Help for package mppR

Type:

Package

Version:

1.5.0

Date:

2024-02-22

Title:

Multi-Parent Population QTL Analysis

Description:

Analysis of experimental multi-parent populations to detect regions of the genome (called quantitative trait loci, QTLs) influencing phenotypic traits measured in unique and multiple environments. The population must be composed of crosses between a set of at least three parents (e.g. factorial design, 'diallel', or nested association mapping). The functions cover data processing, QTL detection, and results visualization. The implemented methodology is described in Garin, Wimmer, Mezmouk, Malosetti and van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>, in Garin, Malosetti and van Eeuwijk (2020) <doi:10.1007/s00122-020-03621-0>, and in Garin, Diallo, Tekete, Thera, ..., and Rami (2024) <doi:10.1093/genetics/iyae003>.

License:

GPL-3

Imports:

dplyr, ggplot2, graphics, grDevices, igraph, Matrix, methods, nlme, parallel, qtl, stats, utils

Depends:

R(≥ 3.1.0)

Suggests:

knitr, rmarkdown, testthat

RoxygenNote:

7.2.3

Encoding:

UTF-8

URL:

https://github.com/vincentgarin/mppR

BugReports:

https://github.com/vincentgarin/mppR/issues

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2024-02-22 15:33:08 UTC; garin

Author:

Vincent Garin

[aut, cre], Valentin Wimmer [aut], Dietrich Borchardt [ctb, dtc], Fred van Eeuwijk [ctb, ths], Marcos Malosetti [ctb, ths]

Maintainer:

Vincent Garin <vincent.garin6@gmail.com>

Repository:

CRAN

Date/Publication:

2024-02-22 17:20:02 UTC

Cross validation partition

Description

Partition the genotype indices into training and validation sets for cross-validation (CV).

Usage

CV_partition(cross.ind, k = 5)

Arguments

cross.ind

Character vector with the same length as the number of genotypes which specifies to which cross each genotype belongs.

k

Numeric value representing the number of subsets (fold) into which data are spread within cross. Default = 5.

Details

The genotype indices are randomly assigned within cross to k subsets (folds). Then each subset is used once as validation set, the remaining data go in the training set.

Value

Return:

fold

List of k lists (one for each fold). Each fold list contains two vectors with genotypes indices of the training ($train.set) and the validation set ($val.set).

Author(s)

Vincent Garin

Examples


data(mppData)

part.cv <- CV_partition(cross.ind = mppData$cross.ind, k = 5)

part.cv[[1]]$train.set
part.cv[[1]]$val.set

Determine EC effects

Description

Determine the effect of environmental covariates (EC) on the mean of a trait across environments and the time range where this effect is the strongest. The procedure was originally proposed by Li et al. (2018).

Usage

EC_effect(
  trait_env_mean,
  crop_duration,
  EC_list,
  type,
  min_win = 20,
  sel_criteria = "global",
  plot = TRUE,
  plot_dir = NULL,
  p_title = "EC_plot",
  env_nm = NULL
)

Arguments

trait_env_mean

vector of trait mean over environment.

crop_duration

numerical value indicating the crop duration.

EC_list

list EC parameter matrix. one per environment. The order of the environment must be the same as the one of the trait mean.

type

character string vector indicating the type of statistic that correspond to the EC. Either the cumulated sum ('sum') or the average ('mean').

min_win

Numerical value indicating the minimum size of the range between start and end day when the EC values are measured. Default = 20

sel_criteria

Character specifying the selection criteria. Default = 'global'

plot

Logical value indicating if a plot of the EC effects over time should be returned. Default = FALSE,

plot_dir

Directory where the plot should be returned. Default = NULL

p_title

Title of the plot. Default = 'EC_plot'

env_nm

Optional vector of environment name. Default = NULL.

Value

Return:

data.frame that contains the following elements for each EC (line). The first line is the value of the EC in the different environments:

Start and end date of the optimal window
R2 of correlation between trait and EC
Direction of the correlation
Average R2 value over all tested windows
EC value in the different environments for the optimal time window

Author(s)

Vincent Garin

References

Li, X., Guo, T., Mu, Q., Li, X., & Yu, J. (2018). Genomic and environmental determinants and their interplay underlying phenotypic plasticity. Proceedings of the National Academy of Sciences, 115(26), 6679-6684.

IBD coding for `mppData` objects

Description

The function first converts genotype data into ABH format. Then it calculates within cross identical by descent (IBD) probabilities.

Usage

IBD.mppData(
  mppData,
  het.miss.par = TRUE,
  subcross.ind = NULL,
  par.per.subcross = NULL,
  type,
  F.gen = NULL,
  BC.gen = NULL,
  type.mating = NULL,
  error.prob = 1e-04,
  map.function = "haldane"
)

Arguments

mppData

An object of class mppData. the mppData must have been processed using: create.mppData, QC.mppData, and IBS.mppData.

het.miss.par

Logical value. if het.miss.par = TRUE, the function will use the offspring segregation to try to infer the allele that was transmitted by the heterozygous or missing parent at a particular locus to make the ABH conversion. Default = TRUE.

subcross.ind

Optional character vector specifying to which sub-cross each genotype belong. Default = NULL.

par.per.subcross

Optional three columns Character matrix specifying : 1) the sub-cross indicators; 2) the parents 1 identifiers of the sub-crosses; 3) the parents 2 identifiers of the sub-crosses. Default = NULL.

type

Character indicator for the type of population analyzed: type = "F" for Fn (F cross n generations); type = "BC" for BCn (backcross n generations); type = "BCsFt" for backcross followed by selfing; type = "DH" for double haploids; and type = "RIL" for recombinant inbred lines. For RIL type specify if the population was obtain using selfing or sibling mating using type.mating. If type = "RIL" or "DH", the function does not assume heterozygous marker scores for these populations and convert them into missing (NA).

F.gen

Numeric integer representing the number of F generations. For example F.gen = 2 for F2. Default = NULL.

BC.gen

Numeric integer representing the number of backcross generations. For example BC.gen = 1 for single backcross. Default = NULL.

type.mating

Character specifying for a RIL population if it was obtained by selfing ("selfing") or by sibling mating ("sib.mat"). Default = NULL.

error.prob

Numeric value for assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype). Default = 0.0001.

map.function

Character expression specifying the type of map function used to infer the IBD probabilities. possibility to choose between "haldane", "kosambi","c-f","morgan". Default = "haldane".

Details

The function first transforms genotype data into within cross ABH format. The function takes the parents of the different cross as reference and assigns the following scores: "A" if the offspring score is equivalent to parent 1; "B" if it is equivalent to parent 2; "H" if it is heterozygous. The function attributes NA for missing when: 1) the offspring score is missing; 2) the two parents have the same score; or 3) when at least one parental score is missing.

If a parent score is heterozygous or missing (het.miss.par = TRUE), the assignment rules are the following. If the two parents are heterozygous or one parent is heterozygous and the other missing, the offspring get NA since the parental origin can not be inferred with certainty. If one parent is heterozygous or missing and the second parent is homozygous, the function looks at offspring segregating pattern to infer which allele was transmitted by the heterozygous parent. If this is possible we consider the heteroxzygous parent as homozygous for the transmitted allele and use this allele score for ABH assignment.

The ABH assignment can be performed using sub-cross structure providing information about sub-cross in arguments subcross.ind and par.per.subcross.

Then the function calculates the IBD probabilities using read.cross() and calc.genoprob() functions from the R/qtl package (Broman et al. 2009).

The type of population must be specified in argument type. Different population types are possible: F-type ('F'), back-cross ('BC'), backcross followed by selfing ('BCsFt'), double haploid ('DH'), and recombinant imbred lines ('RIL'). The number of F and BC generations can be specified using F.gen and BC.gen. The argument type.mating specifies if F and RIL populations were obtained by selfing or by sibling mating.

DH and RIL populations are read as back-cross by R/qtl. For these two population types, heterozygous scores will be treated as missing values.

Value

an increased mppData object containing the the same elements as the mppData object provided as argument and the following new elements:

geno.IBD

A R/qtl cross.object containing the IBD probabilities.

n.zigo

Numeric value Indicating the number of different genotypes: 2 (AA/BB) or 3 (AA/AB/BB)

type

Character expression indicating the type of population.

Author(s)

Vincent Garin

References

Broman KW, Wu H, Sen S, Churchill GA (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19:889-890.

Broman, K. W., & Sen, S. (2009). A Guide to QTL Mapping with R/qtl (Vol. 46). New York: Springer.

Examples


data(mppData_init)

mppData <- QC.mppData(mppData_init)
mppData <- IBS.mppData(mppData = mppData)

mppData <- IBD.mppData(mppData = mppData, het.miss.par = TRUE, type = 'RIL',
                       type.mating = 'selfing')

IBS coding for `mppData` objects

Description

Transform the genotype marker matrix of a mppData object into Identical by state (IBS) 0, 1, 2 format. The IBS score represent the number of copies of the minor allele.

Usage

IBS.mppData(mppData)

Arguments

mppData

An object of class mppData. The mppData must have been processed using: create.mppData and QC.mppData.

Value

an increased mppData object containing the the same elements as the mppData object provided as argument and the following new elements:

geno.IBS

Marker matrix with marker scores coded as 0, 1, 2 corresponding to the number of copies of the least frequent SNP allele.

allele.ref

matrix with reference allele scores. The first row represents the minor allele (lowest frequency), the second the one represent the major allele (largest frequency) and the two others the heterozygous scores.

Author(s)

Vincent Garin

Examples


data(mppData_init)

mppData <- QC.mppData(mppData_init)

mppData <- IBS.mppData(mppData = mppData)

QTL genetic effects multi-QTL effect model

Description

Compute a multi-QTL model with a list of QTL candidates (QTL) and return the decomposed QTL genetic effects per cross or per parents. The list of QTL can be of different types (cross-specific, parental, ancestral or bi-allelic). The type of QTL effects are specified in the vector Q.eff.

Usage

MQE_gen_effects(mppData = NULL, trait = 1, QTL = NULL, Q.eff, ref.par = NULL)

Arguments

mppData

An object of class mppData

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

QTL

Vector of character markers positions names. Default = NULL.

Q.eff

Character vector indicating for each QTL position the type of QTL effect among: "cr", "par", "anc" and "biall". For details look at mpp_SIM.

ref.par

Optional Character expression defining the parental allele that will be used as reference for the parental model. For the ancestral model, the ancestral class containing the reference parent will be set as reference. This option can only be used if the MPP design is composed of a unique connected part. Default = NULL.

Details

This function computes for each QTL position the genetic effects of the cross, parental, ancestral or SNP allele components. For the cross-specific model (Q.eff = "cr"), the genetics effects represent the substitution effect of an single allele from the parent 2 (or B) with respect to an allele coming from the parent 1 or A. All effects are given in absolute value with the parent that cary the positive allele.

For the parental and the ancestral model (Q.eff = "par" or "anc"), the reference allele is defined per interconneted part. The most frequent parental (ancestral) allele is set as reference. Effects of the other alleles are estimated as deviation with respect to the reference. For more details about reference definition see QTL_gen_effects and design_connectivity.

For the bi-allelic model (Q.eff = "biall"), the genetic effects represent the effects of a single allele copy of the least frequent allele.

Value

Return:

results

List of data.frame (one per QTL) containing the following information:

QTL genetic effects per cross or parent.
Standard error of the QTL effects.
Test statistics of the effects (t-test or Wald statistic).
P-value of the test statistics.
Significance of the QTL effects.
For cross-specific model, parent with the positive additive effects.
For parental and ancestral model, indicator of connected part of the design and reference.
Allele scores of the parents if geno.par is non NULL in the mppData object.

Author(s)

Vincent Garin

Examples


data(mppData)

SIM <- mpp_SIM(mppData = mppData)
QTL <- QTL_select(SIM)

QTL.eff <- MQE_gen_effects(mppData = mppData, QTL = QTL[, 1],
                          Q.eff = c("anc", "par", "biall"))

Multi-QTL effect MPP analysis

Description

Build multi-QTL effects (MQE) models in which different QTL effects (cross-specific, parental, ancestral or bi-allelic) can be assumed at different loci.

Usage

MQE_proc(
  pop.name = "MPP_MQE",
  trait.name = "trait1",
  mppData = NULL,
  trait = 1,
  Q.eff,
  ref.par = NULL,
  threshold = 4,
  window = 30,
  backward = TRUE,
  alpha.bk = 0.05,
  plot.MQE = FALSE,
  CI = FALSE,
  drop = 1.5,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name

Character name of the studied population. Default = "MPP_MQE".

trait.name

Character name of the studied trait. Default = "trait1".

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

Character vector of possible QTL effects the user want to test. Elements of Q.eff can be "cr", "par", "anc" or "biall". For details look at mpp_SIM.

ref.par

Optional Character expression defining the parental allele that will be used as reference to calculate the allelic effects of the parental model. For the ancestral model, the ancestral class containing the reference parent will be set as reference. This option can only be used if the MPP design is composed of a unique connected part. Default = NULL.

threshold

Numeric value representing the -log10(p-value) threshold above which a position can be considered as significant. Default = 4.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 30.

backward

Logical value. If backward = TRUE, the function performs a backward elimination on the list of selected QTLs. Default = TRUE.

alpha.bk

Numeric value indicating the significance level for the backward elimination. Default = 0.05.

plot.MQE

Logical value. If plot.MQE = TRUE, the function will plot the last run of the MQE model determination. Default = FALSE.

CI

Logical value. If CI = TRUE, the function will compute a -log10(pval) drop confidence interval for each QTL using the QTL profile of the last iteration. Default = FALSE.

drop

Numeric -log10(p-value) drop value at the limits of the interval. Default = 1.5.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

verbose

Logical value indicating if the steps of MQE_proc should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results.

Details

The possible QTL effect that the user wants to allow must be specified in Q.eff. The procedure is the following:

Forward regression to determine a MQE model with different possible assumptions for the QTL effect at different loci. The function use.
Optional backward elimination (backward = TRUE) on the final list of detected QTLs.
Estimation of the QTL genetic effects and R squared statistics.
If plot.MQE = TRUE, plot of the last CIM run of the forward regression.
If CI = TRUE, confidence interval calculation based on a CIM- (CIM without cofactor on the selected chromosome) of the last run of the forward regression.

Value

Return:

List containing the following items:

n.QTL

Number of detected QTLs.

QTL

Data.frame with QTL positions.

R2

list containing R squared statistics of the QTL effects. for details see QTL_R2.

QTL.effects

List of genetic effects per QTL.

QTL.CI

If CI = TRUE, confidence interval information of the QTLs.

Some output files are also saved at the location specified (output.loc):

A QTL report (QTL_REPORT.txt) with: 1) the number of detected QTLs; 2) the global R squared statistics; 3) for each QTL, position information and estimated QTL genetic effect per cross or parents.
The list of QTLs (QTL.txt).
The QTL R squared statistics (QTL_R2.txt) (for details see QTL_R2).
General results of the QTL detection process: Number of QTL and global adjusted and non-adjusted R squared statistics. (QTL_genResults.txt).
if plot.MQE = TRUE, a plot of the last QTL detection run profile (plot_MQE.pdf).
If CI = TRUE, the QTL confidence intervals (QTL_CI.txt).

Author(s)

Vincent Garin

Examples


## Not run: 

data(mppData)

# Specify a location where your results will be saved
my.loc <- tempdir()

MQE <- MQE_proc(pop.name = "USNAM", trait.name = "ULA", mppData = mppData,
                Q.eff = c("par", "biall"), verbose = FALSE,
                output.loc = my.loc)
                 

## End(Not run)

Quality control for `mppData` objects

Description

Perform different operations of quality control (QC) on the marker data of an mppData object.

Usage

QC.mppData(
  mppData,
  mk.miss = 0.1,
  gen.miss = 0.25,
  n.lim = 15,
  MAF.pop.lim = 0.05,
  MAF.cr.lim = NULL,
  MAF.cr.miss = TRUE,
  MAF.cr.lim2 = NULL,
  verbose = TRUE,
  n.cores = 1
)

Arguments

mppData

An object of class mppData formed with create.mppData.

mk.miss

Numeric maximum marker missing rate at the whole population level comprised between 0 and 1. Default = 0.1.

gen.miss

Numeric maximum genotype missing rate at the whole population level comprised between 0 and 1. Default = 0.25.

n.lim

Numeric value specifying the minimum cross size. Default = 15.

MAF.pop.lim

Numeric minimum marker minor allele frequency at the population level. Default = 0.05.

MAF.cr.lim

Numeric vector specifying the critical within cross MAF. Marker with a problematic segregation rate in at least one cross is either set as missing within the problematic cross (MAF.cr.miss = TRUE), or remove from the marker matrix (MAF.cr.miss = FALSE). For default value see details.

MAF.cr.miss

Logical value specifying if maker with a too low segregation rate within cross (MAF.cr.lim) should be put as missing (MAF.cr.miss = TRUE) or discarded (MAF.cr.miss = FALSE). Default = TRUE.

MAF.cr.lim2

Numeric. Alternative option for marker MAF filtering. Only markers segregating with a MAF larger than MAF.cr.lim2 in at least one cross will be kept for the analysis. Default = NULL.

verbose

Logical value indicating if the steps of the QC should be printed. Default = TRUE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Details

The different operations of the quality control are the following:

Remove markers with more than two alleles.
Remove markers that are monomorphic or fully missing in the parents.
Remove markers with a missing rate higher than mk.miss.
Remove genotypes with more missing markers than gen.miss.
Remove crosses with less than n.lim genotypes.
Keep only the most polymorphic marker when multiple markers map at the same position.
Check marker minor allele frequency (MAF). Different strategy can be used to control marker MAF:

A) A first possibility is to filter marker based on MAF at the whole population level using MAF.pop.lim, and/or on MAF within crosses using MAF.cr.lim.

The user can give the its own vector of critical values for MAF within cross using MAF.cr.lim. By default, the within cross MAF values are defined by the following function of the cross-size n.ci: MAF(n.ci) = 0.5 if n.ci c [0, 10] and MAF(n.ci) = (4.5/n.ci) + 0.05 if n.ci > 10. This means that up to 10 genotypes, the critical within cross MAF is set to 50 decreases when the number of genotype increases until 5

If the within cross MAF is below the limit in at least one cross, then marker scores of the problematic cross are either put as missing (MAF.cr.miss = TRUE) or the whole marker is discarded (MAF.cr.miss = FALSE). By default, MAF.cr.miss = TRUE which allows to include a larger number of markers and to cover a wider genetic diversity.

B) An alternative is to select only markers that segregate in at least on cross at the MAF.cr.lim2 rate.

Value

a filtered mppData object containing the the same elements as create.mppData after filtering. It contains also the following new elements:

geno.id

Character vector of genotpes identifiers.

ped.mat

Four columns data.frame: 1) the type of genotype: "offspring" for the last genration and "founder" for the genotypes above the offspring in the pedigree; 2) the genotype indicator; 3-4) the parent 1 (2) of each line.

geno.par.clu

Parent marker matrix without monomorphic or completely missing markers.

haplo.map

Genetic map corresponding to the list of marker of the geno.par.clu object.

parents

List of parents.

n.cr

Number of crosses.

n.par

Number of parents.

rem.mk

Vector of markers that have been removed.

rem.geno

Vector of genotypes that have been removed.

Author(s)

Vincent Garin

Examples


data(mppData_init)

mppData <- QC.mppData(mppData = mppData_init, n.lim = 15, MAF.pop.lim = 0.05,
                      MAF.cr.miss = TRUE, mk.miss = 0.1,
                      gen.miss = 0.25, verbose = TRUE)

QTL global and partial R squared

Description

Computes the global and partial (adjusted) R squared of a list of QTLs using a linear model.

Usage

QTL_R2(mppData, trait = 1, QTL = NULL, Q.eff = "cr", glb.only = FALSE)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

QTL

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

Q.eff

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 mpp_SIM. Default = "cr".

glb.only

Logical value. If glb.only = TRUE, only the global and global adjusted R squared will be returned. Default = FALSE.

Details

The function computes R squared statistics using a linear model. The extra variance explained by a full model containing the QTL terms with respect to a reduced model containing only the cross intercept terms and uses the ratio between the residual sum of square of these two models: R2 = 1-(RSS(f))/(RSS(r)).

Partial R squared for each individual QTL position can also be calculated. Two types of partial R squared are returned. The first one uses the difference between the R squared obtained with all QTL positions and the R squared obtain with all position minus the ith one (difference R squared). The second method used only the ith QTL position in the model (single R squared).

For both global and partial R squared, it is possible to obtained adjusted measurements taking the number of degrees of freedom into consideration using an adaptation of the formula given by Utz et al. (2000): R.adj = R-(z/(N-z-n.cr))*(1-R) where z is the total number of estimated components of the genetic effect. N is the total number of phenotypic information, and n.cr is the number of intercept (cross) terms.

Value

Return:

object of class QR2Res containing the following objects:

glb.R2

Global R squared of all QTL terms.

glb.adj.R2

Global adjusted R squared of all QTL terms.

part.R2.diff

Vector of partial R squared doing the difference between the full model and a model minus the ith QTL.

part.adj.R2.diff

Vector of partial adjusted R squared doing the difference between the full model and a model minus the ith QTL.

part.R2.sg

Vector of partial R squared using only the ith QTL.

part.adj.R2.sg

Vector of partial adjusted R squared using only the ith QTL.

Author(s)

Vincent Garin

References

Utz, H. F., Melchinger, A. E., & Schon, C. C. (2000). Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics, 154(4), 1839-1849.

Examples


data(mppData)

SIM <- mpp_SIM(mppData)
QTL <- QTL_select(Qprof = SIM, threshold = 3, window = 20)
Q_R2 <- QTL_R2(mppData = mppData, QTL = QTL, Q.eff = "cr")
summary(Q_R2)

MPP GxE QTL R2

Description

Compute global and partial R2 statistics for MPP GxE QTL using a linear model. The global R2 is the contribution of all QTL positions while the partial R2 is the specific contribution of an individual QTL position.

Usage

QTL_R2_GE(mppData, trait, QTL = NULL, glb.only = FALSE)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

QTL

Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.

glb.only

Logical value. If glb.only = TRUE, only the global and global adjusted R squared will be returned. Default = FALSE.

Value

Return:

List containing the global unadjusted R2, the global adjusted R2, the partial unadjusted R2, and the partial adjusted R2.

Author(s)

Vincent Garin

Examples


data(mppData_GE)

Qpos <- c("PZE.105068880", "PZE.106098900")

R2 <- QTL_R2_GE(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
                QTL = Qpos)

MPP GxE QTL genetic effects

Description

Estimate the QTL parental allelic effects within environment. The estimation is performed using an exact mixed model with function from R package nlme. The significance of the allele effect is assessed using a Wald test.

Usage

QTL_effect_GE(
  mppData,
  trait,
  VCOV = "UN",
  ref_par = NULL,
  QTL = NULL,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

VCOV

VCOV Character expression defining the type of variance covariance structure used. 'CS' for compound symmetry assuming a unique genetic covariance between environments. 'CSE' for cross-specific within environment error term. 'CS_CSE' for both compound symmetry plus cross-specific within environment error term. 'UN' for unstructured environmental variance covariance structure allowing a specific genotypic covariance for each pair of environments. Default = 'UN'

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

QTL

Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

Details

The estimated model is the following:

\underline{y}_{icj} = E_{j} + C_{cj} + \sum_{q=1}^{n_{QTL}} x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}

For further details see the vignette.

Value

Return:

Qeff

List of data.frame (one per QTL) containing the following information:

QTL genetic effects
Standard error of the QTL effects.
Wald statistics of the effects.
P-value of the test statistics.
Significance of the QTL effects.

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

Examples


data(mppData_GE)

Qpos <- c("PZE.105068880", "PZE.106098900")

Qeff <- QTL_effect_GE(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
                      QTL = Qpos)

Qeff

Main and QTL by environment interaction model

Description

The function estimate a QTL model where each parental QTL allelic effect is decomposed into a main effect and a QTL by environment effect (QEI). It allows the user to determine which parental allelic effects have a significant interaction with the environment.

Usage

QTL_effect_main_QEI(
  mppData,
  trait,
  env_id = NULL,
  ref_env = NULL,
  ref_par = NULL,
  VCOV = "UN",
  QTL = NULL,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

env_id

Character vector specifying the environment names. By default, E1, ... En

ref_env

Optional Character expression defining the environment that will be used as reference for the parental model. By default, the last environment is set as reference.

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

VCOV

QTL

Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

Details

The function estimate the following model

y_{icj} = E_j + C_{c_j} + \sum_{q=1}^{n_{QTL}}{x_{i_{q}p}*(\alpha_{p} + \beta_{pj})} + GE_{ijc} + e_{ijc}

where the QTL effect is decomposed into \alpha_{p} that represent the main parental allelic effect across environments and \beta_{pj} which is the QEI effect. allelic effects must be interpreted as deviation with respect to the reference parent ('ref_par') in the reference environment ('ref_env'). By default the reference parent is the one with the highest allelic frequency (e.g. central parent in a NAM population).

The estimation is performed using an exact mixed model with function from R package nlme. The significance of the allele effect is assessed using a Wald test.

Value

Return:

List with one data.frame per QTL that contains the following elements:

To be filled
To be filled
Significance of the parent main effect expressed as the -log10(p-val)
Significance of the parent QTLxE effect expressed as the -log10(p-val)

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

Examples


## Not run: 

data(mppData_GE)

Qpos <- c("PZE.105068880", "PZE.106098900")

Qeff <- QTL_effect_main_QEI(mppData = mppData_GE,
                            trait = c('DMY_CIAM', 'DMY_TUM', 'DMY_INRA_P', 'DMY_KWS'),
                            env_id = c('CIAM', 'TUM', 'INRA', 'KWS'),
                            QTL = Qpos)

Qeff


## End(Not run)

Estimation of a model with main and QTL by environmental sensitivity terms

Description

After estimating which parental allelic effects have a significant interaction with the environment (QEI), the function extends the model for the allelic effect with a significant QEI to characterize this interaction in terms of sensitivity to (a) specific environmental covariate(s).

Usage

QTL_effect_main_QxEC(
  mppData,
  trait,
  env_id = NULL,
  ref_env = NULL,
  ref_par = NULL,
  VCOV = "UN",
  QTL = NULL,
  thre_QTL = 2,
  EC,
  Qmain_QEI = NULL,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

env_id

Character vector specifying the environment names. By default, E1, ... En

ref_env

Optional Character expression defining the environment that will be used as reference for the parental model. By default, the last environment is set as reference.

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. By default, the parent with the largest MAF is set as reference.

VCOV

QTL

Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.

thre_QTL

Numerical value specifying the -log10(p-val) threshold for a parental QTL allele to be considered as significant. By default, thre_QTL = 2, which correspond to a p-value of 0.01.

EC

Numeric matrix with environments as row and environmental covariates (EC) as column. The cell i, j of EC specify the value of the jth EC in environment i.

Qmain_QEI

results from QTL_effect_main_QEI

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

Details

The function first estimate the parental QTL allele main and QTLxE effect using the function QTL_effect_main_QEI. Optionally the output of QTL_effect_main_QEI can be passed through the 'Qmain_QEI' argument. The function consider that a parental QTL allele significantly interacts with the environment if its QTLxE term is significant at the 'thre_QTL' level. Thre_QTL is expressed in terms of -log10(p-val). For example, for p-val = 0.01, thre_QTL = -log10(p-val) = 2. Given this information, the effect of the parental QTL allele with a significant QEI are extended like that \beta_{pj} = EC_j*S_p+l_{p\epsilon} where EC_j represents the EC value in environment j associated with the sensitivity term S_p. The S_{p} determines the rate of change of the parental QTL allelic additive effect given an extra unit of EC. Finally, l_{p\epsilon} is a residual effect. The fitted model becomes:

\underline{y}_{icj} = E_{j} + C_{cj} + \sum_{q=1}^{n_{QTL}} x_{i_{q}p} (\alpha_p + \beta_{pj}) + x_{i_{q}pxE} (\alpha_p + EC_j*S_p+l_{p\epsilon}) + \underline{GE}_{icj} + \underline{e}_{icj}

The estimation is performed using an exact mixed model with function from R package nlme. The significance of S_{p} is assessed using a Wald test.

Value

Return:

List with one data.frame per QTL that contains the following elements:

QTL parent allele main effect expressed as deviation with respect to the reference parent
QTL parent allele effect in environment j expressed as deviation with respect to the reference parent
Significance of the parent main effect expressed as the -log10(p-val)
Significance of the parent QTLxE effect expressed as the -log10(p-val)

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

Examples


## Not run: 

data(mppData_GE)

Qpos <- c("PZE.105068880", "PZE.106098900")

EC <- matrix(c(180, 310, 240, 280), 4, 1)
rownames(EC) <- c('CIAM', 'TUM', 'INRA', 'KWS')
colnames(EC) <- 'cum_rain'

Qeff <- QTL_effect_main_QxEC(mppData = mppData_GE,
                         trait = c('DMY_CIAM', 'DMY_TUM', 'DMY_INRA_P', 'DMY_KWS'),
                         env_id = c('CIAM', 'TUM', 'INRA', 'KWS'),
                         QTL = Qpos, EC = EC)

Qeff


## End(Not run)

Forward regression QTL model

Description

Determines a multi-QTL model using a forward regression.

Usage

QTL_forward(
  mppData = NULL,
  trait = 1,
  Q.eff,
  threshold = 4,
  window = 30,
  n.cores = 1,
  verbose = TRUE
)

Arguments

mppData

An object of class mppData

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

Character vector of possible QTL effects the user want to test. Elements of Q.eff can be "cr", "par", "anc" or "biall". For details look at mpp_SIM.

threshold

Numeric value representing the -log10(p-value) threshold above which a position can be considered as significant. Default = 4.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 30.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

verbose

Logical value indicating if the steps of the forward regression should be printed. Default = TRUE.

Details

Forward regression to determine the a multi-QTL model. The function selects successively QTL positions with -log10(pval) above the threshold. Those positions are added as cofactors for following detection run. The procedure stop when no more position has a -log10(pval) above the threshold.

Value

Return:

QTL

Data.frame of class QTLlist 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-values).

Author(s)

Vincent Garin

Examples


data(mppData)

QTL <- QTL_forward(mppData = mppData, Q.eff = "par")

QTL genetic effects

Description

Computes a multi-QTL model with a list of QTL candidates (QTL) and return the decomposed QTL effects per cross or per parents.

Usage

QTL_gen_effects(
  mppData,
  trait = 1,
  QTL = NULL,
  Q.eff = "cr",
  ref.par = NULL,
  sum_zero = FALSE
)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

QTL

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

Q.eff

ref.par

sum_zero

Optional Logical value specifying if the QTL effect of a parental or an ancestral model should be calculated using the sum to zero constraint. Default = FALSE.

Details

For the parental and the ancestral model (Q.eff = "par" or "anc"), it is possible to estimate maximum n-1 parental or ancestral alleles per interconnected part of the design. For these two models, one parental (ancestral) allele is set as reference per interconnected part of the design. Effects of the other alleles are estimated as deviation with respect to the reference. Connected parts of the design can be determined using Weeks and Williams (1964) method (design_connectivity). By default, the reference allele is the most frequent one. The user can also specify a parental allele that will be used as reference using the argument ref.par. This option is only available if the MPP design is composed of a unique connected part.

For the parental and ancestral model it is also possible to estimate the QTL effects using a sum to zero constraint sum_zero = TRUE. In that case, the effects of the different parental (ancestral) allele will represent the deviation with respect to the average trait value.

For the bi-allelic model (Q.eff = "biall"), the genetic effects represent the effects of a single allele copy of the least frequent allele.

Value

Return:

Object of class QeffRes containing the following elements:

Qeff

List of data.frame (one per QTL) containing the following information:

QTL genetic effects per cross or parent.
Standard error of the QTL effects.
Test statistics of the effects (t-test or Wald statistic).
P-value of the test statistics.
Significance of the QTL effects.
For cross-specific model, parent with the positive additive effects.
For parental and ancestral model, indicator of connected part of the design and reference.
Allele scores of the parents if geno.par is non NULL in the mppData object.

tab.Qeff

data.frame with one column per QTL giving the QTL genetic effects per cross or per parent with its significance. The first two rows indicate the chromosome and the position in cM of each QTL.

Author(s)

Vincent Garin

References

Weeks, D. L., & Williams, D. R. (1964). A note on the determination of connectedness in an N-way cross classification. Technometrics, 6(3), 319-324.

Examples


data(mppData)

# QTL candidates

SIM <- mpp_SIM(mppData)
QTL <- QTL_select(SIM)

# Cross-specific model

QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "cr")
summary(QTL.effects)

# Parental model

QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "par")
summary(QTL.effects)

# Ancestral model

QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "anc")
summary(QTL.effects)

# Bi-allelic model

QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "biall")
summary(QTL.effects)

Predicted QTL global and partial R squared

Description

Compute predicted R squared in a validation set using QTLs detected in a training set. These values are corrected by the heritability her.

Usage

QTL_pred_R2(
  mppData.ts,
  mppData.vs,
  trait = 1,
  Q.eff = "cr",
  QTL = NULL,
  her = 1
)

Arguments

mppData.ts

An object of class mppData for the training set.

mppData.vs

An object of class mppData for the validation set.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

QTL

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

her

Numeric value between 0 and 1 representing the heritability of the trait. her can be a single value or a vector specifying each within cross heritability. Default = 1.

Details

Compute QTLs predicted R squared in a validation set (mppData.vs). These QTLs have been previously detected in a training set (mppData.ts). The global R squared (R2 = cor(y.ts,y.pred.ts)^2) is obtained using the Pearson squared correlation between the observed trait values in the validation set (y.vs) and predicted values using estimated QTL effects in the training set (y.pred.vs = X.vs * B.ts).

After that the values are corrected by the general or within cross heritability her. By default her = 1 which means that the R squared represent the proportion of explained phenotypic variance. The values are returned per cross (R2.cr) or averaged at the population level (glb.R2).

Partial R squared statistics are also calculated for each individual position. The partial R squared are computed by making the difference between the global R squared and the R squared computed without the ith position.

Value

Return:

List containing the following objects:

glb.R2

Global predicted R squared corrected for the heritability of all QTL terms. Doing the average of the within cross predicted R squared (R2.cr)

R2.cr

Within cross predicted R squared corrected for the heritability

part.R2.diff

Vector of predicted partial R squared corrected for the heritability doing the difference between the full model and a model minus the ith QTL.

Author(s)

Vincent Garin

Examples


data(mppData)

folds <- CV_partition(cross.ind = mppData$cross.ind, k = 5)

mppData.ts <- subset(x = mppData, gen.list = folds[[1]]$train.set)

mppData.vs <- subset(x = mppData, gen.list = folds[[1]]$val.set)

SIM <- mpp_SIM(mppData = mppData)
QTL <- QTL_select(SIM)

QTL_pred_R2(mppData.ts = mppData.ts, mppData.vs = mppData.vs, QTL = QTL)

QTL candidates selection

Description

Selection of QTL candidate positions.

Usage

QTL_select(Qprof, threshold = 3, window = 50, verbose = TRUE)

Arguments

Qprof

Object of class QTLprof returned by the function mpp_SIM or mpp_CIM.

threshold

Numeric value representing -log10(p-value) threshold above which a position can be considered as a QTL candidate. Default = 3.

window

Numeric value in centi-Morgan representing the minimum distance between two selected positions. Default = 50.

verbose

Logical value specifying if the detection of no QTL should be printed. Default = TRUE.

Details

The function select QTL positions that are above the given threshold per chromosome. Once a position has been selected, and exclusion window is set around that position. Positions falling into that region will not be candidate anymore. The search continue until there is no more candidate position above the threshold.

Value

Return:

QTL

Data.frame of class QTLlist 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-values).

References

This function is a modification of the QTL.reduce function coming from the Biometris pipepline.

RAP (R Analytical Pipeline) (V0.9.1) May 2011

Authors: Paul Eilers (1), Gerrit Gort (1), Sabine Schnabel (1), Lucia Gutierrez(1, 2), Marcos Malosetti(1), Joost van Heerwaarden, and Fred van Eeuwijk(1)

(1) Wageningen University and Research Center, Netherlands (2) Facultad de Agronomia, UDELAR, Uruguay

Examples


data(mppData)

SIM <- mpp_SIM(mppData)

QTL <- QTL_select(Qprof = SIM, threshold = 3)

Reduced genotype data maize US-NAM population

Description

Selection of markers and genotypes from the maize US nested association mapping (NAM) population (McMullen et al., 2009).

Usage

data(USNAM_geno)

Format

data.frame

Details

Sample of the marker matrix of the US-NAM population. The selection correspond to 102 markers coming from the two first chromosomes present in USNAM_map and the 506 genotypes. These genotypes correspond to the selected phenotypic values in USNAM_pheno. The selected genotypes come from the following crosses: (B73 x CML103), (B73 x CML322), (B73 x CML52), (B73 x Hp301), (B73 x M37W). The data of the 6 parental lines are also included. The data are available on www.panzea.org.

Source

https://www.panzea.org/

References

McMullen, M. D., Kresovich, S., Villeda, H. S., Bradbury, P., Li, H., Sun, Q., ... & Buckler, E. S. (2009). Genetic properties of the maize nested association mapping population. Science, 325(5941), 737-740.

Examples

data(USNAM_geno)

Reduced map maize US-NAM population

Description

Reduced map of the maize US nested association mapping (NAM) population (McMullen et al., 2009).

Usage

data(USNAM_map)

Format

data.frame

Details

Selection of 102 markers from the two first chromosomes of the Maize US-NAM population (McCullen et al., 2009). The data are available on www.panzea.org.

Source

https://www.panzea.org

References

Examples

  data(USNAM_map)

Reduced phenotype data from Maize US-NAM population

Description

Reduced phenotype data from the Maize US nested association mapping (NAM) population (McMullen et al., 2009).

Usage

data(USNAM_pheno)

Format

data.frame

Details

Upper leaf angle (ULA) trait values with genotypes identifiers as rownames. These genotypes correspond to the 500 offspring genotypes of the marker matrix USNAM_geno. The data are available on www.panzea.org.

Source

https://www.panzea.org

References

Examples

data(USNAM_pheno)

Create a multi-parent population data object

Description

This function combines all raw data sources in a single data object of class mppData.

Usage

create.mppData(
  geno.off = NULL,
  geno.par = NULL,
  map = NULL,
  pheno = NULL,
  cross.ind = NULL,
  par.per.cross = NULL
)

Arguments

geno.off

Character marker score matrix of the offspring with genotypes as row and markers as column. Rows names must be the offspring genotypes identifiers similar to the one used in pheno. The columns names must be the marker names similar to the one used in map. Marker scores must be coded using one letter per allele. For example, AA, CC, GG, etc. Missing values must be coded NA.

geno.par

Character marker score matrix of the parents with genotypes as row and markers as column. Rows names must be the parents genotypes identifiers similar to the one used in par.per.cross. The columns names must be the marker names similar to the one used in map. Marker scores must be coded using one letter per allele. For example, AA, CC, GG, etc. Missing values must be coded NA.

map

Three columns data.frame with: 1) character marker identifiers; 2) numeric chromosome; 3) numeric positions in centi-Morgan.The marker identifiers must be identical to the column names of the maker matrices (geno.off and geno.par). The chromosome identifiers must start by 1 and increase by 1 unit, e.g. 1, 2, 3, ...

pheno

Numeric matrix with one column per trait and rownames as genotypes identifiers. The genotypes identifiers must be identical to the rownames of the offspring marker matrix (geno.off).

cross.ind

Character vector indicating to which cross each genotype belongs.

par.per.cross

Three columns Character matrix specifying : 1) the cross indicators; 2) the parents 1 identifiers of the crosses; 3) the parents 2 identifiers of the crosses. The list of crosses must contain the same cross indicators as in cross.ind and they must appear in the same order. The list of parent identifiers must be the same to the rownames of the argument geno.par.

Value

a list of class mppData which contains the following elements

geno.off

Matrix with offspring marker scores.

geno.par

Matrix with parents marker scores.

pheno

Matrix with phenotypic trait values.

map

Data.frame with genetic marker information.

cross.ind

Cross indicator.

par.per.cross

Character matrix information about cross and the parents of the crosses.

The list also contain other arguments that will be filled later in the data processing.

Author(s)

Vincent Garin

Examples


data(USNAM_geno)
data(USNAM_map)
data(USNAM_pheno)

geno.off <- USNAM_geno[7:506, ]
geno.par <- USNAM_geno[1:6, ]
map <- USNAM_map
pheno <-  USNAM_pheno
cross.ind <- substr(rownames(pheno), 1, 4)
par.per.cross <- cbind(unique(cross.ind), rep("B73", 5),
                       rownames(geno.par)[2:6])

mppData <- create.mppData(geno.off = geno.off, geno.par = geno.par,
                          map = map, pheno = pheno, cross.ind = cross.ind,
                          par.per.cross = par.per.cross)

Connected parts of a MPP design

Description

Determine the connected parts of a MPP design using the method of Weeks and Williams (1964) and the package igraph.

Usage

design_connectivity(par_per_cross, plot_des = TRUE, output_loc = NULL)

Arguments

par_per_cross

Three columns character matrix specifying : 1) the cross indicators ; 2) the parents 1 identifiers of the crosses; 3) the parents 2 identifiers of the crosses.

plot_des

Logical value indicating if connected part of the design should be plotted. Default = TRUE.

output_loc

Path where the plot of the design will be saved if the argument is given. Default = NULL.

Value

Return a list with each element representing one connected part of the design and the list of parents contained in this part.

If plot_des = TRUE and output_loc has been specified. A plot of the graph (con_plot.pdf) will be saved at the specified location.

Author(s)

Vincent Garin

References

Weeks, D. L., & Williams, D. R. (1964). A note on the determination of connectedness in an N-way cross classification. Technometrics, 6(3), 319-324.

Examples


data(mppData)

par_per_cross <- mppData$par.per.cross

con.part <- design_connectivity(par_per_cross)

QTL incidence matrix

Description

Build a single position QTL incidences matrix.

Usage

inc_mat_QTL(x, mppData, Q.eff, order.MAF = FALSE, ref_par = NULL)

Arguments

x

Integer value indicating the genetic position on the map (mppData$map) of the QTL incidence matrix.

mppData

An object of class mppData.

Q.eff

order.MAF

Logical value specifying if the QTL incidence matrix should be ordered by allele frequency for a parental and ancestral QTL incidence matrix. The colum will be ordred from the least to the most frequent allele. Default = FALSE.

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

Value

Return:

QTL.mat

QTL incidence matrix. For the cross-specific model, it represents the difference between the number of allele from parent 2 or B and parent 1 or A divided by two. For parental (ancestral) model it represents the expected number of parental (ancestral) allele copies. For the bi-allelic model, it represents the number of copies of the least frequent allele.

Author(s)

Vincent Garin

Examples


data(mppData)

QTLmatCr <- inc_mat_QTL(x = 2, mppData = mppData, Q.eff = "cr")

QTLmatPar <- inc_mat_QTL(x = 2, mppData = mppData, Q.eff = "par")

QTLmatAnc <- inc_mat_QTL(x = 2, mppData = mppData, Q.eff = "anc")

QTLmatBi <- inc_mat_QTL(x = 2, mppData = mppData, Q.eff = "biall")

Complete `mppData` object

Description

Complete mppData object made from a sample data of the maize US nested association mapping (NAM) population. This mppData object went through all the steps of the data processing: create.mppData, QC.mppData, IBS.mppData, IBD.mppData, parent_cluster.mppData. The mppData contain all the data necessary for the QTL analysis procedures.

Usage

data(mppData)

Format

mppData

Details

The complete mppData object is a list containing the following elements:

geno.IBS: IBS genotype marker matrix.
geno.IBD: R/qtl cross.object containing the genotype within cross IBD probabilities.
geno.id: List of genotypes.
allele.ref: Matrix containing for each marker the most and least frequent marker scores and the two heterozygous scores.
geno.par: Parents marker matrix.
geno.par.clu: Parent marker data used to cluster the parents.
par.clu: Parent clustering results.
mono.anc: Positions for which the ancestral clustering was monomorphic.
pheno: Phenotypic trait matrix.
map: Genetic map corresponding to the geno (IBS, IBD, par) arguments.
haplo.map: Genetic map corresponding to geno.par.clu.
cross.ind: Vector indicating to which cross each genotype belongs.
par.per.cross: Matrix with for each cross the parent 1 and 2.
parents: Vector of parents.
type: Type of population.
n.cr: Number of crosses.
n.par: Number of parents.
n.anc: Average number of ancestral group along the genome.
n.zigo: Number of possible allelic computations 2 (AA/BB) or 3 (AA/AB/BB).
rem.mk: Removed markers in the data processing.
rem.gen: Removed genotypes in the data processing.
status: Indicates the level of progression in the data processing.

Examples

  data(mppData)

Example `mppData` object

Description

Example mppData object representing a subset from the maize EU-NAM Flint population (Bauer et al. 2013, Lehermeier et al. 2014, Giraud et al. 2014).

Usage

data(mppData_GE)

Format

mppData

Details

Sample data from the maize EU-NAM Flint population. The genotype data were obtained from http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE50558. The genotypes come from the five following crosses: (UH007 x D152), (UH007 x F03802), (UH007 x F2), (UH007 x F283), and (UH007 x DK105). We selected 100 markers randomly spread on chromosomes five and six. The genetic map was downloaded here http://maizegdb.org/data_center/reference?id=9024747.

The phenotypic data represent the within environment adjusted means for dry matter yield (DMY) calculated at La Coruna (CIAM), at Roggenstein (TUM), at Einbeck (KWS), and at Ploudaniel (INRA_P). The raw plot data were obtained here: http://www.genetics.org/content/198/1/3/suppl/DC1.

References

Bauer, E., Falque, M., Walter, H., Bauland, C., Camisan, C., Campo, L., ... & Altmann, T. (2013). Intraspecific variation of recombination rate in maize. Genome biology, 14(9), R103.

Giraud, H., Lehermeier, C., Bauer, E., Falque, M., Segura, V., Bauland, C., ... & Schipprack, W. (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.

Lehermeier, C., Krämer, N., Bauer, E., Bauland, C., Camisan, C., Campo, L., ... & Moreau, L. (2014). Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics, 198(1), 3-16.

Examples

  data(mppData_GE)

Add new phenotypic values to a mppData object

Description

Add the new phenotypic values contained in 'pheno' to a mppData object.

Usage

mppData_add_pheno(mppData, pheno)

Arguments

mppData

An object of class mppData.

pheno

data.frame with : 1) character genotypes identifiers; 2) numeric trait values. The genotypes identifiers must be identical to mppData$geno.id.

Value

Return:

mppData

New mppData object with new phenotypic values added.

Author(s)

Vincent Garin

Examples


data(mppData)
pheno_new <- data.frame(geno.id = mppData$geno.id, ph1 = rnorm(498))

mppData <- mppData_add_pheno(mppData = mppData, pheno = pheno_new)

`mppData` object with raw data

Description

mppData object with raw genotypic and phenotypic data of a sample of the maize US nested association mapping (NAM) population. Different operations of quality control and data processing still need to be performed before the QTL detection analysis.

Usage

data(mppData_init)

Format

mppData

Details

see examples of the create.mppData.

Examples

  data(mppData_init)

Modify the phenotypic values of a mppData object

Description

Modify the phenotypic values of a mppData object.

Usage

mppData_mdf_pheno(mppData, pheno)

Arguments

mppData

An object of class mppData.

pheno

Two columns data.frame with : 1) character genotypes identifiers; 2) numeric trait values. The genotypes identifiers must be identical to mppData$geno.id. The trait value identifiers must correspond to a trait already in the mppData object.

Value

Return:

mppData

New mppData object with modified phenotypic values added.

Author(s)

Vincent Garin

Examples


data(mppData)
pheno_new <- data.frame(geno.id = mppData$geno.id, ULA = rnorm(498))

mppData <- mppData_mdf_pheno(mppData = mppData, pheno = pheno_new)

MPP GxE Composite Interval Mapping

Description

Computes multi-QTL models with cofactors along the genome using an approximate mixed model computation. An initial variance covariance (VCOV) structure is calculated using function from the nlme package. Then, this information is used to estimate the QTL global and within parental effect significance using a Wald test.

Usage

mppGE_CIM(
  mppData,
  trait,
  VCOV = "UN",
  VCOV_data = "unique",
  cofactors = NULL,
  cof_red = FALSE,
  cof_pval_sign = 0.1,
  window = 20,
  ref_par = NULL,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

VCOV

VCOV_data

Character specifying if the reference VCOV should be formed taking all cofactors into consideration ("unique") or if different VCOVs should be formed by removing the cofactor information that is too close of a tested QTL position ("minus_cof"). Default = "unique"

cofactors

Object of class QTLlist representing a list of selected marker positions obtained with the function QTL_select() or a vector of character marker positions names. Default = NULL.

cof_red

Logical value specifying if the cofactor matrix should be reduced by only keeping the significant allele by environment interaction. Default = FALSE

cof_pval_sign

Numeric value specifying the p-value significance of an allele by environment term to be kept in the model. Default = 0.1

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

Details

The estimated model is the following:

\underline{y}_{icj} = E_{j} + C_{cj} + \sum_{qc=1}^{n_{cof}} x_{i_{qc}p} + \beta_{pj} + x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}

For further details see the vignette.

It is possible to calculate one initial VCOV using a null model with all the cofactors (VCOV_data = "unique") or one VCOV per combination of cofactors (VCOV_data = "minus_cof"). In the later case, the cofactor that fall witin a distance of window on the left and right of a QTL position is removed for the calculation of the initial VCOV. Therefore, N_cof + 1 VCOV are calculated.

Value

Return:

CIM

Data.frame of class QTLprof. with five columns : 1) QTL marker or in between position names; 2) chromosomes; 3) integer position indicators on the chromosome; 4) positions in centi-Morgan; and 5) -log10(p-val) of the global QTL effect across environments 6) p-values of the within environment QTL effects (one column per environment); and p-values of the within environment parental QTL allelic effects (one column per parent environment combination).

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

mppGE_SIM, mppGE_proc

Examples


data(mppData_GE)

cofactors <- mppData_GE$map$mk.names[c(35, 61)]

CIM <- mppGE_CIM(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
                     cofactors = cofactors, window = 20)
                     
Qpos <- QTL_select(CIM)
                      
plot(CIM)

plot_allele_eff_GE(mppData = mppData_GE, nEnv = 2, EnvNames = c('CIAM', 'TUM'),
                   Qprof = CIM, Q.eff = 'par', QTL = Qpos, text.size = 14)

MPP GxE Simple Interval Mapping

Description

Computes single QTL models along the genome using an approximate mixed model computation. An initial variance covariance (VCOV) structure is calculated using function from the nlme package. Then, this information is used to estimate the QTL global and within parental effect significance using a Wald test.

Usage

mppGE_SIM(
  mppData,
  trait,
  VCOV = "UN",
  ref_par = NULL,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100
)

Arguments

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

VCOV

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

Details

The estimated model is the following:

\underline{y}_{icj} = E_{j} + C_{cj} + x_{i_{q}p} * \beta_{pj} + \underline{GE}_{icj} + \underline{e}_{icj}

For further details see the vignette.

Value

Return:

SIM

Author(s)

Vincent Garin

References

Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2021). nlme: Linear and Nonlinear Mixed Effects Models_. R package version 3.1-152, <URL: https://CRAN.R-project.org/package=nlme>.

Examples


data(mppData_GE)

SIM <- mppGE_SIM(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'))

Qpos <- QTL_select(Qprof = SIM, threshold = 3, window = 50)

plot(x = SIM, QTL = Qpos)

plot_allele_eff_GE(mppData = mppData_GE, nEnv = 2, EnvNames = c('CIAM', 'TUM'),
                   Qprof = SIM, Q.eff = 'par', QTL = Qpos, text.size = 14)

MPP GxE QTL analysis

Description

QTL detection in MPP characterized in multiple environments.

Usage

mppGE_proc(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait,
  EnvNames = NULL,
  VCOV = "UN",
  ref_par = NULL,
  VCOV_data = "unique",
  SIM_only = FALSE,
  thre.cof = 4,
  win.cof = 50,
  cof_red = FALSE,
  cof_pval_sign = 0.1,
  window = 20,
  thre.QTL = 4,
  win.QTL = 20,
  text.size = 18,
  n.cores = 1,
  maxIter = 100,
  msMaxIter = 100,
  verbose = TRUE,
  output.loc = NULL
)

Arguments

pop.name

Character name of the studied population. Default = "MPP".

trait.name

Character name of the studied trait. Default = "trait1".

mppData

An object of class mppData.

trait

Character vector specifying which traits (environments) should be used.

EnvNames

character expression indicating the environment or trait labels. By default: Env_1, 2, 3, etc.

VCOV

ref_par

Optional Character expression defining the parental allele that will be used as reference for the parental model. Default = NULL

VCOV_data

Character specifying if the reference VCOV of the CIM profile computation should be formed taking all cofactors into consideration ("unique") or if different VCOVs should be formed by removing the cofactor information that is too close of a tested cofactor position ("minus_cof"). Default = "unique"

SIM_only

Logical value specifying if the procedure should only calculate a SIM profile (no CIM). This option can be used with large dataset to save time. Default = FALSE

thre.cof

Numeric value representing the -log10(p-value) threshold above which a position can be selected as cofactor. Default = 4.

win.cof

Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50.

cof_red

Logical value specifying if the cofactor matrix should be reduced by only keeping the significant allele by environment interaction. Default = FALSE

cof_pval_sign

Numeric value specifying the p-value significance of an allele by environment term to be kept in the model. Default = 0.1

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

thre.QTL

Numeric value representing the -log10(p-value) threshold above which a position can be selected as QTL. Default = 4.

win.QTL

Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

maxIter

maximum number of iterations for the lme optimization algorithm. Default = 100.

msMaxIter

maximum number of iterations for the optimization step inside the lme optimization. Default = 100.

verbose

Logical value indicating if the steps of mpp_proc should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results. Default = NULL.

Details

The procedure is the following:

Simple interval mapping (SIM) to select cofactors (mppGE_SIM).
Composite interval mapping (CIM) with selected cofactors (mppGE_CIM).
Estimation of QTLs additive allelic effect (QTL_effect_GE).
Estimation of the global QTLs effect R squared and individual QTL effect R squared (QTL_R2_GE).

Value

Return:

List containing the following items:

n.QTL

Number of detected QTLs.

cofactors

Data.frame with cofactors positions.

QTL

Data.frame with QTL positions.

Q_eff

list containing the estimated QTL allelic effects.

R2

List containing R squared statistics of the QTL effects

Some output files are also saved at the specified location (output.loc):

The SIM and CIM results in a RData file (SIM.RData, CIM.RData).
The list of cofactors (cofactors.RData).
The list of QTL (QTLs.RData).
The list of QTL allelic effects (QTL_effects.RData).
The QTL R squared statistics (QTL_R2.RData)
The number of detected QTLs and adjusted R2 (Glb_res.RData)
The plot of the CIM profile (QTL_profile.pdf) with dotted vertical lines representing the cofactors positions and the plot of the genetic effects per cross or parents obtained with plot_allele_eff_GE (gen_eff.pdf) with dashed lines representing the QTL positions.

Author(s)

Vincent Garin

Examples


## Not run: 

data(mppData_GE)

MPP_GE_QTL <- mppGE_proc(pop.name = 'EUNAM', trait.name = 'DMY',
mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'),
n.cores = 1, output.loc = tempdir())


## End(Not run)

MPP Composite Interval Mapping

Description

Compute QTL models along the genome using cofactors representing other genetic positions for control.

Usage

mpp_CIM(
  mppData,
  trait = 1,
  Q.eff = "cr",
  cofactors = NULL,
  window = 20,
  plot.gen.eff = FALSE,
  n.cores = 1
)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

cofactors

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be plotted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This functionality is only available for the cross-specific, parental and ancestral models. Default value = FALSE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Details

For more details about the different models, see documentation of the function mpp_SIM. The function returns a -log10(p-value) QTL profile.

Value

Return:

CIM

Data.frame of class 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 plot.gen.eff = TRUE, p-values of the cross or parental QTL effects.

Author(s)

Vincent Garin

Examples


# Cross-specific effect model
#############################

data(mppData)

SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr")

cofactors <- QTL_select(Qprof = SIM, threshold = 3, window = 20)

CIM <- mpp_CIM(mppData = mppData, Q.eff = "cr", cofactors = cofactors,
               window = 20, plot.gen.eff = TRUE)

plot(x = CIM)
plot(x = CIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr")

# Bi-allelic model
##################

cofactors <- mppData$map[c(15, 63), 1]

CIM <- mpp_CIM(mppData = mppData, Q.eff = "biall", cofactors = cofactors,
               window = 20)

plot(x = CIM, type = "h")

MPP cross-validation

Description

Evaluation of MPP QTL detection procedure by cross-validation (CV).

Usage

mpp_CV(
  pop.name = "MPP_CV",
  trait.name = "trait1",
  mppData,
  trait = 1,
  her = 1,
  Rep = 10,
  k = 5,
  Q.eff = "cr",
  thre.cof = 3,
  win.cof = 50,
  N.cim = 1,
  window = 20,
  thre.QTL = 3,
  win.QTL = 20,
  backward = TRUE,
  alpha.bk = 0.05,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name

Character name of the studied population. Default = "MPP_CV".

trait.name

Character name of the studied trait. Default = "trait1".

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

her

Numeric value between 0 and 1 representing the heritability of the trait. her can be a single value or a vector specifying each within cross heritability. Default = 1.

Rep

Numeric value representing the number of repetition of the k-fold procedure. Default = 10.

k

Numeric value representing the number of folds for the within cross partition of the population. Default = 5.

Q.eff

thre.cof

Numeric value representing the -log10(p-value) threshold above which a position can be peaked as a cofactor. Default = 3.

win.cof

Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50.

N.cim

Numeric value specifying the number of time the CIM analysis is repeated. Default = 1.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

thre.QTL

Numeric value representing the -log10(p-value) threshold above which a position can be selected as QTL. Default = 3.

win.QTL

Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.

backward

Logical value. If backward = TRUE, the function performs a backward elimination on the list of selected QTLs. Default = TRUE.

alpha.bk

Numeric value indicating the significance level for the backward elimination. Terms with p-values above this value will iteratively be removed. Default = 0.05.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

verbose

Logical value indicating if the progresses of the CV should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results.

Details

For details on the MPP QTL detection models see mpp_SIM documentation. The CV scheme is adapted from Utz et al. (2000) to the MPP context. A single CV run works like that:

Generation of a k-fold partition of the data. The partition is done within crosses. Each cross is divided into k subsets. Then for the kth repetition, the kth subset is used as validation set, the rest goes into the training set.
For the kth repetition, utilization of the training set for cofactor selection and multi-QTL model determination (mpp_SIM and mpp_CIM). If backward = TRUE, the final list of QTLs is tested simultaneously using a backward elimination (mpp_back_elim).
Use the list of detected QTLs in the training set to calculate the proportion of genetic variance explained by all detected QTLs in the training set (p.ts = R2.ts/h2). Where R2.ts is the adjusted R squared and h2 is the average within cross heritability (her). By default, her = 1, which mean that

For each single QTL effect, difference partial R squared are also calculated. Difference R squared are computed by doing the difference between a model with all QTLs and a model without the ith position. For details about R squared computation and adjustment look at QTL_R2.
Use the estimates of the QTL effects in the training set (B.ts) to predict the phenotypic values of the validation set. y.pred.vs = X.vs*B.ts. Computes the predicted R squared in the validation set using the squared Pearson correlation coefficient between the real values (y.vs) and the predicted values (y.pred.vs). R2.vs = cor(y.ts,y.pred.ts)^2. Then the predicted genetic variance in the validation set (p.vs) is equal to p.vs = R2.vs/h2. For heritability correction, the user can provide a single value for the average within cross heritability or a vector specifying each within cross heritability. By default, her = 1, which means that the results represent the proportion of phenotypic variance explained (predicted) in the training (validation) sets.

The predicted R squared is computed per cross and then averaged at the population level (p.ts). Both results are returned. Partial QTL predicted R squared are also calculated using the difference between the predicted R squared using all QTL and the predicted R squared without QTL i. The bias between p.ts and p.vs is calculated as bias = 1 - (p.vs/p.ts).

Value

List containing the following results items:

CV_res

Data.frame containing for each CV run: 1) the number of detected QTL; 2) the proportion of explained genetic variance in the TS (p.ts); 3) the proportion of predicted genetic variance in the VS (p.vs) at the population level (average of within cross prediction); the bias between p.ts and p.vs (bias = 1-(p.vs/p.ts)).

p.vs.cr

Matrix containing the within cross p.vs for each CV run.

QTL

Data.frame containing: 1) the list of QTL position detected at least one time during the entire CV process; 2) the number of times the position has been detected; 3) the average partial p.ts of the QTL position; 4) the average partial p.vs of the QTL position; 5) the average partial bias of the QTL position.

QTL.profiles

Data.frame -log10(p-value) QTL profiles of the different CV runs.

The results elements return as R object are also saved as text files at the specified output location (output.loc). A transparency plot of the CV results (plot.pdf) is also saved.

Author(s)

Vincent Garin

References

Examples


## Not run: 

data(mppData)

# Specify a location where your results will be saved
my.loc <- tempdir()

CV <- mpp_CV(pop.name = "USNAM", trait.name = "ULA", mppData = mppData,
her = .4, Rep = 1, k = 3, verbose = FALSE, output.loc = my.loc)


## End(Not run)

MPP Simple Interval Mapping

Description

Computes single QTL models along the genome using different models.

Usage

mpp_SIM(mppData, trait = 1, Q.eff = "cr", plot.gen.eff = FALSE, n.cores = 1)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be plotted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This functionality is ony available for the cross-specific, parental and ancestral models. Default value = FALSE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Details

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 (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 (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 (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 (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 (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).

Value

Return:

SIM

Author(s)

Vincent Garin

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")

Backward elimination on QTL candidates

Description

Performs a backward elimination using a list of given QTLs positions. The positions with a p-value above the significance level alpha, are successively removed.

Usage

mpp_back_elim(mppData, trait = 1, QTL = NULL, Q.eff = "cr", alpha = 0.05)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

QTL

Object of class QTLlist representing a list of selected position obtained with the function QTL_select or vector of character marker positions names. Default = NULL.

Q.eff

alpha

Numeric value indicating the level of significance for the backward elimination. Default = 0.05.

Details

The function starts with all QTL positions in the model and test the inclusion of each position as the last in the model. If all position p-values are below alpha the procedure stop. If not the position with the highest p-value is remove and the procedure continue until there is no more unsignificant position.

Value

Return:

QTL

Data.frame of class QTLlist 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-values).

Author(s)

Vincent Garin

Examples


data(mppData)

SIM <- mpp_SIM(mppData)

QTL <- QTL_select(SIM)

QTL.sel <- mpp_back_elim(mppData = mppData, QTL = QTL)

MPP QTL analysis using forward regression

Description

Multi-parent population QTL analysis model using a forward regression.

Usage

mpp_forward(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait = 1,
  Q.eff = "cr",
  ref.par = NULL,
  sum_zero = FALSE,
  threshold = 4,
  window = 30,
  backward = TRUE,
  alpha.bk = 0.05,
  plot.Qprof = FALSE,
  plot.gen.eff = FALSE,
  CI = FALSE,
  drop = 1.5,
  text.size = 18,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name

Character name of the studied population. Default = "MPP".

trait.name

Character name of the studied trait. Default = "trait1".

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

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. For more details see mpp_SIM. Default = "cr".

ref.par

sum_zero

Optional Logical value specifying if the QTL effect of a parental or an ancestral model should be caculated using the sum to zero constraint. Default = FALSE.

threshold

Numeric value representing the -log10(p-value) threshold above which a position can be considered as significant. Default = 4.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 30.

backward

Logical value. If backward = TRUE, the function performs a backward elimination on the list of selected QTLs. Default = TRUE.

alpha.bk

Numeric value indicating the significance level for the backward elimination. Default = 0.05.

plot.Qprof

Logical value. If plot = TRUE, the function will plot the QTL profile last run of the forward regression. Default = FALSE.

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be ploted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This plot will be realized on the last run of the forward regression. This functionality is ony available for the cross-specific, parental and ancestral models. Default value = FALSE.

CI

Logical value. If CI = TRUE, the function will compute a -log10(pval) drop confidence interval for each QTL using the QTL profile of the last iteration. Default = FALSE.

drop

Numeric -log10(p-value) drop value at the limits of the interval. Default = 1.5.

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

verbose

Logical value indicating if the steps of the procedure should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results. By default the function uses the current working directory.

Details

The function run a full MPP QTL detection using models with different possible assumptions concerning the number of alleles at the QTL position. For more details about the different models, see documentation of the function mpp_SIM. The procedure is the following:

Forward regression to determine the a multi-QTL model. The function selects successively QTL positions with -log10(pval) above the threshold. Those positions are added as cofactors for following detection run. The procedure stop when no more position has a -log10(pval) above the threshold (QTL_forward).
If backward = TRUE, backward elimination on the final list of detected QTLs.
Estimation of the QTL genetic effects and R squared statistics (QTL_gen_effects and QTL_R2).
If CI = TRUE, confidence interval calculation based on a CIM- (composite interval mapping removing all cofactors on the scanned chromosome) of the last run of the forward regression.
If plot.Qprof = TRUE, plot of the last run of the forward regression using plot.QTLprof.
If plot.gen.eff = TRUE, plot of the genetic effect distribution along the genome of the last run of the forward regression using plot.QTLprof.

Value

Return:

List containing the following items:

n.QTL

Number of detected QTLs.

QTL

Data.frame with QTL positions.

R2

List containing R squared statistics of the QTL effects. For details see QTL_R2 output section.

QTL.effects

List of QTLs genetic effects. For details see QTL_gen_effects output section.

QTL.CI

If CI = TRUE, confidence interval information of the QTLs.

Some output files are also saved at the specified location (output.loc):

A QTL report (QTL_REPORT.txt) with: 1) the number of detected QTLs; 2) the global R squared statistics; 3) for each QTL, position information (plus confidence interval if CI = TRUE) and estimated QTL genetic effects per cross or parents (for details see QTL_gen_effects).
The list of QTL (QTL.txt).
The QTL R squared statistics (QTL_R2.txt) (for details see QTL_R2).
If CI = TRUE, the QTL confidence intervals (QTL_CI.txt).
General results of the QTL detection process: number of QTLs and global adjusted and non-adjusted R squared statistics (QTL_genResults.txt).
If plot.Qprof = TRUE, the plot of the last regression run (QTL_profile.pdf). If plot.gen.eff = TRUE, plot of the genetic effects per cross or parents (gen_eff.pdf) with dashed lines representing the QTL positions. For more details see plot.QTLprof

Author(s)

Vincent Garin

Examples


## Not run: 

data(mppData)

# Specify a location where your results will be saved
my.loc <- "C:/.../..."

# Cross-specific model

USNAM_cr <- mpp_forward(pop.name = "USNAM", trait.name = "ULA",
                        mppData = mppData, plot.gen.eff = TRUE,
                        plot.Qprof = TRUE, CI = TRUE, output.loc = my.loc)



## End(Not run)

QTL significance threshold by permutation

Description

Determination of an empirical null distribution of the QTL significance threshold for a MPP QTL analysis using permutation test (Churchill and Doerge, 1994).

Usage

mpp_perm(
  mppData,
  trait = 1,
  Q.eff = "cr",
  N = 1000,
  q.val = 0.95,
  verbose = TRUE,
  n.cores = 1
)

Arguments

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

N

Number of permutations. Default = 1000.

q.val

Single numeric value or vector of desired quantiles from the null distribution. Default = 0.95.

verbose

Logical value indicating if progression of the function should be printed. It will not affect the printing of the other functions called by mpp_perm(), especially the printing of asreml(). Default = TRUE.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

Details

Performs N permutations of the trait data and computes each time a genome-wide QTL profile. For every run, it stores the highest -log10(p-val). These values can be used to build a null distribution for the QTL significance threshold. Quantile values can be determined from the previous distribution. For more details about the different possible models and their assumptions see mpp_SIM documentation.

Value

Return:

List with the following object:

max.pval

Vector of the highest genome-wide -log10(p-values).

q.val

Quantile values from the QTL significance threshold null distribution.

seed

Numeric vector of random generated seed values for each permutation.

Author(s)

Vincent Garin

References

Churchill, G. A., & Doerge, R. W. (1994). Empirical threshold values for quantitative trait mapping. Genetics, 138(3), 963-971.

Examples


data(mppData)

Perm <- mpp_perm(mppData = mppData, Q.eff = "cr", N = 5)

MPP QTL analysis

Description

Multi-parent population QTL analysis.

Usage

mpp_proc(
  pop.name = "MPP",
  trait.name = "trait1",
  mppData,
  trait = 1,
  Q.eff = "cr",
  plot.gen.eff = FALSE,
  thre.cof = 3,
  win.cof = 50,
  N.cim = 1,
  window = 20,
  thre.QTL = 3,
  win.QTL = 20,
  backward = TRUE,
  alpha.bk = 0.05,
  ref.par = NULL,
  sum_zero = FALSE,
  CI = FALSE,
  drop = 1.5,
  text.size = 18,
  n.cores = 1,
  verbose = TRUE,
  output.loc
)

Arguments

pop.name

Character name of the studied population. Default = "MPP".

trait.name

Character name of the studied trait. Default = "trait1".

mppData

An object of class mppData.

trait

Numerical or character indicator to specify which trait of the mppData object should be used. Default = 1.

Q.eff

plot.gen.eff

Logical value. If plot.gen.eff = TRUE, the function will save the decomposed genetic effects per cross/parent. These results can be plotted with the function plot.QTLprof to visualize a genome-wide decomposition of the genetic effects. This functionality is ony available for the cross-specific, parental and ancestral models. Default value = FALSE.

thre.cof

Numeric value representing the -log10(p-value) threshold above which a position can be peaked as a cofactor. Default = 3.

win.cof

Numeric value in centi-Morgan representing the minimum distance between two selected cofactors. Default = 50.

N.cim

Numeric value specifying the number of time the CIM analysis is repeated. Default = 1.

window

Numeric distance (cM) on the left and the right of a cofactor position where it is not included in the model. Default = 20.

thre.QTL

Numeric value representing the -log10(p-value) threshold above which a position can be selected as QTL. Default = 3.

win.QTL

Numeric value in centi-Morgan representing the minimum distance between two selected QTLs. Default = 20.

backward

Logical value. If backward = TRUE, the function performs a backward elimination on the list of selected QTLs. Default = TRUE.

alpha.bk

Numeric value indicating the significance level for the backward elimination. Terms with p-values above this value will iteratively be removed. Default = 0.05.

ref.par

Optional Character expression defining the parental allele that will be used as reference to compute QTL effects for the parental model. For the ancestral model, the ancestral class containing the reference parent will be set as reference. This option can only be used if the MPP design is composed of a unique connected part. Default = NULL.

sum_zero

Optional Logical value specifying if the QTL effect of a parental or an ancestral model should be calculated using the sum to zero constraint. Default = FALSE.

CI

Logical value. If CI = TRUE, the function will compute a -log10(pval) drop confidence interval for each QTL after calculating a CIM- profile (without cofactors on the scanned chromosome). Default = FALSE.

drop

Numeric -log10(p-value) drop value at the limits of the interval. Default = 1.5.

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

n.cores

Numeric. Specify here the number of cores you like to use. Default = 1.

verbose

Logical value indicating if the steps of mpp_proc should be printed. Default = TRUE.

output.loc

Path where a folder will be created to save the results.

Details

Simple interval mapping (SIM) to select cofactor (mpp_SIM).
Composite interval mapping (CIM) with selected cofactors (mpp_CIM).
Optional backward elimination on the list of QTL candidates (backward = TRUE) (mpp_back_elim).
Computation of the QTL genetic effects (QTL_gen_effects) and proportion of the phenotypic variation explained by the QTLs (R squared) (QTL_R2).
Optional QTL confidence interval computation from a CIM- profile (excluding cofactors on the scanned chromosome) (argument CI=TRUE).

Value

Return:

List containing the following items:

n.QTL

Number of detected QTLs.

cofactors

Data.frame with cofactors positions.

QTL

Data.frame with QTL positions.

R2

List containing R squared statistics of the QTL effects. For details see QTL_R2 output section.

QTL.effects

List of QTLs genetic effects. For details see QTL_gen_effects output section.

QTL.CI

If CI = TRUE, confidence interval information of the QTLs.

Some output files are also saved at the specified location (output.loc):

A QTL report (QTL_REPORT.txt) with: 1) the number of detected QTLs; 2) the global R squared statistics; 3) for each QTL, position information (plus confidence interval if CI = TRUE) and estimated QTL genetic effects per cross or parents (for details see QTL_gen_effects).
The SIM and CIM results in a text file (SIM.txt, CIM.txt).
The list of cofactors (cofactors.txt).
The list of QTL (QTL.txt).
The QTL R squared statistics (QTL_R2.txt) (for details see QTL_R2).
If CI = TRUE, the QTL confidence intervals (QTL_CI.txt).
General results of the QTL detection process: number of QTLs and global adjusted and non-adjusted R squared statistics (QTL_genResults.txt).
The plot of the CIM profile (QTL_profile.pdf) with dotted vertical lines representing the cofactors positions. If plot.gen.eff = TRUE, plot of the genetic effects per cross or parents (gen_eff.pdf) with dashed lines representing the QTL positions. For more details see plot.QTLprof

Author(s)

Vincent Garin

Examples

 

data(mppData)

# Specify a location where your results will be saved
my.loc <- tempdir()

# Cross-specific model

USNAM_cr <- mpp_proc(pop.name = "USNAM", trait.name = "ULA",
                     mppData = mppData, plot.gen.eff = TRUE, CI = TRUE,
                     verbose = FALSE, output.loc = my.loc)

Parental clustering

Description

Example of parental clustering object.

Usage

data(par_clu)

Details

The parent clustering matrix specifies at each genome position the results of a parent clustering into ancestral groups. The matrix rows represent the position and the columns correspond to each parent. For example, if we have at the ith row (1, 2, 3, 2, 1), this means that parents 1 and 5 are in the same group, that 2 and 4 are in another one and that the third parent was assigned to any group.

Examples

  data(par_clu)

Parent clustering for `mppData` objects

Description

Integrate the parent clustering information to the mppData object. The parent clustering is necessary to compute the ancestral model. If the parent clustering step is skipped, the ancestral model can not be used but the other models (cross-specific, parental, and bi-allelic) can still be computed.

Usage

parent_cluster.mppData(mppData, par.clu = NULL)

Arguments

mppData

An object of class mppData. the mppData must have been processed using: create.mppData, QC.mppData, IBS.mppData, and IBD.mppData.

par.clu

Interger matrix representing the results of a parents genotypes clustering. The columns represent the parental lines and the rows the markers. The columns names must be the same as the parents list of the mppData object. The rownames must be the same as the map marker list of the mppData object. At a particular position, parents with the same value are assumed to inherit from the same ancestor. for more details, see par_clu. Default = NULL.

Details

At a single marker position, two parents can be grouped into a similar ancestral classes if we assume that they receive there allele from a common ancestor. The parent clustering information (par.clu) describe parental relatedness and which parent belong to which ancestral group. For example, at marker i, we could have five parents (pA, pB, pC, pD, pE) and the following clustering information (1, 2, 1, 2, 3). This means that pA and pC received their allele from the same ancestor (A1). pB and pD also have a shared ancestor (A2) who is different from (A1). And pE was not included in any group and can be seen as an independent ancestral group (A3).

The parent clustering information is provided via par.clu. It is an interger matrix with markers in row and parents in columns. At a particular marker position, parents with the same value are assumed to inherit from the same ancestor. for more details, see par_clu.

The marker positions that are considered as monomorphic given the parent clustering information are set back to one allele per parent to still allow the computation of the QTL allelic effect at those positions later.

The parent clustering can be performed using the R package 'clusthaplo' that can be found there: https://cran.r-project.org/src/contrib/Archive/clusthaplo/. The 'clusthaplo' option is not integrated in this version of mppR. However, a version of mppR with function calling clusthaplo can be found on github https://github.com/vincentgarin/mppR (branch master).

Value

An increased mppData object containing the the same elements as the mppData object provided as argument and the following new elements:

par.clu

Integer matrix with rows representing markers and columns corresponding to the parents. At a single marker position, parents with the same value were clustered in the same ancestral group.

n.anc

Average number of ancestral clusters along the genome.

mono.anc

Positions for which the ancestral clustering was monomorphic.

Author(s)

Vincent Garin

Examples


data(mppData_init)
data(par_clu)

mppData <- QC.mppData(mppData_init)
mppData <- IBS.mppData(mppData = mppData)

mppData <- IBD.mppData(mppData = mppData, type = 'RIL',
                       type.mating = 'selfing')
                       
mppData <- parent_cluster.mppData(mppData = mppData, par.clu  = par_clu)

plot QTL profile

Description

Plots the -log10(p-val) profile of a QTL analysis or a genome-wide genetic effect plot using package ggplot2.

Usage

## S3 method for class 'QTLprof'
plot(
  x,
  gen.eff = FALSE,
  mppData,
  Q.eff,
  QTL = NULL,
  type = "l",
  main = "QTL profile",
  threshold = 3,
  text.size = 18,
  ...
)

Arguments

x

Object of class QTLprof returned by the function mpp_SIM or mpp_CIM.

gen.eff

Logical. Specify the type of plot. If gen.eff = FALSE, standard QTL profile. If gen.eff = TRUE, genome-wide genetic effect plot. In that case, the QTLprof object in x must have been calculated with argument plot.gen.eff = TRUE. Default = FALSE.

mppData

An object of class mppData. Only required if gen.eff = TRUE.

Q.eff

Character expression indicating the assumption concerning the QTL effects: 1) "cr" for cross-specific; 2) "par" for parental effects; 3) "anc" for ancestral effects. Only required if gen.eff = TRUE

QTL

Optional argument. List of QTL positions. Object of class QTLlist representing a list of selected position obtained with the function QTL_select or two columns numeric matrix with the chromosome and the position in cM. These positions will be drawn on the graph. Default = NULL.

type

Character expression indicating the type of plot should be drawn: "l" for lines , "h" for vertical bar. Default = "l".

main

Title of the graph. Default = "QTL profile".

threshold

Numeric QTL significance threshold value draw on the plot. Default = 3.

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

...

Ignored.

Details

The user can plot regular QTL profiles (gen.eff = FALSE) with -log10(p-val) plotted against genetic position or genome-wide genetic effects plots (gen.eff = TRUE). To plot the genome-wide genetic effects, the SIM and CIM QTL profile must have been computed with plot.gen.eff = TRUE.

The genome-wide genetic effects plots is a visualisation of the significance of the QTL effect per cross or per parents along the genome. For a cross-specific QTL profile (Q.eff = "cr"): Blue color means that the allele coming from parent A(1) increases the phenotypic value and parent B(2) decreases it and red that parent A(1) decreases the trait and parent B(2) increases it.

For a parental (Q.eff = "par") or an ancestral model (Q.eff = "anc"), the results are given per parents. The significance of the effect must be interpreted as a deviation with respect to the reference of each connected part. The reference allele is always defined as the most frequent one. Blue (Red) colour means a signicative negative (positive) effect with respect to the reference of the connected part.

The reference parental allele can change at each position according to the segregation rate. The parent are plotted from the top to the bottom according to the number of time their allele is set as reference. Therefore interpretation of the genetic effect plot should be done with caution. In that case, the plot should be taken as a rough indication of the signal distribution.

The colour intensity increase with the significance of the effect (p-val). The p-val are transformed into a color code (z). If p-val c [0.00001; 0.05]: z = -log10(p-val). If p-val < 0.00001: z=6. This scale allows to plot only the significant effects (p-val <= 0.05) and prevent the color scale to be determine by highly significant values (p-val < 0.00001). The colours red (positive) and blue (negative) correspond to the sign of the QTL effect.

For both type of plot, the user can pass a list of cofactors or QTL position to the argument QTL. These positions will be drawn on the graph using dotted lines.

Author(s)

Vincent Garin

Examples


data(mppData)

SIM <- mpp_SIM(mppData = mppData)
QTL <- QTL_select(SIM)
plot(x = SIM, QTL = QTL)

SIM <- mpp_SIM(mppData = mppData, Q.eff = "cr", plot.gen.eff = TRUE)
QTL <- QTL_select(SIM)
plot(x = SIM, gen.eff = TRUE, mppData = mppData, Q.eff = "cr", QTL = QTL)

plot QTLxEC effect

Description

Plot allowing the visualization of the QTL parental allelic effect variation given an environmental covariate (EC). The function plot the sensitivity curve of the parent allelic effects.

Usage

plot_QxEC(
  Qeff,
  EC,
  env_id = NULL,
  QTL,
  sign_thre = 0.05,
  EC_id = "EC",
  trait_id = "trait",
  main = "QTLxEC",
  col_vec = NULL,
  text_size = 14
)

Arguments

Qeff

output from the function QTL_effect_main_QxEC.

EC

Numeric matrix containing the EC values of a single covariate with environments as row and EC as column.

env_id

Character vector specifying the environment names. By default, E1, ... En

QTL

Numeric value indicating which QTL to plot

sign_thre

Numeric value indicating the significance threshold for a parent sensitivity slope to be ploted. Default = 0.05

EC_id

Character string indicating the name of the environmental covariate. Default = 'EC'.

trait_id

Character string indicating the name of the trait. Default = 'trait'.

main

Character string title of the plot. Default = 'QTLxEC'

col_vec

Character vector specifying colors for the parent sensitivity lines. Default = NULL

text_size

Numerical value specifying the size of the text in the plot. Default = 14.

Value

QTLxEC sensitivity plot

Author(s)

Vincent Garin

Examples


## Not run: 

data(mppData_GE)

Qpos <- c("PZE.105068880", "PZE.106098900")

EC <- matrix(c(180, 310, 240, 280), 4, 1)
rownames(EC) <- c('CIAM', 'TUM', 'INRA', 'KWS')
colnames(EC) <- 'cum_rain'

Qeff <- QTL_effect_main_QxEC(mppData = mppData_GE,
                         trait = c('DMY_CIAM', 'DMY_TUM', 'DMY_INRA_P', 'DMY_KWS'),
                         env_id = c('CIAM', 'TUM', 'INRA', 'KWS'),
                         QTL = Qpos, EC = EC)

pl <- plot_QxEC(Qeff, EC = EC, env_id = c('CIAM', 'TUM', 'INRA', 'KWS'), 
                QTL = 2, EC_id = 'cum rain', trait_id = 'DMY')


## End(Not run)

plot of genome wide QTL allelic effect significance

Description

Plot of the genome wide significance of the QTL allelic effects in multiple environments.

Usage

plot_allele_eff_GE(
  mppData,
  nEnv,
  EnvNames,
  Qprof,
  Q.eff = "par",
  QTL = NULL,
  ref_par = NULL,
  main = "QTL genetic effects plot",
  text.size = 18
)

Arguments

mppData

An object of class mppData.

nEnv

Numeric number of environment or trait.

EnvNames

character expression indicating the environment or trait labels.

Qprof

object obtained with function mppGE_SIM, mppGE_CIM,

Q.eff

one of "cr", "par", "anc" or "biall". For the moment only "par" is available.

QTL

Optional argument. Object of class QTLlist representing a list of selected position obtained with the function QTL_select or a vector of character marker or in between marker positions names. These positions will be plotted on the graph. Default = NULL.

ref_par

Charater specifying the reference parent. Default = NULL.

main

Title of the graph. Default = "QTL genetic effects plot".

text.size

Numeric value specifying the size of graph axis text elements. Default = 18.

Author(s)

Vincent Garin

Examples


data(mppData_GE)

SIM <- mppGE_SIM(mppData = mppData_GE, trait = c('DMY_CIAM', 'DMY_TUM'))

Qpos <- QTL_select(Qprof = SIM, threshold = 3, window = 50)

plot_allele_eff_GE(mppData = mppData_GE, nEnv = 2, EnvNames = c('CIAM', 'TUM'),
                   Qprof = SIM, Q.eff = 'par', QTL = Qpos, text.size = 14)

Print summary.QR2Res object

Description

Print summary.QR2Res object

Usage

## S3 method for class 'summary.QR2Res'
print(x, ...)

Arguments

x

object of class summary.QR2Res

...

Ignored.

Examples


data(mppData)
SIM <- mpp_SIM(mppData)
QTL <- QTL_select(SIM)
Q_R2 <- QTL_R2(mppData = mppData, QTL = QTL, Q.eff = "cr")
sum.QR2Res <- summary(Q_R2)
print(sum.QR2Res)

Print summary.QeffRes object

Description

Print summary.QeffRes object

Usage

## S3 method for class 'summary.QeffRes'
print(x, ...)

Arguments

x

object of class summary.QeffRes

...

Ignored.

Examples


data(mppData)
SIM <- mpp_SIM(mppData)
QTL <- QTL_select(SIM)
QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "cr")
sum.QeffRes <- summary(QTL.effects)
print(sum.QeffRes)

Print summary.mppData object

Description

Print summary.mppData object

Usage

## S3 method for class 'summary.mppData'
print(x, ...)

Arguments

x

object of class summary.mppData

...

Ignored.

Examples


data(mppData)
sum.mppData <- summary(mppData)
print(sum.mppData)

Subset `mppData` object

Description

Pull out a specified set of markers and/or genotypes from a mppData object.

Usage

## S3 method for class 'mppData'
subset(x, mk.list = NULL, gen.list = NULL, ...)

Arguments

x

An object of class mppData.

mk.list

Optional character vector, numeric position vector or logical vector representing marker to keep. Default = NULL.

gen.list

Optional character vector, numeric position vector or logical vector representing genotypes to keep. Default = NULL.

...

Ignored.

Value

Return:

The mppData object but with only the specified subset of data.

Author(s)

Vincent Garin

Examples


### Marker subset

data(mppData)

# Random selection of markers
mk.list <-  sample(mppData$map[, 1], 50)
mppData_sub <- subset(x = mppData, mk.list = mk.list)

# Selection of chromosome 1 marker
mk.list <-  (mppData$map[, 2] == 1)
mppData_sub <- subset(x = mppData, mk.list = mk.list)

### Genotype subset

# Random selection of genotypes
gen.list <-  sample(mppData$geno.id, 200)
mppData_sub <- subset(x = mppData, gen.list = gen.list)

# Selection of genotype from cross 2 and 5
crosses <- unique(mppData$cross.ind)
gen.list <-  mppData$geno.id[mppData$cross.ind %in% crosses[c(2, 5)]]
mppData_sub <- subset(x = mppData, gen.list = gen.list)

### Marker and genotype subset

mk.list <-  sample(mppData$map[, 1], 50)
gen.list <-  sample(mppData$geno.id, 200)
mppData_sub <- subset(x = mppData, mk.list = mk.list,
gen.list = gen.list)

Summary of `QR2Res` object

Description

summary for object of class QR2Res.

Usage

## S3 method for class 'QR2Res'
summary(object, ...)

Arguments

object

An object of class QR2Res obtained with function QTL_R2.

...

Ignored.

Examples


data(mppData)
SIM <- mpp_SIM(mppData)
QTL <- QTL_select(SIM)
Q_R2 <- QTL_R2(mppData = mppData, QTL = QTL, Q.eff = "cr")
summary(Q_R2)

Summary of `QeffRes` object

Description

summary for object of class QeffRes.

Usage

## S3 method for class 'QeffRes'
summary(object, QTL = NULL, ...)

Arguments

object

An object of class QeffRes obtained with function QTL_gen_effects.

QTL

Numeric vector indicating the QTL positions for which the QTL effect must be printed. Default = NULL.

...

Ignored.

Examples


data(mppData)
SIM <- mpp_SIM(mppData)
QTL <- QTL_select(SIM)
QTL.effects <- QTL_gen_effects(mppData = mppData, QTL = QTL, Q.eff = "cr")
summary(QTL.effects)

Summary of `mppData` object

Description

summary for object of class mppData.

Usage

## S3 method for class 'mppData'
summary(object, ...)

Arguments

object

An object of class mppData.

...

Ignored.

Examples


data(mppData)
summary(mppData)

Cross validation partition

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Determine EC effects

Description

Usage

Arguments

Value

Author(s)

References

IBD coding for mppData objects

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

IBS coding for mppData objects

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

QTL genetic effects multi-QTL effect model

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Multi-QTL effect MPP analysis

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Quality control for mppData objects

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

QTL global and partial R squared

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

MPP GxE QTL R2

Description

Usage

Arguments

Value

Author(s)

Examples

MPP GxE QTL genetic effects

Description

IBD coding for `mppData` objects

IBS coding for `mppData` objects

Quality control for `mppData` objects