Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package

Methods in Molecular Biology

Volumn 1019, Issue , 2013, Pages 299-320

  De Los Campos, Gustavo ^a   Pérez, Paulino ^b   Vazquez, Ana I ^a   Crossa, José ^c  

^a UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^b Colegio de Postgraduados ^*  (Mexico)

^c INTERNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER CIMMYT   (Mexico)

Author keywords

Bayesian; Genomic selection; Shrinkage and genomic prediction; Whole genome prediction

Indexed keywords

ALGORITHM; ARTICLE; BAYES THEOREM; BREEDING; MATHEMATICAL COMPUTING; PLANT BREEDING; PRIORITY JOURNAL;

ANIMALIA;

EID: 84883155162     PISSN: 10643745     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-1-62703-447-0_12     Document Type: Article

References (15)

1. Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103(482):681-686
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
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
4. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819-1829
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
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
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
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
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
12. Habier D, Fernando R, Kizilkaya K, Garrick D (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinforma 12(1):186
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&lr-&id-bnewaF4Uq2wC&oi-fnd&pg-PR5&dq- mrode+animal+breeding+genetics& ots-05EITYRwMh&sig-aDDnpg69HSI4- acAmi38yTAVMjqg
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
15. Casella G, George EI (1992) Explaining the Gibbs sampler. Am Stat 46:167-174