R/mppGE_proc.R
In vincentgarin/mppGxE: MPP GxE QTL analysis
Defines functions
mppGE_proc
Documented in
mppGE_proc
##############
# mppGE_proc #
##############
#' MPP GxE QTL analysis
#'
#' QTL detection in MPP characterized in multiple environments.
#'
#' The procedure is the following:
#'
#' \enumerate{
#'
#' \item{Simple interval mapping (SIM) to select cofactor
#' (\code{\link{mppGE_SIM}}).}
#'
#' \item{Composite interval mapping (CIM) with selected cofactors
#' (\code{\link{mppGE_CIM}}).}
#'
#' \item{Backward elimination on the list of QTLs
#' (\code{\link{mppGE_back_elim}}).}
#'
#' \item{Optional estimation of QTLs additive allelic effect
#' (\code{\link{mppGE_QTL_effects}}).}
#'
#' }
#'
#'
#' @param pop.name \code{Character} name of the studied population.
#' Default = "MPP".
#'
#' @param trait.name \code{Character} name of the studied trait.
#' Default = "trait1".
#'
#' @param mppData An object of class \code{mppData}.
#'
#' @param trait \code{Character vector} specifying which traits (environments) should be used.
#'
#' @param EnvNames \code{character} expression indicating the environment or trait
#' labels. By default: Env_1, 2, 3, etc.
#'
#' @param Q.eff \code{Character} expression indicating the assumption concerning
#' the QTL effect: 1) "cr" for cross-specific effects; 2) "par" parental
#' effects; 3) "anc" for an ancestral effects; 4) "biall" for a bi-allelic
#' effects. Default = "cr".
#'
#' @param VCOV VCOV \code{Character} expression defining the type of variance
#' covariance structure used. "ID" for identity, "CSRT" for within environment
#' cross-specific residual terms, "CS_CSRT" for compound symmetry with within
#' environment cross-specific residual terms. Default = "CS_CSRT".
#'
#' @param plot.gen.eff \code{Logical} value. If \code{plot.gen.eff = TRUE},
#' the function will save the significance of the QTL allelic effects per
#' cross/parent along the genome. These results can be visualized with the
#' function \code{\link{plot_allele_eff_GE}}. Default value = FALSE.
#'
#' @param thre.cof \code{Numeric} value representing the -log10(p-value)
#' threshold above which a position can be selected as cofactor. Default = 4.
#'
#' @param win.cof \code{Numeric} value in centi-Morgan representing the minimum
#' distance between two selected cofactors. Default = 50 cM.
#'
#' @param N.cim \code{Numeric} value specifying the number of times the CIM
#' analysis is repeated. Default = 1.
#'
#' @param window \code{Numeric} distance (cM) on the left and the right of a
#' cofactor position where it is not included in the model. Default = 20.
#'
#' @param thre.QTL \code{Numeric} value representing the -log10(p-value)
#' threshold above which a position can be selected as QTL. Default = 4.
#'
#' @param win.QTL \code{Numeric} value in centi-Morgan representing the minimum
#' distance between two selected QTLs. Default = 20.
#'
#' @param alpha \code{Numeric} value indicating the level of significance for
#' the backward elimination. Default = 0.01.
#'
#' @param Qeff_est \code{Logical} value specifying if the QTL effects of the
#' final QTL list should be estimated. Default = TRUE.
#'
#' @param text.size \code{Numeric} value specifying the size of graph axis text
#' elements. Default = 18.
#'
#' @param parallel \code{Logical} value specifying if the function should be
#' executed in parallel on multiple cores. To run the function in parallel user must
#' provide clusters in the \code{cluster} argument. \strong{Parallelization is
#' only available for 'ID' model}. Default = FALSE.
#'
#' @param cluster Cluster object obtained with the function \code{makeCluster()}
#' from the parallel package. Default = NULL.
#'
#' @param workspace Size of workspace for the REML routines measured in double
#' precision words (groups of 8 bytes). The default is workspace = 8e6.
#'
#' @param verbose \code{Logical} value indicating if the steps of mpp_proc should
#' be printed. It will not affect the printing of the other functions called by
#' \code{mpp_proc()}, especially the printing of \code{asreml()}. Default = TRUE.
#'
#' @param output.loc Path where a folder will be created to save the results.
#' Default = NULL.
#'
#'
#' @return Return:
#'
#' List containing the following items:
#'
#' \item{n.QTL}{Number of detected QTLs.}
#'
#' \item{cofactors}{\code{Data.frame} with cofactors positions.}
#'
#' \item{QTL}{\code{Data.frame} with QTL positions.}
#'
#' \item{R2}{\code{List} containing R squared statistics of the QTL effects.
#' For details see \code{\link{QTL_R2}} output section.}
#'
#' Some output files are also saved at the specified location
#' (\code{output.loc}):
#'
#' \enumerate{
#'
#' \item{The SIM and CIM results in a text file (SIM.txt, CIM.txt).}
#'
#' \item{The list of cofactors (cofactors.txt).}
#'
#' \item{The list of QTL (QTL.txt).}
#'
#' \item{The QTL R squared statistics (QTL_R2.txt) (for details see
#' \code{\link{QTL_R2}}).}
#'
#' \item{The plot of the CIM profile (QTL_profile.pdf) with dotted vertical
#' lines representing the cofactors positions. If \code{plot.gen.eff = TRUE},
#' plot of the genetic effects per cross or parents obtained with
#' \code{\link{plot_allele_eff_GE}} (gen_eff.pdf) with dashed
#' lines representing the QTL positions.}
#'
#' }
#'
#'
#' @author Vincent Garin
#'
#' @seealso
#'
#' \code{\link{mppGE_CIM}},
#' \code{\link{mppGE_SIM}},
#' \code{\link{mppGE_back_elim}},
#' \code{\link{mppGE_QTL_effects}},
#' \code{\link{plot_allele_eff_GE}}
#'
#' @examples
#'
#'\dontrun{
#'
#' library(asreml)
#'
#' data(mppData_GE)
#'
#' # Specify a location where your results will be saved
#' my.loc <- tempdir()
#'
#' QTL <- mppGE_proc(pop.name = 'EUNAM', trait.name = 'DMY', mppData = mppData_GE,
#' trait = c('DMY_CIAM', 'DMY_TUM'), EnvNames = c('CIAM', 'TUM'),
#' Q.eff = 'par', plot.gen.eff = TRUE, thre.cof = 3,
#' thre.QTL = 3, verbose = FALSE, output.loc = my.loc)
#'
#'}
#'
#' @export
#'
mppGE_proc <- function(pop.name = "MPP", trait.name = "trait1", mppData, trait,
EnvNames = NULL, Q.eff = "cr", VCOV = "CS_CSRT",
plot.gen.eff = FALSE, thre.cof = 4,
win.cof = 50, N.cim = 1, window = 20, thre.QTL = 4,
win.QTL = 20, alpha = 0.01, Qeff_est = TRUE,
text.size = 18, parallel = FALSE,
cluster = NULL, workspace = 8e6, verbose = TRUE,
output.loc = NULL) {
# 1. Check the validity of the parameters that have been introduced
###################################################################
# CheckModelGE(mppData = mppData, trait = trait, Q.eff = Q.eff, VCOV = VCOV,
# plot.gen.eff = plot.gen.eff,
# parallel = parallel, cluster = cluster, EnvNames = EnvNames,
# fct = "SIM")
if(is.null(EnvNames)){
EnvNames <- paste0("Env_", 1:length(trait))
}
# if(is.null(win.cof)){
#
# chr_fact <- factor(mppData$map[, 2], levels = unique(mppData$map[, 2]))
#
# max_chr_len <- tapply(X = mppData$map[, 4], INDEX = chr_fact,
# FUN = function(x) max(x))
# max_chr_len <- max(unlist(max_chr_len))
#
# win.cof <- max_chr_len + 1
#
# }
# 2. Create a directory to store the results
############################################
# create a directory to store the results of the QTL analysis
folder.loc <- file.path(output.loc, paste("QTLGE", pop.name, trait.name, Q.eff,
VCOV, sep = "_"))
dir.create(folder.loc)
# 3. Cofactors selection - SIM
##############################
if(verbose){
cat("\n")
cat("Cofactors selection - SIM")
cat("\n")
cat("\n")
}
SIM <- mppGE_SIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
# save SIM results in output location
write.table(SIM, file = file.path(folder.loc, "SIM.txt"), quote = FALSE,
sep = "\t", row.names = FALSE)
# cofactors selection
cofactors <- QTL_select(Qprof = SIM, threshold = thre.cof, window = win.cof)
if (is.null(cofactors)) { # test if cofactors have been selected
message("No QTL/cofactor position detected based on the SIM profile.")
return(NULL)
}
# 4. Multi-QTL model search - CIM
#################################
if(verbose){
cat("\n")
cat("Multi-QTL model search - CIM")
cat("\n")
cat("\n")
}
CIM <- mppGE_CIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, cofactors = cofactors,
window = window, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
if (N.cim > 1) {
for (i in 1:(N.cim - 1)) {
# take the cofactors of the previous analysis
cofactors <- QTL_select(Qprof = CIM, threshold = thre.cof,
window = win.cof)
if (is.null(cofactors)) { # test if cofactors have been selected
mes.text <- paste("No cofactor position detected in CIM profile nb", i)
message(mes.text)
return(NULL)
} else {
if(verbose){
cat("\n")
cat(paste("CIM scan", (i+1)))
cat("\n")
cat("\n")
}
CIM <- mppGE_CIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, cofactors = cofactors,
window = window, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
}
}
}
# save the list of cofactors
write.table(cofactors[, 1:5], file = file.path(folder.loc, "cofactors.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
# save CIM results
write.table(CIM, file = file.path(folder.loc, "CIM.txt"), quote = FALSE,
sep = "\t", row.names = FALSE)
# select QTL candidates
QTL <- QTL_select(Qprof = CIM, threshold = thre.QTL, window = win.QTL)
if (is.null(QTL)) { # test if QTL have been selected
message("No QTL position detected based on the (last) CIM profile.")
return(NULL)
}
# 5. Backward elimination
#########################
Q_back <- mppGE_back_elim(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = VCOV, QTL = QTL, alpha = alpha,
workspace = workspace)
# save the list of QTLs
if(!is.null(Q_back)){
QTL <- QTL[QTL[, 1] %in% Q_back[, 1], ]
write.table(QTL[, 1:5], file = file.path(folder.loc, "QTL.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
}
# 6. QTL R2
###########
R2 <- mppGE_QTL_R2(mppData = mppData, trait = trait, Q.eff = Q.eff, VCOV = VCOV,
QTL = QTL[, 1], workspace = workspace)
# save R2 results
QTL.R2 <- data.frame(QTL[, 1:5], round(R2[[3]], 2), round(R2[[4]], 2),
round(R2[[5]], 2), round(R2[[6]], 2),
stringsAsFactors = FALSE)
colnames(QTL.R2)[6:9] <- c("R2.diff", "adj.R2.diff", "R2.sg", "adj.R2.sg")
write.table(QTL.R2, file = file.path(folder.loc, "QTL_R2.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
# 7. Compute QTL effects
########################
if(Qeff_est){
Qeff <- mppGE_QTL_effects(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = VCOV, QTL = QTL, workspace = workspace)
} else { Qeff <- NULL }
# 8. Plots
##########
if(verbose){
cat("\n")
cat("Results processing")
cat("\n")
cat("\n")
}
main.cim <- paste("CIM", pop.name, trait.name, Q.eff, VCOV)
main.Qeff <- paste("QTL gen. effects", pop.name, trait.name, Q.eff, VCOV)
if(Q.eff == "biall"){t_plot <- "h"} else {t_plot <- "l"}
pdf(file.path(folder.loc, "QTL_profile.pdf"), height = 10, width = 16)
print(plot(x = CIM, QTL = cofactors, type = t_plot, main = main.cim,
threshold = thre.QTL, text.size = text.size))
dev.off()
if (plot.gen.eff) {
pdf(file.path(folder.loc, "gen_eff.pdf"), height = 10, width = 16)
print(plot_allele_eff_GE(mppData = mppData, nEnv = length(trait),
EnvNames = EnvNames, Qprof = CIM, Q.eff = Q.eff,
QTL = QTL, main = main.Qeff, text.size = text.size))
dev.off()
}
# 9. QTL report
###############
if(Qeff_est){
QTL_report_GE(out.file = file.path(folder.loc, "QTL_REPORT.txt"),
main = paste(pop.name, trait.name, Q.eff, VCOV),
QTL.info = QTL[, c(1, 2, 4, 5)], QTL.effects = Qeff,
R2 = R2)
}
### Return R object
results <- list(n.QTL = dim(QTL)[1], cofactors = cofactors[, 1:5],
QTL = QTL[, 1:5], R2 = R2, Qeff = Qeff)
return(results)
}
vincentgarin/mppGxE documentation built on June 25, 2022, 2:45 p.m.
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Defines functions mppGE_proc
Documented in mppGE_proc
##############
# mppGE_proc #
##############
#' MPP GxE QTL analysis
#'
#' QTL detection in MPP characterized in multiple environments.
#'
#' The procedure is the following:
#'
#' \enumerate{
#'
#' \item{Simple interval mapping (SIM) to select cofactor
#' (\code{\link{mppGE_SIM}}).}
#'
#' \item{Composite interval mapping (CIM) with selected cofactors
#' (\code{\link{mppGE_CIM}}).}
#'
#' \item{Backward elimination on the list of QTLs
#' (\code{\link{mppGE_back_elim}}).}
#'
#' \item{Optional estimation of QTLs additive allelic effect
#' (\code{\link{mppGE_QTL_effects}}).}
#'
#' }
#'
#'
#' @param pop.name \code{Character} name of the studied population.
#' Default = "MPP".
#'
#' @param trait.name \code{Character} name of the studied trait.
#' Default = "trait1".
#'
#' @param mppData An object of class \code{mppData}.
#'
#' @param trait \code{Character vector} specifying which traits (environments) should be used.
#'
#' @param EnvNames \code{character} expression indicating the environment or trait
#' labels. By default: Env_1, 2, 3, etc.
#'
#' @param Q.eff \code{Character} expression indicating the assumption concerning
#' the QTL effect: 1) "cr" for cross-specific effects; 2) "par" parental
#' effects; 3) "anc" for an ancestral effects; 4) "biall" for a bi-allelic
#' effects. Default = "cr".
#'
#' @param VCOV VCOV \code{Character} expression defining the type of variance
#' covariance structure used. "ID" for identity, "CSRT" for within environment
#' cross-specific residual terms, "CS_CSRT" for compound symmetry with within
#' environment cross-specific residual terms. Default = "CS_CSRT".
#'
#' @param plot.gen.eff \code{Logical} value. If \code{plot.gen.eff = TRUE},
#' the function will save the significance of the QTL allelic effects per
#' cross/parent along the genome. These results can be visualized with the
#' function \code{\link{plot_allele_eff_GE}}. Default value = FALSE.
#'
#' @param thre.cof \code{Numeric} value representing the -log10(p-value)
#' threshold above which a position can be selected as cofactor. Default = 4.
#'
#' @param win.cof \code{Numeric} value in centi-Morgan representing the minimum
#' distance between two selected cofactors. Default = 50 cM.
#'
#' @param N.cim \code{Numeric} value specifying the number of times the CIM
#' analysis is repeated. Default = 1.
#'
#' @param window \code{Numeric} distance (cM) on the left and the right of a
#' cofactor position where it is not included in the model. Default = 20.
#'
#' @param thre.QTL \code{Numeric} value representing the -log10(p-value)
#' threshold above which a position can be selected as QTL. Default = 4.
#'
#' @param win.QTL \code{Numeric} value in centi-Morgan representing the minimum
#' distance between two selected QTLs. Default = 20.
#'
#' @param alpha \code{Numeric} value indicating the level of significance for
#' the backward elimination. Default = 0.01.
#'
#' @param Qeff_est \code{Logical} value specifying if the QTL effects of the
#' final QTL list should be estimated. Default = TRUE.
#'
#' @param text.size \code{Numeric} value specifying the size of graph axis text
#' elements. Default = 18.
#'
#' @param parallel \code{Logical} value specifying if the function should be
#' executed in parallel on multiple cores. To run the function in parallel user must
#' provide clusters in the \code{cluster} argument. \strong{Parallelization is
#' only available for 'ID' model}. Default = FALSE.
#'
#' @param cluster Cluster object obtained with the function \code{makeCluster()}
#' from the parallel package. Default = NULL.
#'
#' @param workspace Size of workspace for the REML routines measured in double
#' precision words (groups of 8 bytes). The default is workspace = 8e6.
#'
#' @param verbose \code{Logical} value indicating if the steps of mpp_proc should
#' be printed. It will not affect the printing of the other functions called by
#' \code{mpp_proc()}, especially the printing of \code{asreml()}. Default = TRUE.
#'
#' @param output.loc Path where a folder will be created to save the results.
#' Default = NULL.
#'
#'
#' @return Return:
#'
#' List containing the following items:
#'
#' \item{n.QTL}{Number of detected QTLs.}
#'
#' \item{cofactors}{\code{Data.frame} with cofactors positions.}
#'
#' \item{QTL}{\code{Data.frame} with QTL positions.}
#'
#' \item{R2}{\code{List} containing R squared statistics of the QTL effects.
#' For details see \code{\link{QTL_R2}} output section.}
#'
#' Some output files are also saved at the specified location
#' (\code{output.loc}):
#'
#' \enumerate{
#'
#' \item{The SIM and CIM results in a text file (SIM.txt, CIM.txt).}
#'
#' \item{The list of cofactors (cofactors.txt).}
#'
#' \item{The list of QTL (QTL.txt).}
#'
#' \item{The QTL R squared statistics (QTL_R2.txt) (for details see
#' \code{\link{QTL_R2}}).}
#'
#' \item{The plot of the CIM profile (QTL_profile.pdf) with dotted vertical
#' lines representing the cofactors positions. If \code{plot.gen.eff = TRUE},
#' plot of the genetic effects per cross or parents obtained with
#' \code{\link{plot_allele_eff_GE}} (gen_eff.pdf) with dashed
#' lines representing the QTL positions.}
#'
#' }
#'
#'
#' @author Vincent Garin
#'
#' @seealso
#'
#' \code{\link{mppGE_CIM}},
#' \code{\link{mppGE_SIM}},
#' \code{\link{mppGE_back_elim}},
#' \code{\link{mppGE_QTL_effects}},
#' \code{\link{plot_allele_eff_GE}}
#'
#' @examples
#'
#'\dontrun{
#'
#' library(asreml)
#'
#' data(mppData_GE)
#'
#' # Specify a location where your results will be saved
#' my.loc <- tempdir()
#'
#' QTL <- mppGE_proc(pop.name = 'EUNAM', trait.name = 'DMY', mppData = mppData_GE,
#' trait = c('DMY_CIAM', 'DMY_TUM'), EnvNames = c('CIAM', 'TUM'),
#' Q.eff = 'par', plot.gen.eff = TRUE, thre.cof = 3,
#' thre.QTL = 3, verbose = FALSE, output.loc = my.loc)
#'
#'}
#'
#' @export
#'
mppGE_proc <- function(pop.name = "MPP", trait.name = "trait1", mppData, trait,
EnvNames = NULL, Q.eff = "cr", VCOV = "CS_CSRT",
plot.gen.eff = FALSE, thre.cof = 4,
win.cof = 50, N.cim = 1, window = 20, thre.QTL = 4,
win.QTL = 20, alpha = 0.01, Qeff_est = TRUE,
text.size = 18, parallel = FALSE,
cluster = NULL, workspace = 8e6, verbose = TRUE,
output.loc = NULL) {
# 1. Check the validity of the parameters that have been introduced
###################################################################
# CheckModelGE(mppData = mppData, trait = trait, Q.eff = Q.eff, VCOV = VCOV,
# plot.gen.eff = plot.gen.eff,
# parallel = parallel, cluster = cluster, EnvNames = EnvNames,
# fct = "SIM")
if(is.null(EnvNames)){
EnvNames <- paste0("Env_", 1:length(trait))
}
# if(is.null(win.cof)){
#
# chr_fact <- factor(mppData$map[, 2], levels = unique(mppData$map[, 2]))
#
# max_chr_len <- tapply(X = mppData$map[, 4], INDEX = chr_fact,
# FUN = function(x) max(x))
# max_chr_len <- max(unlist(max_chr_len))
#
# win.cof <- max_chr_len + 1
#
# }
# 2. Create a directory to store the results
############################################
# create a directory to store the results of the QTL analysis
folder.loc <- file.path(output.loc, paste("QTLGE", pop.name, trait.name, Q.eff,
VCOV, sep = "_"))
dir.create(folder.loc)
# 3. Cofactors selection - SIM
##############################
if(verbose){
cat("\n")
cat("Cofactors selection - SIM")
cat("\n")
cat("\n")
}
SIM <- mppGE_SIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
# save SIM results in output location
write.table(SIM, file = file.path(folder.loc, "SIM.txt"), quote = FALSE,
sep = "\t", row.names = FALSE)
# cofactors selection
cofactors <- QTL_select(Qprof = SIM, threshold = thre.cof, window = win.cof)
if (is.null(cofactors)) { # test if cofactors have been selected
message("No QTL/cofactor position detected based on the SIM profile.")
return(NULL)
}
# 4. Multi-QTL model search - CIM
#################################
if(verbose){
cat("\n")
cat("Multi-QTL model search - CIM")
cat("\n")
cat("\n")
}
CIM <- mppGE_CIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, cofactors = cofactors,
window = window, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
if (N.cim > 1) {
for (i in 1:(N.cim - 1)) {
# take the cofactors of the previous analysis
cofactors <- QTL_select(Qprof = CIM, threshold = thre.cof,
window = win.cof)
if (is.null(cofactors)) { # test if cofactors have been selected
mes.text <- paste("No cofactor position detected in CIM profile nb", i)
message(mes.text)
return(NULL)
} else {
if(verbose){
cat("\n")
cat(paste("CIM scan", (i+1)))
cat("\n")
cat("\n")
}
CIM <- mppGE_CIM(trait = trait, mppData = mppData, Q.eff = Q.eff,
VCOV = VCOV, cofactors = cofactors,
window = window, plot.gen.eff = plot.gen.eff,
parallel = parallel, cluster = cluster,
workspace = workspace)
}
}
}
# save the list of cofactors
write.table(cofactors[, 1:5], file = file.path(folder.loc, "cofactors.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
# save CIM results
write.table(CIM, file = file.path(folder.loc, "CIM.txt"), quote = FALSE,
sep = "\t", row.names = FALSE)
# select QTL candidates
QTL <- QTL_select(Qprof = CIM, threshold = thre.QTL, window = win.QTL)
if (is.null(QTL)) { # test if QTL have been selected
message("No QTL position detected based on the (last) CIM profile.")
return(NULL)
}
# 5. Backward elimination
#########################
Q_back <- mppGE_back_elim(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = VCOV, QTL = QTL, alpha = alpha,
workspace = workspace)
# save the list of QTLs
if(!is.null(Q_back)){
QTL <- QTL[QTL[, 1] %in% Q_back[, 1], ]
write.table(QTL[, 1:5], file = file.path(folder.loc, "QTL.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
}
# 6. QTL R2
###########
R2 <- mppGE_QTL_R2(mppData = mppData, trait = trait, Q.eff = Q.eff, VCOV = VCOV,
QTL = QTL[, 1], workspace = workspace)
# save R2 results
QTL.R2 <- data.frame(QTL[, 1:5], round(R2[[3]], 2), round(R2[[4]], 2),
round(R2[[5]], 2), round(R2[[6]], 2),
stringsAsFactors = FALSE)
colnames(QTL.R2)[6:9] <- c("R2.diff", "adj.R2.diff", "R2.sg", "adj.R2.sg")
write.table(QTL.R2, file = file.path(folder.loc, "QTL_R2.txt"),
quote = FALSE, sep = "\t", row.names = FALSE)
# 7. Compute QTL effects
########################
if(Qeff_est){
Qeff <- mppGE_QTL_effects(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = VCOV, QTL = QTL, workspace = workspace)
} else { Qeff <- NULL }
# 8. Plots
##########
if(verbose){
cat("\n")
cat("Results processing")
cat("\n")
cat("\n")
}
main.cim <- paste("CIM", pop.name, trait.name, Q.eff, VCOV)
main.Qeff <- paste("QTL gen. effects", pop.name, trait.name, Q.eff, VCOV)
if(Q.eff == "biall"){t_plot <- "h"} else {t_plot <- "l"}
pdf(file.path(folder.loc, "QTL_profile.pdf"), height = 10, width = 16)
print(plot(x = CIM, QTL = cofactors, type = t_plot, main = main.cim,
threshold = thre.QTL, text.size = text.size))
dev.off()
if (plot.gen.eff) {
pdf(file.path(folder.loc, "gen_eff.pdf"), height = 10, width = 16)
print(plot_allele_eff_GE(mppData = mppData, nEnv = length(trait),
EnvNames = EnvNames, Qprof = CIM, Q.eff = Q.eff,
QTL = QTL, main = main.Qeff, text.size = text.size))
dev.off()
}
# 9. QTL report
###############
if(Qeff_est){
QTL_report_GE(out.file = file.path(folder.loc, "QTL_REPORT.txt"),
main = paste(pop.name, trait.name, Q.eff, VCOV),
QTL.info = QTL[, c(1, 2, 4, 5)], QTL.effects = Qeff,
R2 = R2)
}
### Return R object
results <- list(n.QTL = dim(QTL)[1], cofactors = cofactors[, 1:5],
QTL = QTL[, 1:5], R2 = R2, Qeff = Qeff)
return(results)
}
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