Nothing
################
# QTLModelSIM #
################
# function to compute a single position QTL model
QTLModelSIM <- function(x, mppData, trait, cross.mat, Q.eff, VCOV,
plot.gen.eff){
# 1. formation of the QTL incidence matrix
###########################################
QTL <- inc_mat_QTL(x = x, mppData = mppData, Q.eff = Q.eff, order.MAF = TRUE)
QTL.el <- dim(QTL)[2] # number of QTL elements
ref.name <- colnames(QTL)
# 2. model computation
######################
### 2.1 homogeneous residual variance error
if(VCOV == "h.err"){
model <- tryCatch(expr = lm(trait ~ - 1 + cross.mat + QTL),
error = function(e) NULL)
if (is.null(model)){
if(plot.gen.eff) {
if(Q.eff == "cr"){ results <- c(0, rep(1, mppData$n.cr))
} else { results <- c(0, rep(1, mppData$n.par)) }
} else { results <- 0 }
} else {
if(!("QTL" %in% rownames(anova(model)))){ # QTL effect could not be
# estimated probably due to
# singularities.
if(plot.gen.eff) {
if(Q.eff == "cr"){ results <- c(0, rep(1, mppData$n.cr))
} else { results <- c(0, rep(1, mppData$n.par)) }
} else { results <- 0 }
} else {
if(plot.gen.eff){
gen.eff <- QTL_pval(mppData = mppData, model = model,
Q.eff = Q.eff, x = x)
results <- c(-log10(anova(model)$Pr[2]), gen.eff)
} else { results <- -log10(anova(model)$Pr[2]) }
}
}
### 2.2 HRT REML or cross-specific variance residual terms
} else if ((VCOV == "h.err.as") || (VCOV == "cr.err")){
# dataset <- data.frame(QTL = QTL,
# cr.mat = factor(mppData$cross.ind,
# levels = unique(mppData$cross.ind)),
# trait = trait)
# colnames(dataset)[1:QTL.el] <- paste0("Q", 1:QTL.el)
#
# formula.QTL <- paste("+", paste0("Q", 1:QTL.el), collapse = " ")
# formula.fix <- paste("trait~-1+cr.mat", formula.QTL)
#
# if(VCOV == "h.err.as"){ formula.R <- "~idv(units)"
# } else if (VCOV == "cr.err") {formula.R <- "~at(cr.mat):units"}
#
# model <- tryCatch(expr = asreml::asreml(fixed = as.formula(formula.fix),
# rcov = as.formula(formula.R),
# data = dataset, trace = FALSE,
# na.method.Y = "omit",
# na.method.X = "omit"),
# error = function(e) NULL)
### 2.3 random pedigree + HVRT or + CSRT
} else if ((VCOV == "pedigree") || (VCOV == "ped_cr.err")){
# dataset <- data.frame(QTL = QTL,
# cr.mat = factor(mppData$cross.ind,
# levels = unique(mppData$cross.ind)),
# trait = trait, genotype = mppData$geno.id)
# colnames(dataset)[1:QTL.el] <- paste0("Q", 1:QTL.el)
#
#
# formula.QTL <- paste("+", paste0("Q", 1:QTL.el), collapse = " ")
# formula.fix <- paste("trait~1", formula.QTL)
#
# if(VCOV == "pedigree"){ formula.R <- "~idv(units)"
# } else if (VCOV == "ped_cr.err") {formula.R <- "~at(cr.mat):units"}
#
# model <- tryCatch(expr = asreml::asreml(fixed = as.formula(formula.fix),
# random = ~ ped(genotype),
# rcov = as.formula(formula.R),
# ginverse = list(genotype = ped.mat.inv),
# data = dataset, trace = FALSE,
# na.method.Y = "omit",
# na.method.X = "omit"),
# error = function(e) NULL)
}
# 3. organise the results for the mixed models similar for all models
#####################################################################
# if(VCOV != "h.err"){
#
# if (is.null(model)){
#
# if(plot.gen.eff) {
#
# if(Q.eff == "cr"){ results <- c(0, rep(1, mppData$n.cr))
#
# } else { results <- c(0, rep(1, mppData$n.par)) }
#
# } else { results <- 0 }
#
# } else {
#
# W.stat <- sum(asreml::wald(model)[2:(QTL.el+1), 3])
#
# if(W.stat == 0){
#
# if(plot.gen.eff) {
#
# if(Q.eff == "cr"){ results <- c(0, rep(1, mppData$n.cr))
#
# } else { results <- c(0, rep(1, mppData$n.par)) }
#
# } else { results <- 0 }
#
# } else {
#
# df <- sum(asreml::wald(model)[2:(QTL.el+1), 1])
#
# pval <- pchisq(W.stat, df, lower.tail = FALSE)
#
# results <- -log10(pval)
#
# if(plot.gen.eff){
#
# gen.eff <- QTL_pval_mix(model = model, Q.eff = Q.eff, QTL.el = QTL.el,
# x = x, ref.name = ref.name,
# par.clu = mppData$par.clu,
# par.names = mppData$parents, fct = "SIM")
#
# results <- c(results, gen.eff)
#
# }
#
# }
#
# }
#
# }
return(results)
}
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