README.md

Bayesian Genomic Linear Models Applied to GE Genome Selection

Bayesian Genomic Linear Models Applied to GE Genome Selection - Development version 0.6.5

\[Last README update: 2018-11-13\]

Maturing LGPL, Version 3.0 Status of the Repo: Active Dowloads from the CRAN CRAN

Table of contents

News of this version (0.6.5)

Changes:

See the last updates in NEWS.

Instructions for proper implementation

Installation

To complete installation of dev version of BGGE from GitHub, you must have previously installed the devtools package.

install.packages('devtools')
devtools::install_github('italo/BGGE')

If you want to use the stable version of BGGE package, install it from CRAN.

install.packages('BGGE')

Load the package

library(BGGE)

Example of simple usage of the package

library(BGGE)
library(BGLR)
data(wheat)

X <- scale(wheat.X, scale = TRUE, center = TRUE)
rownames(X) <- 1:599
pheno_geno <- data.frame(env = gl(n = 4, k = 599), 
                         GID = gl(n=599, k=1, length = 599*4),
                         value = as.vector(wheat.Y))

# Creating kernel for GE model

K <- getK(Y = pheno_geno, X = X, kernel = "GB", model = "MM")
y <- as.vector(wheat.Y)

fit <- BGGE(y = y, K = K, ne = rep(599, 4))
fit
## Model Fitted with: 
##  1000  Iterations, burning the first  200  and thining every  3 
## 
##  Some predicted Values: 
##  [1]  -0.2737  -0.4081  -0.3799   0.3694  -0.0362   0.2354   0.2840
##  [8]  -0.1194  -0.7742  -1.0280
## 
##  Use str() function to found more datailed information.
plot(fit)

Others params

| params | Use | | ------- | --------------------------------------------------------------------- | | XF | Design matrix for fixed effects. | | ite | Number of iterations. | | ne | Number of subjects by environment. | | burn | Number of iterations to be discarded as burn-in. | | thin | Thinin interval. | | verbose | Should report be printed on screen? | | tol | tolerance for zero. Default is 1e-10 | | R2 | the proportion of variance expected to be explained by the regression |

Citation

First option, by the paper.

@article {Granato2018,
    author = {Granato, Italo and Cuevas, Jaime and Luna-V{\'a}zquez, Francisco J. and Crossa, Jos{\'e} and Montesinos-L{\'o}pez, Osval A. and Burgue{\~n}o, Juan and Fritsche-Neto, Roberto},
    title = {BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype {\texttimes} Environment Interaction Models},
    year = {2018},
    doi = {10.1534/g3.118.200435},
    publisher = {G3: Genes, Genomes, Genetics},
    URL = {http://www.g3journal.org/content/early/2018/07/25/g3.118.200435},
    eprint = {http://www.g3journal.org/content/early/2018/07/25/g3.118.200435.full.pdf},
    journal = {G3: Genes, Genomes, Genetics}
}

Second option, by the package

citation('BGGE')
## 
## To cite package 'BGGE' in publications use:
## 
##   Italo Granato, Luna-Vázquez Francisco J. and Cuevas Jaime
##   (2018). BGGE: Bayesian Genomic Linear Models Applied to GE
##   Genome Selection. R package version 0.6.5.
##   https://github.com/italo-granato/BGGE
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {BGGE: Bayesian Genomic Linear Models Applied to GE Genome Selection},
##     author = {Italo Granato and Luna-Vázquez {Francisco J.} and Cuevas Jaime},
##     year = {2018},
##     note = {R package version 0.6.5},
##     url = {https://github.com/italo-granato/BGGE},
##   }

Contributions

If you have any suggestions or feedback, I would love to hear about it. Feel free to report new issues in this link, also if you want to request a feature/report a bug, or make a pull request if you can contribute.

Research and implementations

Authors



italo-granato/BGGE documentation built on May 20, 2019, 1:29 p.m.