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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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.
    2. 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.

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