R/mpp_SIM.R
In vincentgarin/mppR: Multi-Parent Population QTL Analysis
Defines functions
mpp_SIM
Documented in
mpp_SIM
###########
# mpp_SIM #
###########
#' MPP Simple Interval Mapping
#'
#' Computes single QTL models along the genome using different models.
#'
#' The implemented models vary according to the number of alleles assumed at the
#' QTL position and their origin. Four assumptions for the QTL effect are
#' possible.
#'
#' Concerning the type of QTL effect, the first option is a cross-specific QTL
#' effects model (\code{Q.eff = "cr"}). In this model, the QTL effects are
#' assumed to be nested within cross which leads to the estimation of one
#' parameter per cross. The cross-specific model corresponds to the
#' disconnected model described in Blanc et al. 2006.
#'
#' A second possibility is the parental model (\code{Q.eff = "par"}). The
#' parental model assumes one QTL effect (allele) per parent that are independent
#' from the genetic background. This means that QTL coming form parent i has the
#' same effect in all crosses where this parent is used. This model is supposed
#' to produce better estimates of the QTL due to larger sample size when parents
#' are shared between crosses.
#'
#' In a connected MPP (\code{\link{design_connectivity}}), if np - 1 < nc, where
#' np is the number of parents and nc the number of crosses, the parental model
#' should be more powerful than the cross-specific model because it estimate
#' a reduced number of QTL parameters. This gain in power will be only true if
#' the assumption of constant parental effect through crosses holds. Calculated
#' with HRT assumption, the parental model corresponds to the connected model
#' presented in Blanc et al. (2006).
#'
#' The third type of model is the ancestral model (\code{Q.eff = "anc"}). This
#' model tries to use genetic relatedness that could exist between parents.
#' Indeed, the parental model assumes that parent are independent which is not
#' the case. Using genetic relatedness between the parents, it is possible group
#' these parents into a reduced number of ancestral cluster. Parents belonging
#' to the same ancestral group are assumed to transmit the same allele
#' (Jansen et al. 2003; Leroux et al. 2014). The ancestral model estimate
#' therefore one QTL effect
#' per ancestral class. Once again, the theoretical expectation is a gain of
#' QTL detection power by the reduction of the number of parameters to estimate.
#' The HRT ancestral model correspond to the linkage desequilibrium
#' linkage analysis (LDLA) models used by Bardol et al. (2013) or
#' Giraud et al. (2014).
#'
#' The final possibility is the bi-allelic model (\code{Q.eff = "biall"}).
#' Bi-allelic genetic predictor are a single vector with value 0, 1 or 2
#' corresponding to the number of allele copy of the least frequent SNP allele.
#' Relatedness between lines is therefore defined via identical by state (IBS)
#' measurement. This model corresponds to models used for association mapping.
#' For example, it is similar to model B in Wurschum et al. (2012) or
#' association mapping model in Liu et al. (2012).
#'
#' @param mppData An object of class \code{mppData}.
#'
#' @param trait \code{Numerical} or \code{character} indicator to specify which
#' trait of the \code{mppData} object should be used. Default = 1.
#'
#' @param Q.eff \code{Character} expression indicating the assumption concerning
#' the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental; 3) "anc"
#' for ancestral; 4) "biall" for a bi-allelic. For more details see
#' \code{\link{mpp_SIM}}. Default = "cr".
#'
#' @param plot.gen.eff \code{Logical} value. If \code{plot.gen.eff = TRUE},
#' the function will save the decomposed genetic effects per cross/parent.
#' These results can be plotted with the function \code{\link{plot.QTLprof}}
#' to visualize a genome-wide decomposition of the genetic effects.
#' \strong{This functionality is ony available for the cross-specific,
#' parental and ancestral models.}
#' Default value = FALSE.
#'
#' @param n.cores \code{Numeric}. Specify here the number of cores you like to
#' use. Default = 1.
#'
#'
#' @return Return:
#'
#' \item{SIM }{\code{Data.frame} of class \code{QTLprof}. with five columns :
#' 1) QTL marker names; 2) chromosomes;
#' 3) interger position indicators on the chromosome;
#' 4) positions in centi-Morgan; and 5) -log10(p-val). And if
#' \code{plot.gen.eff = TRUE}, p-values of the cross or parental QTL effects.}
#'
#' @author Vincent Garin
#'
#' @seealso \code{\link{plot.QTLprof}}
#'
#' @references
#'
#' Bardol, N., Ventelon, M., Mangin, B., Jasson, S., Loywick, V., Couton, F., ...
#' & Moreau, L. (2013). Combined linkage and linkage disequilibrium QTL mapping
#' in multiple families of maize (Zea mays L.) line crosses highlights
#' complementarities between models based on parental haplotype and single locus
#' polymorphism. Theoretical and applied genetics, 126(11), 2717-2736.
#'
#' Blanc, G., Charcosset, A., Mangin, B., Gallais, A., & Moreau, L. (2006).
#' Connected populations for detecting quantitative trait loci and testing for
#' epistasis: an application in maize. Theoretical and Applied Genetics,
#' 113(2), 206-224.
#'
#' Giraud, H., Lehermeier, C., Bauer, E., Falque, M., Segura, V., Bauland,
#' C., ... & Moreau, L. (2014). Linkage Disequilibrium with Linkage Analysis
#' of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid
#' Performance in the Flint and Dent Heterotic Groups of Maize. Genetics,
#' 198(4), 1717-1734.
#'
#' Jansen, R. C., Jannink, J. L., & Beavis, W. D. (2003). Mapping quantitative
#' trait loci in plant breeding populations. Crop Science, 43(3), 829-834.
#'
#' Leroux, D., Rahmani, A., Jasson, S., Ventelon, M., Louis, F., Moreau, L.,
#' & Mangin, B. (2014). Clusthaplo: a plug-in for MCQTL to enhance QTL detection
#' using ancestral alleles in multi-cross design. Theoretical and Applied
#' Genetics, 127(4), 921-933.
#'
#' Liu, W., Reif, J. C., Ranc, N., Della Porta, G., & Wurschum, T. (2012).
#' Comparison of biometrical approaches for QTL detection in multiple
#' segregating families. Theoretical and Applied Genetics, 125(5), 987-998.
#'
#' Meuwissen T and Luo, Z. (1992). Computing inbreeding coefficients in large
#' populations. Genetics Selection Evolution, 24(4), 305-313.
#'
#' Wurschum, T., Liu, W., Gowda, M., Maurer, H. P., Fischer, S., Schechert, A.,
#' & Reif, J. C. (2012). Comparison of biometrical models for joint linkage
#' association mapping. Heredity, 108(3), 332-340.
#'
#' @examples
#'
#'
#' # Cross-specific model
#' ######################
#'
#' data(mppData)
#'
#' SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr", plot.gen.eff = TRUE)
#'
#' plot(x = SIM)
#' plot(x = SIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr")
#'
#'
#' # Bi-allelic model
#' ##################
#'
#' SIM <- mpp_SIM(mppData = mppData, Q.eff = "biall")
#'
#' plot(x = SIM, type = "h")
#'
#' @export
#'
mpp_SIM <- function(mppData, trait = 1, Q.eff = "cr",
plot.gen.eff = FALSE, n.cores = 1) {
# 1. Check data format and arguments
####################################
check.model.comp(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = 'h.err', plot.gen.eff = plot.gen.eff,
n.cores = n.cores, fct = "SIM")
# 2. Form required elements for the analysis
############################################
### 2.1 trait values
t_val <- sel_trait(mppData = mppData, trait = trait)
### 2.3 cross matrix (cross intercept)
cross.mat <- IncMat_cross(cross.ind = mppData$cross.ind)
### 2.4 Optional cluster
if(n.cores > 1){
parallel <- TRUE
cluster <- makeCluster(n.cores)
} else {
parallel <- FALSE
cluster <- NULL
}
vect.pos <- 1:dim(mppData$map)[1]
# 3. computation of the SIM profile (genome scan)
#################################################
if (parallel) {
log.pval <- parLapply(cl = cluster, X = vect.pos, fun = QTLModelSIM,
mppData = mppData, trait = t_val,
cross.mat = cross.mat, Q.eff = Q.eff, VCOV = 'h.err',
plot.gen.eff = plot.gen.eff)
} else {
log.pval <- lapply(X = vect.pos, FUN = QTLModelSIM,
mppData = mppData, trait = t_val, cross.mat = cross.mat,
Q.eff = Q.eff, VCOV = 'h.err', plot.gen.eff = plot.gen.eff)
}
if(n.cores > 1){stopCluster(cluster)}
log.pval <- t(data.frame(log.pval))
if(plot.gen.eff){log.pval[is.na(log.pval)] <- 1}
log.pval[, 1] <- check.inf(x = log.pval[, 1]) # check if there are -/+ Inf value
log.pval[is.na(log.pval[, 1]), 1] <- 0
# 4. form the results
#####################
SIM <- data.frame(mppData$map, log.pval)
if(plot.gen.eff){
if(Q.eff == "cr"){ Qeff_names <- unique(mppData$cross.ind)
} else { Qeff_names <- mppData$parents }
colnames(SIM)[5:dim(SIM)[2]] <- c("log10pval", Qeff_names)
} else {colnames(SIM)[5] <- "log10pval"}
class(SIM) <- c("QTLprof", "data.frame")
### 4.1: Verify the positions for which model could not be computed
if(sum(SIM$log10pval == 0) > 0) {
if (sum(SIM$log10pval) == 0){
warning("the computation of the QTL model failled for all positions")
} else {
list.pos <- mppData$map[(SIM$log10pval == 0), 1]
prob_pos <- paste(list.pos, collapse = ", ")
message("the computation of the QTL model failed for the following ",
"positions: ", prob_pos,
". This could be due to singularities or function issues")
}
}
return(SIM)
}
vincentgarin/mppR documentation built on March 13, 2024, 7:30 p.m.
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Defines functions mpp_SIM
Documented in mpp_SIM
###########
# mpp_SIM #
###########
#' MPP Simple Interval Mapping
#'
#' Computes single QTL models along the genome using different models.
#'
#' The implemented models vary according to the number of alleles assumed at the
#' QTL position and their origin. Four assumptions for the QTL effect are
#' possible.
#'
#' Concerning the type of QTL effect, the first option is a cross-specific QTL
#' effects model (\code{Q.eff = "cr"}). In this model, the QTL effects are
#' assumed to be nested within cross which leads to the estimation of one
#' parameter per cross. The cross-specific model corresponds to the
#' disconnected model described in Blanc et al. 2006.
#'
#' A second possibility is the parental model (\code{Q.eff = "par"}). The
#' parental model assumes one QTL effect (allele) per parent that are independent
#' from the genetic background. This means that QTL coming form parent i has the
#' same effect in all crosses where this parent is used. This model is supposed
#' to produce better estimates of the QTL due to larger sample size when parents
#' are shared between crosses.
#'
#' In a connected MPP (\code{\link{design_connectivity}}), if np - 1 < nc, where
#' np is the number of parents and nc the number of crosses, the parental model
#' should be more powerful than the cross-specific model because it estimate
#' a reduced number of QTL parameters. This gain in power will be only true if
#' the assumption of constant parental effect through crosses holds. Calculated
#' with HRT assumption, the parental model corresponds to the connected model
#' presented in Blanc et al. (2006).
#'
#' The third type of model is the ancestral model (\code{Q.eff = "anc"}). This
#' model tries to use genetic relatedness that could exist between parents.
#' Indeed, the parental model assumes that parent are independent which is not
#' the case. Using genetic relatedness between the parents, it is possible group
#' these parents into a reduced number of ancestral cluster. Parents belonging
#' to the same ancestral group are assumed to transmit the same allele
#' (Jansen et al. 2003; Leroux et al. 2014). The ancestral model estimate
#' therefore one QTL effect
#' per ancestral class. Once again, the theoretical expectation is a gain of
#' QTL detection power by the reduction of the number of parameters to estimate.
#' The HRT ancestral model correspond to the linkage desequilibrium
#' linkage analysis (LDLA) models used by Bardol et al. (2013) or
#' Giraud et al. (2014).
#'
#' The final possibility is the bi-allelic model (\code{Q.eff = "biall"}).
#' Bi-allelic genetic predictor are a single vector with value 0, 1 or 2
#' corresponding to the number of allele copy of the least frequent SNP allele.
#' Relatedness between lines is therefore defined via identical by state (IBS)
#' measurement. This model corresponds to models used for association mapping.
#' For example, it is similar to model B in Wurschum et al. (2012) or
#' association mapping model in Liu et al. (2012).
#'
#' @param mppData An object of class \code{mppData}.
#'
#' @param trait \code{Numerical} or \code{character} indicator to specify which
#' trait of the \code{mppData} object should be used. Default = 1.
#'
#' @param Q.eff \code{Character} expression indicating the assumption concerning
#' the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental; 3) "anc"
#' for ancestral; 4) "biall" for a bi-allelic. For more details see
#' \code{\link{mpp_SIM}}. Default = "cr".
#'
#' @param plot.gen.eff \code{Logical} value. If \code{plot.gen.eff = TRUE},
#' the function will save the decomposed genetic effects per cross/parent.
#' These results can be plotted with the function \code{\link{plot.QTLprof}}
#' to visualize a genome-wide decomposition of the genetic effects.
#' \strong{This functionality is ony available for the cross-specific,
#' parental and ancestral models.}
#' Default value = FALSE.
#'
#' @param n.cores \code{Numeric}. Specify here the number of cores you like to
#' use. Default = 1.
#'
#'
#' @return Return:
#'
#' \item{SIM }{\code{Data.frame} of class \code{QTLprof}. with five columns :
#' 1) QTL marker names; 2) chromosomes;
#' 3) interger position indicators on the chromosome;
#' 4) positions in centi-Morgan; and 5) -log10(p-val). And if
#' \code{plot.gen.eff = TRUE}, p-values of the cross or parental QTL effects.}
#'
#' @author Vincent Garin
#'
#' @seealso \code{\link{plot.QTLprof}}
#'
#' @references
#'
#' Bardol, N., Ventelon, M., Mangin, B., Jasson, S., Loywick, V., Couton, F., ...
#' & Moreau, L. (2013). Combined linkage and linkage disequilibrium QTL mapping
#' in multiple families of maize (Zea mays L.) line crosses highlights
#' complementarities between models based on parental haplotype and single locus
#' polymorphism. Theoretical and applied genetics, 126(11), 2717-2736.
#'
#' Blanc, G., Charcosset, A., Mangin, B., Gallais, A., & Moreau, L. (2006).
#' Connected populations for detecting quantitative trait loci and testing for
#' epistasis: an application in maize. Theoretical and Applied Genetics,
#' 113(2), 206-224.
#'
#' Giraud, H., Lehermeier, C., Bauer, E., Falque, M., Segura, V., Bauland,
#' C., ... & Moreau, L. (2014). Linkage Disequilibrium with Linkage Analysis
#' of Multiline Crosses Reveals Different Multiallelic QTL for Hybrid
#' Performance in the Flint and Dent Heterotic Groups of Maize. Genetics,
#' 198(4), 1717-1734.
#'
#' Jansen, R. C., Jannink, J. L., & Beavis, W. D. (2003). Mapping quantitative
#' trait loci in plant breeding populations. Crop Science, 43(3), 829-834.
#'
#' Leroux, D., Rahmani, A., Jasson, S., Ventelon, M., Louis, F., Moreau, L.,
#' & Mangin, B. (2014). Clusthaplo: a plug-in for MCQTL to enhance QTL detection
#' using ancestral alleles in multi-cross design. Theoretical and Applied
#' Genetics, 127(4), 921-933.
#'
#' Liu, W., Reif, J. C., Ranc, N., Della Porta, G., & Wurschum, T. (2012).
#' Comparison of biometrical approaches for QTL detection in multiple
#' segregating families. Theoretical and Applied Genetics, 125(5), 987-998.
#'
#' Meuwissen T and Luo, Z. (1992). Computing inbreeding coefficients in large
#' populations. Genetics Selection Evolution, 24(4), 305-313.
#'
#' Wurschum, T., Liu, W., Gowda, M., Maurer, H. P., Fischer, S., Schechert, A.,
#' & Reif, J. C. (2012). Comparison of biometrical models for joint linkage
#' association mapping. Heredity, 108(3), 332-340.
#'
#' @examples
#'
#'
#' # Cross-specific model
#' ######################
#'
#' data(mppData)
#'
#' SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr", plot.gen.eff = TRUE)
#'
#' plot(x = SIM)
#' plot(x = SIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr")
#'
#'
#' # Bi-allelic model
#' ##################
#'
#' SIM <- mpp_SIM(mppData = mppData, Q.eff = "biall")
#'
#' plot(x = SIM, type = "h")
#'
#' @export
#'
mpp_SIM <- function(mppData, trait = 1, Q.eff = "cr",
plot.gen.eff = FALSE, n.cores = 1) {
# 1. Check data format and arguments
####################################
check.model.comp(mppData = mppData, trait = trait, Q.eff = Q.eff,
VCOV = 'h.err', plot.gen.eff = plot.gen.eff,
n.cores = n.cores, fct = "SIM")
# 2. Form required elements for the analysis
############################################
### 2.1 trait values
t_val <- sel_trait(mppData = mppData, trait = trait)
### 2.3 cross matrix (cross intercept)
cross.mat <- IncMat_cross(cross.ind = mppData$cross.ind)
### 2.4 Optional cluster
if(n.cores > 1){
parallel <- TRUE
cluster <- makeCluster(n.cores)
} else {
parallel <- FALSE
cluster <- NULL
}
vect.pos <- 1:dim(mppData$map)[1]
# 3. computation of the SIM profile (genome scan)
#################################################
if (parallel) {
log.pval <- parLapply(cl = cluster, X = vect.pos, fun = QTLModelSIM,
mppData = mppData, trait = t_val,
cross.mat = cross.mat, Q.eff = Q.eff, VCOV = 'h.err',
plot.gen.eff = plot.gen.eff)
} else {
log.pval <- lapply(X = vect.pos, FUN = QTLModelSIM,
mppData = mppData, trait = t_val, cross.mat = cross.mat,
Q.eff = Q.eff, VCOV = 'h.err', plot.gen.eff = plot.gen.eff)
}
if(n.cores > 1){stopCluster(cluster)}
log.pval <- t(data.frame(log.pval))
if(plot.gen.eff){log.pval[is.na(log.pval)] <- 1}
log.pval[, 1] <- check.inf(x = log.pval[, 1]) # check if there are -/+ Inf value
log.pval[is.na(log.pval[, 1]), 1] <- 0
# 4. form the results
#####################
SIM <- data.frame(mppData$map, log.pval)
if(plot.gen.eff){
if(Q.eff == "cr"){ Qeff_names <- unique(mppData$cross.ind)
} else { Qeff_names <- mppData$parents }
colnames(SIM)[5:dim(SIM)[2]] <- c("log10pval", Qeff_names)
} else {colnames(SIM)[5] <- "log10pval"}
class(SIM) <- c("QTLprof", "data.frame")
### 4.1: Verify the positions for which model could not be computed
if(sum(SIM$log10pval == 0) > 0) {
if (sum(SIM$log10pval) == 0){
warning("the computation of the QTL model failled for all positions")
} else {
list.pos <- mppData$map[(SIM$log10pval == 0), 1]
prob_pos <- paste(list.pos, collapse = ", ")
message("the computation of the QTL model failed for the following ",
"positions: ", prob_pos,
". This could be due to singularities or function issues")
}
}
return(SIM)
}
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