Package 'polyqtlR' reference manual

Title:	QTL Analysis in Autopolyploid Bi-Parental F1 Populations
Description:	Quantitative trait loci (QTL) analysis and exploration of meiotic patterns in autopolyploid bi-parental F1 populations. For all ploidy levels, identity-by-descent (IBD) probabilities can be estimated. Significance thresholds, exploring QTL allele effects and visualising results are provided. For more background and to reference the package see <doi:10.1093/bioinformatics/btab574>.
Authors:	Peter Bourke [aut, cre], Christine Hackett [ctb], Chris Maliepaard [ctb], Geert van Geest [ctb], Roeland Voorrips [ctb], Johan Willemsen [ctb]
Maintainer:	Peter Bourke <[email protected]>
License:	GPL-3
Version:	0.1.1
Built:	2024-11-02 04:59:34 UTC
Source:	https://github.com/cran/polyqtlR

Calculate Best Linear Unbiased Estimates using linear mixed model from `nlme` package

Description

Calculation of BLUEs from data frame of genotype names and phenotypes (assuming repeated measurements)

Usage

BLUE(data, model, random, genotype.ID)
BLUE(data, model, random, genotype.ID)

Arguments

`data`	Data frame of genotype codes and corresponding phenotypes
`model`	The model specification of fixed terms, eg. Yield ~ Clones
`random`	The random component of the model (repeat structure, can be nested), eg. ~1 \| Blocks if only Blocks are used
`genotype.ID`	The colname used to describe genotypes, e.g. "Clones"

Value

A data-frame with columns "geno" for the genotype names, and "blue" for the BLUEs.

Examples

data("Phenotypes_4x")
blue <- BLUE(data = Phenotypes_4x,model = pheno~geno,random = ~1|year,genotype.ID = "geno")
data("Phenotypes_4x")
blue <- BLUE(data = Phenotypes_4x,model = pheno~geno,random = ~1|year,genotype.ID = "geno")

Best Linear Unbiased Estimates of phenotype

Description

Best Linear Unbiased Estimates of phenotype

Usage

BLUEs.pheno
BLUEs.pheno

Format

An object of class data.frame with 50 rows and 2 columns.

Build a multi-QTL model using step-wise procedure of checking genetic co-factors.

Description

The function check_cofactors initially fits all significant QTL positions as co-factors, both individually and in combination. Significance thresholds are re-estimated each time, yielding threshold-corrected LOD scores. If this leads to a change in the estimated position of QTL, or detection of subsequent peaks, a second round of co-factor inclusion is performed for all new QTL or novel QTL combinations. Finally, the multi-QTL model that maximises the individual significance of each QTL is returned as a data.frame. This can be directly passed to the function PVE to estimate the percentage variance explained by the full multi-QTL model and all possible sub-models. Note: this function estimates the most likely QTL positions by maximising the threshold-corrected LOD at QTL peaks. Non-additive interactions between QTL may be missed as a result. It is recommended to run a manual co-factor analysis as well, as described in the package vignette.

Usage

check_cofactors(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  LOD_data = NULL,
  min_res = 20,
  test_full_model = FALSE,
  verbose = TRUE,
  ...
)
check_cofactors(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  LOD_data = NULL,
  min_res = 20,
  test_full_model = FALSE,
  verbose = TRUE,
  ...
)

Arguments

`IBD_list`	List of IBD_probabilities as estimated using one of the various methods available (e.g. `estimate_IBD`).
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the population identifiers (F1 names) (must be a colname of `Phenotype.df`)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model (must be a colname of `Phenotype.df`)
`LOD_data`	Output of `QTLscan` function. Since v.0.1.0 this argument is optional - function will re-run a `QTLscan` if not provided. Indeed, it may be desirable to not specify `LOD_data` if argument `test_full_model` is `TRUE`, as this will first combine the best results using additive-effects or allelic interaction-effects models before searching for additional QTL.
`min_res`	The minimum genetic distance (resolution) assumed possible to consider 2 linked QTL (on the same linkage group) as independent. By default a value of 20 cM is used. This is not to suggest that 20 cM is a realistic resolution in a practical mapping study, but it provides the function with a criterion to consider 2 significant QTL within this distance as one and the same. For this purpose, 20 cM seems a reasonable value to use. In practice, closely linked QTL will generally "explain" all the variation at nearby positions, making it unlikely to be able to disentangle their effects. QTL positions will vary slightly when co-factors are introduced, but again this variation is presumed not to exceed 20 cM either side.
`test_full_model`	By default `FALSE`, in which case the normal additive-effects model is used in `QTLscan`. If set to `TRUE`, then both the additive and full models are run for each genome-wide scan.
`verbose`	Logical, by default `TRUE` - should progress messages be printed to the console?
`...`	Option to pass extra arguments to `QTLscan`, for example specifying `ncores` for parallel processing, or changing the default settings of the permutation test (by default the number of permutations to perform = 1000 and alpha = 0.05). For a full list of options see the documentation of `QTLscan`.

Value

Data frame with the following columns:

LG: Linkage group identifier
cM: CentiMorgan position
deltaLOD: The difference between the LOD score at the peak and the significance threshold (always positive, otherwise the QTL would not be significant)
CofactorID: An identifier giving the co-factor model used in detecting the QTL (if no co-factors were included then NA). The co-factor model is described by concatenating all co-factor positions with a '+', so for example 1_10+4_20 would mean a co-factor model with 2 positions included as co-factors, namely 10 cM on linkage group 1 and 20 cM on linkage group 4.

Examples

data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
check_cofactors(IBD_list=IBD_4x,Phenotype.df=BLUEs.pheno,
genotype.ID="Geno",trait.ID="BLUE",LOD_data=qtl_LODs.4x)
data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
check_cofactors(IBD_list=IBD_4x,Phenotype.df=BLUEs.pheno,
genotype.ID="Geno",trait.ID="BLUE",LOD_data=qtl_LODs.4x)

Function to extract the phased map from a mappoly.map object

Description

Convert MAPpoly.map object into a phased maplist, needed for IBD estimation

Usage

convert_mappoly_to_phased.maplist(mappoly_object)
convert_mappoly_to_phased.maplist(mappoly_object)

Arguments

mappoly_object

An object of class 'mappoly.map', for example output of the function mappoly::est_rf_hmm_sequential

Value

A phased.maplist, with linkage group names LG1 etc. Each list item is a data.frame with columns marker, position followed by the phased map, coded in 1 and 0 for presence/absence of SNP (alternative) allele on parental homologues (h) numbered 1:ploidy for parent 1 and ploidy + 1 : 2*ploidy for parent 2.

Examples

## Not run: 
library("mappoly")
phased.maplist <- convert_mappoly_to_phased.maplist(maps.hexafake)

## End(Not run)
## Not run: 
library("mappoly")
phased.maplist <- convert_mappoly_to_phased.maplist(maps.hexafake)

## End(Not run)

Predict recombination breakpoints using IBD probabilities

Description

The function count_recombinations returns a list of all predicted recombination breakpoints. The output can be passed using the argument recombination_data to the function visualiseHaplo, where the predicted breakpoints overlay the haplotypes. Alternatively, a genome-wide visualisation of the recombination landscape both per linkage group and per individual can be generated using the function plotRecLS, which can be useful in identifying problematic areas of the linkage maps, or problematic individuals in the population. Currently, recombination break-points are only estimated from bivalents in meiosis; any offspring resulting from a predicted multivalent is excluded from the analysis and will be returned with a NA value.

Usage

count_recombinations(IBD_list, plausible_pairing_prob = 0.3)
count_recombinations(IBD_list, plausible_pairing_prob = 0.3)

Arguments

IBD_list

List of IBD_probabilities as estimated using one of the various methods available (e.g. estimate_IBD).

plausible_pairing_prob

The minimum probability of a pairing configuration needed to analyse an individual's IBD data. The default setting of 0.3 accommodates scenarios where e.g. two competing plausible pairing scenarios are possible. In such situations, both pairing configurations (also termed "valencies") would be expected to have a probability close to 0.5. Both are then considered, and the output contains the probability of both situations. These can then be used to generate a probabilistic recombination landscape. In some cases, it may not be possible to discern the pairing in one of the parents due to a lack of recombination (ie. full parental haplotypes were transmitted). In such cases, having a lower threshold here will allow more offspring to be analysed without affecting the quality of the predictions. If a more definite set of predictions is required, simply increase plausible_pairing_prob to eliminate such uncertainty. These individuals will then be returned with a NA value. In any case, it is always helpful to visualise the output using the function visualiseHaplo.

Value

A nested list corresponding to each linkage group. Within each LG, a list with 3 items is returned, specifying the plausible_pairing_prob, the map and the predicted recombinations in each individual in the mapping population. Per individual, all valencies with a probability greater than plausible_pairing_prob are returned, specifying both the Valent_probability and the best estimate of the cM position of the recombination_breakpoints involving pairs of homologues A, B, C etc. (in the order parent 1, parent 2). If no recombinations are predicted, a NA value is given instead.

Examples

data("IBD_4x")
recom.ls <- count_recombinations(IBD_4x)
data("IBD_4x")
recom.ls <- count_recombinations(IBD_4x)

Estimate the Genotypic Information Coefficient (GIC)

Description

Function to estimate the GIC per homologue using IBD probabilities

Usage

estimate_GIC(IBD_list)
estimate_GIC(IBD_list)

Arguments

IBD_list

List of IBD probabilities

Value

A nested list; each list element (per linkage group) contains the following items:

GIC :: Matrix of GIC values estimated from the IBD probabilities
map :: Integrated linkage map positions of markers used in IBD calculation
parental_phase :: The parental marker phasing, coded in 1 and 0's

Examples

data("IBD_4x")
GIC_4x <- estimate_GIC(IBD_list = IBD_4x)
data("IBD_4x")
GIC_4x <- estimate_GIC(IBD_list = IBD_4x)

Generate IBD probabilities from marker genotypes and a phased linkage map

Description

estimate_IBD is a function for creating identity-by-descent (IBD) probabilities. Two computational methods are offered: by default IBD probabilites are estimated using hidden Markov models, but a heuristic method based on Bourke et al. (2014) is also included. Basic input data for this function are marker genotypes (either discrete marker dosages (ie scores 0, 1, ..., ploidy representing the number of copies of the marker allele), or the probabilities of these dosages) and a phased linkage map. Details on each of the methods are included under method

Usage

estimate_IBD(
  input_type = "discrete",
  genotypes,
  phased_maplist,
  method = "hmm",
  remove_markers = NULL,
  ploidy,
  ploidy2 = NULL,
  parent1 = "P1",
  parent2 = "P2",
  individuals = "all",
  log = NULL,
  map_function = "haldane",
  bivalent_decoding = TRUE,
  error = 0.01,
  full_multivalent_hexa = FALSE,
  verbose = FALSE,
  ncores = 1,
  fix_threshold = 0.1,
  factor_dist = 1
)
estimate_IBD(
  input_type = "discrete",
  genotypes,
  phased_maplist,
  method = "hmm",
  remove_markers = NULL,
  ploidy,
  ploidy2 = NULL,
  parent1 = "P1",
  parent2 = "P2",
  individuals = "all",
  log = NULL,
  map_function = "haldane",
  bivalent_decoding = TRUE,
  error = 0.01,
  full_multivalent_hexa = FALSE,
  verbose = FALSE,
  ncores = 1,
  fix_threshold = 0.1,
  factor_dist = 1
)

Arguments

`input_type`	Can be either one of 'discrete' or 'probabilistic'. For the former (default), `dosage_matrix` must be supplied, while for the latter `probgeno_df` must be supplied. Note that probabilistic genotypes can only be accepted if the `method` is default ('hmm').
`genotypes`	Marker genotypes, either a 2d matrix of integer marker scores or a data.frame of dosage probabilities. Details are as follows: discrete : If `input_type` is 'discrete', `genotypes` is a matrix of marker dosage scores with markers in rows and individuals in columns. Both (marker) rownames and (individual or sample) colnames are needed. probabilistic : If `input_type` is 'probabilistic', `genotypes` is a data frame as read from the scores file produced by function `saveMarkerModels` of R package `fitPoly`, or alternatively, a data frame containing at least the following columns: SampleName : Name of the sample (individual) MarkerName : Name of the marker P0 : Probabilities of dosage score '0' P1, P2... etc. : Probabilities of dosage score '1' etc. (up to max offspring dosage, e.g. P4 for tetraploid population)
`phased_maplist`	A list of phased linkage maps, the output of `polymapR::create_phased_maplist`
`method`	The method used to estimate IBD probabilities, either `"hmm"` or `"heur"`. By default, the Hidden Markov Model (hmm) method is used. This uses an approach developed by Zheng et al (2016), and implemented in the 'TetraOrigin' package. However, unlike the original TetraOrigin software, it does not re-estimate parental linkage phase, as this is assumed to have been generated during map construction. Alternatively, a heuristic algorithm can be employed (`method = "heur"`), providing computational efficiency at higher ploidy levels (hexaploid, octoploid etc.), but at the cost of some accuracy. If `method = "hmm"` is specified, only diploid, triploid, autotetraploid and autohexaploid populations are currently allowed, while `method = "heur"` caters for all possible ploidy levels. Furthermore, the argument `bivalent_decoding` can only be set to `FALSE` in the case of the 'hmm' method (i.e. allowing for the possibility of multivalent formation and double reduction).
`remove_markers`	Optional vector of marker names to remove from the maps. Default is `NULL`.
`ploidy`	Integer. Ploidy of the organism.
`ploidy2`	Optional integer, by default `NULL`. Ploidy of parent 2, if different from parent 1.
`parent1`	Identifier of parent 1, by default assumed to be `"P1"`
`parent2`	Identifier of parent 2, by default assumed to be `"P2"`
`individuals`	By default "all" offspring are included, but otherwise a subset can be selected, using a vector of offspring indexing numbers (1,2, etc.) according to their order in `dosage_matrix`
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.
`map_function`	Mapping function to use when converting map distances to recombination frequencies. Currently only `"haldane"` or `"kosambi"` are allowed.
`bivalent_decoding`	Option to consider only bivalent pairing during formation of gametes (ignored for diploid populations, as only bivalents possible there), by default `TRUE`
`error`	The (prior) probability of errors in the offspring dosages, usually assumed to be small but non-zero
`full_multivalent_hexa`	Option to allow multivalent pairing in both parents at the hexaploid level, by default `FALSE`. Note that if `TRUE`, a very large available RAM may be required (>= 32Gb) to process the data.
`verbose`	Logical, by default `TRUE`. Should progress messages be written?
`ncores`	How many CPU cores should be used in the evaluation? By default 1 core is used.
`fix_threshold`	If `method = "heur"`, the threshold to fix the IBD probabilities while correcting for the sum of probabilities.
`factor_dist`	If `method = "heur"`, the factor by which to increase or decrease the recombination frequencies as calculated from the map distances.

Value

A list of IBD probabilities, organised by linkage group (as given in the input phased_maplist). Each list item is itself a list containing the following:

IBDtype: The type of IBD; for this function only "genotypeIBD" are calculated.
IBDarray: A 3d array of IBD probabilities, with dimensions marker, genotype-class and F1 individual.
map: A 3-column data-frame specifying chromosome, marker and position (in cM)
parental_phase: Phasing of the markers in the parents, as given in the input phased_maplist
marginal.likelihoods: A list of marginal likelihoods of different valencies if method "hmm" was used, otherwise NULL
valency: The predicted valency that maximised the marginal likelihood, per offspring. For method "heur", NULL
offspring: Offspring names
biv_dec: Logical, whether bivalent decoding was used in the estimation of the F1 IBD probabilities.
gap: The size of the gap (in cM) used when interpolating the IBD probabilities. See function spline_IBD for details.
genocodes: Ordered list of genotype codes used to represent different genotype classes.
pairing: log likelihoods of each of the different pairing scenarios considered (can be used e.g. for post-mapping check of preferential pairing)
ploidy: ploidy of parent 1
ploidy2: ploidy of parent 2
method: The method used, either "hmm" (default) or "heur". See argument method
error: The error prior used, if method "hmm" was used, otherwise NULL

References

Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological sequence analysis: Probabilistic models of proteins and nucleic acids. Cambridge: Cambridge University Press.
Hackett et al. (2013) Linkage analysis and QTL mapping using SNP dosage data in a tetraploid potato mapping population. PLoS One 8(5): e63939
Zheng et al. (2016) Probabilistic multilocus haplotype reconstruction in outcrossing tetraploids. Genetics 203: 119-131
Bourke P.M. (2014) QTL analysis in polyploids: Model testing and power calculations. Wageningen University (MSc thesis)

Examples

data("phased_maplist.4x", "SNP_dosages.4x")
estimate_IBD(phased_maplist=phased_maplist.4x,genotypes=SNP_dosages.4x,ploidy=4)
data("phased_maplist.4x", "SNP_dosages.4x")
estimate_IBD(phased_maplist=phased_maplist.4x,genotypes=SNP_dosages.4x,ploidy=4)

Explore the possible segregation type of a QTL peak using Schwarz Information Criterion

Description

Function to explore the possible segregation type at a QTL position using the Schwarz Information Criterion

Usage

exploreQTL(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  linkage_group,
  LOD_data,
  cM = NULL,
  QTLconfig = NULL,
  plotBIC = TRUE,
  deltaBIC = 6,
  testAllele_Effects = TRUE,
  log = NULL
)
exploreQTL(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  linkage_group,
  LOD_data,
  cM = NULL,
  QTLconfig = NULL,
  plotBIC = TRUE,
  deltaBIC = 6,
  testAllele_Effects = TRUE,
  log = NULL
)

Arguments

`IBD_list`	List of IBD probabilities
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the population identifiers (F1 names) (must be a colname of `Phenotype.df`)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model (must be a colname of `Phenotype.df`)
`linkage_group`	Numeric identifier of the linkage group being tested, based on the order of `IBD_list`. Only a single linkage group is allowed.
`LOD_data`	Output of `QTLscan` function
`cM`	By default `NULL`, in which case the position of maximum LOD score is taken as the position of interest. Otherwise, the cM position to be explored.
`QTLconfig`	Nested list of homologue configurations and modes of action of QTL to be explored and compared, the output of `segMaker`. Note that a default List is available of all possible bi-allelic QTL if none is provided. Each list element is itself a list with components homs : a vector of length at least 1, describing the proposed homologues the functional allele Q is on mode : Vector of same length as `homs` with codes "a" for additive and "d" for dominant.
`plotBIC`	Logical, with default `TRUE` - should the calculated BIC values be plotted?
`deltaBIC`	Numeric, by default 6. Configurations within this distance of the minimum BIC are considered plausible.
`testAllele_Effects`	Logical, with default `TRUE` - should the effects of the different alleles be tested using the most likely QTL configuration?
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.

Value

List with the following items:

linkage_group: Linkage group of the QTL peak being explored
cM: CentiMorgan position of the locus being explored
BIC: Vector of BIC values corresponding to elements of QTLconfig provided for testing
Allele.effects: Summary of the means and standard errors of groups with (+) and without(-) the specified allele combinations for the most likely QTLconfig if testAllele_Effects = TRUE (NULL otherwise).
genotype.means: A one-column matrix of mean phenotype values of offspring classes, with rownames corresponding to the genotype class. If the probability of certain genotype classes is 0 (e.g. double reduction classes where no double reduction occurred), then the genotype mean for that class will be NA

Examples

data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
exploreQTL(IBD_list = IBD_4x,
           Phenotype.df = BLUEs.pheno,
           genotype.ID = "Geno",
           trait.ID = "BLUE",
           linkage_group = 1,
           LOD_data = qtl_LODs.4x)
data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
exploreQTL(IBD_list = IBD_4x,
           Phenotype.df = BLUEs.pheno,
           genotype.ID = "Geno",
           trait.ID = "BLUE",
           linkage_group = 1,
           LOD_data = qtl_LODs.4x)

Function to find the position of maximum LOD on a particular linkage group

Description

Given QTL output, this function returns the position of maximum LOD for a specified linkage group.

Usage

findPeak(LOD_data, linkage_group, verbose = TRUE)
findPeak(LOD_data, linkage_group, verbose = TRUE)

Arguments

`LOD_data`	Output of `QTLscan` function.
`linkage_group`	Numeric identifier of the linkage group being tested, based on the order of `IBD_list`. Only a single linkage group is allowed.
`verbose`	Should messages be written to standard output? By default `TRUE`.

Examples

data("qtl_LODs.4x")
findPeak(LOD_data=qtl_LODs.4x,linkage_group=1)
data("qtl_LODs.4x")
findPeak(LOD_data=qtl_LODs.4x,linkage_group=1)

Function to find a LOD - x support interval around a QTL position

Description

Given QTL output, this function returns the LOD - x support for a specified linkage group, taking the maximum LOD position as the desired QTL peak.

Usage

findSupport(LOD_data, linkage_group, LOD_support = 2)
findSupport(LOD_data, linkage_group, LOD_support = 2)

Arguments

`LOD_data`	Output of `QTLscan` function.
`linkage_group`	Numeric identifier of the linkage group being tested, based on the order of `IBD_list`. Only a single linkage group is allowed.
`LOD_support`	The level of support around a QTL peak, by default 2 (giving a LOD - 2 support interval, the range of positions with a LOD score within 2 LOD units of the maximum LOD on that linkage group).

Examples

data("qtl_LODs.4x")
findSupport(LOD_data=qtl_LODs.4x,linkage_group=1)
data("qtl_LODs.4x")
findSupport(LOD_data=qtl_LODs.4x,linkage_group=1)

Genotypic Information Coefficient for example tetraploid

Description

Genotypic Information Coefficient for example tetraploid

Usage

GIC_4x
GIC_4x

Format

An object of class list of length 2.

Identical by descent probabilities for example tetraploid

Description

Identical by descent probabilities for example tetraploid

Usage

IBD_4x
IBD_4x

Format

An object of class list of length 2.

Import IBD probabilities as estimated by TetraOrigin or PolyOrigin

Description

Imports the IBD probability output of TetraOrigin (Mathematica software) or PolyOrigin (julia software) into the same format as natively-estimated IBD probabilities from the polyqtlR package.

Usage

import_IBD(
  method,
  folder = NULL,
  filename,
  bivalent_decoding = TRUE,
  error = 0.01,
  log = NULL
)
import_IBD(
  method,
  folder = NULL,
  filename,
  bivalent_decoding = TRUE,
  error = 0.01,
  log = NULL
)

Arguments

`method`	The method used for IBD estimation, either "TO" for TetraOrigin or "PO" for PolyOrigin
`folder`	The path to the folder in which the Tetra/PolyOrigin (ie. TetraOrigin or PolyOrigin) output is contained, default is `NULL` if files are in working directory.
`filename`	If method = "TO", the (vector of) character filename stem(s) of the `.csv` file(s) containing the output of TetraOrigin (stem = without ".csv"). Should be in order according to LG/chromosome numbering. If method = "PO", then simply specify the PolyOrigin filename stem here (as the output is not split into separate linkage groups in PolyOrigin). A PolyOrigin file with name <filename>_polyancestry.csv and its corresponding log file <filename>.log will then be searched for.
`bivalent_decoding`	Logical, if method = "TO" you must specify `TRUE` if only bivalent pairing was allowed in TetraOrigin (in offspring deciding step), otherwise specify `FALSE` if multivalent pairing was also allowed. If method = "PO", this will be automatically detected, so no need to specify (will be ignored).
`error`	If method = "TO", the offspring error prior used in the offspring decoding step of TetraOrigin, by default assumed to be 0.01. For method = "PO", this is automatically read in.
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.

Value

Returns a list with the following items:

`IBDtype :`	Always "genotypeIBD" for the output of TetraOrigin
`IBDarray :`	An array of IBD probabilities. The dimensions of the array are: markers, genotype classes and individuals.
`map :`	Integrated linkage map positions of markers used in IBD calculation
`parental_phase :`	The parental marker phasing as used by TetraOrigin, recoded in 1 and 0's
`marginal.likelihoods :`	A list of marginal likelihoods of different valencies, currently `NULL`
`valency :`	The predicted valency that maximised the marginal likelihood, per offspring. Currently `NULL`
`offspring :`	Offspring names
`biv_dec :`	Logical, the bivalent_decoding parameter specified.
`gap :`	The gap size used in IBD interpolation if performed by `spline_IBD`. At this stage, `NULL`
`genocodes :`	Ordered list of genotype codes used to represent different genotype classes.
`pairing :`	log likelihoods of each of the different pairing scenarios considered (can be used e.g. for post-mapping check of preferential pairing)
`ploidy :`	The ploidy of parent 1, by default assumed to be 4
`ploidy2 :`	The ploidy of parent 2, by default assumed to be 4
`method :`	The method used, either "hmm_TO" (TetraOrigin) or "hmm_PO" (PolyOrigin)
`error :`	The error prior used in the calculation in TetraOrigin, assumed to be 0.01

Examples

## Not run: 
## These examples demonstrate the function call for both methods, but won't run without input files
## from either package, hence this call will normally result in an Error:
IBD_TO <- import_IBD(method = "TO", filename = paste0("test_LinkageGroup",1:5,"_Summary"),
bivalent_decoding = FALSE, error = 0.05)
## Equivalent call for PolyOrigin output:
IBD_PO <- import_IBD(method = "PO",filename = "test")

## End(Not run)
## Not run: 
## These examples demonstrate the function call for both methods, but won't run without input files
## from either package, hence this call will normally result in an Error:
IBD_TO <- import_IBD(method = "TO", filename = paste0("test_LinkageGroup",1:5,"_Summary"),
bivalent_decoding = FALSE, error = 0.05)
## Equivalent call for PolyOrigin output:
IBD_PO <- import_IBD(method = "PO",filename = "test")

## End(Not run)

Re-estimate marker dosages given IBD input estimated using a high error prior.

Description

Function to correct marker dosage scores given a list of previously estimated IBD probabilities. This may prove useful to correct genotyping errors. Running the estimate_IBD function with a high error prior will result in suppressed predictions of double recombination events, associated with genotyping errors. By forcing the HMM to penalise double recombinations heavily, a smoothed haplotype landscape is achieved in which individual genotype observations are down-weighted. This smoothed output is then used to re-estimate marker dosages, dependent on (correct) parental scores. An alternative strategy is to use the function maxL_IBD over a range of error priors first, and use the resulting $maxL_IBD output as input here (as the IBD_list). In this case, set the argument min_error_prior to a low value (0.005 say) to avoid issues.

Usage

impute_dosages(
  IBD_list,
  dosage_matrix,
  parent1 = "P1",
  parent2 = "P2",
  rounding_error = 0.05,
  min_error_prior = 0.1,
  verbose = TRUE
)
impute_dosages(
  IBD_list,
  dosage_matrix,
  parent1 = "P1",
  parent2 = "P2",
  rounding_error = 0.05,
  min_error_prior = 0.1,
  verbose = TRUE
)

Arguments

`IBD_list`	List of IBD probabilities
`dosage_matrix`	An integer matrix with markers in rows and individuals in columns. Note that probabilistic genotypes are not currently catered for here.
`parent1`	The identifier of parent 1, by default "P1"
`parent2`	The identifier of parent 2, by default "P2"
`rounding_error`	The maximum deviation from an integer value that an inputed value can have, by default 0.05. For example, an imputed score of 2.97 or 3.01 would both be rounded to a dosage of 3, while 2.87 would be deemed too far from an integer score, and would be made missing. If you find the output contains too many missing values, a possibility would be to increase the `rounding_error`. However this may also introduce more errors in the output!
`min_error_prior`	Suggestion for a suitably high error prior to be used in IBD calculations to ensure IBD smoothing is achieved. If IBD probabilities were estimated with a smaller error prior, the function aborts.
`verbose`	Should messages be written to standard output?

Examples

## Not run: 
# Toy example only, as this will result in an Error: the original error prior was too low
data("IBD_4x","SNP_dosages.4x")
impute_dosages(IBD_list=IBD_4x,dosage_matrix=SNP_dosages.4x)

## End(Not run)
## Not run: 
# Toy example only, as this will result in an Error: the original error prior was too low
data("IBD_4x","SNP_dosages.4x")
impute_dosages(IBD_list=IBD_4x,dosage_matrix=SNP_dosages.4x)

## End(Not run)

Wrapper function to run estimate_IBD function over multiple error priors

Description

Function to run the estimate_IBD function over a range of possible error priors. The function returns a merged set of results that maximise the marginal likelihood per individual, i.e. allowing a per-individual error rate within the options provided in the errors argument.

Usage

maxL_IBD(errors = c(0.01, 0.05, 0.1, 0.2), ...)
maxL_IBD(errors = c(0.01, 0.05, 0.1, 0.2), ...)

Arguments

`errors`	Vector of offspring error priors to test (each between 0 and 1)
`...`	Arguments passed to `estimate_IBD`.

Value

A list containing the following components:

maxL_IBD: A nested list as would have been returned by the estimate_IBD function, but composite across error priors to maximise the marginal likelihoods. Note that the $error values per linkage group are now the average error prior across the population per linkage group
MML: A 3d array of the maximal marginal likelihoods, per error prior. Dimensions are individuals, linkage groups, error priors.
error_per_ind: A matrix of the most likely genotyping error rates per individual (in rows) for each linkage group (in columns)
errors: The error priors used (i.e. the input vector is returned for later reference.)

Examples

## Not run: 
data("phased_maplist.4x","SNP_dosages.4x")
maxL_IBD(phased_maplist=phased_maplist.4x,genotypes=SNP_dosages.4x,
ploidy=4,errors=c(0.01,0.02,0.05,0.1))

## End(Not run)
## Not run: 
data("phased_maplist.4x","SNP_dosages.4x")
maxL_IBD(phased_maplist=phased_maplist.4x,genotypes=SNP_dosages.4x,
ploidy=4,errors=c(0.01,0.02,0.05,0.1))

## End(Not run)

Generate a 'report' of predicted meiotic behaviour in an F1 population

Description

Function to extract the chromosome pairing predictions as estimated by estimate_IBD. Apart from producing an overview of the pairing during parental meiosis (including counts of multivalents, per linkage group per parent), the function also applies a simple chi-squared test to look for evidence of non-random pairing behaviour from the bivalent counts (deviations from a polysomic model)

Usage

meiosis_report(IBD_list, visualise = FALSE, precision = 2)
meiosis_report(IBD_list, visualise = FALSE, precision = 2)

Arguments

`IBD_list`	List of IBD probabilities as estimated by `estimate_IBD` using method 'hmm', or externally (e.g. using TetraOrigin)
`visualise`	Logical, by default `FALSE`. If `TRUE`, a plot of the pairing results is produced per LG. In order to flag extreme deviations from the expected numbers (associated with polysomic inheritance, considered the Null hypothesis), barplots are coloured according to the level of significance of the X2 test. Plots showing red bars indicate extreme deviations from a polysomic pattern.
`precision`	To how many decimal places should summed probabilities per bivalent pairing be rounded? By default 2.

Value

The function returns a nested list, with one element per linkage group in the same order as the input IBD list. Per linkage group, a list is returned containing the following components:

P1_multivalents: The count of multivalents in parent 1 (only relevant if bivalent_decoding = FALSE during IBD calculation)
P2_multivalents: Similarly, the count of multivalents in parent 2
P1_pairing: The counts of each bivalent pairing predicted in parent 1, with an extra column Pr(X2) which gives the p-value of the X2 test of the off-diagonal terms in the matrix. In the case of a tetraploid, pairing A with B automatically implies C with D pairing, so the count table contains a lot of redundancy. The table should be read using both row and column names, so row A and column B corresponds to the count of individuals with A and B pairing (and hence C and D pairing). In a hexaploid, A-B pairing does not imply a particular pairing configuration in the remaining homologues. In this case, row A and column B is the count of individuals where A and B were predicted to have paired, summed over all three bivalent configurations with A and B paired (AB-CD-EF, AB-CE-DF, AB-CF,DE).
P2_pairing: Same as P1_pairing, except using parent 2
ploidy: The ploidy of parent 1
ploidy2: The ploidy of parent 2

Examples

data("IBD_4x")
mr.ls<-meiosis_report(IBD_list = IBD_4x)
data("IBD_4x")
mr.ls<-meiosis_report(IBD_list = IBD_4x)

Example output of meiosis report function

Description

Example output of meiosis report function

Usage

mr.ls
mr.ls

Format

An object of class list of length 2.

Phased maplist for example tetraploid

Description

Phased maplist for example tetraploid

Usage

phased_maplist.4x
phased_maplist.4x

Format

An object of class list of length 2.

Phenotypes for example tetraploid

Description

Phenotypes for example tetraploid

Usage

Phenotypes_4x
Phenotypes_4x

Format

An object of class data.frame with 150 rows and 3 columns.

Plot the results of QTL scan.

Description

Up to package v.0.0.9, there were three plotting functions for the output of QTLscan, namely plotQTL, plotLinearQTL and plotLinearQTL_list. Since release 0.1.0, the functionality of all three functions has been combined into a single general plotting function, named plotQTL. The plot layout is now specified by a new argument layout, allowing the user to plot results for single chromosomes separately, or together either adjacently or in a grid layout. Results from multiple analyses can be overlaid. Previously, it was possible to call the function plotQTL multiple times and overlay subsequent plots using the argument overlay = TRUE. This approach is no longer supported. Instead, if multiple results are to be overlaid, they can be provided as a list of QTLscan or singleMarkerRegression outputs. Note however that this is only possible using the default layout. If significance thresholds are present, the default behaviour is to rescale LOD values so that multiple plots can be combined with overlapping signficance thresholds. This rescaling behaviour can also be disabled (by setting rescale = FALSE). Note that not all arguments may be appropriate for all layouts.

Usage

plotQTL(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)

plotLinearQTL(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)

plotLinearQTL_list(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)
plotQTL(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)

plotLinearQTL(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)

plotLinearQTL_list(
  LOD_data,
  layout = "l",
  inter_chm_gap = 5,
  ylimits = NULL,
  sig.unit = "LOD",
  plot_type = "lines",
  colour = c("black", "red", "dodgerblue", "sienna4"),
  add_xaxis = TRUE,
  add_rug = TRUE,
  add_thresh = TRUE,
  override_thresh = NULL,
  thresh.lty = 3,
  thresh.lwd = 2,
  thresh.col = "darkred",
  return_plotData = FALSE,
  show_thresh_CI = FALSE,
  use_LG_names = TRUE,
  axis_label.cex = 1,
  custom_LG_names = NULL,
  LGdiv.col = "gray42",
  ylab.at = 2.5,
  highlight_positions = NULL,
  mainTitle = FALSE,
  rescale = TRUE,
  ...
)

Arguments

`LOD_data`	Output of `QTLscan` function. If you wish to overlay multiple genome-wide `QTLscan` outputs, then first compile these into a single list and pass this to `LOD_data`, for example `LOD_data = list(qtl_res1, qtl_res2)`. If this is passed as a named list and `add_legend = TRUE`, these names will be used in the legend as well.
`layout`	There are three possible plot layouts - single chromosome plots ("s"), genome-wide plots arranged adjacently in a linear fashion ("l") which is also the default, and genome-wide plots arranged in a grid ("g"), i.e. a grid of single chromosome plots. In the latter case, a suitable grid dimension will be determined based on the number of linkage groups detected in `LOD_data`. If you wish to overlay results from multiple multi-chromosome analyses, use the default layout.
`inter_chm_gap`	The gap size (in units of cM) between successive chromosomes when `layout = "l"`. By default a gap of 5 cM is used. Normally the user should not need to change this.
`ylimits`	Use to specify ylimits of plot region, though by default `NULL` in which case a suitable plot region is automatically used.
`sig.unit`	Label to use on the y-axis for significance units, by default assumed to be LOD score.
`plot_type`	Plots can be either in line drawings ("lines", default) or scatter plot format ("points").
`colour`	Vector of colours to be used in the plotting. A default set of 4 colours is provided, the first of which is used when results from a single QTL scan are to be plotted.
`add_xaxis`	Should an x-axis be drawn? If multiple QTL analyses are performed on different traits, specifying this to be `FALSE` and using `par(mar=c(0,4.1,4.1,2.1))` allows subsequent plots to be neatly stacked.
`add_rug`	Logical, by default `TRUE` - should original marker points be added to plot?
`add_thresh`	Logical, by default `TRUE` - should a significance threshold be added to plot?
`override_thresh`	By default `NULL`. Can be used to specify a (numeric) value for the significance threshold, overriding any stored in `LOD_data`. If you wish to override thresholds for multiple analyses (so, when `LOD_data` is a list of QTL outputs), can also provide a vector of numeric values here.
`thresh.lty`	Gives user control over the line type of the significance threshold to be drawn. Default threshold lty is 3.
`thresh.lwd`	Gives user control over the line width of the significance threshold to be drawn. Default threshold lwd is 2.
`thresh.col`	Gives user control over the line colour of the significance threshold to be drawn. Default threshold colour is dark red. If plotting multiple analyses with `rescale = FALSE`, it can be useful to provide the same colours to this argument as to `colour`, so that LOD profiles can be linked to their respective LOD thresholds.
`return_plotData`	Logical, by default `FALSE`. If `TRUE`, then the x and y coordinates of the plot data are returned when `layout = "l"`, which can be useful for subsequent plot manipulations and overlays. For other layouts, no plot data is returned.
`show_thresh_CI`	Logical, by default `FALSE`. Should confidence interval bounds around LOD threshold be shown if available? If `LOD_data` is a list from multiple analyses, this option is ignored to prevent plot becoming too cluttered.
`use_LG_names`	Logical, by default `TRUE`. Should original character LG names (the names of list `LOD_data`) be used as axis labels? If `FALSE`, numbering is used instead.
`axis_label.cex`	Argument to adjust the size of the axis labels. Can be useful if there are many linkage groups to plot
`custom_LG_names`	Option to specify a vector that contains custom linkage group names. By default `NULL`. See previous argument `use_LG_names`, which is the usual manner for controlling x-axis labels.
`LGdiv.col`	Colour of dividing lines between linkage groups when `layout = "l"`, by default grey.
`ylab.at`	Distance from the y-axis to place label (by default at 2.5 points)
`highlight_positions`	Option to include a (list of) positions to highlight (e.g. peak QTL positions). Each list element should be a 2-column data.frame with columns giving the linkage group numbers (numeric) and the corresponding cM positions (numeric) to highlight. If `LOD_data` is the result of a single genome-wide scan, it is also possible to just directly provide the 2-column data.frame (again, with column 1 containing linkage group numbers and column 2 containing corresponding cM positions). If `LOD_data` has been provided as a list of multiple analyses, you may wish to highlight different positions from each analysis. Then `highlight_positions` should also be a list of the same length and in the same order as `LOD_data`. Each data.frame of positions will be coloured in the same colour as the LOD output. If no position is to be highlighted for some analyses, add the corresponding list element as `NULL`. For example, if you wish to highlight positions for analyses 1 and 3 in a 3-analysis overlay, then use something like `highlight_positions = list(data.frame(lg = 1, cM = 50),NULL,data.frame(lg=c(2,3),cM=c(11,99)))`. The default setting is `NULL`, meaning no positions are highlighted.
`mainTitle`	Option to supply vector of plot titles if `layout = "s"` or `layout = "g"`. Argument ignored if using the default layout. Single character vector also allowed and will be recycled. For no plot titles, leave as default, i.e. `FALSE`
`rescale`	If results from multiple analyses are to be overlaid and different significance thresholds are detected, then by default plots will be rescaled so that threshold lines overlap. This behaviour can be disabled by setting `rescale = FALSE`.
`...`	Arguments passed to `plot`, and `lines` or `points` as appropriate (see argument `plot_type`).

Value

The plot data, if return_plotData = TRUE. Otherwise NULL. Output is returned invisibly

Examples

## Not run: 
data("qtl_LODs.4x")
plotQTL(LOD_data = qtl_LODs.4x,layout = "l")

## End(Not run)
## Not run: 
data("qtl_LODs.4x")
plotQTL(LOD_data = qtl_LODs.4x,layout = "l")

## End(Not run)

Plot the recombination landscape across the genome

Description

Function which visualises the recombination landscape in two ways: per linkage group, and per individual. For the first analysis, a rudimentary spline is also fitted to estimate the recombination rate along a grid of positions defined by gap, which is also returned by the function.

Usage

plotRecLS(
  recombination_data,
  plot_per_LG = TRUE,
  plot_per_ind = TRUE,
  gap = 1,
  ...
)
plotRecLS(
  recombination_data,
  plot_per_LG = TRUE,
  plot_per_ind = TRUE,
  gap = 1,
  ...
)

Arguments

`recombination_data`	Data on predicted recombination events, as returned by the function `count_recombinations`
`plot_per_LG`	Logical argument, plot recombination events per linkage group? By default `TRUE`.
`plot_per_ind`	Logical argument, plot recombination events per individual? By default `TRUE`.
`gap`	The size (in cM) of the gap used to define the grid of positions to define the window in which to estimate recombination rate. By default 1 cM. Interpolated positions are taken to be the centre of an interval, so a 1 cM gap would result in predictions for positions 0.5 cM, 1.5 cM etc.
`...`	Option to pass extra arguments to the `plot` function for the per_LG plots. This may lead to conflicts with arguments already declared internally (such as `main` for example).

Value

A list with two elements, per_LG and per_individual. The first of these is itself a list with the same length as recombination_data, giving the estimated recombination rates along the linkage group. This rate is simply estimated as the (weighted) count of recombination breakpoints divided by the population size.

Examples

data("Rec_Data_4x")
plotRecLS(Rec_Data_4x)
data("Rec_Data_4x")
plotRecLS(Rec_Data_4x)

Function to determine the percentage variance explained (PVE) of a (maximal) QTL model, and explore sub-models.

Description

This function builds a (maximal) QTL model from previously detected QTL peaks and outputs the percentage variance explained (PVE) of the full QTL model and all sub-models. It uses a similar approach to the fitting of genetic co-factors in the function QTLscan. The PVE is very similar to but not exactly equal to the adjusted R2 returned in QTLscan at each position (and note: in the former case, these R2 values are per-locus, while this function can estimate the PVE combined over multiple loci). The discrepancy has to do with how PVE is calculated using the formula 100(1 - RSS0/RSS1), where RSS0 and RSS1 are the residual sums of squares of the NULL and QTL models, respectively.

Usage

PVE(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  block = NULL,
  QTL_df = NULL,
  prop_Pheno_rep = 0.5,
  log = NULL,
  verbose = FALSE
)
PVE(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  block = NULL,
  QTL_df = NULL,
  prop_Pheno_rep = 0.5,
  log = NULL,
  verbose = FALSE
)

Arguments

`IBD_list`	List of IBD probabilities
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the offspring identifiers (F1 names)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model
`block`	The blocking factor to be used, if any (must be colname of `Phenotype.df`). By default `NULL`, in which case no blocking structure (for unreplicated experiments)
`QTL_df`	A 2-column data frame of previously-detected QTL; column 1 gives linkage group identifiers, column 2 specifies the cM position of the QTL. If not specified, an error results. It can be convenient to generate a compatible data.frame by first running the function `check_cofactors` to build a multi-QTL model.
`prop_Pheno_rep`	The minimum proportion of phenotypes represented across blocks. If less than this, the individual is removed from the analysis. If there is incomplete data, the missing phenotypes are imputed using the mean values across the recorded observations.
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.
`verbose`	Should messages be written to standard output?

Value

A list with percentage variance explained of maximal QTL model and all sub-models

Examples

data("IBD_4x","Phenotypes_4x")
PVE(IBD_list = IBD_4x,
    Phenotype.df = Phenotypes_4x,
    genotype.ID = "geno",trait.ID = "pheno",
    block = "year",
    QTL_df = data.frame(LG=1,cM=12.3))
data("IBD_4x","Phenotypes_4x")
PVE(IBD_list = IBD_4x,
    Phenotype.df = Phenotypes_4x,
    genotype.ID = "geno",trait.ID = "pheno",
    block = "year",
    QTL_df = data.frame(LG=1,cM=12.3))

QTL output for example tetraploid

Description

QTL output for example tetraploid

Usage

qtl_LODs.4x
qtl_LODs.4x

Format

An object of class list of length 6.

General QTL function that allows for co-factors, completely randomised block designs and the possibility to derive LOD thresholds using a permutation test

Description

Function to run QTL analysis using IBD probabilties given (possibly replicated) phenotypes, assuming randomised experimental design

Usage

QTLscan(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  block = NULL,
  cofactor_df = NULL,
  allelic_interaction = FALSE,
  folder = NULL,
  filename.short,
  prop_Pheno_rep = 0.5,
  perm_test = FALSE,
  N_perm.max = 1000,
  alpha = 0.05,
  gamma = 0.05,
  ncores = 1,
  log = NULL,
  verbose = TRUE,
  ...
)
QTLscan(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  block = NULL,
  cofactor_df = NULL,
  allelic_interaction = FALSE,
  folder = NULL,
  filename.short,
  prop_Pheno_rep = 0.5,
  perm_test = FALSE,
  N_perm.max = 1000,
  alpha = 0.05,
  gamma = 0.05,
  ncores = 1,
  log = NULL,
  verbose = TRUE,
  ...
)

Arguments

`IBD_list`	List of IBD probabilities
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the offspring identifiers (F1 names)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model
`block`	The blocking factor to be used, if any (must be colname of `Phenotype.df`). By default `NULL`, in which case no blocking structure (for unreplicated experiments)
`cofactor_df`	A 3-column data frame of co-factor(s); column 1 gives the numeric linkage group identifier(s), column 2 specifies the cM position of the co-factor(s), column 3 specifies whether the QTL was fitted using "a" = additive effects or "f" = full allelic interactions (note that any other symbol for the full model will also be accepted, as long as it is not "a"). For backward compatibility with package versions <= 0.0.9, it is possible to just supply the first two columns, in which case an additive-effects model is assumed for each cofactor (so, a third column will be automatically filled with "a"). By default `cofactor_df = NULL`, in which case no co-factors are included in the analysis.
`allelic_interaction`	The QTL detection model can be for additive main effects only (by default `allelic_interaction = FALSE`). If `TRUE`, then the full model is used (i.e. all possible genotype combinations are included as predictors in the model). This runs the risk of overfitting, especially if double reduction was also allowed. Both types of analyses can ideally be performed and compared. Note that if IBD probabilities were estimated using the "heuristic" method rather than the HMM method (see `estimate_IBD`), then IBDs are actually haplotype probabilities rather than genotype probabilities, meaning that allelic interaction effects cannot be included in the model.
`folder`	If markers are to be used as co-factors, the path to the folder in which the imported IBD probabilities is contained can be provided here. By default this is `NULL`, if files are in working directory.
`filename.short`	If TetraOrigin was used and co-factors are being included, the shortened stem of the filename of the `.csv` files containing the output of TetraOrigin, i.e. without the tail "_LinkageGroupX_Summary.csv" which is added by default to all output of TetraOrigin.
`prop_Pheno_rep`	The minimum proportion of phenotypes represented across blocks. If less than this, the individual is removed from the analysis. If there is incomplete data, the missing phenotypes are imputed using the mean values across the recorded observations.
`perm_test`	Logical, by default `FALSE`. If `TRUE`, a permutation test will be performed to determine a genome-wide significance threshold.
`N_perm.max`	The maximum number of permutations to run if `perm_test` is `TRUE`; by default this is 1000.
`alpha`	The P-value to be used in the selection of a threshold if `perm_test` is `TRUE`, by default 0.05 (i.e. the 0.95 quantile).
`gamma`	The width of the confidence intervals used around the permutation test threshold using the approach of Nettleton & Doerge (2000), by default 0.05.
`ncores`	Number of cores to use if parallel computing is required. Works both for Windows and UNIX (using `doParallel`). Use `parallel::detectCores()` to find out how many cores you have available.
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.
`verbose`	Logical, by default `TRUE`. Should messages be printed during running?
`...`	Arguments passed to `plot`

Value

A nested list; each list element (per linkage group) contains the following items:

QTL.res: Single matrix of QTL results with columns chromosome, position, LOD, adj.r.squared and PVE (percentage variance explained).
Perm.res: If perm_test = FALSE, this will be NULL. Otherwise, Perm.res contains a list of the results of the permutation test, with list items "quantile","threshold" and "scores". Quantile refers to which quantile of scores was used to determine the threshold. Note that scores are each of the maximal LOD scores across the entire genome scan per permutation, thus returning a genome-wide threshold rather than a chromosome-specific threshold. If the latter is preferred, restricting the IBD_list to a single chromosome and re-running the permutation test will provide the desired threshold.
Residuals: If a blocking factor or co-factors are used, this is the (named) vector of residuals used as input for the QTL scan. Otherwise, this is the set of (raw) phenotypes used in the QTL scan.
Map: Original map of genetic marker positions upon which the IBDs were based, most often used for adding rug of marker positions to QTL plots.
LG_names: Names of the linkage groups
allelic_interaction: Whether argument allelic_interaction was TRUE or FALSE in the QTL scan

Examples

data("IBD_4x","Phenotypes_4x")
qtl_LODs.4x <- QTLscan(IBD_list = IBD_4x,
                       Phenotype.df = Phenotypes_4x,
                       genotype.ID = "geno",
                       trait.ID = "pheno",
                       block = "year")
data("IBD_4x","Phenotypes_4x")
qtl_LODs.4x <- QTLscan(IBD_list = IBD_4x,
                       Phenotype.df = Phenotypes_4x,
                       genotype.ID = "geno",
                       trait.ID = "pheno",
                       block = "year")

Recombination data for example tetraploid

Description

Recombination data for example tetraploid

Usage

Rec_Data_4x
Rec_Data_4x

Format

An object of class list of length 2.

Expected segregation for all markers types of a diploid cross

Description

Expected segregation for all markers types of a diploid cross

Usage

segList_2x
segList_2x

Format

An object of class list of length 8.

Expected segregation for all markers types of a triploid cross (4 x 2)

Description

Expected segregation for all markers types of a triploid cross (4 x 2)

Usage

segList_3x
segList_3x

Format

An object of class list of length 27.

Expected segregation for all markers types of a triploid cross (2 x 4)

Description

Expected segregation for all markers types of a triploid cross (2 x 4)

Usage

segList_3x_24
segList_3x_24

Format

An object of class list of length 27.

Expected segregation for all markers types of a tetraploid cross

Description

Expected segregation for all markers types of a tetraploid cross

Usage

segList_4x
segList_4x

Format

An object of class list of length 224.

Expected segregation for all markers types of a hexaploid cross

Description

Expected segregation for all markers types of a hexaploid cross

Usage

segList_6x
segList_6x

Format

An object of class list of length 3735.

Create a list of possible QTL segregation types

Description

Function to generate list of segregation types for the exploreQTL function

Usage

segMaker(ploidy, segtypes, modes = c("a", "d"))
segMaker(ploidy, segtypes, modes = c("a", "d"))

Arguments

`ploidy`	The ploidy of the population. Currently assumed to be an even number for this function.
`segtypes`	List of QTL segregation types to consider, so e.g. c(1,0) would mean all possible simplex x nulliplex QTL (ie. 4 QTL, on each of homologues 1 - 4 of parent 1). Note that symmetrical QTL types that cannot be distinguished are not automatically removed and need to be manually identified. If this is an issue, use the inbuilt list for tetraploids provided with the package to search the full model space. Such an inbuilt list is currently only available for tetraploids, and is available from the `exploreQTL` function.
`modes`	Character vector of modes of QTL action to consider, with options "a" for "additive" and "d" for dominant QTL action.

Run a single marker regression using marker dosages

Description

Function to run a single marker regression using marker dosages

Usage

singleMarkerRegression(
  dosage_matrix,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  maplist = NULL,
  perm_test = FALSE,
  N_perm = 1000,
  alpha = 0.05,
  ncores = 1,
  return_R2 = FALSE,
  log = NULL
)
singleMarkerRegression(
  dosage_matrix,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  maplist = NULL,
  perm_test = FALSE,
  N_perm = 1000,
  alpha = 0.05,
  ncores = 1,
  return_R2 = FALSE,
  log = NULL
)

Arguments

`dosage_matrix`	An integer matrix with markers in rows and individuals in columns. All markers in this matrix will be tested for association with the trait.
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the population identifiers (F1 names) (must be a colname of `Phenotype.df`)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model (must be a colname of `Phenotype.df`)
`maplist`	Option to include linkage map in the format returned by `MDSMap_from_list` from `polymapR`. If maplist is not specified (by default `NULL`) then no ordering of markers from dosage-matrix is performed. Note that all markers in dosage_matrix are tested; markers with dosages that were not on the maplist will be assigned unordered to linkage group 0 with dummy cM positions 1,2,3 etc.
`perm_test`	Logical, by default `FALSE`. If `TRUE`, a permutation test will be performed to determine a genome-wide significance threshold.
`N_perm`	Integer. The number of permutations to run if `perm_test` is `TRUE`; by default this is 1000.
`alpha`	Numeric. The P-value to be used in the selection of a threshold if `perm_test` is `TRUE`; by default 0.05 (i.e. the 0.95 quantile).
`ncores`	Number of cores to use if parallel processing required. Works both for Windows and UNIX (using `doParallel`). Use `parallel::detectCores()` to find out how many cores you have available.
`return_R2`	Should the (adjusted) R2 of the model fit also be determined?
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.

Value

A list containing the following components:

QTL.res: The -log(p) of the model fit per marker are returned as "LOD" scores, although "LOP" would have been a better description. If requested, R2 values are also returned in column "R2adj"
Perm.res: The results of the permutation test if performed, otherwise NULL
Map: The linkage map if provided, otherwise NULL
LG_names: Names of the linkage groups, if a map was provided, otherwise NULL

Examples

data("SNP_dosages.4x","BLUEs.pheno")
Trait_1.smr <- singleMarkerRegression(dosage_matrix = SNP_dosages.4x,
Phenotype.df = BLUEs.pheno,genotype.ID = "Geno",trait.ID = "BLUE")
data("SNP_dosages.4x","BLUEs.pheno")
Trait_1.smr <- singleMarkerRegression(dosage_matrix = SNP_dosages.4x,
Phenotype.df = BLUEs.pheno,genotype.ID = "Geno",trait.ID = "BLUE")

SNP marker dosage data for example tetraploid

Description

SNP marker dosage data for example tetraploid

Usage

SNP_dosages.4x
SNP_dosages.4x

Format

An object of class matrix (inherits from array) with 186 rows and 52 columns.

Fit splines to IBD probabilities

Description

Fits splines to IBD probabilities at a grid of positions at user-defined spacing.

Usage

spline_IBD(IBD_list, gap, method = "cubic", ncores = 1, log = NULL)
spline_IBD(IBD_list, gap, method = "cubic", ncores = 1, log = NULL)

Arguments

`IBD_list`	List of IBD probabilities
`gap`	The size (in centiMorgans) of the gap between splined positions
`method`	One of two options, either "linear" or "cubic". The default method (cubic) fits cubic splines, and although more accurate, becomes computationally expensive in higher-density data-sets, where the linear option may be preferable.
`ncores`	Number of cores to use, by default 1 only. Works both for Windows and UNIX (using `doParallel`). Use `parallel::detectCores()` to find out how many cores you have available. Note that with large datasets, using multiple cores will use large amounts of memory (RAM). Single-core or e.g. 2-core evaluations, although slower, is less memory-intensive.
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.

Value

Returns a list of similar format as IBD_list, with a splined IBD_array in place of the original IBD_array

Examples

data("IBD_4x")
IBD_4x.spl <- spline_IBD(IBD_list = IBD_4x, gap = 1)
data("IBD_4x")
IBD_4x.spl <- spline_IBD(IBD_list = IBD_4x, gap = 1)

Thin out map data

Description

thinmap is a function for thinning out an integrated map, in order that IBD estimation runs more quickly. Especially useful for maps with very high marker densities for which the estimate_IBD function is to be used.

Usage

thinmap(
  maplist,
  dosage_matrix,
  bin_size = 1,
  bounds = NULL,
  remove_markers = NULL,
  plot_maps = TRUE,
  use_SN_phase = FALSE,
  parent1 = "P1",
  parent2 = "P2",
  log = NULL
)
thinmap(
  maplist,
  dosage_matrix,
  bin_size = 1,
  bounds = NULL,
  remove_markers = NULL,
  plot_maps = TRUE,
  use_SN_phase = FALSE,
  parent1 = "P1",
  parent2 = "P2",
  log = NULL
)

Arguments

`maplist`	A list of maps. In the first column marker names and in the second their position.
`dosage_matrix`	An integer matrix with markers in rows and individuals in columns.
`bin_size`	Numeric. Size (in cM) of the bins to include. By default, a bin size of 1 cM is used. Larger `bin_size` results in fewer markers being left on the resulting map.
`bounds`	Numeric vector. If `NULL` (by default) then all positions are included, however if specified then output is limited to a specific region, which may be useful if fine-mapping a region of interest.
`remove_markers`	Optional vector of marker names to remove from the maps. Default is `NULL`.
`plot_maps`	Logical. Plot the marker positions of the selected markers using `polymapR::plot_map`.
`use_SN_phase`	Logical, by default `FALSE`. If `TRUE`, then 1x0 and 0x1 are binned per phase, to increase coverage of these marker types across parental homologues. If not, at most one of each are retained per bin.
`parent1`	Identifier of parent 1, by default assumed to be `"P1"`
`parent2`	Identifier of parent 2, by default assumed to be `"P2"`
`log`	Character string specifying the log filename to which standard output should be written. If NULL log is send to stdout.

Value

A maplist of the same structure as the input maplist, but with fewer markers based on the bin_size.

Examples

data("phased_maplist.4x","SNP_dosages.4x")
maplist_thin<-thinmap(maplist=phased_maplist.4x,dosage_matrix=SNP_dosages.4x)
data("phased_maplist.4x","SNP_dosages.4x")
maplist_thin<-thinmap(maplist=phased_maplist.4x,dosage_matrix=SNP_dosages.4x)

Visualise Genotypic Information Coefficient

Description

Function to visualise the GIC of a certain region

Usage

visualiseGIC(
  GIC_list,
  add_rug = TRUE,
  add_leg = FALSE,
  ylimits = NULL,
  gic.cex = 1,
  show_markers = TRUE,
  add.mainTitle = TRUE,
  plot.cols = NULL
)
visualiseGIC(
  GIC_list,
  add_rug = TRUE,
  add_leg = FALSE,
  ylimits = NULL,
  gic.cex = 1,
  show_markers = TRUE,
  add.mainTitle = TRUE,
  plot.cols = NULL
)

Arguments

`GIC_list`	List of GIC data, the output of `estimate_GIC`
`add_rug`	Should original marker positions be added to the plot?
`add_leg`	Should a legend be added to the plot?
`ylimits`	Optional argument to control the plotting area, by default `NULL`
`gic.cex`	Option to increase the size of the GIC
`show_markers`	Should markers be shown?
`add.mainTitle`	Should a main title be added to the plot?
`plot.cols`	Optional argument to specify plot colours, otherwise suitable contrasting colours are chosen

Value

The phased map data for the specified region, recoded into 1's and 0's.

Examples

data("GIC_4x")
visualiseGIC(GIC_list = GIC_4x)
data("GIC_4x")
visualiseGIC(GIC_list = GIC_4x)

Visualise haplotypes in certain individuals in a certain region

Description

Function to visualise the haplotypes of a certain region in certain individuals

Usage

visualiseHaplo(
  IBD_list,
  display_by = c("phenotype", "name"),
  linkage_group = NULL,
  Phenotype.df = NULL,
  genotype.ID = NULL,
  trait.ID = NULL,
  pheno_range = NULL,
  cM_range = "all",
  highlight_region = NULL,
  select_offspring = NULL,
  recombinant_scan = NULL,
  allele_fish = NULL,
  presence_threshold = 0.95,
  xlabl = TRUE,
  ylabl = TRUE,
  mainTitle = NULL,
  multiplot = NULL,
  append = FALSE,
  colPal = c("white", "navyblue", "darkred"),
  hap.wd = 0.4,
  recombination_data = NULL,
  reset_par = TRUE,
  log = NULL
)
visualiseHaplo(
  IBD_list,
  display_by = c("phenotype", "name"),
  linkage_group = NULL,
  Phenotype.df = NULL,
  genotype.ID = NULL,
  trait.ID = NULL,
  pheno_range = NULL,
  cM_range = "all",
  highlight_region = NULL,
  select_offspring = NULL,
  recombinant_scan = NULL,
  allele_fish = NULL,
  presence_threshold = 0.95,
  xlabl = TRUE,
  ylabl = TRUE,
  mainTitle = NULL,
  multiplot = NULL,
  append = FALSE,
  colPal = c("white", "navyblue", "darkred"),
  hap.wd = 0.4,
  recombination_data = NULL,
  reset_par = TRUE,
  log = NULL
)

Arguments

`IBD_list`	List of IBD probabilities
`display_by`	Option to display a subset of the population's haplotypes either by `"phenotype"` or `"name"`. If `"phenotype"` is supplied, then `Phenotype.df`,`genotype.ID`,`trait.ID` and `pheno_range` must also be specified. if `"name"` is supplied, then `select_offspring` must be specified.
`linkage_group`	Numeric identifier of the linkage group being examined, based on the order of `IBD_list`. Only a single linkage group is allowed. If `IBD_list` corresponds to a single linkage group, default value of `NULL` will suffice
`Phenotype.df`	A data.frame containing phenotypic values, which can be used to select a subset of the population to visualise (with extreme phenotypes for example). By default `NULL`, in which case a subset of the population may be selected using the `select_offspring` argument.
`genotype.ID`	The colname of `Phenotype.df` that contains the population identifiers (F1 names) (must be a colname of `Phenotype.df`)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model (must be a colname of `Phenotype.df`)
`pheno_range`	Vector of numeric bounds of the phenotypic scores to include (offspring selection).
`cM_range`	Vector of numeric bounds of the genetic region to be explored. If none are specified, the default of `"all"` means all cM positions will be included.
`highlight_region`	Option to hightlight a particular genetic region on the plot; can be a single position or a vector of 2 positions. By default `NULL`.
`select_offspring`	Vector of offspring identifiers to visualise, must be supplied if `display_by` = `"name"`. Specifying `"all"` will result in all offspring haplotypes being visualised.
`recombinant_scan`	Vector of homologue numbers between which to search for recombinant offspring in the visualised region and selected individuals. By default `NULL`, in which case no search is preformed.
`allele_fish`	Vector of homologue numbers of interest, for which to search for offspring that carry these homologues (in the visualised region). By default `NULL`, in which case no search ("fishing") is performed.
`presence_threshold`	Numeric. The minimum probability used to declare presence of a homologue in an individual. This is only needed if a `recombinant_scan` is performed. By default a value of 0.95 is used. When searching for recombinants, this value is also used to denote the proportion of loci carrying the required number of homologues (i.e. by default 95 per cent of loci should have between 0.95 and 1.1 copies of the specified recombinant homologues).
`xlabl`	Logical, by default `TRUE`. Should an x-axis label be used?
`ylabl`	Logical, by default `TRUE`. Should a y-axis label be used?
`mainTitle`	Option to override default plot titles with a (vector of) captions. By default `NULL`.
`multiplot`	Vector of integers. By default `NULL` so haplotypes are plotted singly; otherwise a vector specifying the number of rows and columns in the plot layout.
`append`	Option to allow user to append new plots to spaces generated by `multiplot`, otherwise these are filled with blank plots. By default `FALSE`. If `TRUE`, then a large enough `multiplot` grid should be generated to make this option meaningful.
`colPal`	Colour palette to use in the visualisation (best to provide 3 colours).
`hap.wd`	The width of the haplotype tracks to be plotted, generally recommended to be about 0.4 (default value)
`recombination_data`	List object as returned by the function `count_recombinations`. By default `NULL`, in which case no overlay of predicted recombination events is performed. However, it can be useful to visualise predicted recombination events, particularly as this might help inform the choice of argument `plausible_pairing_prob` of that function. See `count_recombinations` for more details.
`reset_par`	By default `TRUE`, reset par on exit.
`log`	Character string specifying the log filename to which standard output should be written. If `NULL` log is send to stdout.

Value

If recombinant_scan vector is supplied, a vector of recombinant offspring ID in the region of interest (otherwise NULL).

Examples

data("IBD_4x")
visualiseHaplo(IBD_list = IBD_4x,
               display_by = "name",
               linkage_group = 1,
               select_offspring = "all",
               multiplot = c(3,3))
data("IBD_4x")
visualiseHaplo(IBD_list = IBD_4x,
               display_by = "name",
               linkage_group = 1,
               select_offspring = "all",
               multiplot = c(3,3))

Visualise pairing of parental homologues

Description

Function to visualise the pairing of parental homologues across the population using graph, with nodes to denote parental homologues and edges to denote deviations from expected proportions under a polysomic model of inheritance

Usage

visualisePairing(
  meiosis_report.ls,
  pos.col = "red",
  neg.col = "blue",
  parent,
  max.lwd = 20,
  datawidemax,
  add.label = TRUE,
  return.data = FALSE,
  ...
)
visualisePairing(
  meiosis_report.ls,
  pos.col = "red",
  neg.col = "blue",
  parent,
  max.lwd = 20,
  datawidemax,
  add.label = TRUE,
  return.data = FALSE,
  ...
)

Arguments

`meiosis_report.ls`	List output of function `meiosis_report`
`pos.col`	Colour corresponding to excess of pairing associations predicted (positive deviations), by default red
`neg.col`	Colour corresponding to lack of pairing associations predicted (negative deviations), by default blue
`parent`	The parent, either "P1" (mother) or "P2 (father)
`max.lwd`	Maximum line width, by default 20
`datawidemax`	This argument is currently a work-around to allow multiple plots to have the same scale (line thicknesses consistent). No default is provided. To estimate this value, simply set argument `return.data = TRUE`, and record the maximum absolute value over columns 'count', which are the deviations from random expectations. This should be done over multiple function calls if e.g. comparing both P1 and P2 values. When a global maximum (absolute) deviation is known, re-run the function with this value for `datawidemax`. The line width specified by `max.lwd` will then be used for this, and all other line widths re-scaled accordingly.
`add.label`	Should a label be applied, giving the maximum deviation in the plot? By default `TRUE`
`return.data`	Should plot data be returned? By default `FALSE`
`...`	Optional arguments passed to `plot.igraph`

Value

If return.data = TRUE, the values for pairwise deviations from the expected numbers are returned, useful for determining the value datawidemax to provide consistent scaling across multiple plots

Examples

data("mr.ls")
visualisePairing(meiosis_report.ls = mr.ls,
                 parent = "P1",
                 datawidemax = 3)
data("mr.ls")
visualisePairing(meiosis_report.ls = mr.ls,
                 parent = "P1",
                 datawidemax = 3)

Visualise QTL homologue effects around a QTL position

Description

Function to visualise the effect of parental homologues around a QTL peak across the population.

Usage

visualiseQTLeffects(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  linkage_group,
  LOD_data,
  cM_range = NULL,
  col.pal = c("purple4", "white", "seagreen"),
  point.density = 50,
  zero.sum = FALSE,
  allelic_interaction = FALSE,
  exploreQTL_output = NULL,
  return_plotData = FALSE
)
visualiseQTLeffects(
  IBD_list,
  Phenotype.df,
  genotype.ID,
  trait.ID,
  linkage_group,
  LOD_data,
  cM_range = NULL,
  col.pal = c("purple4", "white", "seagreen"),
  point.density = 50,
  zero.sum = FALSE,
  allelic_interaction = FALSE,
  exploreQTL_output = NULL,
  return_plotData = FALSE
)

Arguments

`IBD_list`	List of IBD probabilities
`Phenotype.df`	A data.frame containing phenotypic values
`genotype.ID`	The colname of `Phenotype.df` that contains the population identifiers (F1 names) (must be a colname of `Phenotype.df`)
`trait.ID`	The colname of `Phenotype.df` that contains the response variable to use in the model (must be a colname of `Phenotype.df`)
`linkage_group`	Numeric identifier of the linkage group being tested, based on the order of `IBD_list`. Only a single linkage group is allowed.
`LOD_data`	Output of `QTLscan` function
`cM_range`	If required, the plotting region can be restricted to a specified range of centiMorgan positions (provided as a vector of start and end positions).
`col.pal`	Vector of colours to use in the visualisations (it is best to provide two or three colours for simplicity). By default, effects will be coloured from purple to green through white.
`point.density`	Parameter to increase the smoothing of homologue effect tracks
`zero.sum`	How allele substitution effect should be defined. If `FALSE` (by default), the effect of each homologue is computed relative to the overall phenotypic mean, otherwise contrasts (against offspring without the inherited homologue) are used.
`allelic_interaction`	By default `FALSE`, in which case the additive effects of parental alleles are visualised. If `TRUE`, a plot of the mean effect of combinations of parental alleles is visualised instead. `exploreQTL_output` is required in this case.
`exploreQTL_output`	If `allelic_interaction = TRUE`, the output of the function `exploreQTL` must be provided.
`return_plotData`	Logical, by default `FALSE`. If `TRUE`, plot data is returned, otherwise `NULL`.

Value

The estimated effects of the homologues, used in the visualisation

Examples

data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
visualiseQTLeffects(IBD_list = IBD_4x,
                    Phenotype.df = BLUEs.pheno,
                    genotype.ID = "Geno",
                    trait.ID = "BLUE",
                    linkage_group = 2,
                    LOD_data = qtl_LODs.4x)
data("IBD_4x","BLUEs.pheno","qtl_LODs.4x")
visualiseQTLeffects(IBD_list = IBD_4x,
                    Phenotype.df = BLUEs.pheno,
                    genotype.ID = "Geno",
                    trait.ID = "BLUE",
                    linkage_group = 2,
                    LOD_data = qtl_LODs.4x)

Package 'polyqtlR'

Help Index

Calculate Best Linear Unbiased Estimates using linear mixed model from nlme package

Description

Usage

Arguments

Value

Examples

Best Linear Unbiased Estimates of phenotype

Description

Usage

Format

Build a multi-QTL model using step-wise procedure of checking genetic co-factors.

Description

Usage

Arguments

Value

Examples

Function to extract the phased map from a mappoly.map object

Description

Usage

Arguments

Value

Examples

Predict recombination breakpoints using IBD probabilities

Description

Usage

Arguments

Value

Examples

Estimate the Genotypic Information Coefficient (GIC)

Description

Usage

Arguments

Value

Examples

Generate IBD probabilities from marker genotypes and a phased linkage map

Description

Usage

Arguments

Value

References

Examples

Explore the possible segregation type of a QTL peak using Schwarz Information Criterion

Description

Usage

Arguments

Value

Examples

Function to find the position of maximum LOD on a particular linkage group

Description

Usage

Arguments

Examples

Function to find a LOD - x support interval around a QTL position

Description

Usage

Arguments

Examples

Genotypic Information Coefficient for example tetraploid

Description

Usage

Format

Identical by descent probabilities for example tetraploid

Description

Usage

Format

Import IBD probabilities as estimated by TetraOrigin or PolyOrigin

Description

Usage

Arguments

Value

Examples

Re-estimate marker dosages given IBD input estimated using a high error prior.

Description

Usage

Arguments

Examples

Wrapper function to run estimate_IBD function over multiple error priors

Description

Calculate Best Linear Unbiased Estimates using linear mixed model from `nlme` package