IDEAS home Printed from https://ideas.repec.org/a/caa/jnlcjs/v65y2020i12id83-2020-cjas.html
   My bibliography  Save this article

The use of genomic data and imputation methods in dairy cattle breeding

Author

Listed:
  • Anita Klímová

    (Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic
    Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague - Uhříněves, Czech Republic)

  • Eva Kašná

    (Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague - Uhříněves, Czech Republic)

  • Karolína Machová

    (Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

  • Michaela Brzáková

    (Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague - Uhříněves, Czech Republic)

  • Josef Přibyl

    (Department of Genetics and Breeding of Farm Animals, Institute of Animal Science, Prague - Uhříněves, Czech Republic)

  • Luboš Vostrý

    (Department of Genetics and Breeding, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague - Suchdol, Czech Republic)

Abstract

The inclusion of animal genotype data has contributed to the development of genomic selection. Animals are selected not only based on pedigree and phenotypic data but also on the basis of information about their genotypes. Genomic information helps to increase the accuracy of selection of young animals and thus enables a reduction of the generation interval. Obtaining information about genotypes in the form of SNPs (single nucleotide polymorphisms) has led to the development of new chips for genotyping. Several methods of genomic comparison have been developed as a result. One of the methods is data imputation, which allows the missing SNPs to be calculated using low-density chips to high-density chips. Through imputations, it is possible to combine information from diverse sets of chips and thus obtain more information about genotypes at a lower cost. Increasing the amount of data helps increase the reliability of predicting genomic breeding values. Imputation methods are increasingly used in genome-wide association studies. When classical genotyping and genome-wide sequencing data are combined, this option helps to increase the chances of identifying loci that are associated with economically significant traits.

Suggested Citation

  • Anita Klímová & Eva Kašná & Karolína Machová & Michaela Brzáková & Josef Přibyl & Luboš Vostrý, 2020. "The use of genomic data and imputation methods in dairy cattle breeding," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 65(12), pages 445-453.
  • Handle: RePEc:caa:jnlcjs:v:65:y:2020:i:12:id:83-2020-cjas
    DOI: 10.17221/83/2020-CJAS
    as

    Download full text from publisher

    File URL: http://cjas.agriculturejournals.cz/doi/10.17221/83/2020-CJAS.html
    Download Restriction: free of charge

    File URL: http://cjas.agriculturejournals.cz/doi/10.17221/83/2020-CJAS.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/83/2020-CJAS?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bryan N Howie & Peter Donnelly & Jonathan Marchini, 2009. "A Flexible and Accurate Genotype Imputation Method for the Next Generation of Genome-Wide Association Studies," PLOS Genetics, Public Library of Science, vol. 5(6), pages 1-15, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Svensson & Matilda Rentoft & Anna M Dahlin & Emma Lundholm & Pall I Olason & Andreas Sjödin & Carin Nylander & Beatrice S Melin & Johan Trygg & Erik Johansson, 2020. "A whole-genome sequenced control population in northern Sweden reveals subregional genetic differences," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-18, September.
    2. Chuan Gao & Nan Wang & Xiuqing Guo & Julie T Ziegler & Kent D Taylor & Anny H Xiang & Yang Hai & Steven J Kridel & Jerry L Nadler & Fouad Kandeel & Leslie J Raffel & Yii-Der I Chen & Jill M Norris & J, 2015. "A Comprehensive Analysis of Common and Rare Variants to Identify Adiposity Loci in Hispanic Americans: The IRAS Family Study (IRASFS)," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    3. Paul S de Vries & Maria Sabater-Lleal & Daniel I Chasman & Stella Trompet & Tarunveer S Ahluwalia & Alexander Teumer & Marcus E Kleber & Ming-Huei Chen & Jie Jin Wang & John R Attia & Riccardo E Mario, 2017. "Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-22, January.
    4. Bo Jiang & Jun S. Liu, 2015. "Bayesian Partition Models for Identifying Expression Quantitative Trait Loci," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1350-1361, December.
    5. Rakesh Chettier & Lesa Nelson & James W Ogilvie & Hans M Albertsen & Kenneth Ward, 2015. "Haplotypes at LBX1 Have Distinct Inheritance Patterns with Opposite Effects in Adolescent Idiopathic Scoliosis," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-11, February.
    6. Michel S. Naslavsky & Marilia O. Scliar & Guilherme L. Yamamoto & Jaqueline Yu Ting Wang & Stepanka Zverinova & Tatiana Karp & Kelly Nunes & José Ricardo Magliocco Ceroni & Diego Lima Carvalho & Carlo, 2022. "Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    7. Steinrücken, Matthias & Paul, Joshua S. & Song, Yun S., 2013. "A sequentially Markov conditional sampling distribution for structured populations with migration and recombination," Theoretical Population Biology, Elsevier, vol. 87(C), pages 51-61.
    8. Anshuman Sewda & A J Agopian & Elizabeth Goldmuntz & Hakon Hakonarson & Bernice E Morrow & Fadi Musfee & Deanne Taylor & Laura E Mitchell & on behalf of the Pediatric Cardiac Genomics Consortium, 2020. "Gene-based analyses of the maternal genome implicate maternal effect genes as risk factors for conotruncal heart defects," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-15, June.
    9. Lin Yuan & Chang-An Yuan & De-Shuang Huang, 2017. "FAACOSE: A Fast Adaptive Ant Colony Optimization Algorithm for Detecting SNP Epistasis," Complexity, Hindawi, vol. 2017, pages 1-10, September.
    10. E P A van Iperen & G K Hovingh & F W Asselbergs & A H Zwinderman, 2017. "Extending the use of GWAS data by combining data from different genetic platforms," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-11, February.
    11. Liqiong Xue & Chunming Pan & Zhaohui Gu & Shuangxia Zhao & Bing Han & Wei Liu & Shaoying Yang & Shasha Yu & Yixuan Sun & Jun Liang & Guanqi Gao & Xiaomei Zhang & Guoyue Yuan & Changgui Li & Wenhua Du , 2013. "Genetic Heterogeneity of Susceptibility Gene in Different Ethnic Populations: Refining Association Study of PTPN22 for Graves’ Disease in a Chinese Han Population," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    12. Carl Nettelblad, 2013. "Breakdown of Methods for Phasing and Imputation in the Presence of Double Genotype Sharing," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-5, March.
    13. Joseph Vijai & Tomas Kirchhoff & Kasmintan A Schrader & Jennifer Brown & Ana Virginia Dutra-Clarke & Christopher Manschreck & Nichole Hansen & Rohini Rau-Murthy & Kara Sarrel & Jennifer Przybylo & Soh, 2013. "Susceptibility Loci Associated with Specific and Shared Subtypes of Lymphoid Malignancies," PLOS Genetics, Public Library of Science, vol. 9(1), pages 1-11, January.
    14. Viinikainen, Jutta & Bryson, Alex & Böckerman, Petri & Kari, Jaana T. & Lehtimäki, Terho & Raitakari, Olli & Viikari, Jorma & Pehkonen, Jaakko, 2022. "Does better education mitigate risky health behavior? A mendelian randomization study," Economics & Human Biology, Elsevier, vol. 46(C).
    15. Cavin K Ward-Caviness & Paul S de Vries & Kerri L Wiggins & Jennifer E Huffman & Lisa R Yanek & Lawrence F Bielak & Franco Giulianini & Xiuqing Guo & Marcus E Kleber & Tim Kacprowski & Stefan Groß & A, 2019. "Mendelian randomization evaluation of causal effects of fibrinogen on incident coronary heart disease," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-18, May.
    16. Ani Manichaikul & Xin-Qun Wang & Solomon K Musani & David M Herrington & Wendy S Post & James G Wilson & Stephen S Rich & Annabelle Rodriguez, 2015. "Association of the Lipoprotein Receptor SCARB1 Common Missense Variant rs4238001 with Incident Coronary Heart Disease," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-16, May.
    17. Morten Dybdahl Krebs & Gonçalo Espregueira Themudo & Michael Eriksen Benros & Ole Mors & Anders D. Børglum & David Hougaard & Preben Bo Mortensen & Merete Nordentoft & Michael J. Gandal & Chun Chieh F, 2021. "Associations between patterns in comorbid diagnostic trajectories of individuals with schizophrenia and etiological factors," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    18. Heejung Shim & Daniel I Chasman & Joshua D Smith & Samia Mora & Paul M Ridker & Deborah A Nickerson & Ronald M Krauss & Matthew Stephens, 2015. "A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    19. Sijie Wu & Manfei Zhang & Xinzhou Yang & Fuduan Peng & Juan Zhang & Jingze Tan & Yajun Yang & Lina Wang & Yanan Hu & Qianqian Peng & Jinxi Li & Yu Liu & Yaqun Guan & Chen Chen & Merel A Hamer & Tamar , 2018. "Genome-wide association studies and CRISPR/Cas9-mediated gene editing identify regulatory variants influencing eyebrow thickness in humans," PLOS Genetics, Public Library of Science, vol. 14(9), pages 1-22, September.
    20. Mette K Andersen & Emil Jørsboe & Line Skotte & Kristian Hanghøj & Camilla H Sandholt & Ida Moltke & Niels Grarup & Timo Kern & Yuvaraj Mahendran & Bolette Søborg & Peter Bjerregaard & Christina V L L, 2020. "The derived allele of a novel intergenic variant at chromosome 11 associates with lower body mass index and a favorable metabolic phenotype in Greenlanders," PLOS Genetics, Public Library of Science, vol. 16(1), pages 1-17, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlcjs:v:65:y:2020:i:12:id:83-2020-cjas. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.