IDEAS home Printed from https://ideas.repec.org/a/spr/jagbes/v22y2017i2d10.1007_s13253-017-0277-6.html
   My bibliography  Save this article

Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach

Author

Listed:
  • Rohan Fernando

    (Iowa State University)

  • Ali Toosi

    (Iowa State University)

  • Anna Wolc

    (Iowa State University)

  • Dorian Garrick

    (Iowa State University)

  • Jack Dekkers

    (Iowa State University)

Abstract

Data that are collected for whole-genome prediction can also be used for genome-wide association studies (GWAS). This paper discusses how Bayesian multiple-regression methods that are used for whole-genome prediction can be adapted for GWAS. It is argued here that controlling the posterior type I error rate (PER) is more suitable than controlling the genomewise error rate (GER) for controlling false positives in GWAS. It is shown here that under ideal conditions, i.e., when the model is correctly specified, PER can be controlled by using Bayesian posterior probabilities that are easy to obtain. Computer simulation was used to examine the properties of this Bayesian approach when the ideal conditions were not met. Results indicate that even then useful inferences can be made.

Suggested Citation

  • Rohan Fernando & Ali Toosi & Anna Wolc & Dorian Garrick & Jack Dekkers, 2017. "Application of Whole-Genome Prediction Methods for Genome-Wide Association Studies: A Bayesian Approach," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(2), pages 172-193, June.
    • Handle: RePEc:spr:jagbes:v:22:y:2017:i:2:d:10.1007_s13253-017-0277-6
    DOI: 10.1007/s13253-017-0277-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13253-017-0277-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13253-017-0277-6?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. Ben J Hayes & Jennie Pryce & Amanda J Chamberlain & Phil J Bowman & Mike E Goddard, 2010. "Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits," PLOS Genetics, Public Library of Science, vol. 6(9), pages 1-11, September.
    2. Emre Karaman & Hao Cheng & Mehmet Z Firat & Dorian J Garrick & Rohan L Fernando, 2016. "An Upper Bound for Accuracy of Prediction Using GBLUP," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-18, August.
    3. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shibo Wang & Fangjie Xie & Shizhong Xu, 2022. "Estimating genetic variance contributed by a quantitative trait locus: A random model approach," PLOS Computational Biology, Public Library of Science, vol. 18(3), pages 1-30, March.
    2. Marion Patxot & Daniel Trejo Banos & Athanasios Kousathanas & Etienne J. Orliac & Sven E. Ojavee & Gerhard Moser & Alexander Holloway & Julia Sidorenko & Zoltan Kutalik & Reedik Mägi & Peter M. Vissch, 2021. "Probabilistic inference of the genetic architecture underlying functional enrichment of complex traits," Nature Communications, Nature, vol. 12(1), pages 1-16, December.

    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    11. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    12. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    13. Dvorkin Daniel & Biehs Brian & Kechris Katerina, 2013. "A graphical model method for integrating multiple sources of genome-scale data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 469-487, August.
    14. Robert A. Connolly & Richard J. Rendleman, 2012. "What It Takes to Win on the PGA TOUR (If Your Name Is “Tiger” or If It Isn't)," Interfaces, INFORMS, vol. 42(6), pages 554-576, December.
    15. Yuan Zhao, 2014. "Cross-sector fund performance comparison: the role of real estate mutual funds," ERES eres2014_213, European Real Estate Society (ERES).
    16. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    17. David Ardia & Lukasz Gatarek & Lennart F. hoogerheide, 2014. "A New Bootstrap Test for the Validity of a Set of Marginal Models for Multiple Dependent Time Series: an Application to Risk Analysis," Cahiers de recherche 1413, CIRPEE.
    18. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2021. "Do actively managed US mutual funds produce positive alpha?," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 472-492.
    19. Ilias Thomas & Alex M. Dickens & Jussi P. Posti & Endre Czeiter & Daniel Duberg & Tim Sinioja & Matilda Kråkström & Isabel R. A. Retel Helmrich & Kevin K. W. Wang & Andrew I. R. Maas & Ewout W. Steyer, 2022. "Serum metabolome associated with severity of acute traumatic brain injury," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    20. Zhou, Jia & Li, Yang & Zheng, Zemin & Li, Daoji, 2022. "Reproducible learning in large-scale graphical models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

    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:spr:jagbes:v:22:y:2017:i:2:d:10.1007_s13253-017-0277-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.