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Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package

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Genome-Wide Association Studies and Genomic Prediction

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1019))

Abstract

The BLR (Bayesian linear regression) package of R implements several Bayesian regression models for continuous traits. The package was originally developed for implementing the Bayesian LASSO (BL) of Park and Casella (J Am Stat Assoc 103(482):681–686, 2008), extended to accommodate fixed effects and regressions on pedigree using methods described by de los Campos et al. (Genetics 182(1):375–385, 2009). In 2010 we further developed the code into an R-package, reprogrammed some internal aspects of the algorithm in the C language to increase computational speed, and further documented the package (Plant Genome J 3(2):106–116, 2010). The first version of BLR was launched in 2010 and since then the package has been used for multiple publications and is being routinely used for genomic evaluations in some animal and plant breeding programs. In this article we review the models implemented by BLR and illustrate the use of the package with examples.

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References

  1. Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103(482):681–686

    Article  CAS  Google Scholar 

  2. de los Campos G, Naya H, Gianola D, Crossa J, Legarra A, Manfredi E, Weigel K, Cotes JM (2009) Predicting quantitative traits with regression models for dense molecular markers and pedigree. Genetics 182(1):375–385

    Article  PubMed  Google Scholar 

  3. Pérez P, de los Campos G, Crossa J, Gianola D (2010) Genomic-enabled prediction based on molecular markers and pedigree using the Bayesian linear regression package in R. Plant Genome J 3(2):106–116

    Article  Google Scholar 

  4. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829

    PubMed  CAS  Google Scholar 

  5. de los Campos G, Hickey JM, Detwyler HD, Pong-Wong R, Calus MPL (2013) Whole genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345

    Article  PubMed  Google Scholar 

  6. R Development Core Team (2010) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [Internet]. R Foundation for Statistical Computing. http://www.R-project.org

  7. Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1):55–67

    Article  Google Scholar 

  8. Vazquez AI, Bates DM, Rosa GJM, Gianola D, Weigel KA (2010) Technical note: an R package for fitting generalized linear mixed models in animal breeding1. J Anim Sci 88(2):497–504

    Article  PubMed  CAS  Google Scholar 

  9. Bates D, Vazquez AI (2009) Pedigreemm: pedigree-based mixed-effects models V 0.2-4 [Internet]. http://cran.r-project.org/web/packages/pedigreemm/index.html

  10. McLaren CG, Bruskiewich RM, Portugal AM, Cosico AB (2005) The international rice information system. A platform for meta-analysis of rice crop data. Plant Physiol 139(2):637–642

    Article  PubMed  CAS  Google Scholar 

  11. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Soc Series B (Stat Methodol) 64(4):583–639

    Article  Google Scholar 

  12. Habier D, Fernando R, Kizilkaya K, Garrick D (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinforma 12(1):186

    Article  Google Scholar 

  13. Mrode RA, Thompson R (2005) Linear models for the prediction of animal breeding values [Internet]. Cabi. [cited 15 Aug 2012] http://books.google.com/books?hl=en%26;lr=%26;id=bnewaF4Uq2wC%26;oi=fnd%26;pg=PR5%26;dq=mrode+animal+breeding+genetics%26;ots=05EITYRwMh%26;sig=aDDnpg69HSI4acAmi38yTAVMjqg

  14. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6:721–741

    Article  PubMed  CAS  Google Scholar 

  15. Casella G, George EI (1992) Explaining the Gibbs sampler. Am Stat 46:167–174

    Google Scholar 

Download references

Acknowledgments

de los Campos, Pérez, and Crossa acknowledge financial support from the International Maize and Wheat Improvement Center (CIMMYT). Pérez and de los Campos were also supported by NIH Grants R01GM101219-01 and R01GM099992-01A1.

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de los Campos, G., Pérez, P., Vazquez, A.I., Crossa, J. (2013). Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-Package. In: Gondro, C., van der Werf, J., Hayes, B. (eds) Genome-Wide Association Studies and Genomic Prediction. Methods in Molecular Biology, vol 1019. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-447-0_12

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  • DOI: https://doi.org/10.1007/978-1-62703-447-0_12

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-446-3

  • Online ISBN: 978-1-62703-447-0

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