Type: Package
Title: Random-Effect Multiple QTL Mapping in Autopolyploids
Version: 0.2.4
Maintainer: Gabriel de Siqueira Gesteira <gdesiqu@ncsu.edu>
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>.
License: GPL-3
URL: https://gabrielgesteira.github.io/QTLpoly/
BugReports: https://github.com/gabrielgesteira/QTLpoly/issues
Encoding: UTF-8
LazyData: TRUE
LazyDataCompression: xz
Depends: R (≥ 4.0)
Imports: ggplot2 (≥ 3.1), abind (≥ 1.4), MASS (≥ 7.3), gtools (≥ 3.9.2), CompQuadForm, Matrix, RLRsim, mvtnorm, nlme, quadprog, parallel, doParallel, foreach, stats, methods, mappoly, Rcpp (≥ 0.12.19)
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
Suggests: rmarkdown, devtools, knitr
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2024-03-20 18:48:26 UTC; gdesiqu
Author: Guilherme da Silva Pereira ORCID iD [aut], Marcelo Mollinari ORCID iD [ctb], Gabriel de Siqueira Gesteira ORCID iD [ctb, cre], Zhao-Bang Zeng ORCID iD [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)
Repository: CRAN
Date/Publication: 2024-03-25 20:50:06 UTC

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

Format

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

See Also

read_data, fit_model

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

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

See Also

permutations

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)

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, ...)

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, gdasilv@ncsu.edu

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.

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.

See Also

read_data, remim

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")

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
)

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, gdasilv@ncsu.edu

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.

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.

See Also

read_data, remim

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")

Simulated autohexaploid dataset.

Description

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

Usage

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, mmollin@ncsu.edu

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

Examples

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

Format

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

Author(s)

Marcelo Mollinari, mmollin@ncsu.edu

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.

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, 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.

See Also

hexafake, pheno6x

Examples

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

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, mmollin@ncsu.edu

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.

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

See Also

hexafake, pheno6x

Examples

library(mappoly)
plot_map_list(maps6x)

Adapt to sommer MNR core function

Description

Adapts genomic incidence and relationship (varcov) matrices to run using sommer's C++ core function (v. 4.0 or higher) Function adapted from sommer v. 3.6 (Author: Giovanny Covarrubias-Pazaran)

Usage

mmer_adapted(
  Y,
  X = NULL,
  Z = NULL,
  R = NULL,
  W = NULL,
  method = "NR",
  init = NULL,
  iters = 20,
  tolpar = 0.001,
  tolparinv = 1e-06,
  draw = FALSE,
  silent = FALSE,
  constraint = TRUE,
  EIGEND = FALSE,
  forced = NULL,
  IMP = FALSE,
  complete = TRUE,
  check.model = TRUE,
  restrained = NULL,
  REML = TRUE,
  init.equal = TRUE,
  date.warning = TRUE,
  verbose = FALSE
)

Author(s)

Gabriel de Siqueira Gesteira, gdesiqu@ncsu.edu

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.

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.

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, ...)

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, gdasilv@ncsu.edu

References

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.

See Also

read_data, remim

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

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.

See Also

read_data

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)

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
)

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, gdasilv@ncsu.edu, Gabriel de Siqueira Gesteira, gdesiqu@ncsu.edu

References

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.

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.

See Also

read_data

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

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

See Also

read_data, null_model, search_qtl

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)

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), ...)

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, gdasilv@ncsu.edu

References

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

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.

See Also

feim

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)

Autotetraploid potato phenotypes

Description

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

Usage

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, gdasilv@ncsu.edu

References

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

head(pheno4x)

Simulated phenotypes

Description

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

Usage

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, gdasilv@ncsu.edu

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.

See Also

simulate_qtl, pheno4x

Examples

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
)

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, gdasilv@ncsu.edu

References

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.

See Also

profile_qtl, remim

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)

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
)

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, gdasilv@ncsu.edu

References

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.

See Also

read_data, remim, fit_model

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)

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)

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, gdasilv@ncsu.edu

References

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.

See Also

read_data, remim, profile_qtl

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

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)

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", ...)

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, gdasilv@ncsu.edu, with modifications by Gabriel Gesteira, gdesiqu@ncsu.edu

References

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.

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

See Also

read_data, remim, fit_model

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)

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, ...)

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, gdasilv@ncsu.edu, with minor updates by Gabriel de Siqueira Gesteira, gdesiqu@ncsu.edu

References

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.

See Also

maps6x, pheno6x

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)

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
)

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, gdasilv@ncsu.edu, Gabriel de Siqueira Gesteira, gdesiqu@ncsu.edu

References

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.

See Also

maps6x, pheno6x

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)

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, ...)

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, gdasilv@ncsu.edu

References

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

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.

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

See Also

read_data

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)

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
)

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, gdasilv@ncsu.edu, Getúlio Caixeta Ferreira, getulio.caifer@gmail.com

References

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

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.

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

See Also

read_data

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

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

See Also

read_data, null_model

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)

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, ...)

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, gdasilv@ncsu.edu

References

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.

See Also

read_data

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