Package 'qtlpoly'

Title:	Random-Effect Multiple QTL Mapping in Autopolyploids
Description:	Performs random-effect multiple interval mapping (REMIM) in full-sib families of autopolyploid species based on restricted maximum likelihood (REML) estimation and score statistics, as described in Pereira et al. (2020) <doi:10.1534/genetics.120.303080>.
Authors:	Guilherme da Silva Pereira [aut] , Marcelo Mollinari [ctb] , Gabriel de Siqueira Gesteira [ctb, cre] , Zhao-Bang Zeng [ctb] , Long Qu [ctb] (R code for variance component tests using score statistics in R/varComp.R), Giovanny Covarrubias-Pazaran [ctb] (C code for fitting mixed models with REML estimation in src/MNR.cpp)
Maintainer:	Gabriel de Siqueira Gesteira <[email protected]>
License:	GPL-3
Version:	0.2.4
Built:	2024-11-21 06:52:44 UTC
Source:	CRAN

Autotetraploid potato dataset

Description

A dataset of the B2721 population which derived from a cross between two tetraploid potato varieties: Atlantic × B1829-5.

Usage

B2721
B2721

Format

An object of class mappoly.data from the package mappoly.

Author(s)

Marcelo Mollinari, [email protected]

References

Mollinari M, Garcia AAF (2019) Linkage analysis and haplotype phasing in experimental autopolyploid populations with high ploidy level using hidden Markov models, G3: Genes|Genomes|Genetics 9 (10): 3297-3314. doi:10.1534/g3.119.400378

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2020) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. doi:10.1534/genetics.120.303080.

Pereira GS, Mollinari M, Schumann MJ, Clough ME, Zeng ZB, Yencho C (2021) The recombination landscape and multiple QTL mapping in a Solanum tuberosum cv. ‘Atlantic’-derived F_1 population. Heredity. doi:10.1038/s41437-021-00416-x.

Examples

library(mappoly)
print(B2721)
library(mappoly)
print(B2721)

Prediction of QTL-based breeding values from REMIM model

Description

Computes breeding values for each genotyped individual based on multiple QTL models

Usage

breeding_values(data, fitted)

## S3 method for class 'qtlpoly.bvalues'
plot(x, pheno.col = NULL, ...)
breeding_values(data, fitted)

## S3 method for class 'qtlpoly.bvalues'
plot(x, pheno.col = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`fitted`	an object of class `qtlpoly.fitted`.
`x`	an object of class `qtlpoly.bvalues` to be plotted.
`pheno.col`	a numeric vector with the phenotype column numbers to be plotted; if `NULL`, all phenotypes from `'data'` will be included.
`...`	currently ignored

Value

An object of class qtlpoly.bvalues which is a list of results for each trait containing the following components:

`pheno.col`	a phenotype column number.
`y.hat`	a column matrix of breeding value for each individual.

A ggplot2 histogram with the distribution of breeding values.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob) #5,7
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data = data, model = remim.mod, probs = "joint", polygenes = "none")

  # Predict genotypic values
  y.hat = breeding_values(data = data, fitted = fitted.mod)
  plot(y.hat)
  
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob) #5,7
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data = data, model = remim.mod, probs = "joint", polygenes = "none")

  # Predict genotypic values
  y.hat = breeding_values(data = data, fitted = fitted.mod)
  plot(y.hat)

Fixed-effect interval mapping (FEIM)

Description

Performs interval mapping using the single-QTL, fixed-effect model proposed by Hackett et al. (2001).

Usage

feim(
  data = data,
  pheno.col = NULL,
  w.size = 15,
  sig.lod = 7,
  d.sint = 1.5,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.feim'
print(x, pheno.col = NULL, sint = NULL, ...)
feim(
  data = data,
  pheno.col = NULL,
  w.size = 15,
  sig.lod = 7,
  d.sint = 1.5,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.feim'
print(x, pheno.col = NULL, sint = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed; if `NULL` (default), all phenotypes from `'data'` will be included.
`w.size`	a number representing the window size (in centiMorgans) to be avoided on either side of QTL already in the model when looking for a new QTL, e.g. 15 (default).
`sig.lod`	the vector of desired significance LOD thresholds (usually permutation-based) for declaring a QTL for each trait, e.g. 5 (default); if a single value is provided, the same LOD threshold will be applied to all traits.
`d.sint`	a $d$ value to subtract from logarithm of the odds ( $LOD-d$ ) for support interval calculation, e.g. $d=1.5$ (default) represents approximate 95% support interval.
`plot`	a suffix for the file's name containing plots of every algorithm step, e.g. "remim" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.feim` to be printed.
`sint`	whether `"upper"` or `"lower"` support intervals should be printed; if `NULL` (default), QTL peak information will be printed.
`...`	currently ignored

Value

An object of class qtlpoly.feim which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`LRT`	a vector containing LRT values.
`LOD`	a vector containing LOD scores.
`AdjR2`	a vector containing adjusted $R^2$ .
`qtls`	a data frame with information from the mapped QTL.
`lower`	a data frame with information from the lower support interval of mapped QTL.
`upper`	a data frame with information from the upper support interval of mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Hackett CA, Bradshaw JE, McNicol JW (2001) Interval mapping of quantitative trait loci in autotetraploid species, Genetics 159: 1819-1832.

Examples

  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 5)

  # Perform remim
  feim.mod = feim(data = data, sig.lod = 7)

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 5)

  # Perform remim
  feim.mod = feim(data = data, sig.lod = 7)

Fits multiple QTL models

Description

Fits alternative multiple QTL models by performing variance component estimation using REML.

Usage

fit_model(
  data,
  model,
  probs = "joint",
  polygenes = "none",
  keep = TRUE,
  verbose = TRUE,
  pheno.col = NULL
)

## S3 method for class 'qtlpoly.fitted'
summary(object, pheno.col = NULL, ...)
fit_model(
  data,
  model,
  probs = "joint",
  polygenes = "none",
  keep = TRUE,
  verbose = TRUE,
  pheno.col = NULL
)

## S3 method for class 'qtlpoly.fitted'
summary(object, pheno.col = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.profile` or `qtlpoly.remim`.
`probs`	a character string indicating if either `"joint"` (genotypes) or `"marginal"` (parental gametes) conditional probabilities should be used.
`polygenes`	a character string indicating if either `"none"`, `"most"` or `"all"` QTL should be used as polygenes.
`keep`	if `TRUE` (default), stores all matrices and estimates from fitted model; if `FALSE`, nothing is stored.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`pheno.col`	a numeric vector with the phenotype column numbers to be summarized; if `NULL` (default), all phenotypes from `'data'` will be included.
`object`	an object of class `qtlpoly.fitted` to be summarized.
`...`	currently ignored

Value

An object of class qtlpoly.fitted which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`fitted`	a sommer object of class `mmer`.
`qtls`	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Covarrubias-Pazaran G (2016) Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11 (6): 1–15. doi:10.1371/journal.pone.0156744.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data=data, model=remim.mod, probs="joint", polygenes="none")
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data=data, model=remim.mod, probs="joint", polygenes="none")

Fits multiple QTL models

Description

Fits alternative multiple QTL models by performing variance component estimation using REML.

Usage

fit_model2(
  data,
  model,
  probs = "joint",
  polygenes = "none",
  keep = TRUE,
  verbose = TRUE,
  pheno.col = NULL
)
fit_model2(
  data,
  model,
  probs = "joint",
  polygenes = "none",
  keep = TRUE,
  verbose = TRUE,
  pheno.col = NULL
)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.profile` or `qtlpoly.remim`.
`probs`	a character string indicating if either `"joint"` (genotypes) or `"marginal"` (parental gametes) conditional probabilities should be used.
`polygenes`	a character string indicating if either `"none"`, `"most"` or `"all"` QTL should be used as polygenes.
`keep`	if `TRUE` (default), stores all matrices and estimates from fitted model; if `FALSE`, nothing is stored.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`pheno.col`	a numeric vector with the phenotype column numbers to be summarized; if `NULL` (default), all phenotypes from `'data'` will be included.

Value

An object of class qtlpoly.fitted which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`fitted`	a sommer object of class `mmer`.
`qtls`	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Covarrubias-Pazaran G (2016) Genome-assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11 (6): 1–15. doi:10.1371/journal.pone.0156744.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data=data, model=remim.mod, probs="joint", polygenes="none")
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data=data, model=remim.mod, probs="joint", polygenes="none")

Simulated autohexaploid dataset.

Description

A dataset of a hypothetical autohexaploid full-sib population containing three homology groups

Usage

hexafake
hexafake

Format

An object of class mappoly.data which contains a list with the following components:

plody: ploidy level = 6
n.ind: number individuals = 300
n.mrk: total number of markers = 1500
ind.names: the names of the individuals
mrk.names: the names of the markers
dosage.p1: a vector containing the dosage in parent P for all n.mrk markers
dosage.p2: a vector containing the dosage in parent Q for all n.mrk markers
chrom: a vector indicating the chromosome each marker belongs. Zero indicates that the marker was not assigned to any chromosome
genome.pos: Physical position of the markers into the sequence
geno.dose: a matrix containing the dosage for each markers (rows) for each individual (columns). Missing data are represented by ploidy_level + 1 = 7
n.phen: There are no phenotypes in this simulation
phen: There are no phenotypes in this simulation
chisq.pval: vector containing p-values for all markers associated to the chi-square test for the expected segregation patterns under Mendelian segregation

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

library(mappoly)
plot(hexafake)
library(mappoly)
plot(hexafake)

Autotetraploid potato map

Description

A real autotetraploid potato map containing 12 homology groups from a tetraploid potato full-sib family (Atlantic x B1829-5).

Usage

maps4x
maps4x

Format

An object of class "mappoly.map" from the package mappoly, which is a list of 12 linkage groups (LGs)

Author(s)

Marcelo Mollinari, [email protected]

References

Pereira GS, Gemenet DC, Mollinari M, Olukolu BA, Wood JC, Mosquera V, Gruneberg WJ, Khan A, Buell CR, Yencho GC, Zeng ZB (2019) Multiple QTL mapping in autopolyploids: a random-effect model approach with application in a hexaploid sweetpotato full-sib population, Genetics 215 (3): 579-595. doi:10.1534/genetics.120.303080.

Examples

library(mappoly)
plot_map_list(maps4x)
library(mappoly)
plot_map_list(maps4x)

Simulated autohexaploid map

Description

A simulated map containing three homology groups of a hypotetical cross between two autohexaploid individuals.

Usage

maps6x
maps6x

Format

An object of class "mappoly.map" from the package mappoly, which is a list of three linkage groups (LGs):

LG 1: 538 markers distributed along 112.2 cM
LG 2: 329 markers distributed along 54.6 cM
LG 3: 443 markers distributed along 98.2 cM

Author(s)

Marcelo Mollinari, [email protected]

References

Examples

library(mappoly)
plot_map_list(maps6x)
library(mappoly)
plot_map_list(maps6x)

Modify QTL model

Description

Adds or removes QTL manually from a given model.

Usage

modify_qtl(
  model,
  pheno.col = NULL,
  add.qtl = NULL,
  drop.qtl = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.modify'
print(x, pheno.col = NULL, ...)
modify_qtl(
  model,
  pheno.col = NULL,
  add.qtl = NULL,
  drop.qtl = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.modify'
print(x, pheno.col = NULL, ...)

Arguments

`model`	an object of class `qtlpoly.model` containing the QTL to be modified.
`pheno.col`	a phenotype column number whose model will be modified or printed.
`add.qtl`	a marker position number to be added.
`drop.qtl`	a marker position number to be removed.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.modify` to be printed.
`...`	currently ignored

Value

An object of class qtlpoly.modify which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Modify model
  modified.mod = modify_qtl(model = remim.mod, pheno.col = 1, drop.qtl = 18)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Modify model
  modified.mod = modify_qtl(model = remim.mod, pheno.col = 1, drop.qtl = 18)

Null model

Description

Creates a null model (with no QTL) for each trait.

Usage

null_model(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.null'
print(x, pheno.col = NULL, ...)

## S3 method for class 'qtlpoly.null'
print(x, pheno.col = NULL, ...)
null_model(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.null'
print(x, pheno.col = NULL, ...)

## S3 method for class 'qtlpoly.null'
print(x, pheno.col = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`offset.data`	a data frame with the same dimensions of `data$pheno` containing offset variables; if `NULL` (default), no offset variables are considered.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed; if `NULL`, all phenotypes from `'data'` will be included.
`n.clusters`	number of parallel processes to spawn.
`plot`	a suffix for the file's name containing simple plots of every QTL search round, e.g. "null" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.null` to be printed.
`...`	currently ignored

Value

An object of class qtlpoly.null which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL (`NULL` at this point).

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null models
  null.mod = null_model(data = data, pheno.col = 1, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null models
  null.mod = null_model(data = data, pheno.col = 1, n.clusters = 1)

Null model

Description

Creates a null model (with no QTL) for each trait.

Usage

null_model2(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)
null_model2(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

Arguments

`data`	an object of class `qtlpoly.data`.
`offset.data`	a data frame with the same dimensions of `data$pheno` containing offset variables; if `NULL` (default), no offset variables are considered.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed; if `NULL`, all phenotypes from `'data'` will be included.
`n.clusters`	number of parallel processes to spawn.
`plot`	a suffix for the file's name containing simple plots of every QTL search round, e.g. "null" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.

Value

An object of class qtlpoly.null which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL (`NULL` at this point).

Author(s)

Guilherme da Silva Pereira, [email protected], Gabriel de Siqueira Gesteira, [email protected]

References

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null models
  null.mod = null_model(data = data, pheno.col = 1, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null models
  null.mod = null_model(data = data, pheno.col = 1, n.clusters = 1)

Model optimization

Description

Tests each QTL at a time and updates its position (if it changes) or drops the QTL (if non-significant).

Usage

optimize_qtl(
  data,
  offset.data = NULL,
  model,
  sig.bwd = 0.05,
  score.null = NULL,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.optimize'
print(x, pheno.col = NULL, ...)
optimize_qtl(
  data,
  offset.data = NULL,
  model,
  sig.bwd = 0.05,
  score.null = NULL,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.optimize'
print(x, pheno.col = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`offset.data`	a data frame with the same dimensions of `data$pheno` containing offset variables; if `NULL` (default), no offset variables are considered.
`model`	an object of class `qtlpoly.model` containing the QTL to be optimized.
`sig.bwd`	the desired score-based p-value threshold for backward elimination, e.g. 0.0001 (default).
`score.null`	an object of class `qtlpoly.null` with results of score statistics from resampling.
`polygenes`	if `TRUE` all QTL but the one being tested are treated as a single polygenic effect, if `FALSE` (default) all QTL effect variances have to estimated.
`n.clusters`	number of parallel processes to spawn.
`plot`	a suffix for the file's name containing plots of every QTL optimization round, e.g. "optimize" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.optimize` to be printed.
`pheno.col`	a numeric vector with the phenotype columns to be printed; if `NULL`, all phenotypes from `'data'` will be included.
`...`	currently ignored

Value

An object of class qtlpoly.optimize which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Zou F, Fine JP, Hu J, Lin DY (2004) An efficient resampling method for assessing genome-wide statistical significance in mapping quantitative trait loci. Genetics 168 (4): 2307-16. doi:10.1534/genetics.104.031427

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data = data, pheno.col = 1,n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data = data, pheno.col = 1,n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)

Fixed-effect interval mapping (FEIM) model permutations

Description

Stores maximum LOD scores for a number of permutations of given phenotypes.

Usage

permutations(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.sim = 1000,
  probs = c(0.9, 0.95),
  n.clusters = NULL,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.perm'
print(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)

## S3 method for class 'qtlpoly.perm'
plot(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)
permutations(
  data,
  offset.data = NULL,
  pheno.col = NULL,
  n.sim = 1000,
  probs = c(0.9, 0.95),
  n.clusters = NULL,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.perm'
print(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)

## S3 method for class 'qtlpoly.perm'
plot(x, pheno.col = NULL, probs = c(0.9, 0.95), ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`offset.data`	a subset of the data object to be used in permutation calculations.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed; if `NULL` (default), all phenotypes from `'data'` will be included.
`n.sim`	a number of simulations, e.g. 1000 (default).
`probs`	a vector of probability values in [0, 1] representing the quantiles, e.g. c(0.90, 0.95) for the 90% and 95% quantiles.
`n.clusters`	a number of parallel processes to spawn.
`seed`	an integer for the `set.seed()` function; if `NULL`, no reproducible seeds are set.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.perm` to be printed or plotted.
`...`	currently ignored

Value

An object of class qtlpoly.perm which contains a list of results for each trait with the maximum LOD score per permutation.

LOD score thresholds for given quantiles for each trait.

A ggplot2 histogram with the distribution of ordered maximum LOD scores and thresholds for given quantiles for each trait.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Churchill GA, Doerge RW (1994) Empirical threshold values for quantitative trait mapping, Genetics 138: 963-971.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Perform permutations
  perm = permutations(data = data, pheno.col = 1, n.sim = 10, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Perform permutations
  perm = permutations(data = data, pheno.col = 1, n.sim = 10, n.clusters = 1)

Autotetraploid potato phenotypes

Description

A subset of phenotypes from a tetraploid potato full-sib family (Atlantic x B1829-5).

Usage

pheno4x
pheno4x

Format

A data frame of phenotypes with 156 named individuals in rows and three named phenotypes in columns, which are:

FM07: Foliage maturity evaluated in 2007.
FM08: Foliage maturity evaluated in 2008.
FM14: Foliage maturity evaluated in 2014.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

head(pheno4x)
head(pheno4x)

Simulated phenotypes

Description

A simulated data set of phenotypes for a hipotetical autohexaploid species map.

Usage

pheno6x
pheno6x

Format

A data frame of phenotypes with 300 named individuals in rows and three named phenotypes in columns, which are:

T32: 3 QTLs, with heritabilities of 0.20 (LG 1 at 32.03 cM), 0.15 (LG 1 at 95.02 cM) and 0.30 (LG 2 at 40.01 cM).
T17: 1 QTL, with heritability of 0.15 (LG 3 at 34.51 cM).
T45: no QTLs.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

head(pheno6x)
head(pheno6x)

Logarithm of P-value (LOP) profile plots

Description

Plots profiled logarithm of score-based P-values (LOP) from individual or combined traits.

Usage

plot_profile(
  data = data,
  model = model,
  pheno.col = NULL,
  sup.int = FALSE,
  main = NULL,
  legend = "bottom",
  ylim = NULL,
  grid = FALSE
)
plot_profile(
  data = data,
  model = model,
  pheno.col = NULL,
  sup.int = FALSE,
  main = NULL,
  legend = "bottom",
  ylim = NULL,
  grid = FALSE
)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.profile` or `qtlpoly.remim`.
`pheno.col`	a numeric vector with the phenotype column numbers to be plotted; if `NULL`, all phenotypes from `'data'` will be included.
`sup.int`	if `TRUE`, support interval are shown as shaded areas; if `FALSE` (default), no support interval is show.
`main`	a character string with the main title; if `NULL`, no title is shown.
`legend`	legend position (either "bottom", "top", "left" or "right"); if `NULL`, no legend is shown.
`ylim`	a numeric value pair supplying the limits of y-axis, e.g. c(0,10); if `NULL` (default), limits will be provided automatically.
`grid`	if `TRUE`, profiles will be organized in rows (one per trait); if `FALSE` (default), profiles will appear superimposed. Only effective when plotting profiles from more than one trait.

Value

A ggplot2 with the LOP profiles for each trait.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Plot profile
  plot_profile(data = data, model = remim.mod, grid = FALSE)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Plot profile
  plot_profile(data = data, model = remim.mod, grid = FALSE)

QTL heritability and significance plot

Description

Creates a plot where dot sizes and colors represent the QTLs heritabilities and their p-values, respectively.

Usage

plot_qtl(
  data = data,
  model = model,
  fitted = fitted,
  pheno.col = NULL,
  main = NULL,
  drop.pheno = TRUE,
  drop.lgs = TRUE
)
plot_qtl(
  data = data,
  model = model,
  fitted = fitted,
  pheno.col = NULL,
  main = NULL,
  drop.pheno = TRUE,
  drop.lgs = TRUE
)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.profile` or `qtlpoly.remim`.
`fitted`	an object of class `qtlpoly.fitted`.
`pheno.col`	the desired phenotype column numbers to be plotted. The order here specifies the order of plotting (from top to bottom.)
`main`	plot title; if `NULL` (the default), no title is shown.
`drop.pheno`	if `FALSE`, shows the names of all traits from `pheno.col`, even of those with no QTLs; if `TRUE` (the default), shows only the traits with QTL(s).
`drop.lgs`	if `FALSE`, shows all linkage groups, even those with no QTL; if `TRUE` (the default), shows only the linkage groups with QTL(s).

Value

A ggplot2 with dots representing the QTLs.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data, remim.mod, probs="joint", polygenes="none")

  # Plot QTL
  plot_qtl(data, remim.mod, fitted.mod)
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data, remim.mod, probs="joint", polygenes="none")

  # Plot QTL
  plot_qtl(data, remim.mod, fitted.mod)

QTLs with respective support interval plots

Description

Creates a plot where colored bars represent the support intervals for QTL peaks (black dots).

Usage

plot_sint(data, model, pheno.col = NULL, main = NULL, drop = FALSE)
plot_sint(data, model, pheno.col = NULL, main = NULL, drop = FALSE)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.profile` or `qtlpoly.remim`.
`pheno.col`	a numeric vector with the phenotype column numbers to be plotted; if `NULL`, all phenotypes from `'data'` will be included.
`main`	a character string with the main title; if `NULL`, no title will be shown.
`drop`	if `TRUE`, phenotypes with no QTL will be dropped; if `FALSE` (default), all phenotypes will be shown.

Value

A ggplot2 with QTL bars for each linkage group.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Plot support intervals
  plot_sint(data = data, model = remim.mod)
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Plot support intervals
  plot_sint(data = data, model = remim.mod)

QTL profiling

Description

Generates the score-based genome-wide profile conditional to the selected QTL.

Usage

profile_qtl(
  data,
  model,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.profile'
print(x, pheno.col = NULL, sint = NULL, ...)
profile_qtl(
  data,
  model,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.profile'
print(x, pheno.col = NULL, sint = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`model`	an object of class `qtlpoly.model` containing the QTL to be profiled.
`d.sint`	a $d$ value to subtract from logarithm of p-value ( $LOP-d$ ) for support interval calculation, e.g. $d=1.5$ (default) represents approximate 95% support interval.
`polygenes`	if `TRUE` all QTL but the one being tested are treated as a single polygenic effect, if `FALSE` (default) all QTL effect variances have to estimated.
`n.clusters`	number of parallel processes to spawn.
`plot`	a suffix for the file's name containing plots of every QTL profiling round, e.g. "profile" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.profile` to be printed.
`pheno.col`	a numeric vector with the phenotype column numbers to be plotted; if `NULL`, all phenotypes from `'data'` will be included.
`sint`	whether `"upper"` or `"lower"` support intervals should be printed; if `NULL` (default), only QTL peak information will be printed.
`...`	currently ignored

Value

An object of class qtlpoly.profile which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.
`lower`	a data frame with information from the lower support interval of mapped QTL.
`upper`	a data frame with information from the upper support interval of mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)

  # Profile model
  profile.mod = profile_qtl(data = data, model = optimize.mod, d.sint = 1.5, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data = data, model = null.mod,
w.size = 15, sig.fwd = 0.01, n.clusters = 1)

  # Optimize model
  optimize.mod = optimize_qtl(data = data, model = search.mod, sig.bwd = 0.0001, n.clusters = 1)

  # Profile model
  profile.mod = profile_qtl(data = data, model = optimize.mod, d.sint = 1.5, n.clusters = 1)

QTL allele effect estimation

Description

Computes allele specific and allele combination (within-parent) heritable effects from multiple QTL models.

Usage

qtl_effects(ploidy = 6, fitted, pheno.col = NULL, verbose = TRUE)

## S3 method for class 'qtlpoly.effects'
plot(x, pheno.col = NULL, p1 = "P1", p2 = "P2", ...)
qtl_effects(ploidy = 6, fitted, pheno.col = NULL, verbose = TRUE)

## S3 method for class 'qtlpoly.effects'
plot(x, pheno.col = NULL, p1 = "P1", p2 = "P2", ...)

Arguments

`ploidy`	a numeric value of ploidy level of the cross (currently, only 2, 4 or 6).
`fitted`	a fitted multiple QTL model of class `qtlpoly.fitted`.
`pheno.col`	a numeric vector with the phenotype column numbers to be plotted; if `NULL`, all phenotypes from `'fitted'` will be included.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.effects` to be plotted.
`p1`	a character string with the first parent name, e.g. `"P1"` (default).
`p2`	a character string with the second parent name, e.g. `"P2"` (default).
`...`	currently ignored

Value

An object of class qtlpoly.effects which is a list of results for each containing the following components:

`pheno.col`	a phenotype column number.
`y.hat`	a vector with the predicted values.

A ggplot2 barplot with parental allele and allele combination effects.

Author(s)

Guilherme da Silva Pereira, [email protected], with modifications by Gabriel Gesteira, [email protected]

References

Kempthorne O (1955) The correlation between relatives in a simple autotetraploid population, Genetics 40: 168-174.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data, model=remim.mod, probs="joint", polygenes="none")

  # Estimate effects
  est.effects = qtl_effects(ploidy = 4, fitted = fitted.mod, pheno.col = 1)

  # Plot results
  plot(est.effects)
  
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

  # Fit model
  fitted.mod = fit_model(data, model=remim.mod, probs="joint", polygenes="none")

  # Estimate effects
  est.effects = qtl_effects(ploidy = 4, fitted = fitted.mod, pheno.col = 1)

  # Plot results
  plot(est.effects)

Read genotypic and phenotypic data

Description

Reads files in specific formats and creates a qtlpoly.data object to be used in subsequent analyses.

Usage

read_data(
  ploidy = 6,
  geno.prob,
  geno.dose = NULL,
  double.reduction = FALSE,
  pheno,
  weights = NULL,
  step = 1,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.data'
print(x, detailed = FALSE, ...)
read_data(
  ploidy = 6,
  geno.prob,
  geno.dose = NULL,
  double.reduction = FALSE,
  pheno,
  weights = NULL,
  step = 1,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.data'
print(x, detailed = FALSE, ...)

Arguments

`ploidy`	a numeric value of ploidy level of the cross.
`geno.prob`	an object of class `mappoly.genoprob` from mappoly.
`geno.dose`	an object of class `mappoly.data` from mappoly.
`double.reduction`	if `TRUE`, double reduction genotypes are taken into account; if `FALSE`, no double reduction genotypes are considered.
`pheno`	a data frame of phenotypes (columns) with individual names (rows) identical to individual names in `geno.prob` and/or `geno.dose` object.
`weights`	a data frame of phenotype weights (columns) with individual names (rows) identical to individual names in `pheno` object.
`step`	a numeric value of step size (in centiMorgans) where tests will be performed, e.g. 1 (default); if `NULL`, tests will be performed at every marker.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.data` to be printed.
`detailed`	if `TRUE`, detailed information on linkage groups and phenotypes in shown; if `FALSE`, no details are printed.
`...`	currently ignored

Value

An object of class qtlpoly.data which is a list containing the following components:

`ploidy`	a scalar with ploidy level.
`nlgs`	a scalar with the number of linkage groups.
`nind`	a scalar with the number of individuals.
`nmrk`	a scalar with the number of marker positions.
`nphe`	a scalar with the number of phenotypes.
`lgs.size`	a vector with linkage group sizes.
`cum.size`	a vector with cumulative linkage group sizes.
`lgs.nmrk`	a vector with number of marker positions per linkage group.
`cum.nmrk`	a vector with cumulative number of marker positions per linkage group.
`lgs`	a list with selected marker positions per linkage group.
`lgs.all`	a list with all marker positions per linkage group.
`step`	a scalar with the step size.
`pheno`	a data frame with phenotypes.
`G`	a list of relationship matrices for each marker position.
`Z`	a list of conditional probability matrices for each marker position for genotypes.
`X`	a list of conditional probability matrices for each marker position for alleles.
`Pi`	a matrix of identical-by-descent shared alleles among genotypes.

Author(s)

Guilherme da Silva Pereira, [email protected], with minor updates by Gabriel de Siqueira Gesteira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

Read genotypic and phenotypic data

Description

Reads files in specific formats and creates a qtlpoly.data object to be used in subsequent analyses.

Usage

read_data2(
  ploidy = 6,
  geno.prob,
  geno.dose = NULL,
  double.reduction = FALSE,
  pheno,
  weights = NULL,
  step = 1,
  verbose = TRUE
)
read_data2(
  ploidy = 6,
  geno.prob,
  geno.dose = NULL,
  double.reduction = FALSE,
  pheno,
  weights = NULL,
  step = 1,
  verbose = TRUE
)

Arguments

`ploidy`	a numeric value of ploidy level of the cross.
`geno.prob`	an object of class `mappoly.genoprob` from mappoly.
`geno.dose`	an object of class `mappoly.data` from mappoly.
`double.reduction`	if `TRUE`, double reduction genotypes are taken into account; if `FALSE`, no double reduction genotypes are considered.
`pheno`	a data frame of phenotypes (columns) with individual names (rows) identical to individual names in `geno.prob` and/or `geno.dose` object.
`weights`	a data frame of phenotype weights (columns) with individual names (rows) identical to individual names in `pheno` object.
`step`	a numeric value of step size (in centiMorgans) where tests will be performed, e.g. 1 (default); if `NULL`, tests will be performed at every marker.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.

Value

An object of class qtlpoly.data which is a list containing the following components:

`ploidy`	a scalar with ploidy level.
`nlgs`	a scalar with the number of linkage groups.
`nind`	a scalar with the number of individuals.
`nmrk`	a scalar with the number of marker positions.
`nphe`	a scalar with the number of phenotypes.
`lgs.size`	a vector with linkage group sizes.
`cum.size`	a vector with cumulative linkage group sizes.
`lgs.nmrk`	a vector with number of marker positions per linkage group.
`cum.nmrk`	a vector with cumulative number of marker positions per linkage group.
`lgs`	a list with selected marker positions per linkage group.
`lgs.all`	a list with all marker positions per linkage group.
`step`	a scalar with the step size.
`pheno`	a data frame with phenotypes.
`G`	a list of relationship matrices for each marker position.
`Z`	a list of conditional probability matrices for each marker position for genotypes.
`X`	a list of conditional probability matrices for each marker position for alleles.
`Pi`	a matrix of identical-by-descent shared alleles among genotypes.

Author(s)

Guilherme da Silva Pereira, [email protected], Gabriel de Siqueira Gesteira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)
  
# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

Random-effect multiple interval mapping (REMIM)

Description

Automatic function that performs REMIM algorithm using score statistics.

Usage

remim(
  data,
  pheno.col = NULL,
  w.size = 15,
  sig.fwd = 0.01,
  sig.bwd = 1e-04,
  score.null = NULL,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  n.rounds = Inf,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.remim'
print(x, pheno.col = NULL, sint = NULL, ...)
remim(
  data,
  pheno.col = NULL,
  w.size = 15,
  sig.fwd = 0.01,
  sig.bwd = 1e-04,
  score.null = NULL,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  n.rounds = Inf,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.remim'
print(x, pheno.col = NULL, sint = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed or printed; if `NULL` (default), all phenotypes from `'data'` will be included.
`w.size`	the window size (in centiMorgans) to avoid on either side of QTL already in the model when looking for a new QTL, e.g. 15 (default).
`sig.fwd`	the desired score-based significance level for forward search, e.g. 0.01 (default).
`sig.bwd`	the desired score-based significance level for backward elimination, e.g. 0.001 (default).
`score.null`	an object of class `qtlpoly.null` with results of score statistics from resampling.
`d.sint`	a $d$ value to subtract from logarithm of p-value ( $LOP-d$ ) for support interval calculation, e.g. $d=1.5$ (default) represents approximate 95% support interval.
`polygenes`	if `TRUE` all QTL already in the model are treated as a single polygenic effect; if `FALSE` (default) all QTL effect variances have to estimated.
`n.clusters`	number of parallel processes to spawn.
`n.rounds`	number of search rounds; if `Inf` (default) forward search will stop when no more significant positions can be found.
`plot`	a suffix for the file's name containing plots of every algorithm step, e.g. "remim"; if `NULL` (default), no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.remim` to be printed.
`sint`	whether `"upper"` or `"lower"` support intervals should be printed; if `NULL` (default), only QTL peak information will be printed.
`...`	currently ignored

Value

An object of class qtlpoly.remim which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.
`lower`	a data frame with information from the lower support interval of mapped QTL.
`upper`	a data frame with information from the upper support interval of mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152 (3): 1203–16.

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

Random-effect multiple interval mapping (REMIM)

Description

Automatic function that performs REMIM algorithm using score statistics.

Usage

remim2(
  data,
  pheno.col = NULL,
  w.size = 15,
  sig.fwd = 0.01,
  sig.bwd = 1e-04,
  score.null = NULL,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  n.rounds = Inf,
  plot = NULL,
  verbose = TRUE
)
remim2(
  data,
  pheno.col = NULL,
  w.size = 15,
  sig.fwd = 0.01,
  sig.bwd = 1e-04,
  score.null = NULL,
  d.sint = 1.5,
  polygenes = FALSE,
  n.clusters = NULL,
  n.rounds = Inf,
  plot = NULL,
  verbose = TRUE
)

Arguments

`data`	an object of class `qtlpoly.data`.
`pheno.col`	a numeric vector with the phenotype columns to be analyzed or printed; if `NULL` (default), all phenotypes from `'data'` will be included.
`w.size`	the window size (in centiMorgans) to avoid on either side of QTL already in the model when looking for a new QTL, e.g. 15 (default).
`sig.fwd`	the desired score-based significance level for forward search, e.g. 0.01 (default).
`sig.bwd`	the desired score-based significance level for backward elimination, e.g. 0.001 (default).
`score.null`	an object of class `qtlpoly.null` with results of score statistics from resampling.
`d.sint`	a $d$ value to subtract from logarithm of p-value ( $LOP-d$ ) for support interval calculation, e.g. $d=1.5$ (default) represents approximate 95% support interval.
`polygenes`	if `TRUE` all QTL already in the model are treated as a single polygenic effect; if `FALSE` (default) all QTL effect variances have to estimated.
`n.clusters`	number of parallel processes to spawn.
`n.rounds`	number of search rounds; if `Inf` (default) forward search will stop when no more significant positions can be found.
`plot`	a suffix for the file's name containing plots of every algorithm step, e.g. "remim"; if `NULL` (default), no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`sint`	whether `"upper"` or `"lower"` support intervals should be printed; if `NULL` (default), only QTL peak information will be printed.

Value

An object of class qtlpoly.remim which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.
`lower`	a data frame with information from the lower support interval of mapped QTL.
`upper`	a data frame with information from the upper support interval of mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected], Getúlio Caixeta Ferreira, [email protected]

References

Kao CH, Zeng ZB, Teasdale RD (1999) Multiple interval mapping for quantitative trait loci. Genetics 152 (3): 1203–16.

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim2(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Search for QTL
  remim.mod = remim2(data = data, pheno.col = 1, w.size = 15, sig.fwd = 0.0011493379,
sig.bwd = 0.0002284465, d.sint = 1.5, n.clusters = 1)

QTL forward search

Description

Searches for QTL and adds them one at a time to a multiple random-effect QTL model based on score statistics.

Usage

search_qtl(
  data,
  offset.data = NULL,
  model,
  w.size = 15,
  sig.fwd = 0.2,
  score.null = NULL,
  polygenes = FALSE,
  n.rounds = Inf,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.search'
print(x, pheno.col = NULL, ...)
search_qtl(
  data,
  offset.data = NULL,
  model,
  w.size = 15,
  sig.fwd = 0.2,
  score.null = NULL,
  polygenes = FALSE,
  n.rounds = Inf,
  n.clusters = NULL,
  plot = NULL,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.search'
print(x, pheno.col = NULL, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`offset.data`	a data frame with the same dimensions of `data$pheno` containing offset variables; if `NULL` (default), no offset variables are considered.
`model`	an object of class `qtlpoly.model` from which a forward search will start.
`w.size`	the window size (in cM) to avoid on either side of QTL already in the model when looking for a new QTL.
`sig.fwd`	the desired score-based p-value threshold for forward search, e.g. 0.01 (default).
`score.null`	an object of class `qtlpoly.null` with results of score statistics from resampling.
`polygenes`	if `TRUE` all QTL but the one being tested are treated as a single polygenic effect; if `FALSE` (default) all QTL effect variances have to estimated.
`n.rounds`	number of search rounds; if `Inf` (default) forward search will stop when no more significant positions can be found.
`n.clusters`	number of parallel processes to spawn.
`plot`	a suffix for the file's name containing plots of every QTL search round, e.g. "search" (default); if `NULL`, no file is produced.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.search` to be printed.
`pheno.col`	a numeric vector with the phenotype column numbers to be printed; if `NULL`, all phenotypes from `'data'` will be included.
`...`	currently ignored

Value

An object of class qtlpoly.search which contains a list of results for each trait with the following components:

`pheno.col`	a phenotype column number.
`stat`	a vector containing values from score statistics.
`pval`	a vector containing p-values from score statistics.
`qtls`	a data frame with information from the mapped QTL.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Qu L, Guennel T, Marshall SL (2013) Linear score tests for variance components in linear mixed models and applications to genetic association studies. Biometrics 69 (4): 883–92.

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data, model = null.mod, w.size = 15, sig.fwd = 0.01, n.clusters = 1)
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Build null model
  null.mod = null_model(data, pheno.col = 1, n.clusters = 1)

  # Perform forward search
  search.mod = search_qtl(data, model = null.mod, w.size = 15, sig.fwd = 0.01, n.clusters = 1)

Simulations of multiple QTL

Description

Simulate new phenotypes with a given number of QTL and creates new object with the same structure of class qtlpoly.data from an existing genetic map.

Usage

simulate_qtl(
  data,
  mu = 0,
  h2.qtl = c(0.3, 0.2, 0.1),
  var.error = 1,
  linked = FALSE,
  n.sim = 1000,
  missing = TRUE,
  w.size = 20,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.simul'
print(x, detailed = FALSE, ...)
simulate_qtl(
  data,
  mu = 0,
  h2.qtl = c(0.3, 0.2, 0.1),
  var.error = 1,
  linked = FALSE,
  n.sim = 1000,
  missing = TRUE,
  w.size = 20,
  seed = 123,
  verbose = TRUE
)

## S3 method for class 'qtlpoly.simul'
print(x, detailed = FALSE, ...)

Arguments

`data`	an object of class `qtlpoly.data`.
`mu`	simulated phenotype mean, e.g. 0 (default).
`h2.qtl`	vector with QTL heritabilities, e.g. `c(0.3, 0.2, 0.1)` for three QTL (default); if `NULL`, only error is simulated.
`var.error`	simulated error variance, e.g. 1 (default).
`linked`	if `TRUE` (default), at least two QTL will be linked; if `FALSE`, QTL will be randomly assigned along the genetic map. Linkage is defined by a genetic distance smaller than the selected `w.size`.
`n.sim`	number of simulations, e.g. 1000 (default).
`missing`	if `TRUE` (default), phenotypes are simulated with the same number of missing data observed in `data$pheno`.
`w.size`	the window size (in centiMorgans) between two (linked) QTL, e.g. 20 (default).
`seed`	integer for the `set.seed()` function.
`verbose`	if `TRUE` (default), current progress is shown; if `FALSE`, no output is produced.
`x`	an object of class `qtlpoly.sim` to be printed.
`detailed`	if `TRUE`, detailed information on linkage groups and phenotypes in shown; if `FALSE`, no details are printed.
`...`	currently ignored

Value

An object of class qtlpoly.sim which contains a list of results with the same structure of class qtlpoly.data.

Author(s)

Guilherme da Silva Pereira, [email protected]

References

Examples

  
  # Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Simulate new phenotypes
  sim.dat = simulate_qtl(data = data, n.sim = 1)
  sim.dat
  

# Estimate conditional probabilities using mappoly package
  library(mappoly)
  library(qtlpoly)
  genoprob4x = lapply(maps4x[c(5)], calc_genoprob)
  data = read_data(ploidy = 4, geno.prob = genoprob4x, pheno = pheno4x, step = 1)

  # Simulate new phenotypes
  sim.dat = simulate_qtl(data = data, n.sim = 1)
  sim.dat

Package 'qtlpoly'

Help Index

Autotetraploid potato dataset

Description

Usage

Format

Author(s)

References

Examples

Prediction of QTL-based breeding values from REMIM model

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Fixed-effect interval mapping (FEIM)

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Fits multiple QTL models

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Fits multiple QTL models

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Simulated autohexaploid dataset.

Description

Usage

Format

Author(s)

References

Examples

Autotetraploid potato map

Description

Usage

Format

Author(s)

References

See Also

Examples

Simulated autohexaploid map

Description

Usage

Format

Author(s)

References

See Also

Examples

Modify QTL model

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Null model

Description

Usage