1
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Leonard AS, Mapel XM, Pausch H. Pangenome-genotyped structural variation improves molecular phenotype mapping in cattle. Genome Res 2024; 34:300-309. [PMID: 38355307 PMCID: PMC10984387 DOI: 10.1101/gr.278267.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
Expression and splicing quantitative trait loci (e/sQTL) are large contributors to phenotypic variability. Achieving sufficient statistical power for e/sQTL mapping requires large cohorts with both genotypes and molecular phenotypes, and so, the genomic variation is often called from short-read alignments, which are unable to comprehensively resolve structural variation. Here we build a pangenome from 16 HiFi haplotype-resolved cattle assemblies to identify small and structural variation and genotype them with PanGenie in 307 short-read samples. We find high (>90%) concordance of PanGenie-genotyped and DeepVariant-called small variation and confidently genotype close to 21 million small and 43,000 structural variants in the larger population. We validate 85% of these structural variants (with MAF > 0.1) directly with a subset of 25 short-read samples that also have medium coverage HiFi reads. We then conduct e/sQTL mapping with this comprehensive variant set in a subset of 117 cattle that have testis transcriptome data, and find 92 structural variants as causal candidates for eQTL and 73 for sQTL. We find that roughly half of the top associated structural variants affecting expression or splicing are transposable elements, such as SV-eQTL for STN1 and MYH7 and SV-sQTL for CEP89 and ASAH2 Extensive linkage disequilibrium between small and structural variation results in only 28 additional eQTL and 17 sQTL discovered when including SVs, although many top associated SVs are compelling candidates.
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Affiliation(s)
| | - Xena M Mapel
- Animal Genomics, ETH Zurich, 8092 Zurich, Switzerland
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092 Zurich, Switzerland
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2
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Rüegg AB, van der Weijden VA, de Sousa JA, von Meyenn F, Pausch H, Ulbrich SE. Developmental progression continues during embryonic diapause in the roe deer. Commun Biol 2024; 7:270. [PMID: 38443549 PMCID: PMC10914810 DOI: 10.1038/s42003-024-05944-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/21/2024] [Indexed: 03/07/2024] Open
Abstract
Embryonic diapause in mammals is a temporary developmental delay occurring at the blastocyst stage. In contrast to other diapausing species displaying a full arrest, the blastocyst of the European roe deer (Capreolus capreolus) proliferates continuously and displays considerable morphological changes in the inner cell mass. We hypothesised that developmental progression also continues during this period. Here we evaluate the mRNA abundance of developmental marker genes in embryos during diapause and elongation. Our results show that morphological rearrangements of the epiblast during diapause correlate with gene expression patterns and changes in cell polarity. Immunohistochemical staining further supports these findings. Primitive endoderm formation occurs during diapause in embryos composed of around 3,000 cells. Gastrulation coincides with elongation and thus takes place after embryo reactivation. The slow developmental progression makes the roe deer an interesting model for unravelling the link between proliferation and differentiation and requirements for embryo survival.
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Affiliation(s)
- Anna B Rüegg
- ETH Zurich, Animal Physiology, Institute of Agricultural Sciences, Zurich, Switzerland
| | - Vera A van der Weijden
- ETH Zurich, Animal Physiology, Institute of Agricultural Sciences, Zurich, Switzerland
- Max-Planck Institute for Molecular Genetics, Berlin, Germany
| | - João Agostinho de Sousa
- ETH Zurich, Laboratory of Nutrition and Metabolic Epigenetics, Institute of Food, Nutrition and Health, Zurich, Switzerland
| | - Ferdinand von Meyenn
- ETH Zurich, Laboratory of Nutrition and Metabolic Epigenetics, Institute of Food, Nutrition and Health, Zurich, Switzerland
| | - Hubert Pausch
- ETH Zurich, Animal Genomics, Institute of Agricultural Sciences, Zurich, Switzerland
| | - Susanne E Ulbrich
- ETH Zurich, Animal Physiology, Institute of Agricultural Sciences, Zurich, Switzerland.
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3
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Mapel XM, Kadri NK, Leonard AS, He Q, Lloret-Villas A, Bhati M, Hiltpold M, Pausch H. Author Correction: Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle. Nat Commun 2024; 15:1506. [PMID: 38374319 PMCID: PMC10876946 DOI: 10.1038/s41467-024-45727-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024] Open
Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Qiongyu He
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | | | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Maya Hiltpold
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland.
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4
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Mapel XM, Kadri NK, Leonard AS, He Q, Lloret-Villas A, Bhati M, Hiltpold M, Pausch H. Molecular quantitative trait loci in reproductive tissues impact male fertility in cattle. Nat Commun 2024; 15:674. [PMID: 38253538 PMCID: PMC10803364 DOI: 10.1038/s41467-024-44935-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
Breeding bulls are well suited to investigate inherited variation in male fertility because they are genotyped and their reproductive success is monitored through semen analyses and thousands of artificial inseminations. However, functional data from relevant tissues are lacking in cattle, which prevents fine-mapping fertility-associated genomic regions. Here, we characterize gene expression and splicing variation in testis, epididymis, and vas deferens transcriptomes of 118 mature bulls and conduct association tests between 414,667 molecular phenotypes and 21,501,032 genome-wide variants to identify 41,156 regulatory loci. We show broad consensus in tissue-specific and tissue-enriched gene expression between the three bovine tissues and their human and murine counterparts. Expression- and splicing-mediating variants are more than three times as frequent in testis than epididymis and vas deferens, highlighting the transcriptional complexity of testis. Finally, we identify genes (WDR19, SPATA16, KCTD19, ZDHHC1) and molecular phenotypes that are associated with quantitative variation in male fertility through transcriptome-wide association and colocalization analyses.
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Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | - Qiongyu He
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
| | | | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- Roslin Institute, The University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG, UK
| | - Maya Hiltpold
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitatstrasse 2, 8092, Zurich, Switzerland.
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5
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Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
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Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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6
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Nosková A, Li C, Wang X, Leonard AS, Pausch H, Kadri N. Exploiting public databases of genomic variation to quantify evolutionary constraint on the branch point sequence in 30 plant and animal species. Nucleic Acids Res 2023; 51:12069-12075. [PMID: 37953306 PMCID: PMC10711541 DOI: 10.1093/nar/gkad970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/06/2023] [Accepted: 10/18/2023] [Indexed: 11/14/2023] Open
Abstract
The branch point sequence is a degenerate intronic heptamer required for the assembly of the spliceosome during pre-mRNA splicing. Disruption of this motif may promote alternative splicing and eventually cause phenotype variation. Despite its functional relevance, the branch point sequence is not included in most genome annotations. Here, we predict branch point sequences in 30 plant and animal species and attempt to quantify their evolutionary constraints using public variant databases. We find an implausible variant distribution in the databases from 16 of 30 examined species. Comparative analysis of variants from whole-genome sequencing shows that variants submitted from exome sequencing or false positive variants are widespread in public databases and cause these irregularities. We then investigate evolutionary constraint with largely unbiased public variant databases in 14 species and find that the fourth and sixth position of the branch point sequence are more constrained than coding nucleotides. Our findings show that public variant databases should be scrutinized for possible biases before they qualify to analyze evolutionary constraint.
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Affiliation(s)
- Adéla Nosková
- Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Chao Li
- Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Xiaolong Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | | | - Hubert Pausch
- Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
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7
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Sanchez MP, Tribout T, Kadri NK, Chitneedi PK, Maak S, Hozé C, Boussaha M, Croiseau P, Philippe R, Spengeler M, Kühn C, Wang Y, Li C, Plastow G, Pausch H, Boichard D. Correction: Sequence-based GWAS meta-analyses for beef production traits. Genet Sel Evol 2023; 55:79. [PMID: 37957580 PMCID: PMC10642027 DOI: 10.1186/s12711-023-00852-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023] Open
Affiliation(s)
- Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
| | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Praveen K Chitneedi
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Steffen Maak
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Chris Hozé
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
- Eliance, 75595, Paris, France
| | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Pascal Croiseau
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Romain Philippe
- INRAE, USC1061 GAMAA, Université de Limoges, 87060, Limoges, France
| | | | - Christa Kühn
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
- Agricultural and Environmental Faculty, University Rostock, 18059, Rostock, Germany
- Friedrich-Loefer-Institut (FLI), Insel Riems, 17493, Greifswald, Germany
| | - Yining Wang
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
| | - Changxi Li
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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8
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Bhati M, Mapel XM, Lloret-Villas A, Pausch H. Structural variants and short tandem repeats impact gene expression and splicing in bovine testis tissue. Genetics 2023; 225:iyad161. [PMID: 37655920 PMCID: PMC10627265 DOI: 10.1093/genetics/iyad161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 06/05/2023] [Accepted: 08/24/2023] [Indexed: 09/02/2023] Open
Abstract
Structural variants (SVs) and short tandem repeats (STRs) are significant sources of genetic variation. However, the impacts of these variants on gene regulation have not been investigated in cattle. Here, we genotyped and characterized 19,408 SVs and 374,821 STRs in 183 bovine genomes and investigated their impact on molecular phenotypes derived from testis transcriptomes. We found that 71% STRs were multiallelic. The vast majority (95%) of STRs and SVs were in intergenic and intronic regions. Only 37% SVs and 40% STRs were in high linkage disequilibrium (LD) (R2 > 0.8) with surrounding SNPs/insertions and deletions (Indels), indicating that SNP-based association testing and genomic prediction are blind to a nonnegligible portion of genetic variation. We showed that both SVs and STRs were more than 2-fold enriched among expression and splicing QTL (e/sQTL) relative to SNPs/Indels and were often associated with differential expression and splicing of multiple genes. Deletions and duplications had larger impacts on splicing and expression than any other type of SV. Exonic duplications predominantly increased gene expression either through alternative splicing or other mechanisms, whereas expression- and splicing-associated STRs primarily resided in intronic regions and exhibited bimodal effects on the molecular phenotypes investigated. Most e/sQTL resided within 100 kb of the affected genes or splicing junctions. We pinpoint candidate causal STRs and SVs associated with the expression of SLC13A4 and TTC7B and alternative splicing of a lncRNA and CAPP1. We provide a catalog of STRs and SVs for taurine cattle and show that these variants contribute substantially to gene expression and splicing variation.
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Affiliation(s)
- Meenu Bhati
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Xena Marie Mapel
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | | | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
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9
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O’Callaghan E, Navarrete-Lopez P, Štiavnická M, Sánchez JM, Maroto M, Pericuesta E, Fernández-González R, O’Meara C, Eivers B, Kelleher MM, Evans RD, Mapel XM, Lloret-Villas A, Pausch H, Balastegui-Alarcón M, Avilés M, Sanchez-Rodriguez A, Roldan ERS, McDonald M, Kenny DA, Fair S, Gutiérrez-Adán A, Lonergan P. Adenylate kinase 9 is essential for sperm function and male fertility in mammals. Proc Natl Acad Sci U S A 2023; 120:e2305712120. [PMID: 37812723 PMCID: PMC10589668 DOI: 10.1073/pnas.2305712120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 08/23/2023] [Indexed: 10/11/2023] Open
Abstract
Despite passing routine laboratory tests for semen quality, bulls used in artificial insemination exhibit significant variation in fertility. Routine analysis of fertility data identified a dairy bull with extreme subfertility (10% pregnancy rate). To characterize the subfertility phenotype, a range of in vitro, in vivo, and molecular assays were carried out. Sperm from the subfertile bull exhibited reduced motility and severely reduced caffeine-induced hyperactivation compared to controls. Ability to penetrate the zona pellucida, cleavage rate, cleavage kinetics, and blastocyst yield after IVF or AI were significantly lower than in control bulls. Whole-genome sequencing from semen and RNA sequencing of testis tissue revealed a critical mutation in adenylate kinase 9 (AK9) that impaired splicing, leading to a premature termination codon and a severely truncated protein. Mice deficient in AK9 were generated to further investigate the function of the gene; knockout males were phenotypically indistinguishable from their wild-type littermates but produced immotile sperm that were incapable of normal fertilization. These sperm exhibited numerous abnormalities, including a low ATP concentration and reduced motility. RNA-seq analysis of their testis revealed differential gene expression of components of the axoneme and sperm flagellum as well as steroid metabolic processes. Sperm ultrastructural analysis showed a high percentage of sperm with abnormal flagella. Combined bovine and murine data indicate the essential metabolic role of AK9 in sperm motility and/or hyperactivation, which in turn affects sperm binding and penetration of the zona pellucida. Thus, AK9 has been found to be directly implicated in impaired male fertility in mammals.
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Affiliation(s)
- Elena O’Callaghan
- Animal and Crop Sciences, School of Agriculture and Food Science, University College Dublin, Belfield, DublinD04 V1W8, Ireland
| | - Paula Navarrete-Lopez
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Miriama Štiavnická
- Department of Biological Sciences, Bernal Institute, Faculty of Science and Engineering, University of Limerick, LimerickV94 T9PX, Ireland
| | - José M. Sánchez
- Animal and Crop Sciences, School of Agriculture and Food Science, University College Dublin, Belfield, DublinD04 V1W8, Ireland
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Maria Maroto
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Eva Pericuesta
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Raul Fernández-González
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Ciara O’Meara
- National Cattle Breeding Centre, County KildareW91 WF59, Ireland
| | - Bernard Eivers
- National Cattle Breeding Centre, County KildareW91 WF59, Ireland
| | - Margaret M. Kelleher
- Irish Cattle Breeding Federation, Link Road, Ballincollig, County CorkP31 D452, Ireland
| | - Ross D. Evans
- Irish Cattle Breeding Federation, Link Road, Ballincollig, County CorkP31 D452, Ireland
| | - Xena M. Mapel
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Zürich8092, Switzerland
| | - Audald Lloret-Villas
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Zürich8092, Switzerland
| | - Hubert Pausch
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Zürich8092, Switzerland
| | - Miriam Balastegui-Alarcón
- Departamento de Biología Celular e Histología, Universidad de Murcia-Instituto Murciano de Investigación Biosanitaria Pascual Parrilla, Murcia30120, Spain
| | - Manuel Avilés
- Departamento de Biología Celular e Histología, Universidad de Murcia-Instituto Murciano de Investigación Biosanitaria Pascual Parrilla, Murcia30120, Spain
| | - Ana Sanchez-Rodriguez
- Departmento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, Madrid28006, Spain
| | - Eduardo R. S. Roldan
- Departmento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias Naturales, Madrid28006, Spain
| | - Michael McDonald
- Animal and Crop Sciences, School of Agriculture and Food Science, University College Dublin, Belfield, DublinD04 V1W8, Ireland
| | - David A. Kenny
- Animal and Bioscience Department, Teagasc, Animal and Grassland Research and Innovation Centre, Grange, Dunsany, County MeathC15 PW93, Ireland
| | - Sean Fair
- Department of Biological Sciences, Bernal Institute, Faculty of Science and Engineering, University of Limerick, LimerickV94 T9PX, Ireland
| | - Alfonso Gutiérrez-Adán
- Departamento de Reproducción Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria-Centro Nacional integrado en la Agencia Estatal Consejo Superior de Investigaciones Científicas, Madrid28040, Spain
| | - Patrick Lonergan
- Animal and Crop Sciences, School of Agriculture and Food Science, University College Dublin, Belfield, DublinD04 V1W8, Ireland
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10
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Sanchez MP, Tribout T, Kadri NK, Chitneedi PK, Maak S, Hozé C, Boussaha M, Croiseau P, Philippe R, Spengeler M, Kühn C, Wang Y, Li C, Plastow G, Pausch H, Boichard D. Sequence-based GWAS meta-analyses for beef production traits. Genet Sel Evol 2023; 55:70. [PMID: 37828440 PMCID: PMC10568825 DOI: 10.1186/s12711-023-00848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Accepted: 10/04/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Combining the results of within-population genome-wide association studies (GWAS) based on whole-genome sequences into a single meta-analysis (MA) is an accurate and powerful method for identifying variants associated with complex traits. As part of the H2020 BovReg project, we performed sequence-level MA for beef production traits. Five partners from France, Switzerland, Germany, and Canada contributed summary statistics from sequence-based GWAS conducted with 54,782 animals from 15 purebred or crossbred populations. We combined the summary statistics for four growth, nine morphology, and 15 carcass traits into 16 MA, using both fixed effects and z-score methods. RESULTS The fixed-effects method was generally more informative to provide indication on potentially causal variants, although we combined substantially different traits in each MA. In comparison with within-population GWAS, this approach highlighted (i) a larger number of quantitative trait loci (QTL), (ii) QTL more frequently located in genomic regions known for their effects on growth and meat/carcass traits, (iii) a smaller number of genomic variants within the QTL, and (iv) candidate variants that were more frequently located in genes. MA pinpointed variants in genes, including MSTN, LCORL, and PLAG1 that have been previously associated with morphology and carcass traits. We also identified dozens of other variants located in genes associated with growth and carcass traits, or with a function that may be related to meat production (e.g., HS6ST1, HERC2, WDR75, COL3A1, SLIT2, MED28, and ANKAR). Some of these variants overlapped with expression or splicing QTL reported in the cattle Genotype-Tissue Expression atlas (CattleGTEx) and could therefore regulate gene expression. CONCLUSIONS By identifying candidate genes and potential causal variants associated with beef production traits in cattle, MA demonstrates great potential for investigating the biological mechanisms underlying these traits. As a complement to within-population GWAS, this approach can provide deeper insights into the genetic architecture of complex traits in beef cattle.
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Affiliation(s)
- Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
| | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Praveen K Chitneedi
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Steffen Maak
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
| | - Chris Hozé
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
- Eliance, 75595, Paris, France
| | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Pascal Croiseau
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Romain Philippe
- INRAE, USC1061 GAMAA, Université de Limoges, 87060, Limoges, France
| | | | - Christa Kühn
- Research Institute for Farm Animal Biology (FBN), 18196, Dummerstorf, Germany
- Agricultural and Environmental faculty, University Rostock, 18059, Rostock, Germany
- Friedrich-Loeffler-Institut (FLI), 17493, Greifswald, Insel Riems, Germany
| | - Yining Wang
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
| | - Changxi Li
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB, T4L 1W1, Canada
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Graham Plastow
- Department of Agricultural, Food and Nutritional Science, Livestock Gentec, University of Alberta, Edmonton, AB, T6G 2HI, Canada
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8092, Zurich, Switzerland
| | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
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11
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Xia X, Zhang F, Li S, Luo X, Peng L, Dong Z, Pausch H, Leonard AS, Crysnanto D, Wang S, Tong B, Lenstra JA, Han J, Li F, Xu T, Gu L, Jin L, Dang R, Huang Y, Lan X, Ren G, Wang Y, Gao Y, Ma Z, Cheng H, Ma Y, Chen H, Pang W, Lei C, Chen N. Structural variation and introgression from wild populations in East Asian cattle genomes confer adaptation to local environment. Genome Biol 2023; 24:211. [PMID: 37723525 PMCID: PMC10507960 DOI: 10.1186/s13059-023-03052-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/07/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Structural variations (SVs) in individual genomes are major determinants of complex traits, including adaptability to environmental variables. The Mongolian and Hainan cattle breeds in East Asia are of taurine and indicine origins that have evolved to adapt to cold and hot environments, respectively. However, few studies have investigated SVs in East Asian cattle genomes and their roles in environmental adaptation, and little is known about adaptively introgressed SVs in East Asian cattle. RESULTS In this study, we examine the roles of SVs in the climate adaptation of these two cattle lineages by generating highly contiguous chromosome-scale genome assemblies. Comparison of the two assemblies along with 18 Mongolian and Hainan cattle genomes obtained by long-read sequencing data provides a catalog of 123,898 nonredundant SVs. Several SVs detected from long reads are in exons of genes associated with epidermal differentiation, skin barrier, and bovine tuberculosis resistance. Functional investigations show that a 108-bp exonic insertion in SPN may affect the uptake of Mycobacterium tuberculosis by macrophages, which might contribute to the low susceptibility of Hainan cattle to bovine tuberculosis. Genotyping of 373 whole genomes from 39 breeds identifies 2610 SVs that are differentiated along a "north-south" gradient in China and overlap with 862 related genes that are enriched in pathways related to environmental adaptation. We identify 1457 Chinese indicine-stratified SVs that possibly originate from banteng and are frequent in Chinese indicine cattle. CONCLUSIONS Our findings highlight the unique contribution of SVs in East Asian cattle to environmental adaptation and disease resistance.
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Affiliation(s)
- Xiaoting Xia
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Fengwei Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Shuang Li
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Xiaoyu Luo
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Lixin Peng
- National Engineering Research Center for Non-Food Biorefinery, Guangxi Academy of Sciences, 98 Daling Road, Nanning, China
| | - Zheng Dong
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Danang Crysnanto
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Shikang Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Bin Tong
- The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, School of Life Sciences, Inner Mongolia University, Hohhot, China
| | - Johannes A Lenstra
- Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Jianlin Han
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi, Kenya
- CAAS-ILRI Joint Laboratory On Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agriculture Sciences (CAAS), Beijing, China
| | - Fuyong Li
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Tieshan Xu
- Tropical Crops Genetic Resources Institute, Chinese Academy of Tropical Agricultural Sciences, Haikou, China
| | - Lihong Gu
- Institute of Animal Science & Veterinary Medicine, Hainan Academy of Agricultural Sciences, Haikou, China
| | - Liangliang Jin
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Ruihua Dang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yongzhen Huang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Xianyong Lan
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Gang Ren
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yu Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Yuanpeng Gao
- College of Veterinary Medicine, Northwest A&F University, Xianyang, Yangling, China
| | - Zhijie Ma
- Qinghai Academy of Animal Science and Veterinary Medicine, Qinghai University, Xining, China
| | - Haijian Cheng
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
- Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Shandong Key Lab of Animal Disease Control and Breeding, Jinan, China
| | - Yun Ma
- Key Laboratory of Ruminant Molecular and Cellular Breeding of Ningxia Hui Autonomous Region, School of Agriculture, Ningxia University, Yinchuan, China
| | - Hong Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China
| | - Weijun Pang
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
| | - Chuzhao Lei
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
| | - Ningbo Chen
- Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling, Xianyang, China.
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12
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Smith TPL, Bickhart DM, Boichard D, Chamberlain AJ, Djikeng A, Jiang Y, Low WY, Pausch H, Demyda-Peyrás S, Prendergast J, Schnabel RD, Rosen BD. The Bovine Pangenome Consortium: democratizing production and accessibility of genome assemblies for global cattle breeds and other bovine species. Genome Biol 2023; 24:139. [PMID: 37337218 DOI: 10.1186/s13059-023-02975-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 05/19/2023] [Indexed: 06/21/2023] Open
Abstract
The Bovine Pangenome Consortium (BPC) is an international collaboration dedicated to the assembly of cattle genomes to develop a more complete representation of cattle genomic diversity. The goal of the BPC is to provide genome assemblies and a community-agreed pangenome representation to replace breed-specific reference assemblies for cattle genomics. The BPC invites partners sharing our vision to participate in the production of these assemblies and the development of a common, community-approved, pangenome reference as a public resource for the research community ( https://bovinepangenome.github.io/ ). This community-driven resource will provide the context for comparison between studies and the future foundation for cattle genomic selection.
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Affiliation(s)
- Timothy P L Smith
- US Meat Animal Research Center, USDA-ARS, Clay Center, NE, 68933, USA
| | | | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC, 3083, Australia
| | - Appolinaire Djikeng
- Centre for Tropical Livestock Genetics and Health, ILRI Kenya, Nairobi, 30709-00100, Kenya
- Centre for Tropical Livestock Genetics and Health, Easter Bush, Midlothian, EH25 9RG, UK
| | - Yu Jiang
- Center for Ruminant Genetics and Evolution, Northwest A&F University, Yangling, 712100, China
| | - Wai Y Low
- The Davies Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA, 5371, Australia
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Sebastian Demyda-Peyrás
- Departamento de Producción Animal, Facultad de Ciencias Veterinarias, Universidad Nacional de La Plata, 1900, La Plata, Argentina
- Consejo Superior de Investigaciones Científicas Y Tecnológicas (CONICET), CCT-La Plata, 1900, La Plata, Argentina
| | - James Prendergast
- Centre for Tropical Livestock Genetics and Health, Easter Bush, Midlothian, EH25 9RG, UK
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, 65211, USA
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
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13
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Leonard AS, Crysnanto D, Mapel XM, Bhati M, Pausch H. Graph construction method impacts variation representation and analyses in a bovine super-pangenome. Genome Biol 2023; 24:124. [PMID: 37217946 PMCID: PMC10204317 DOI: 10.1186/s13059-023-02969-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 05/10/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Several models and algorithms have been proposed to build pangenomes from multiple input assemblies, but their impact on variant representation, and consequently downstream analyses, is largely unknown. RESULTS We create multi-species super-pangenomes using pggb, cactus, and minigraph with the Bos taurus taurus reference sequence and eleven haplotype-resolved assemblies from taurine and indicine cattle, bison, yak, and gaur. We recover 221 k nonredundant structural variations (SVs) from the pangenomes, of which 135 k (61%) are common to all three. SVs derived from assembly-based calling show high agreement with the consensus calls from the pangenomes (96%), but validate only a small proportion of variations private to each graph. Pggb and cactus, which also incorporate base-level variation, have approximately 95% exact matches with assembly-derived small variant calls, which significantly improves the edit rate when realigning assemblies compared to minigraph. We use the three pangenomes to investigate 9566 variable number tandem repeats (VNTRs), finding 63% have identical predicted repeat counts in the three graphs, while minigraph can over or underestimate the count given its approximate coordinate system. We examine a highly variable VNTR locus and show that repeat unit copy number impacts the expression of proximal genes and non-coding RNA. CONCLUSIONS Our findings indicate good consensus between the three pangenome methods but also show their individual strengths and weaknesses that need to be considered when analysing different types of variants from multiple input assemblies.
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Affiliation(s)
- Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland.
| | - Danang Crysnanto
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Xena M Mapel
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Meenu Bhati
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8092, Zurich, Switzerland.
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14
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Lloret-Villas A, Pausch H, Leonard AS. The size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle. Genet Sel Evol 2023; 55:33. [PMID: 37170101 PMCID: PMC10173671 DOI: 10.1186/s12711-023-00809-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/02/2023] [Indexed: 05/13/2023] Open
Abstract
BACKGROUND Low-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available. METHODS Sequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data. RESULTS DeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed. CONCLUSIONS Variant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.
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Affiliation(s)
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, Universitätstrasse 2, Zürich, 8092, Switzerland
| | - Alexander S Leonard
- Animal Genomics, ETH Zürich, Universitätstrasse 2, Zürich, 8092, Switzerland
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15
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Abstract
Cattle are a well-suited "model organism" to study the genetic underpinnings of variation in male reproductive performance. The adoption of artificial insemination and genomic prediction in many cattle breeds provide access to microarray-derived genotypes and repeated measurements for semen quality and insemination success in several thousand bulls. Similar-sized mapping cohorts with phenotypes for male fertility are not available for most other species precluding powerful association testing. The repeated measurements of the artificial insemination bulls' semen quality enable the differentiation between transient and biologically relevant trait fluctuations, and thus, are an ideal source of phenotypes for variance components estimation and genome-wide association testing. Genome-wide case-control association testing involving bulls with either aberrant sperm quality or low insemination success revealed several causal recessive loss-of-function alleles underpinning monogenic reproductive disorders. These variants are routinely monitored with customised genotyping arrays in the male selection candidates to avoid the use of subfertile or infertile bulls for artificial insemination and natural service. Genome-wide association studies with quantitative measurements of semen quality and insemination success revealed quantitative trait loci for male fertility, but the underlying causal variants remain largely unknown. Moreover, these loci explain only a small part of the heritability of male fertility. Integrating genome-wide association studies with gene expression and other omics data from male reproductive tissues is required for the fine-mapping of candidate causal variants underlying variation in male reproductive performance in cattle.
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Affiliation(s)
- Hubert Pausch
- Animal Genomics, Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland.
| | - Xena Marie Mapel
- Animal Genomics, Department of Environmental Systems Science, ETH Zurich, Universitaetstrasse 2, 8092 Zurich, Switzerland
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16
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Nosková A, Mehrotra A, Kadri NK, Lloret-Villas A, Neuenschwander S, Hofer A, Pausch H. Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs. BMC Genomics 2023; 24:192. [PMID: 37038103 PMCID: PMC10084639 DOI: 10.1186/s12864-023-09295-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/04/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). RESULTS We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. CONCLUSIONS Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
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Affiliation(s)
- A Nosková
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland.
| | - A Mehrotra
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | - N K Kadri
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | | | | | - A Hofer
- SUISAG, Allmend 10, 6204, Sempach, Switzerland
| | - H Pausch
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
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17
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Han H, McGivney BA, Allen L, Bai D, Corduff LR, Davaakhuu G, Davaasambuu J, Dorjgotov D, Hall TJ, Hemmings AJ, Holtby AR, Jambal T, Jargalsaikhan B, Jargalsaikhan U, Kadri NK, MacHugh DE, Pausch H, Readhead C, Warburton D, Dugarjaviin M, Hill EW. Common protein-coding variants influence the racing phenotype in galloping racehorse breeds. Commun Biol 2022; 5:1320. [PMID: 36513809 PMCID: PMC9748125 DOI: 10.1038/s42003-022-04206-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/01/2022] [Indexed: 12/14/2022] Open
Abstract
Selection for system-wide morphological, physiological, and metabolic adaptations has led to extreme athletic phenotypes among geographically diverse horse breeds. Here, we identify genes contributing to exercise adaptation in racehorses by applying genomics approaches for racing performance, an end-point athletic phenotype. Using an integrative genomics strategy to first combine population genomics results with skeletal muscle exercise and training transcriptomic data, followed by whole-genome resequencing of Asian horses, we identify protein-coding variants in genes of interest in galloping racehorse breeds (Arabian, Mongolian and Thoroughbred). A core set of genes, G6PC2, HDAC9, KTN1, MYLK2, NTM, SLC16A1 and SYNDIG1, with central roles in muscle, metabolism, and neurobiology, are key drivers of the racing phenotype. Although racing potential is a multifactorial trait, the genomic architecture shaping the common athletic phenotype in horse populations bred for racing provides evidence for the influence of protein-coding variants in fundamental exercise-relevant genes. Variation in these genes may therefore be exploited for genetic improvement of horse populations towards specific types of racing.
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Affiliation(s)
- Haige Han
- grid.411638.90000 0004 1756 9607Inner Mongolia Key Laboratory of Equine Genetics, Breeding and Reproduction, College of Animal Science, Equine Research Center, Inner Mongolia Agricultural University, Hohhot, 010018 China
| | - Beatrice A. McGivney
- grid.496984.ePlusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, A96 W5T3 Ireland
| | - Lucy Allen
- grid.417905.e0000 0001 2186 5933Royal Agricultural University, Cirencester, Gloucestershire GL7 6JS UK
| | - Dongyi Bai
- grid.411638.90000 0004 1756 9607Inner Mongolia Key Laboratory of Equine Genetics, Breeding and Reproduction, College of Animal Science, Equine Research Center, Inner Mongolia Agricultural University, Hohhot, 010018 China
| | - Leanne R. Corduff
- grid.496984.ePlusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, A96 W5T3 Ireland
| | - Gantulga Davaakhuu
- grid.425564.40000 0004 0587 3863Institute of Biology, Mongolian Academy of Sciences, Peace Avenue 54B, Ulaanbaatar, 13330 Mongolia
| | - Jargalsaikhan Davaasambuu
- Ajnai Sharga Horse Racing Team, Encanto Town 210-11, Ikh Mongol State Street, 26th Khoroo, Bayanzurkh district Ulaanbaatar, 13312 Mongolia
| | - Dulguun Dorjgotov
- grid.440461.30000 0001 2191 7895School of Industrial Technology, Mongolian University of Science and Technology, Ulaanbaatar, 661 Mongolia
| | - Thomas J. Hall
- grid.7886.10000 0001 0768 2743UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin D04 V1W8 Ireland
| | - Andrew J. Hemmings
- grid.417905.e0000 0001 2186 5933Royal Agricultural University, Cirencester, Gloucestershire GL7 6JS UK
| | - Amy R. Holtby
- grid.496984.ePlusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, A96 W5T3 Ireland
| | - Tuyatsetseg Jambal
- grid.440461.30000 0001 2191 7895School of Industrial Technology, Mongolian University of Science and Technology, Ulaanbaatar, 661 Mongolia
| | - Badarch Jargalsaikhan
- grid.444534.60000 0000 8485 883XDepartment of Obstetrics and Gynecology, Mongolian National University of Medical Sciences, Ulaanbaatar, 14210 Mongolia
| | - Uyasakh Jargalsaikhan
- Ajnai Sharga Horse Racing Team, Encanto Town 210-11, Ikh Mongol State Street, 26th Khoroo, Bayanzurkh district Ulaanbaatar, 13312 Mongolia
| | - Naveen K. Kadri
- grid.5801.c0000 0001 2156 2780Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - David E. MacHugh
- grid.7886.10000 0001 0768 2743UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin D04 V1W8 Ireland ,grid.7886.10000 0001 0768 2743UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin D04 V1W8 Ireland
| | - Hubert Pausch
- grid.5801.c0000 0001 2156 2780Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Carol Readhead
- grid.20861.3d0000000107068890Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125 USA
| | - David Warburton
- grid.42505.360000 0001 2156 6853The Saban Research Institute, Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA 90027 USA
| | - Manglai Dugarjaviin
- grid.411638.90000 0004 1756 9607Inner Mongolia Key Laboratory of Equine Genetics, Breeding and Reproduction, College of Animal Science, Equine Research Center, Inner Mongolia Agricultural University, Hohhot, 010018 China
| | - Emmeline W. Hill
- grid.496984.ePlusvital Ltd, The Highline, Dun Laoghaire Business Park, Dublin, A96 W5T3 Ireland ,grid.7886.10000 0001 0768 2743UCD School of Agriculture and Food Science, University College Dublin, Belfield, Dublin D04 V1W8 Ireland
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18
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Li C, Wu Y, Chen B, Cai Y, Guo J, Leonard AS, Kalds P, Zhou S, Zhang J, Zhou P, Gan S, Jia T, Pu T, Suo L, Li Y, Zhang K, Li L, Purevdorj M, Wang X, Li M, Wang Y, Liu Y, Huang S, Sonstegard T, Wang MS, Kemp S, Pausch H, Chen Y, Han JL, Jiang Y, Wang X. Markhor-derived Introgression of a Genomic Region Encompassing PAPSS2 Confers High-altitude Adaptability in Tibetan Goats. Mol Biol Evol 2022; 39:6830663. [PMID: 36382357 PMCID: PMC9728798 DOI: 10.1093/molbev/msac253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Understanding the genetic mechanism of how animals adapt to extreme conditions is fundamental to determine the relationship between molecular evolution and changing environments. Goat is one of the first domesticated species and has evolved rapidly to adapt to diverse environments, including harsh high-altitude conditions with low temperature and poor oxygen supply but strong ultraviolet radiation. Here, we analyzed 331 genomes of domestic goats and wild caprid species living at varying altitudes (high > 3000 m above sea level and low < 1200 m), along with a reference-guided chromosome-scale assembly (contig-N50: 90.4 Mb) of a female Tibetan goat genome based on PacBio HiFi long reads, to dissect the genetic determinants underlying their adaptation to harsh conditions on the Qinghai-Tibetan Plateau (QTP). Population genomic analyses combined with genome-wide association studies (GWAS) revealed a genomic region harboring the 3'-phosphoadenosine 5'-phosphosulfate synthase 2 (PAPSS2) gene showing strong association with high-altitude adaptability (PGWAS = 3.62 × 10-25) in Tibetan goats. Transcriptomic data from 13 tissues revealed that PAPSS2 was implicated in hypoxia-related pathways in Tibetan goats. We further verified potential functional role of PAPSS2 in response to hypoxia in PAPSS2-deficient cells. Introgression analyses suggested that the PAPSS2 haplotype conferring the high-altitude adaptability in Tibetan goats originated from a recent hybridization between goats and a wild caprid species, the markhor (Capra falconeri). In conclusion, our results uncover a hitherto unknown contribution of PAPSS2 to high-altitude adaptability in Tibetan goats on QTP, following interspecific introgression and natural selection.
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Affiliation(s)
| | | | | | | | | | | | - Peter Kalds
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Shiwei Zhou
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China,College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Jingchen Zhang
- College of Veterinary Medicine, Northwest A&F University, Yangling 712100, China
| | - Ping Zhou
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Shangqu Gan
- Institute of Animal Husbandry and Veterinary Medicine, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China,State Key Laboratory of Sheep Genetic Improvement and Healthy Breeding, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China
| | - Ting Jia
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Tianchun Pu
- Beijing Key Laboratory of Captive Wildlife Technologies, Beijing Zoo, Beijing 100044, China
| | - Langda Suo
- Institute of Animal Science, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850009, China
| | - Yan Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ke Zhang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Lan Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Myagmarsuren Purevdorj
- Lab of Animal Genetics and Animal Reproductive Technology, Research Institute of Animal Husbandry, Mongolian University of Life Sciences, Ulaanbaatar 17024, Mongolia
| | - Xihong Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Ming Li
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Yu Wang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Yao Liu
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | - Shuhong Huang
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | | | - Ming-Shan Wang
- Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 94720
| | - Stephen Kemp
- Livestock Genetics Program, International Livestock Research Institute (ILRI), Nairobi 30709-00100, Kenya
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, 8092 Zürich, Switzerland
| | - Yulin Chen
- International Joint Agriculture Research Center for Animal Bio-Breeding, Ministry of Agriculture and Rural Affairs/Key Laboratory of Animal Genetics, Breeding and Reproduction of Shaanxi Province, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China
| | | | - Yu Jiang
- Corresponding authors: E-mails: ; ;
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19
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Mapel XM, Hiltpold M, Kadri NK, Witschi U, Pausch H. Erratum to “Bull fertility and semen quality are not correlated with dairy and production traits in Brown Swiss cattle” (JDS Commun. 3:120–125). JDS Communications 2022; 3:378. [PMID: 36342882 PMCID: PMC9623789 DOI: 10.3168/jdsc.2022-3-5-378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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20
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Leonard AS, Crysnanto D, Fang ZH, Heaton MP, Vander Ley BL, Herrera C, Bollwein H, Bickhart DM, Kuhn KL, Smith TPL, Rosen BD, Pausch H. Structural variant-based pangenome construction has low sensitivity to variability of haplotype-resolved bovine assemblies. Nat Commun 2022; 13:3012. [PMID: 35641504 PMCID: PMC9156671 DOI: 10.1038/s41467-022-30680-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Accepted: 05/10/2022] [Indexed: 12/12/2022] Open
Abstract
Advantages of pangenomes over linear reference assemblies for genome research have recently been established. However, potential effects of sequence platform and assembly approach, or of combining assemblies created by different approaches, on pangenome construction have not been investigated. Here we generate haplotype-resolved assemblies from the offspring of three bovine trios representing increasing levels of heterozygosity that each demonstrate a substantial improvement in contiguity, completeness, and accuracy over the current Bos taurus reference genome. Diploid coverage as low as 20x for HiFi or 60x for ONT is sufficient to produce two haplotype-resolved assemblies meeting standards set by the Vertebrate Genomes Project. Structural variant-based pangenomes created from the haplotype-resolved assemblies demonstrate significant consensus regardless of sequence platform, assembler algorithm, or coverage. Inspecting pangenome topologies identifies 90 thousand structural variants including 931 overlapping with coding sequences; this approach reveals variants affecting QRICH2, PRDM9, HSPA1A, TAS2R46, and GC that have potential to affect phenotype.
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Affiliation(s)
- Alexander S Leonard
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland.
| | - Danang Crysnanto
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Zih-Hua Fang
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland
| | - Michael P Heaton
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE, 68933, USA
| | - Brian L Vander Ley
- Great Plains Veterinary Educational Center, University of Nebraska-Lincoln, Lincoln, NE, 68588, USA
| | - Carolina Herrera
- Clinic of Reproductive Medicine, Department for Farm Animals, University of Zurich, 8057, Zurich, Switzerland
| | - Heinrich Bollwein
- Clinic of Reproductive Medicine, Department for Farm Animals, University of Zurich, 8057, Zurich, Switzerland
| | - Derek M Bickhart
- Dairy Forage Research Center, USDA-ARS, 1925 Linden Drive, Madison, WI, 53706, USA
| | - Kristen L Kuhn
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE, 68933, USA
| | - Timothy P L Smith
- U.S. Meat Animal Research Center, USDA-ARS, 844 Road 313, Clay Center, NE, 68933, USA
| | - Benjamin D Rosen
- Animal Genomics and Improvement Laboratory, USDA-ARS, 10300 Baltimore Ave, Beltsville, MD, 20705, USA.
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, 8006, Zurich, Switzerland.
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21
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Hiltpold M, Janett F, Mapel XM, Kadri NK, Fang ZH, Schwarzenbacher H, Seefried FR, Spengeler M, Witschi U, Pausch H. A 1-bp deletion in bovine QRICH2 causes low sperm count and immotile sperm with multiple morphological abnormalities. Genet Sel Evol 2022; 54:18. [PMID: 35255804 PMCID: PMC8900305 DOI: 10.1186/s12711-022-00710-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/17/2022] [Indexed: 12/16/2022] Open
Abstract
Abstract
Background
Semen quality and insemination success are monitored in artificial insemination bulls to ensure high male fertility rates. Only ejaculates that fulfill minimum quality requirements are processed and eventually used for artificial inseminations. We examined 70,990 ejaculates from 1343 Brown Swiss bulls to identify bulls from which all ejaculates were rejected due to low semen quality. This procedure identified a bull that produced 12 ejaculates with an aberrantly small number of sperm (0.2 ± 0.2 × 109 sperm per mL) which were mostly immotile due to multiple morphological abnormalities.
Results
The genome of this bull was sequenced at a 12× coverage to investigate a possible genetic cause. Comparing the sequence variant genotypes of this bull with those from 397 fertile bulls revealed a 1-bp deletion in the coding sequence of the QRICH2 gene which encodes the glutamine rich 2 protein, as a compelling candidate causal variant. This 1-bp deletion causes a frameshift in translation and a premature termination codon (ENSBTAP00000018337.1:p.Cys1644AlafsTer52). The analysis of testis transcriptomes from 76 bulls showed that the transcript with the premature termination codon is subject to nonsense-mediated mRNA decay. The 1-bp deletion resides in a 675-kb haplotype that includes 181 single nucleotide polymorphisms (SNPs) from the Illumina BovineHD Bead chip. This haplotype segregates at a frequency of 5% in the Brown Swiss cattle population. Our analysis also identified another bull that carried the 1-bp deletion in the homozygous state. Semen analyses from the second bull confirmed low sperm concentration and immotile sperm with multiple morphological abnormalities that primarily affect the sperm flagellum and, to a lesser extent, the sperm head.
Conclusions
A recessive loss-of-function allele of the bovine QRICH2 gene likely causes low sperm concentration and immotile sperm with multiple morphological abnormalities. Routine sperm analyses unambiguously identify homozygous bulls for this allele. A direct gene test can be implemented to monitor the frequency of the undesired allele in cattle populations.
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22
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Mapel XM, Hiltpold M, Kadri NK, Witschi U, Pausch H. Bull fertility and semen quality are not correlated with dairy and production traits in Brown Swiss cattle. JDS Commun 2022; 3:120-125. [PMID: 36339738 PMCID: PMC9623726 DOI: 10.3168/jdsc.2021-0164] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/21/2021] [Indexed: 05/31/2023]
Abstract
Undisturbed reproduction is key for successful breeding of beef and dairy cattle. Improving reproductive ability can be difficult because of antagonistic relationships with other economically relevant traits. In cattle, thorough investigation of female fertility revealed unfavorable genetic correlations with various production phenotypes. However, the correlation between male reproductive ability and production traits remains poorly understood. Here, we investigated the genetic relationships among and between male fertility characteristics and economically relevant traits in a population of Brown Swiss cattle. We performed GWAS with imputed genotypes at nearly 12 million sequence variants for semen quality (sperm head and tail anomalies, motility, concentration, and volume), male fertility, and 57 production phenotypes. Allele substitution effects were then correlated on a trait-by-trait basis to estimate genetic correlations. Correlations between male reproductive characteristics and traits of economic value were small and ranged from -0.0681 to 0.0787. Among the semen quality parameters, sperm motility was negatively correlated with anomalies (head: r = -0.7083 ± 0.0002; tail: r = -0.7739 ± 0.0002) and volume (r = -0.1266 ± 0.0003), whereas volume was negatively correlated with concentration (r = -0.3503 ± 0.0002). Sire nonreturn rate was negatively correlated with sperm anomalies (head: r = -0.1640 ± 0.0002; tail: r = -0.1580 ± 0.0002) and positively correlated with motility (r = 0.1598 ± 0.0002). A meta-analysis of male reproductive traits identified 2 quantitative trait loci: a previously described region on chromosome 6 showed pleiotropic effects and a novel region on chromosome 11 was associated with sperm head anomalies. In conclusion, our results suggest that selection for economically important dairy and production phenotypes has little impact on semen quality and fertility of Brown Swiss bulls.
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Affiliation(s)
- Xena Marie Mapel
- Animal Genomics, ETH Zürich, Universitätsstrasse 2, 8006 Zürich, Switzerland
| | - Maya Hiltpold
- Animal Genomics, ETH Zürich, Universitätsstrasse 2, 8006 Zürich, Switzerland
| | - Naveen Kumar Kadri
- Animal Genomics, ETH Zürich, Universitätsstrasse 2, 8006 Zürich, Switzerland
| | - Ulrich Witschi
- Swissgenetics, Meielenfeldweg 12, 3052 Zollikofen, Switzerland
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, Universitätsstrasse 2, 8006 Zürich, Switzerland
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23
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Flisikowski K, Perleberg C, Niu G, Winogrodzki T, Bak A, Liang W, Grodziecki A, Zhang Y, Pausch H, Flisikowska T, Klinger B, Perkowska A, Kind A, Switonski M, Janssen KP, Saur D, Schnieke A. Wild-type APC Influences the Severity of Familial Adenomatous Polyposis. Cell Mol Gastroenterol Hepatol 2021; 13:669-671.e3. [PMID: 34774804 PMCID: PMC8777002 DOI: 10.1016/j.jcmgh.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 02/07/2023]
Affiliation(s)
- Krzysztof Flisikowski
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Carolin Perleberg
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany; Center of Integrated Protein Science Munich, Division of Clinical Pharmacology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Guanglin Niu
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Thomas Winogrodzki
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Agnieszka Bak
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Wei Liang
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Alessandro Grodziecki
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Yue Zhang
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | | | - Tatiana Flisikowska
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Bernhard Klinger
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Anna Perkowska
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poznan, Poland
| | - Alexander Kind
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich Germany
| | - Marek Switonski
- Department of Genetics and Animal Breeding, Poznan University of Life Sciences, Poznan, Poland
| | - Klaus-Peter Janssen
- Department of Surgery, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dieter Saur
- Translational Cancer Research and Institute for Experimental Cancer Therapy, School of Medicine, Technical University of Munich, Munich, Germany; Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany; Division of Translational Cancer Research, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Angelika Schnieke
- Livestock Biotechnology, School of Life Sciences, Technical University of Munich, Munich, Germany; ZIEL Institute for Food and Health, School of Life Sciences, Technical University of Munich, Munich, Germany
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Kadri NK, Mapel XM, Pausch H. The intronic branch point sequence is under strong evolutionary constraint in the bovine and human genome. Commun Biol 2021; 4:1206. [PMID: 34675361 PMCID: PMC8531310 DOI: 10.1038/s42003-021-02725-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 09/29/2021] [Indexed: 12/30/2022] Open
Abstract
The branch point sequence is a cis-acting intronic motif required for mRNA splicing. Despite their functional importance, branch point sequences are not routinely annotated. Here we predict branch point sequences in 179,476 bovine introns and investigate their variability using a catalogue of 29.4 million variants detected in 266 cattle genomes. We localize the bovine branch point within a degenerate heptamer "nnyTrAy". An adenine residue at position 6, that acts as branch point, and a thymine residue at position 4 of the heptamer are more strongly depleted for mutations than coding sequences suggesting extreme purifying selection. We provide evidence that mutations affecting these evolutionarily constrained residues lead to alternative splicing. We confirm evolutionary constraints on branch point sequences using a catalogue of 115 million SNPs established from 3,942 human genomes of the gnomAD database.
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Affiliation(s)
- Naveen Kumar Kadri
- grid.5801.c0000 0001 2156 2780Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Xena Marie Mapel
- grid.5801.c0000 0001 2156 2780Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
| | - Hubert Pausch
- grid.5801.c0000 0001 2156 2780Animal Genomics, ETH Zürich, Universitätstrasse 2, 8092 Zürich, Switzerland
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25
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Nosková A, Hiltpold M, Janett F, Echtermann T, Fang ZH, Sidler X, Selige C, Hofer A, Neuenschwander S, Pausch H. Infertility due to defective sperm flagella caused by an intronic deletion in DNAH17 that perturbs splicing. Genetics 2021; 217:6041611. [PMID: 33724408 DOI: 10.1093/genetics/iyaa033] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 12/08/2020] [Indexed: 12/30/2022] Open
Abstract
Artificial insemination in pig (Sus scrofa domesticus) breeding involves the evaluation of the semen quality of breeding boars. Ejaculates that fulfill predefined quality requirements are processed, diluted and used for inseminations. Within short time, eight Swiss Large White boars producing immotile sperm that had multiple morphological abnormalities of the sperm flagella were noticed at a semen collection center. The eight boars were inbred on a common ancestor suggesting that the novel sperm flagella defect is a recessive trait. Transmission electron microscopy cross-sections revealed that the immotile sperm had disorganized flagellar axonemes. Haplotype-based association testing involving microarray-derived genotypes at 41,094 SNPs of six affected and 100 fertile boars yielded strong association (P = 4.22 × 10-15) at chromosome 12. Autozygosity mapping enabled us to pinpoint the causal mutation on a 1.11 Mb haplotype located between 3,473,632 and 4,587,759 bp. The haplotype carries an intronic 13-bp deletion (Chr12:3,556,401-3,556,414 bp) that is compatible with recessive inheritance. The 13-bp deletion excises the polypyrimidine tract upstream exon 56 of DNAH17 (XM_021066525.1: c.8510-17_8510-5del) encoding dynein axonemal heavy chain 17. Transcriptome analysis of the testis of two affected boars revealed that the loss of the polypyrimidine tract causes exon skipping which results in the in-frame loss of 89 amino acids from DNAH17. Disruption of DNAH17 impairs the assembly of the flagellar axoneme and manifests in multiple morphological abnormalities of the sperm flagella. Direct gene testing may now be implemented to monitor the defective allele in the Swiss Large White population and prevent the frequent manifestation of a sterilizing sperm tail disorder in breeding boars.
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Affiliation(s)
- Adéla Nosková
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Maya Hiltpold
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Fredi Janett
- Clinic of Reproductive Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | - Thomas Echtermann
- Division of Swine Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | - Zih-Hua Fang
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
| | - Xaver Sidler
- Division of Swine Medicine, Vetsuisse Faculty, University of Zurich, 8057 Zurich, Switzerland
| | | | | | - Stefan Neuenschwander
- Animal Genetics, Institute of Agricultural Science, ETH Zürich, 8092 Zürich, Switzerland
| | - Hubert Pausch
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, 8315 Lindau, Switzerland
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Mehrotra A, Bhushan B, A K, Singh A, Panda S, Bhati M, Panigrahi M, Dutt T, Mishra BP, Pausch H, Kumar A. Genome-wide SNP data unravel the ancestry and signatures of divergent selection in Ghurrah pigs of India. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104587] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Lloret-Villas A, Bhati M, Kadri NK, Fries R, Pausch H. Investigating the impact of reference assembly choice on genomic analyses in a cattle breed. BMC Genomics 2021; 22:363. [PMID: 34011274 PMCID: PMC8132449 DOI: 10.1186/s12864-021-07554-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Reference-guided read alignment and variant genotyping are prone to reference allele bias, particularly for samples that are greatly divergent from the reference genome. A Hereford-based assembly is the widely accepted bovine reference genome. Haplotype-resolved genomes that exceed the current bovine reference genome in quality and continuity have been assembled for different breeds of cattle. Using whole genome sequencing data of 161 Brown Swiss cattle, we compared the accuracy of read mapping and sequence variant genotyping as well as downstream genomic analyses between the bovine reference genome (ARS-UCD1.2) and a highly continuous Angus-based assembly (UOA_Angus_1). RESULTS Read mapping accuracy did not differ notably between the ARS-UCD1.2 and UOA_Angus_1 assemblies. We discovered 22,744,517 and 22,559,675 high-quality variants from ARS-UCD1.2 and UOA_Angus_1, respectively. The concordance between sequence- and array-called genotypes was high and the number of variants deviating from Hardy-Weinberg proportions was low at segregating sites for both assemblies. More artefactual INDELs were genotyped from UOA_Angus_1 than ARS-UCD1.2 alignments. Using the composite likelihood ratio test, we detected 40 and 33 signatures of selection from ARS-UCD1.2 and UOA_Angus_1, respectively, but the overlap between both assemblies was low. Using the 161 sequenced Brown Swiss cattle as a reference panel, we imputed sequence variant genotypes into a mapping cohort of 30,499 cattle that had microarray-derived genotypes using a two-step imputation approach. The accuracy of imputation (Beagle R2) was very high (0.87) for both assemblies. Genome-wide association studies between imputed sequence variant genotypes and six dairy traits as well as stature produced almost identical results from both assemblies. CONCLUSIONS The ARS-UCD1.2 and UOA_Angus_1 assemblies are suitable for reference-guided genome analyses in Brown Swiss cattle. Although differences in read mapping and genotyping accuracy between both assemblies are negligible, the choice of the reference genome has a large impact on detecting signatures of selection that already reached fixation using the composite likelihood ratio test. We developed a workflow that can be adapted and reused to compare the impact of reference genomes on genome analyses in various breeds, populations and species.
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Affiliation(s)
| | - Meenu Bhati
- Animal Genomics, ETH Zürich, Lindau, 8315 Switzerland
| | | | - Ruedi Fries
- Chair of Animal Breeding, TU München, Freising-Weihenstephan, 85354 Germany
| | - Hubert Pausch
- Animal Genomics, ETH Zürich, Lindau, 8315 Switzerland
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Nosková A, Bhati M, Kadri NK, Crysnanto D, Neuenschwander S, Hofer A, Pausch H. Characterization of a haplotype-reference panel for genotyping by low-pass sequencing in Swiss Large White pigs. BMC Genomics 2021; 22:290. [PMID: 33882824 PMCID: PMC8061004 DOI: 10.1186/s12864-021-07610-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 04/13/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND The key-ancestor approach has been frequently applied to prioritize individuals for whole-genome sequencing based on their marginal genetic contribution to current populations. Using this approach, we selected 70 key ancestors from two lines of the Swiss Large White breed that have been selected divergently for fertility and fattening traits and sequenced their genomes with short paired-end reads. RESULTS Using pedigree records, we estimated the effective population size of the dam and sire line to 72 and 44, respectively. In order to assess sequence variation in both lines, we sequenced the genomes of 70 boars at an average coverage of 16.69-fold. The boars explained 87.95 and 95.35% of the genetic diversity of the breeding populations of the dam and sire line, respectively. Reference-guided variant discovery using the GATK revealed 26,862,369 polymorphic sites. Principal component, admixture and fixation index (FST) analyses indicated considerable genetic differentiation between the lines. Genomic inbreeding quantified using runs of homozygosity was higher in the sire than dam line (0.28 vs 0.26). Using two complementary approaches, we detected 51 signatures of selection. However, only six signatures of selection overlapped between both lines. We used the sequenced haplotypes of the 70 key ancestors as a reference panel to call 22,618,811 genotypes in 175 pigs that had been sequenced at very low coverage (1.11-fold) using the GLIMPSE software. The genotype concordance, non-reference sensitivity and non-reference discrepancy between thus inferred and Illumina PorcineSNP60 BeadChip-called genotypes was 97.60, 98.73 and 3.24%, respectively. The low-pass sequencing-derived genomic relationship coefficients were highly correlated (r > 0.99) with those obtained from microarray genotyping. CONCLUSIONS We assessed genetic diversity within and between two lines of the Swiss Large White pig breed. Our analyses revealed considerable differentiation, even though the split into two populations occurred only few generations ago. The sequenced haplotypes of the key ancestor animals enabled us to implement genotyping by low-pass sequencing which offers an intriguing cost-effective approach to increase the variant density over current array-based genotyping by more than 350-fold.
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Affiliation(s)
- Adéla Nosková
- Animal Genomics, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland.
| | - Meenu Bhati
- Animal Genomics, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland
| | | | - Danang Crysnanto
- Animal Genomics, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland
| | | | | | - Hubert Pausch
- Animal Genomics, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland
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29
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Hiltpold M, Kadri NK, Janett F, Witschi U, Schmitz-Hsu F, Pausch H. Autosomal recessive loci contribute significantly to quantitative variation of male fertility in a dairy cattle population. BMC Genomics 2021; 22:225. [PMID: 33784962 PMCID: PMC8010996 DOI: 10.1186/s12864-021-07523-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/05/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Cattle are ideally suited to investigate the genetics of male fertility. Semen from individual bulls is used for thousands of artificial inseminations for which the fertilization success is monitored. Results from the breeding soundness examination and repeated observations of semen quality complement the fertility evaluation for each bull. RESULTS In a cohort of 3881 Brown Swiss bulls that had genotypes at 683,609 SNPs, we reveal four novel recessive QTL for male fertility on BTA1, 18, 25, and 26 using haplotype-based association testing. A QTL for bull fertility on BTA1 is also associated with sperm head shape anomalies. All other QTL are not associated with any of the semen quality traits investigated. We perform complementary fine-mapping approaches using publicly available transcriptomes as well as whole-genome sequencing data of 125 Brown Swiss bulls to reveal candidate causal variants. We show that missense or nonsense variants in SPATA16, VWA3A, ENSBTAG00000006717 and ENSBTAG00000019919 are in linkage disequilibrium with the QTL. Using whole-genome sequence data, we detect strong association (P = 4.83 × 10- 12) of a missense variant (p.Ile193Met) in SPATA16 with male fertility. However, non-coding variants exhibit stronger association at all QTL suggesting that variants in regulatory regions contribute to variation in bull fertility. CONCLUSION Our findings in a dairy cattle population provide evidence that recessive variants may contribute substantially to quantitative variation in male fertility in mammals. Detecting causal variants that underpin variation in male fertility remains difficult because the most strongly associated variants reside in poorly annotated non-coding regions.
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Affiliation(s)
- Maya Hiltpold
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland.
| | - Naveen Kumar Kadri
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland
| | - Fredi Janett
- Clinic of Reproductive Medicine, Vetsuisse Faculty, University of Zurich, 8057, Zurich, Switzerland
| | | | | | - Hubert Pausch
- Animal Genomics, Institute of Agricultural Sciences, ETH Zürich, Eschikon 27, 8315, Lindau, Switzerland
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30
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Niu G, Bak A, Nusselt M, Zhang Y, Pausch H, Flisikowska T, Schnieke AE, Flisikowski K. Allelic Expression Imbalance Analysis Identified YAP1 Amplification in p53- Dependent Osteosarcoma. Cancers (Basel) 2021; 13:cancers13061364. [PMID: 33803512 PMCID: PMC8002920 DOI: 10.3390/cancers13061364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Osteosarcoma (OS) is a highly heterogenous cancer, making the identification of genetic driving factors difficult. Genetic factors, such as heritable mutations of Rb1 and TP53, are associated with an increased risk of OS. We previously generated pigs carrying a mutated TP53 gene, which develop OS at high frequency. RNA sequencing and allelic expression imbalance analysis identified an amplification of YAP1 involved in p53- dependent OS progression. The inactivation of YAP1 inhibits proliferation, migration, and invasion, and leads to the silencing of TP63 and reconstruction of p16 expression in p53-deficient porcine OS cells. This study confirms the importance of p53/YAP1 network in cancer. Abstract Osteosarcoma (OS) is a primary bone malignancy that mainly occurs during adolescent growth, suggesting that bone growth plays an important role in the aetiology of the disease. Genetic factors, such as heritable mutations of Rb1 and TP53, are associated with an increased risk of OS. Identifying driver mutations for OS has been challenging due to the complexity of bone growth-related pathways and the extensive intra-tumoral heterogeneity of this cancer. We previously generated pigs carrying a mutated TP53 gene, which develop OS at high frequency. RNA sequencing and allele expression imbalance (AEI) analysis of OS and matched healthy control samples revealed a highly significant AEI (p = 2.14 × 10−39) for SNPs in the BIRC3-YAP1 locus on pig chromosome 9. Analysis of copy number variation showed that YAP1 amplification is associated with the AEI and the progression of OS. Accordingly, the inactivation of YAP1 inhibits proliferation, migration, and invasion, and leads to the silencing of TP63 and reconstruction of p16 expression in p53-deficient porcine OS cells. Increased p16 mRNA expression correlated with lower methylation of its promoter. Altogether, our study provides molecular evidence for the role of YAP1 amplification in the progression of p53-dependent OS.
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Affiliation(s)
- Guanglin Niu
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Agnieszka Bak
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Melanie Nusselt
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Yue Zhang
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Hubert Pausch
- Institute of Agricultural Sciences, ETH Zurich, 8092 Zurich, Switzerland;
| | - Tatiana Flisikowska
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Angelika E. Schnieke
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
| | - Krzysztof Flisikowski
- Chair of Livestock Biotechnology, Technical University of Munich, 85354 Freising, Germany; (G.N.); (A.B.); (M.N.); (Y.Z.); (T.F.); (A.E.S.)
- Correspondence:
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31
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Mehrotra A, Bhushan B, Kumar A, Panigrahi M, A K, Singh A, Tiwari AK, Pausch H, Dutt T, Mishra BP. A 1.6 Mb region on SSC2 is associated with antibody response to classical swine fever vaccination in a mixed pig population. Anim Biotechnol 2021; 33:1128-1133. [PMID: 33451274 DOI: 10.1080/10495398.2021.1873145] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Classical Swine Fever (CSF) is a contagious viral disease of pigs which is endemic in several parts of the world, including India. Prophylactic vaccination using live attenuated vaccine is the preferred method of control. However, there is significant inter-individual variation in the antibody response to vaccination. In this study, we measured the E2 antibody blocking percentage after 21 days of CSF vaccination in a mixed pig population consisting of Landrace, indigenous Ghurrah pigs, and their crossbreds. A Genome Wide Association Study (GWAS) carried out using single-SNP and haplotype based methods detected a 1.6 Mb region on SSC2 (28.92-30.52 Mb) as significantly associated with antibody response to CSF vaccination. The significant region and 1 Mb flanking sequences encompass 3 genes - EIF3M, DNAJC24 and ARL14EP, which code for proteins involved in Pestivirus replication and host immune response system. Our results combined with previous studies on immune response of pigs present this region as a suitable candidate for future functional investigations.
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Affiliation(s)
- Arnav Mehrotra
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Amit Kumar
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Karthikeyan A
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Akansha Singh
- Division of Animal Genetics, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Ashok K Tiwari
- Biological Standardization Division, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | | | - Triveni Dutt
- Division of Livestock Production and Management, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
| | - Bishnu P Mishra
- Animal Biotechnology, ICAR - Indian Veterinary Research Institute, Bareilly, UP, India
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Flossmann G, Wurmser C, Pausch H, Tenghe A, Dodenhoff J, Dahinten G, Götz KU, Russ I, Fries R. A nonsense mutation of bone morphogenetic protein-15 (BMP15) causes both infertility and increased litter size in pigs. BMC Genomics 2021; 22:38. [PMID: 33413103 PMCID: PMC7792226 DOI: 10.1186/s12864-020-07343-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Accepted: 12/22/2020] [Indexed: 11/24/2022] Open
Abstract
Background Atypical external genitalia are often a sign of reproductive organ pathologies and infertility with both environmental or genetic causes, including karyotypic abnormalities. Genome-wide association studies (GWAS) provide a means for identifying chromosomal regions harboring deleterious DNA-variants causing such phenotypes. We performed a GWAS to unravel the causes of incidental cases of atypically small vulvae in German Landrace gilts. Results A case-control GWAS involving Illumina porcine SNP60 BeadChip-called genotypes of 17 gilts with atypically small vulvae and 1818 control animals (fertile German Landrace sows) identified a significantly associated region on the X-chromosome (P = 8.81 × 10− 43). Inspection of whole-genome sequencing data in the critical area allowed us to pinpoint a likely causal variant in the form of a nonsense mutation of bone morphogenetic protein-15 (BMP15; Sscrofa11.1_X:g.44618787C>T, BMP15:p.R212X). The mutant allele occurs at a frequency of 6.2% in the German Landrace breeding population. Homozygous gilts exhibit underdeveloped, most likely not functional ovaries and are not fertile. Male carriers do not seem to manifest defects. Heterozygous sows produce 0.41±0.02 (P=4.5 × 10-83) piglets more than wildtype animals. However, the mutant allele’s positive effect on litter size accompanies a negative impact on lean meat growth. Conclusion Our results provide an example for the power of GWAS in identifying the genetic causes of a fuzzy phenotype and add to the list of natural deleterious BMP15 mutations that affect fertility in a dosage-dependent manner, the first time in a poly-ovulatory species. We advise eradicating the mutant allele from the German Landrace breeding population since the adverse effects on the lean meat growth outweigh the larger litter size in heterozygous sows. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07343-x.
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Affiliation(s)
- Gabriele Flossmann
- Lehrstuhl für Tierzucht, Technische Universität München, Freising, Germany.
| | - Christine Wurmser
- Lehrstuhl für Tierzucht, Technische Universität München, Freising, Germany
| | - Hubert Pausch
- Lehrstuhl für Tierzucht, Technische Universität München, Freising, Germany.,Animal Genomics, ETH Zürich, Zürich, Switzerland
| | - Amabel Tenghe
- Lehrstuhl für Tierzucht, Technische Universität München, Freising, Germany
| | - Jörg Dodenhoff
- Institut für Tierzucht, Bayerische Landesanstalt für Landwirtschaft, Poing, Germany
| | - Günther Dahinten
- Institut für Tierzucht, Bayerische Landesanstalt für Landwirtschaft, Poing, Germany
| | - Kay-Uwe Götz
- Institut für Tierzucht, Bayerische Landesanstalt für Landwirtschaft, Poing, Germany
| | - Ingolf Russ
- Tierzuchtforschung e. V. München, Poing, Germany
| | - Ruedi Fries
- Lehrstuhl für Tierzucht, Technische Universität München, Freising, Germany
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Hermisdorff IDC, Costa RB, de Albuquerque LG, Pausch H, Kadri NK. Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome. BMC Genomics 2020; 21:772. [PMID: 33167856 PMCID: PMC7654006 DOI: 10.1186/s12864-020-07184-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Accepted: 10/26/2020] [Indexed: 11/22/2022] Open
Abstract
Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07184-8.
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Affiliation(s)
- Isis da Costa Hermisdorff
- School of Veterinary Medicine and Animal Science, Federal University of Bahia (UFBA), Salvador, Brazil.,Animal Genomics, ETH Zurich, Zurich, Switzerland
| | - Raphael Bermal Costa
- School of Veterinary Medicine and Animal Science, Federal University of Bahia (UFBA), Salvador, Brazil
| | - Lucia Galvão de Albuquerque
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo, Brazil
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Nosková A, Wurmser C, Crysnanto D, Sironen A, Uimari P, Fries R, Andersson M, Pausch H. Deletion of porcine BOLL is associated with defective acrosomes and subfertility in Yorkshire boars. Anim Genet 2020; 51:945-949. [PMID: 32975846 DOI: 10.1111/age.12998] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/21/2020] [Indexed: 12/30/2022]
Abstract
A recessive sperm defect of Yorkshire boars was detected more than a decade ago. Affected boars produce ejaculates that contain spermatozoa with defective acrosomes, resulting in low fertility. The acrosome defect was mapped to porcine chromosome 15 but the causal mutation has not been identified. We re-analyzed microarray-derived genotypes of affected boars and confirmed that the acrosome defect maps to a 12.24 Mb segment of porcine chromosome 15. To detect the mutation causing defective acrosomes, we sequenced the genomes of two affected and three unaffected boars to an average coverage of 11-fold. Read depth analysis revealed a 55 kb deletion that is associated with the acrosome defect. The deletion encompasses the BOLL gene encoding the boule homolog, an RNA binding protein which is an evolutionarily conserved member of the DAZ (Deleted in AZoospermia) gene family. Lack of BOLL expression causes spermatogenic arrest and sperm maturation failure in many species. Boars that carry the deletion in the homozygous state produce sperm but their acrosomes are defective, suggesting that lack of porcine BOLL compromises acrosome formation. Our findings warrant further research to investigate the role of BOLL during spermatogenesis and sperm maturation in pigs.
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Affiliation(s)
- A Nosková
- Animal Genomics, ETH Zürich, Eschikon 27, Lindau, 8315, Switzerland
| | - C Wurmser
- Chair of Animal Breeding, TU München, Liesel-Beckmann-Str. 1, Freising, 85354, Germany
| | - D Crysnanto
- Animal Genomics, ETH Zürich, Eschikon 27, Lindau, 8315, Switzerland
| | - A Sironen
- Natural Resources Institute Finland (Luke), Jokioinen, 31600, Finland
| | - P Uimari
- Department of Agricultural Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - R Fries
- Chair of Animal Breeding, TU München, Liesel-Beckmann-Str. 1, Freising, 85354, Germany
| | - M Andersson
- Department of Production Animal Medicine, University of Helsinki, Helsinki, 00014, Finland
| | - H Pausch
- Animal Genomics, ETH Zürich, Eschikon 27, Lindau, 8315, Switzerland
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Crysnanto D, Pausch H. Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery. Genome Biol 2020; 21:184. [PMID: 32718320 PMCID: PMC7385871 DOI: 10.1186/s13059-020-02105-0%0a%0a] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 07/14/2020] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND The current bovine genomic reference sequence was assembled from a Hereford cow. The resulting linear assembly lacks diversity because it does not contain allelic variation, a drawback of linear references that causes reference allele bias. High nucleotide diversity and the separation of individuals by hundreds of breeds make cattle ideally suited to investigate the optimal composition of variation-aware references. RESULTS We augment the bovine linear reference sequence (ARS-UCD1.2) with variants filtered for allele frequency in dairy (Brown Swiss, Holstein) and dual-purpose (Fleckvieh, Original Braunvieh) cattle breeds to construct either breed-specific or pan-genome reference graphs using the vg toolkit. We find that read mapping is more accurate to variation-aware than linear references if pre-selected variants are used to construct the genome graphs. Graphs that contain random variants do not improve read mapping over the linear reference sequence. Breed-specific augmented and pan-genome graphs enable almost similar mapping accuracy improvements over the linear reference. We construct a whole-genome graph that contains the Hereford-based reference sequence and 14 million alleles that have alternate allele frequency greater than 0.03 in the Brown Swiss cattle breed. Our novel variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping for SNPs and Indels. CONCLUSIONS We develop the first variation-aware reference graph for an agricultural animal ( https://doi.org/10.5281/zenodo.3759712 ). Our novel reference structure improves sequence read mapping and variant genotyping over the linear reference. Our work is a first step towards the transition from linear to variation-aware reference structures in species with high genetic diversity and many sub-populations.
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36
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Crysnanto D, Pausch H. Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery. Genome Biol 2020; 21:184. [PMID: 32718320 PMCID: PMC7385871 DOI: 10.1186/s13059-020-02105-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Accepted: 07/14/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The current bovine genomic reference sequence was assembled from a Hereford cow. The resulting linear assembly lacks diversity because it does not contain allelic variation, a drawback of linear references that causes reference allele bias. High nucleotide diversity and the separation of individuals by hundreds of breeds make cattle ideally suited to investigate the optimal composition of variation-aware references. RESULTS We augment the bovine linear reference sequence (ARS-UCD1.2) with variants filtered for allele frequency in dairy (Brown Swiss, Holstein) and dual-purpose (Fleckvieh, Original Braunvieh) cattle breeds to construct either breed-specific or pan-genome reference graphs using the vg toolkit. We find that read mapping is more accurate to variation-aware than linear references if pre-selected variants are used to construct the genome graphs. Graphs that contain random variants do not improve read mapping over the linear reference sequence. Breed-specific augmented and pan-genome graphs enable almost similar mapping accuracy improvements over the linear reference. We construct a whole-genome graph that contains the Hereford-based reference sequence and 14 million alleles that have alternate allele frequency greater than 0.03 in the Brown Swiss cattle breed. Our novel variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping for SNPs and Indels. CONCLUSIONS We develop the first variation-aware reference graph for an agricultural animal ( https://doi.org/10.5281/zenodo.3759712 ). Our novel reference structure improves sequence read mapping and variant genotyping over the linear reference. Our work is a first step towards the transition from linear to variation-aware reference structures in species with high genetic diversity and many sub-populations.
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37
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Fang ZH, Nosková A, Crysnanto D, Neuenschwander S, Vögeli P, Pausch H. A 63-bp insertion in exon 2 of the porcine KIF21A gene is associated with arthrogryposis multiplex congenita. Anim Genet 2020; 51:820-823. [PMID: 32686171 DOI: 10.1111/age.12984] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 06/05/2020] [Accepted: 06/22/2020] [Indexed: 12/30/2022]
Abstract
A recessive form of arthrogryposis multiplex congenita (AMC) was detected 20 years ago in the Swiss Large White (SLW) pig population. A diagnostic marker test enabled the identification of carrier animals, but the underlying causal mutation remains unknown. To identify the mutation underlying AMC, we collected SNP chip genotyping data for 11 affected piglets and 23 healthy pigs. Association testing using 47 829 SNPs confirmed that AMC maps to SSC5 (P = 9.4 × 10-13 ). Subsequent autozygosity mapping revealed a common 6.06 Mb region (from 66 757 970 to 72 815 151 bp) of extended homozygosity in 11 piglets affected by AMC. Using WGS data, we detected a 63-bp insertion compatible with the recessive inheritance of AMC in the second exon of KIF21A gene encoding Kinesin Family Member 21A. The 63-bp insertion is predicted to introduce a premature stop codon in KIF21A gene (p.Val41_Phe42insTer) that truncates 1614 amino acids (~97%) from the protein. We found that this deleterious allele still segregates at a frequency of 0.1% in the SLW pig population. Carrier animals can now be detected unambiguously and excluded from breeding.
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Affiliation(s)
- Z-H Fang
- Animal Genomics, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
| | - A Nosková
- Animal Genomics, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
| | - D Crysnanto
- Animal Genomics, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
| | - S Neuenschwander
- Animal Genetics unit, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
| | - P Vögeli
- Animal Genetics unit, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
| | - H Pausch
- Animal Genomics, Institute of Agricultural Science, D-USYS, ETH Zürich, Zürich, 8092, Switzerland
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van den Berg I, Xiang R, Jenko J, Pausch H, Boussaha M, Schrooten C, Tribout T, Gjuvsland AB, Boichard D, Nordbø Ø, Sanchez MP, Goddard ME. Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds. Genet Sel Evol 2020; 52:37. [PMID: 32635893 PMCID: PMC7339598 DOI: 10.1186/s12711-020-00556-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 06/26/2020] [Indexed: 12/14/2022] Open
Abstract
Background Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. Results To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10−8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk. Conclusions Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.
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Affiliation(s)
- Irene van den Berg
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.
| | - Ruidong Xiang
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
| | - Janez Jenko
- GENO SA, Storhamargata 44, 2317, Hamar, Norway
| | | | - Mekki Boussaha
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Thierry Tribout
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Didier Boichard
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | | | - Marie-Pierre Sanchez
- Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France
| | - Mike E Goddard
- Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, VIC, 3083, Australia.,Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, VIC, 3010, Australia
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Hiltpold M, Niu G, Kadri NK, Crysnanto D, Fang ZH, Spengeler M, Schmitz-Hsu F, Fuerst C, Schwarzenbacher H, Seefried FR, Seehusen F, Witschi U, Schnieke A, Fries R, Bollwein H, Flisikowski K, Pausch H. Activation of cryptic splicing in bovine WDR19 is associated with reduced semen quality and male fertility. PLoS Genet 2020; 16:e1008804. [PMID: 32407316 PMCID: PMC7252675 DOI: 10.1371/journal.pgen.1008804] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 05/27/2020] [Accepted: 04/28/2020] [Indexed: 12/30/2022] Open
Abstract
Cattle are ideally suited to investigate the genetics of male reproduction, because semen quality and fertility are recorded for all ejaculates of artificial insemination bulls. We analysed 26,090 ejaculates of 794 Brown Swiss bulls to assess ejaculate volume, sperm concentration, sperm motility, sperm head and tail anomalies and insemination success. The heritability of the six semen traits was between 0 and 0.26. Genome-wide association testing on 607,511 SNPs revealed a QTL on bovine chromosome 6 that was associated with sperm motility (P = 2.5 x 10−27), head (P = 2.0 x 10−44) and tail anomalies (P = 7.2 x 10−49) and insemination success (P = 9.9 x 10−13). The QTL harbors a recessive allele that compromises semen quality and male fertility. We replicated the effect of the QTL on fertility (P = 7.1 x 10−32) in an independent cohort of 2481 Brown Swiss bulls. The analysis of whole-genome sequencing data revealed that a synonymous variant (BTA6:58373887C>T, rs474302732) in WDR19 encoding WD repeat-containing protein 19 was in linkage disequilibrium with the fertility-associated haplotype. WD repeat-containing protein 19 is a constituent of the intraflagellar transport complex that is essential for the physiological function of motile cilia and flagella. Bioinformatic and transcription analyses revealed that the BTA6:58373887 T-allele activates a cryptic exonic splice site that eliminates three evolutionarily conserved amino acids from WDR19. Western blot analysis demonstrated that the BTA6:58373887 T-allele decreases protein expression. We make the remarkable observation that, in spite of negative effects on semen quality and bull fertility, the BTA6:58373887 T-allele has a frequency of 24% in the Brown Swiss population. Our findings are the first to uncover a variant that is associated with quantitative variation in semen quality and male fertility in cattle. In cattle farming, artificial insemination is the most common method of breeding. To ensure high fertilization rates, ejaculate quality and insemination success are closely monitored in artificial insemination bulls. We analyse semen quality, insemination success and microarray-called genotypes at more than 600,000 genome-wide SNP markers of 794 bulls to identify a recessive allele that compromises semen quality. We take advantage of whole-genome sequencing to pinpoint a variant in the coding sequence of WDR19 encoding WD repeat-containing protein 19 that activates a novel exonic splice site. Our results indicate that cryptic splicing in WDR19 is associated with reduced male reproductive performance. This is the first report of a variant that contributes to quantitative variation in bovine semen quality.
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Affiliation(s)
| | - Guanglin Niu
- Livestock Biotechnology, TU München, Freising, Germany
| | | | | | - Zih-Hua Fang
- Animal Genomics, ETH Zürich, Lindau, Switzerland
| | | | | | | | | | | | - Frauke Seehusen
- Institute of Veterinary Pathology, University of Zurich, Zurich, Switzerland
| | | | | | - Ruedi Fries
- Animal Breeding, TU München, Freising, Germany
| | - Heinrich Bollwein
- Clinic of Reproductive Medicine, University of Zurich, Zürich, Switzerland
| | | | - Hubert Pausch
- Animal Genomics, ETH Zürich, Lindau, Switzerland
- * E-mail:
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Grilz-Seger G, Reiter S, Neuditschko M, Wallner B, Rieder S, Leeb T, Jagannathan V, Mesarič M, Cotman M, Pausch H, Lindgren G, Velie B, Horna M, Brem G, Druml T. A Genome-Wide Association Analysis in Noriker Horses Identifies a SNP Associated With Roan Coat Color. J Equine Vet Sci 2020; 88:102950. [PMID: 32303326 DOI: 10.1016/j.jevs.2020.102950] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/28/2020] [Accepted: 01/28/2020] [Indexed: 01/20/2023]
Abstract
The roan coat color in horses is characterized by dispersed white hair and dark points. This phenotype segregates in a broad range of horse breeds, while the underlying genetic background is still unknown. Previous studies mapped the roan locus to the KIT gene on equine chromosome 3 (ECA3). However, this association could not be validated across different horse breeds. Performing a genome-wide association analysis (GWAS) in Noriker horses, we identified a single nucleotide polymorphism (SNP) (ECA3:g.79,543.439 A > G) in the intron 17 of the KIT gene. The G -allele of the top associated SNP was present in other roan horses, namely Quarter Horse, Murgese, Slovenian, and Belgian draught horse, while it was absent in a panel of 15 breeds, including 657 non-roan horses. In further 379 gray Lipizzan horses, eight animals exhibited a heterozygous genotype (A/G). Comparative whole-genome sequence analysis of the KIT region revealed two deletions in the downstream region (ECA3:79,533,217_79,533,224delTCGTCTTC; ECA3:79,533,282_79,533,285delTTCT) and a 3 bp deletion combined with 17 bp insertion in intron 20 of KIT (ECA3:79,588,128_79,588,130delinsTTATCTCTATAGTAGTT). Within the Noriker sample, these loci were in complete linkage disequilibrium (LD) with the identified top SNP. Based upon pedigree information and historical records, we were able to trace back the genetic origin of roan coat color to a baroque gene pool. Furthermore, our data suggest allelic heterogeneity and the existence of additional roan alleles in ponies and breeds related to the English Thoroughbred. In order to study the roan phenotype segregating in those breeds, further association and verification studies are required.
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Affiliation(s)
- Gertrud Grilz-Seger
- Department of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria.
| | - Simone Reiter
- Department of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria
| | | | - Barbara Wallner
- Department of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria
| | | | - Tosso Leeb
- Department of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Vidhya Jagannathan
- Department of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
| | - Matjaz Mesarič
- Clinic for Reproduction and Large Animals, University of Ljubljana, Veterinary Faculty, Ljubljana, Slovenia
| | - Markus Cotman
- Department for Preclinical Sciences, University of Ljubljana, Veterinary Faculty, Ljubljana, Slovenia
| | | | - Gabriella Lindgren
- Department of Animal Breeding & Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden; Livestock Genetics, Department of Biosystems, KU Leuven, Leuven, Belgium
| | - Brandon Velie
- School of Life and Environmental Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Michaela Horna
- Department of Animal Husbandry, Slovak University of Agriculture in Nitra, Nitra, Slovakia
| | - Gottfried Brem
- Department of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria
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Bhati M, Kadri NK, Crysnanto D, Pausch H. Assessing genomic diversity and signatures of selection in Original Braunvieh cattle using whole-genome sequencing data. BMC Genomics 2020; 21:27. [PMID: 31914939 PMCID: PMC6950892 DOI: 10.1186/s12864-020-6446-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Accepted: 12/31/2019] [Indexed: 02/07/2023] Open
Abstract
Background Autochthonous cattle breeds are an important source of genetic variation because they might carry alleles that enable them to adapt to local environment and food conditions. Original Braunvieh (OB) is a local cattle breed of Switzerland used for beef and milk production in alpine areas. Using whole-genome sequencing (WGS) data of 49 key ancestors, we characterize genomic diversity, genomic inbreeding, and signatures of selection in Swiss OB cattle at nucleotide resolution. Results We annotated 15,722,811 SNPs and 1,580,878 Indels including 10,738 and 2763 missense deleterious and high impact variants, respectively, that were discovered in 49 OB key ancestors. Six Mendelian trait-associated variants that were previously detected in breeds other than OB, segregated in the sequenced key ancestors including variants causal for recessive xanthinuria and albinism. The average nucleotide diversity (1.6 × 10− 3) was higher in OB than many mainstream European cattle breeds. Accordingly, the average genomic inbreeding derived from runs of homozygosity (ROH) was relatively low (FROH = 0.14) in the 49 OB key ancestor animals. However, genomic inbreeding was higher in OB cattle of more recent generations (FROH = 0.16) due to a higher number of long (> 1 Mb) runs of homozygosity. Using two complementary approaches, composite likelihood ratio test and integrated haplotype score, we identified 95 and 162 genomic regions encompassing 136 and 157 protein-coding genes, respectively, that showed evidence (P < 0.005) of past and ongoing selection. These selection signals were enriched for quantitative trait loci related to beef traits including meat quality, feed efficiency and body weight and pathways related to blood coagulation, nervous and sensory stimulus. Conclusions We provide a comprehensive overview of sequence variation in Swiss OB cattle genomes. With WGS data, we observe higher genomic diversity and less inbreeding in OB than many European mainstream cattle breeds. Footprints of selection were detected in genomic regions that are possibly relevant for meat quality and adaptation to local environmental conditions. Considering that the population size is low and genomic inbreeding increased in the past generations, the implementation of optimal mating strategies seems warranted to maintain genetic diversity in the Swiss OB cattle population.
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Affiliation(s)
- Meenu Bhati
- Animal Genomics, ETH Zürich, Zürich, Switzerland.
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42
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Fang ZH, Pausch H. Multi-trait meta-analyses reveal 25 quantitative trait loci for economically important traits in Brown Swiss cattle. BMC Genomics 2019; 20:695. [PMID: 31481029 PMCID: PMC6724290 DOI: 10.1186/s12864-019-6066-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 08/27/2019] [Indexed: 01/02/2023] Open
Abstract
Background Little is known about the genetic architecture of economically important traits in Brown Swiss cattle because only few genome-wide association studies (GWAS) have been carried out in this breed. Moreover, most GWAS have been performed for single traits, thus not providing detailed insights into potentially existing pleiotropic effects of trait-associated loci. Results To compile a comprehensive catalogue of large-effect quantitative trait loci (QTL) segregating in Brown Swiss cattle, we carried out association tests between partially imputed genotypes at 598,016 SNPs and daughter-derived phenotypes for more than 50 economically important traits, including milk production, growth and carcass quality, body conformation, reproduction and calving traits in 4578 artificial insemination bulls from two cohorts of Brown Swiss cattle (Austrian-German and Swiss populations). Across-cohort multi-trait meta-analyses of the results from the single-trait GWAS revealed 25 quantitative trait loci (QTL; P < 8.36 × 10− 8) for economically relevant traits on 17 Bos taurus autosomes (BTA). Evidence of pleiotropy was detected at five QTL located on BTA5, 6, 17, 21 and 25. Of these, two QTL at BTA6:90,486,780 and BTA25:1,455,150 affect a diverse range of economically important traits, including traits related to body conformation, calving, longevity and milking speed. Furthermore, the QTL at BTA6:90,486,780 seems to be a target of ongoing selection as evidenced by an integrated haplotype score of 2.49 and significant changes in allele frequency over the past 25 years, whereas either no or only weak evidence of selection was detected at all other QTL. Conclusions Our findings provide a comprehensive overview of QTL segregating in Brown Swiss cattle. Detected QTL explain between 2 and 10% of the variation in the estimated breeding values and thus may be considered as the most important QTL segregating in the Brown Swiss cattle breed. Multi-trait association testing boosts the power to detect pleiotropic QTL and assesses the full spectrum of phenotypes that are affected by trait-associated variants. Electronic supplementary material The online version of this article (10.1186/s12864-019-6066-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zih-Hua Fang
- Animal Genomics, Institute of Agricultural Science, ETH Zürich, 8092, Zürich, Switzerland.
| | - Hubert Pausch
- Animal Genomics, Institute of Agricultural Science, ETH Zürich, 8092, Zürich, Switzerland
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43
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Crysnanto D, Wurmser C, Pausch H. Accurate sequence variant genotyping in cattle using variation-aware genome graphs. Genet Sel Evol 2019; 51:21. [PMID: 31092189 PMCID: PMC6521551 DOI: 10.1186/s12711-019-0462-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 05/03/2019] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Genotyping of sequence variants typically involves, as a first step, the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of all the DNA sequence variation within a species, reference allele bias may occur at highly polymorphic or divergent regions of the genome. Graph-based methods facilitate the comparison of sequencing reads to a variation-aware genome graph, which incorporates a collection of non-redundant DNA sequences that segregate within a species. We compared the accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely-used methods, i.e., GATK and SAMtools, which rely on linear reference genomes using whole-genome sequencing data from 49 Original Braunvieh cattle. RESULTS We discovered 21,140,196, 20,262,913, and 20,668,459 polymorphic sites using GATK, Graphtyper, and SAMtools, respectively. Comparisons between sequence variant genotypes and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the smallest number of Mendelian inconsistencies between sequence-derived single nucleotide polymorphisms and indels in nine sire-son pairs. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all the tools evaluated, particularly for animals that were sequenced at low coverage. Following imputation, the concordance between sequence- and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24% for GATK, Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but less than GATK. CONCLUSIONS Sequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants, which is not possible with the current implementation of state-of-the-art methods that rely on linear reference genomes.
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Iso-Touru T, Wurmser C, Venhoranta H, Hiltpold M, Savolainen T, Sironen A, Fischer K, Flisikowski K, Fries R, Vicente-Carrillo A, Alvarez-Rodriguez M, Nagy S, Mutikainen M, Peippo J, Taponen J, Sahana G, Guldbrandtsen B, Simonen H, Rodriguez-Martinez H, Andersson M, Pausch H. A splice donor variant in CCDC189 is associated with asthenospermia in Nordic Red dairy cattle. BMC Genomics 2019; 20:286. [PMID: 30975085 PMCID: PMC6460654 DOI: 10.1186/s12864-019-5628-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 03/20/2019] [Indexed: 01/10/2023] Open
Abstract
Background Cattle populations are highly amenable to the genetic mapping of male reproductive traits because longitudinal data on ejaculate quality and dense microarray-derived genotypes are available for thousands of artificial insemination bulls. Two young Nordic Red bulls delivered sperm with low progressive motility (i.e., asthenospermia) during a semen collection period of more than four months. The bulls were related through a common ancestor on both their paternal and maternal ancestry. Thus, a recessive mode of inheritance of asthenospermia was suspected. Results Both bulls were genotyped at 54,001 SNPs using the Illumina BovineSNP50 Bead chip. A scan for autozygosity revealed that they were identical by descent for a 2.98 Mb segment located on bovine chromosome 25. This haplotype was not found in the homozygous state in 8557 fertile bulls although five homozygous haplotype carriers were expected (P = 0.018). Whole genome-sequencing uncovered that both asthenospermic bulls were homozygous for a mutation that disrupts a canonical 5′ splice donor site of CCDC189 encoding the coiled-coil domain containing protein 189. Transcription analysis showed that the derived allele activates a cryptic splice site resulting in a frameshift and premature termination of translation. The mutated CCDC189 protein is truncated by more than 40%, thus lacking the flagellar C1a complex subunit C1a-32 that is supposed to modulate the physiological movement of the sperm flagella. The mutant allele occurs at a frequency of 2.5% in Nordic Red cattle. Conclusions Our study in cattle uncovered that CCDC189 is required for physiological movement of sperm flagella thus enabling active progression of spermatozoa and fertilization. A direct gene test may be implemented to monitor the asthenospermia-associated allele and prevent the birth of homozygous bulls that are infertile. Our results have been integrated in the Online Mendelian Inheritance in Animals (OMIA) database (https://omia.org/OMIA002167/9913/). Electronic supplementary material The online version of this article (10.1186/s12864-019-5628-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Terhi Iso-Touru
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Christine Wurmser
- Chair of Animal Breeding, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | | | - Maya Hiltpold
- Animal Genomics, ETH Zurich, 8001, Zurich, Switzerland
| | | | - Anu Sironen
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Konrad Fischer
- Chair of Livestock Biotechnology, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | - Krzysztof Flisikowski
- Chair of Livestock Biotechnology, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | - Ruedi Fries
- Chair of Animal Breeding, Technische Universität München, 85354, Freising-Weihenstephan, Germany
| | | | - Manuel Alvarez-Rodriguez
- Department of Clinical and Experimental Medicine, Linköping University, 58183, Linköping, Sweden
| | | | - Mervi Mutikainen
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Jaana Peippo
- Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | | | - Goutam Sahana
- Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Department of Molecular Biology and Genetics, Aarhus University, 8830, Tjele, Denmark
| | | | | | | | - Hubert Pausch
- Animal Genomics, ETH Zurich, 8001, Zurich, Switzerland.
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45
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Druml T, Grilz-Seger G, Neuditschko M, Horna M, Ricard A, Pausch H, Brem G. Novel insights into Sabino1 and splashed white coat color patterns in horses. Anim Genet 2018; 49:249-253. [PMID: 29635692 PMCID: PMC6001536 DOI: 10.1111/age.12657] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/18/2018] [Indexed: 11/27/2022]
Abstract
Within the framework of genome‐wide analyses using the novel Axiom® genotyping array, we investigated the distribution of two previously described coat color patterns, namely sabino1 (SBI), associated with the KIT gene (KI16+1037A), and splashed white, associated with the PAX3 gene (ECA6:g.11429753C>T; PAX3C70Y), including a total of 899 horses originating from eight different breeds (Achal Theke, Purebred Arabian, Partbred Arabian, Anglo‐Arabian, Shagya Arabian, Haflinger, Lipizzan and Noriker). Based on the data we collected we were able to demonstrate that, besides Quarter horses, the PAX3C70Y allele is also present in Noriker (seven out of 189) and Lipizzan (three out of 329) horses. The SB1 allele was present in three breeds (Haflinger, 14 out of 98; Noriker, four out of 189; Lipizzan one out of 329). Furthermore, we examined the phenotypes of SB1‐ and PAX3C70Y‐carrier horses for their characteristic white spotting patterns. None of the SB1/sb1‐carrier horses met the criteria defining the Sabino1 pattern according to current applied protocols. From 10 heterozygous PAX3C70Y‐carrier horses, two had nearly a splashed white phenotype. The results of this large‐scale experiment on the genetic association of white spotting patterns in horses underline the influence of gene interactions and population differences on complex traits such as Sabino1 and splashed white.
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Affiliation(s)
- T Druml
- Institute of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Veterinärplatz 1, A-1210, Vienna, Austria
| | | | - M Neuditschko
- Institute of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Veterinärplatz 1, A-1210, Vienna, Austria.,Agroscope, Swiss National Stud Farm, Les Longs Prés, CH-1580, Avenches, Switzerland
| | - M Horna
- Department of Animal Husbandry, Slovak University of Agriculture in Nitra, Tr. A. Hlinku 2, 949 76, Nitra, Slovak Republic
| | - A Ricard
- UMR 1313 Génétique Animale et Biologie Intégrative, Institut National de la Recherche Agronomique, Domaine de Vilvert, Bat 211, 78352, Jouy-en-Josas, France.,Institut Français du Cheval et de l'Equitation, Recherche et Innovation, La Jumenterie du Pin, 61310, Exmes, France
| | - H Pausch
- Animal Genomics, ETH Zürich, CH-8092, Zürich, Switzerland
| | - G Brem
- Institute of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Veterinärplatz 1, A-1210, Vienna, Austria
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46
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Kottmaier M, Bourier F, Pausch H, Reents T, Semmler V, Telishevska M, Koch-Buettner K, Lengauer S, Brooks S, Kornmayer M, Berger F, Rousseva E, Kolb C, Hessling G, Deisenhofer I. P379Safety of uninterrupted periprocedural edoxaban versus phenprocoumon for patients undergoing left atrial catheter ablation procedures. Europace 2018. [DOI: 10.1093/europace/euy015.190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- M Kottmaier
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - F Bourier
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - H Pausch
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - T Reents
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - V Semmler
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - M Telishevska
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - K Koch-Buettner
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - S Lengauer
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - S Brooks
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - M Kornmayer
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - F Berger
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - E Rousseva
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - C Kolb
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - G Hessling
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
| | - I Deisenhofer
- German Heart Center of Munich, Department of Electrophysiology, Munich, Germany
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Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M, Schenkel FS, Sahana G, Govignon-Gion A, Boitard S, Dolezal M, Pausch H, Brøndum RF, Bowman PJ, Thomsen B, Guldbrandtsen B, Lund MS, Servin B, Garrick DJ, Reecy J, Vilkki J, Bagnato A, Wang M, Hoff JL, Schnabel RD, Taylor JF, Vinkhuyzen AAE, Panitz F, Bendixen C, Holm LE, Gredler B, Hozé C, Boussaha M, Sanchez MP, Rocha D, Capitan A, Tribout T, Barbat A, Croiseau P, Drögemüller C, Jagannathan V, Vander Jagt C, Crowley JJ, Bieber A, Purfield DC, Berry DP, Emmerling R, Götz KU, Frischknecht M, Russ I, Sölkner J, Van Tassell CP, Fries R, Stothard P, Veerkamp RF, Boichard D, Goddard ME, Hayes BJ. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat Genet 2018; 50:362-367. [PMID: 29459679 DOI: 10.1038/s41588-018-0056-5] [Citation(s) in RCA: 173] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 01/03/2018] [Indexed: 11/09/2022]
Abstract
Stature is affected by many polymorphisms of small effect in humans 1 . In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10-8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP-seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals.
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Affiliation(s)
- Aniek C Bouwman
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, the Netherlands
| | - Hans D Daetwyler
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
| | - Amanda J Chamberlain
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Carla Hurtado Ponce
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada.,The Semex Alliance, Guelph, Ontario, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, Canada
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | | | - Simon Boitard
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Marlies Dolezal
- Platform of Bioinformatics and Statistics, University of Veterinary Medicine, Vienna, Austria
| | - Hubert Pausch
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Chair of Animal Breeding, Technische Universität München, Freising-Weihenstephan, Germany.,Animal Genomics, ETH Zurich, Zurich, Switzerland
| | - Rasmus F Brøndum
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Phil J Bowman
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | - Bo Thomsen
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Bernt Guldbrandtsen
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Mogens S Lund
- Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Bertrand Servin
- GenPhySE, Université de Toulouse, INRA, INPT, INP-ENVT, Castanet-Tolosan, France
| | - Dorian J Garrick
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - James Reecy
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Johanna Vilkki
- Green Technology, Natural Resources Institute Finland (Luke), Jokioinen, Finland
| | | | - Min Wang
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia
| | - Jesse L Hoff
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Robert D Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Jeremy F Taylor
- Division of Animal Sciences, University of Missouri, Columbia, MO, USA
| | - Anna A E Vinkhuyzen
- University of Queensland, Institute for Molecular Bioscience, St Lucia, Queensland, Australia.,University of Queensland, Queensland Brain Institute, St Lucia, Queensland, Australia
| | - Frank Panitz
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Christian Bendixen
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | - Lars-Erik Holm
- Section for Molecular Genetics and Systems Biology. Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
| | | | - Chris Hozé
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.,Allice, Paris, France
| | - Mekki Boussaha
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | | | - Dominique Rocha
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Aurelien Capitan
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France.,Allice, Paris, France
| | - Thierry Tribout
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Anne Barbat
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Pascal Croiseau
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | | | | | - Christy Vander Jagt
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia
| | | | - Anna Bieber
- Research Institute of Organic Agriculture (FiBL), Frick, Switzerland
| | - Deirdre C Purfield
- Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland
| | - Donagh P Berry
- Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Ireland
| | - Reiner Emmerling
- Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Poing, Germany
| | - Kay-Uwe Götz
- Institute of Animal Breeding, Bavarian State Research Centre for Agriculture, Poing, Germany
| | | | | | - Johann Sölkner
- University of Natural Resources and Life Sciences, Vienna, Austria
| | - Curtis P Van Tassell
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, US Department of Agriculture, Beltsville, MD, USA
| | - Ruedi Fries
- Chair of Animal Breeding, Technische Universität München, Freising-Weihenstephan, Germany
| | - Paul Stothard
- Department of Agricultural, Food and Nutritional Science/Livestock Gentec, University of Alberta, Edmonton, Alberta, Canada
| | - Roel F Veerkamp
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, the Netherlands
| | - Didier Boichard
- GABI, INRA, AgroParisTech, Université Paris Saclay, Jouy-en-Josas, France
| | - Mike E Goddard
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia.,Faculty of Land and Food Resources, University of Melbourne, Parkville, Victoria, Australia
| | - Ben J Hayes
- AgriBio, Centre for AgriBioscience, Department of Economic Development, Jobs, Transport and Resources, Bundoora, Victoria, Australia. .,Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, Queensland, Australia.
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48
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Frischknecht M, Pausch H, Bapst B, Signer-Hasler H, Flury C, Garrick D, Stricker C, Fries R, Gredler-Grandl B. Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle. BMC Genomics 2017; 18:999. [PMID: 29284405 PMCID: PMC5747239 DOI: 10.1186/s12864-017-4390-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 12/15/2017] [Indexed: 01/06/2023] Open
Abstract
Background Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required. Results In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study. Conclusions Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection. Electronic supplementary material The online version of this article (10.1186/s12864-017-4390-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mirjam Frischknecht
- Qualitas AG, Chamerstrasse 56a, 6300, Zug, Switzerland. .,Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Länggasse 85, 3052, Zollikofen, Switzerland.
| | - Hubert Pausch
- Chair of Animal Breeding, Technische Universität München, Liesel-Beckmann-Str. 1, 85354, Freising, Germany.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, 3083, Australia.,ETH Zurich, Tannenstrasse 1, 8092, Zurich, Switzerland
| | - Beat Bapst
- Qualitas AG, Chamerstrasse 56a, 6300, Zug, Switzerland
| | - Heidi Signer-Hasler
- Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Länggasse 85, 3052, Zollikofen, Switzerland
| | - Christine Flury
- Bern University of Applied Sciences, School of Agricultural, Forest and Food Sciences HAFL, Länggasse 85, 3052, Zollikofen, Switzerland
| | - Dorian Garrick
- Institute of Veterinary, Animal & Biomedical Sciences, Massey University, 4442, Palmerston North, New Zealand
| | | | - Ruedi Fries
- Chair of Animal Breeding, Technische Universität München, Liesel-Beckmann-Str. 1, 85354, Freising, Germany
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Druml T, Neuditschko M, Grilz-Seger G, Horna M, Ricard A, Mesarič M, Cotman M, Pausch H, Brem G. Population Networks Associated with Runs of Homozygosity Reveal New Insights into the Breeding History of the Haflinger Horse. J Hered 2017; 109:384-392. [DOI: 10.1093/jhered/esx114] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 12/23/2017] [Indexed: 02/04/2023] Open
Affiliation(s)
- Thomas Druml
- Institute of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria
| | | | | | - Michaela Horna
- Department of Animal Husbandry, Slovak University of Agriculture in Nitra, Nitra-Chrenová, Slovak Republic
| | - Anne Ricard
- Institut National de la Recherche Agronomique, UMR 1313 Génétique Animale et Biologie Intégrative, Jouy-en-Josas, France
- Institut Français du Cheval et de l’Equitation, Recherche et Innovation, Exmes, France
| | - Matjaz Mesarič
- Clinic for Reproduction and Large Animals, Veterinary Faculty, University of Lubljana, Cesta v Mestni log, Ljubljana, Slovenia
| | - Marco Cotman
- Institute of Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Cesta v Mestni log, Ljubljana, Slovenia
| | | | - Gottfried Brem
- Institute of Animal Breeding and Genetics, University of Veterinary Sciences Vienna, Vienna, Austria
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50
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Pausch H, Emmerling R, Gredler-Grandl B, Fries R, Daetwyler HD, Goddard ME. Meta-analysis of sequence-based association studies across three cattle breeds reveals 25 QTL for fat and protein percentages in milk at nucleotide resolution. BMC Genomics 2017; 18:853. [PMID: 29121857 PMCID: PMC5680815 DOI: 10.1186/s12864-017-4263-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 11/02/2017] [Indexed: 11/25/2022] Open
Abstract
Background Genotyping and whole-genome sequencing data have been generated for hundreds of thousands of cattle. International consortia used these data to compile imputation reference panels that facilitate the imputation of sequence variant genotypes for animals that have been genotyped using dense microarrays. Association studies with imputed sequence variant genotypes allow for the characterization of quantitative trait loci (QTL) at nucleotide resolution particularly when individuals from several breeds are included in the mapping populations. Results We imputed genotypes for 28 million sequence variants in 17,229 cattle of the Braunvieh, Fleckvieh and Holstein breeds in order to compile large mapping populations that provide high power to identify QTL for milk production traits. Association tests between imputed sequence variant genotypes and fat and protein percentages in milk uncovered between six and thirteen QTL (P < 1e-8) per breed. Eight of the detected QTL were significant in more than one breed. We combined the results across breeds using meta-analysis and identified a total of 25 QTL including six that were not significant in the within-breed association studies. Two missense mutations in the ABCG2 (p.Y581S, rs43702337, P = 4.3e-34) and GHR (p.F279Y, rs385640152, P = 1.6e-74) genes were the top variants at QTL on chromosomes 6 and 20. Another known causal missense mutation in the DGAT1 gene (p.A232K, rs109326954, P = 8.4e-1436) was the second top variant at a QTL on chromosome 14 but its allelic substitution effects were inconsistent across breeds. It turned out that the conflicting allelic substitution effects resulted from flaws in the imputed genotypes due to the use of a multi-breed reference population for genotype imputation. Conclusions Many QTL for milk production traits segregate across breeds and across-breed meta-analysis has greater power to detect such QTL than within-breed association testing. Association testing between imputed sequence variant genotypes and phenotypes of interest facilitates identifying causal mutations provided the accuracy of imputation is high. However, true causal mutations may remain undetected when the imputed sequence variant genotypes contain flaws. It is highly recommended to validate the effect of known causal variants in order to assess the ability to detect true causal mutations in association studies with imputed sequence variants. Electronic supplementary material The online version of this article (10.1186/s12864-017-4263-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Hubert Pausch
- Animal Genomics, Institute of Agricultural Sciences, ETH Zurich, 8092, Zurich, Switzerland. .,Agriculture Research Division, Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, AgriBio, VIC, 3083, Australia.
| | - Reiner Emmerling
- Institute of Animal Breeding, Bavarian State Research Center for Agriculture, 85586, Grub, Germany
| | | | - Ruedi Fries
- Animal Breeding, Technische Universitaet Muenchen, 85354, Freising, Germany
| | - Hans D Daetwyler
- Agriculture Research Division, Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, AgriBio, VIC, 3083, Australia.,School of Applied Systems Biology, LaTrobe University, Bundoora, VIC, 3083, Australia
| | - Michael E Goddard
- Agriculture Research Division, Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, AgriBio, VIC, 3083, Australia.,Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia
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