IDEAS home Printed from https://ideas.repec.org/a/caa/jnlcjs/v63y2018i4id62-2017-cjas.html
   My bibliography  Save this article

Genomic response to natural selection within alpine cattle breeds

Author

Listed:
  • Nina Moravčíková

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

  • Mojca Simčič

    (Department of Animal Science, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia)

  • Gábor Mészáros

    (Division Livestock Science, University of Natural Resources and Life Sciences Vienna, Vienna, Austria)

  • Johann Sölkner

    (Division Livestock Science, University of Natural Resources and Life Sciences Vienna, Vienna, Austria)

  • Veronika Kukučková

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

  • Michal Vlček

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

  • Anna Trakovická

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

  • Ondrej Kadlečík

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

  • Radovan Kasarda

    (Department of Animal Genetics and Breeding Biology, Slovak University of Agriculture in Nitra, Nitra, Slovakia)

Abstract

The aim of this study was to analyse the genomic regions that have been target of natural selection with respect to identifying the loci responsible mainly for fitness traits across six alpine cattle breeds. The genome-wide scan for selection signatures was performed using genotyping data from totally 465 animals. After applying data quality control, overall 35 873 single nucleotide polymorphisms were useable for the subsequent analysis. The detection of genomic regions affected by natural selection was carried out using the approach of principal component analysis. The analysis was based on the assumption that markers extremely related to the population structure are also candidates for local adaptation potential of the population. Based on the expected false discovery rate equal to 10% up to 1138 loci were identified as outliers. The strongest signals of selection were found in genomic regions on BTA 1, 2, 3, 6, 9, 11, 13, and 22. Most genes located in the identified regions have been previously associated with immunity system as well as body growth and muscle formation that mainly reflect the pressure of both natural and artificial selection in respect to adaptation of analysed breeds to the local environmental conditions. The results also signalized that those regions represent a correlated selection response in way to maintain the fitness of analysed breeds.

Suggested Citation

  • Nina Moravčíková & Mojca Simčič & Gábor Mészáros & Johann Sölkner & Veronika Kukučková & Michal Vlček & Anna Trakovická & Ondrej Kadlečík & Radovan Kasarda, 2018. "Genomic response to natural selection within alpine cattle breeds," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 63(4), pages 136-143.
  • Handle: RePEc:caa:jnlcjs:v:63:y:2018:i:4:id:62-2017-cjas
    DOI: 10.17221/62/2017-CJAS
    as

    Download full text from publisher

    File URL: http://cjas.agriculturejournals.cz/doi/10.17221/62/2017-CJAS.html
    Download Restriction: free of charge

    File URL: http://cjas.agriculturejournals.cz/doi/10.17221/62/2017-CJAS.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.17221/62/2017-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. G. Mészáros & O. Kadlečík & R. Kasarda & J. Sölkner, 2013. "Analysis of longevity in the Slovak Pinzgau population - extension to the animal model," Czech Journal of Animal Science, Czech Academy of Agricultural Sciences, vol. 58(7), pages 289-295.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    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. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    2. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    3. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    4. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    5. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    6. A Bottle & P Aylin, 2011. "Predicting the false alarm rate in multi-institution mortality monitoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1711-1718, September.
    7. Van Hanh Nguyen & Catherine Matias, 2014. "On Efficient Estimators of the Proportion of True Null Hypotheses in a Multiple Testing Setup," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1167-1194, December.
    8. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    9. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    10. Ebrahimi, Nader, 2008. "Simultaneous control of false positives and false negatives in multiple hypotheses testing," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 437-450, March.
    11. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
    12. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    13. Mark Rempel, 2016. "Improving Overnight Loan Identification in Payments Systems," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 549-564, March.
    14. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2015.
    15. Nucera, Federico & Valente, Giorgio, 2013. "Carry trades and the performance of currency hedge funds," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 407-425.
    16. Nickole Moon & Christopher P. Morgan & Ruth Marx-Rattner & Alyssa Jeng & Rachel L. Johnson & Ijeoma Chikezie & Carmen Mannella & Mary D. Sammel & C. Neill Epperson & Tracy L. Bale, 2024. "Stress increases sperm respiration and motility in mice and men," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    17. Axel Gandy & Georg Hahn, 2016. "A Framework for Monte Carlo based Multiple Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1046-1063, December.
    18. Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    19. Iain Melvin & Jason Weston & William Stafford Noble & Christina Leslie, 2011. "Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-8, January.
    20. Won, Joong-Ho & Lim, Johan & Yu, Donghyeon & Kim, Byung Soo & Kim, Kyunga, 2014. "Monotone false discovery rate," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 86-93.

    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:63:y:2018:i:4:id:62-2017-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.