Authors: Gustavo de los Campos gustavoc@msu.edu , Paulino Perez-Rodriguez perpdgo@gmail.com & BOGARD Matthieu M.BOGARD@arvalis.fr
Citations: FW-Function and BGLR R-package.
Before you run the code below, download the FW.BGLR function and the following Sample Data.
You will need to source the FW_BGLR.R
function and load the sample_FW.RData
data set to run the examples below.
The following data and code is reproduced from the supplementary data from de los Campos et al., Nat. Comm., 2020.
Here we present a function to perform the Finlay-Wilkinson (1963) analysis in two steps using the BGLR (Pérez and de los Campos) package in R (R Core Team, 2019). We created the R function FW.BGLR to perform the analysis. The function takes the following arguments:
- pheno: a data.frame with 3 columns, VAR (variety),
ENV (environment), y (response variable).
- X: matrix of markers coded for additive effects (e.g, 0, 1, 2).
- G: matrix with genomic relationships between individuals.
The matrix X contains marker information for the varieties given in the data.frame. This matrix is used to compute the additive relationships between individuals (Lopez-Cruz et al., 2015) if matrix G is not given.
The function returns a list object with the following elements:m
- yHat: vector with predicted values for the response variable.
- VAR: a data frame with the columns ID (varieties),
int (estimated intercept), intSD (estimated standard deviation for intercept),
slope (estimated slope) and slopeSD (estimated standard deviation for slope).
- ENV: vector with environmental effects.
The following R code shows how to load sample data (raw means).
# Load data
load("sample_FW.RData")
#list objects, at this point you should have at least 2 objects: pheno and G.
ls()
The code below shows how to load the function and perform the analysis assuming that objects pheno and G are already loaded in the R environment.
# Loads BGLR library
library(BGLR)
# Loads function for FW analysis
source("FW_BGLR.R")
# Fits FW-regression in two steps
fm<-FW.BGLR(pheno=pheno,G=G,verbose=FALSE)
# Predictions
head(fm$yHat)
# Intercept and slopes
head(fm$VAR)
# Environmental effects
head(fm$ENV)
# Plot Environment effect vs Genotype performance
plot.FW(fm,pheno)
Finlay K.W. and G.N. Wilkinson. 1963. “The Analysis of Adaptation in a Plant-Breeding Programme.” Australian Journal of Agricultural Research 14 (6). CSIRO PUBLISHING:742. https://doi.org/10.1071/AR9630742.
Lopez-Cruz M., J. Crossa, D. Bonnett, S. Dreisigacker, J. Poland, J.L. Jannink, R.P. Singh, E. Autrique, and G. de los Campos. 2015. “Increased Prediction Accuracy in Wheat Breeding Trials Using a Marker x Environment Interaction Genomic Selection Model.” G3 (Bethesda, Md.) 5 (4). G3: Genes, Genomes, Genetics:569–82. https://doi.org/10.1534/g3.114.016097.
Pérez, P. and G. de los Campos. 2014. Genome-Wide Regression and Prediction with the BGLR Statistical Package. Genetics, 198: 483-495. https://doi.org/10.1534/genetics.114.164442.
R Core Team. 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.