`checkAndFilterSNPcoord`

Check compatibility between 2 snps coordinates data set and keep only genotypes SNPs

Description

If the user_SNPcoord data miss some SNPs defined in the .vcf file, an error is raised. If the user_SNPcoord data have additional SNPs, those SNPs will be removed. If the data between those two data-set are inconsistent, an error is raised.

Usage

checkAndFilterSNPcoord(user_SNPcoord, vcf_SNPcoord)

Arguments

Argument	Description
`user_SNPcoord`	SNPs coordinates data coming from the user (`.csv` file)
`vcf_SNPcoord`	SNPs coordinates data coming from the `.vcf` file

Value

The filtered user_SNPcoord data.frame

`checkIndNamesConsistency`

Check individuals in the crossing table are in the haplotype data

Description

Check individuals in the crossing table are in the haplotype data

Usage

checkIndNamesConsistency(crossTable, haplo)

Arguments

Argument	Description
`crossTable`	the crossing table
`haplo`	the haplotype data given by the function `readPhasedGeno`

Value

NULL, raise error if missing individuals are detected.

`initializeSimulation`

Initialise the simulation

Description

Initialise the simulation

Usage

initializeSimulation(haplotypes, SNPcoord)

Arguments

Argument	Description
`haplotypes`	haplotypes of the parents, data.frame with genotype values in row, and individuals'haplotype in columns. The columns name should be `individualName_1` and `individualName_2` for the first/second haplotype of the individual named `individualName`. (the list item named `haplo` return by the function `readPhasedGeno`)
`SNPcoord`	snp coordinates, data.frame of 4 columns: - `chr`: Name of the chromosome holding the SNP - `physPos`: SNP physical position on the chromosome - `linkMapPos`: SNP linkage map position on the chromosome in morgan - `SNPid`: SNP's IDs

Value

breedSimulatR's population object

`engineError`

Main function to raise an engineError (ie. expected error)

Description

This function is similar to the R's stop function and should be used instead to ensure we raise expected errors.

Usage

engineError(message, extra = list(), n_skip_caller = 1)

Arguments

Argument	Description
`message`	error message
`extra`	list of extra information that will be included in the error
`n_skip_caller`	(int, default 1) This is to catch the function where the error happens. 0 will show this function, 1 will show the function calling this one and so on.

`bad_argument`

Helper function to raise an engineError for a "bad argument"

Description

To be used inside functions to check their arguments. the error message will be: "arg must be must_be not not" (adapted from https://adv-r.hadley.nz/conditions.html#signalling)

Usage

bad_argument(
  arg,
  must_be,
  not = NULL,
  errType = "value",
  class = "engineError",
  extra = NULL,
  n_skip_caller = 2
)

Arguments

Argument	Description
`arg`	(character) tested argument name
`must_be`	(character) expected value or type
`not`	(any) provided value
`errType`	"type" is the only recognised value. Use it to report an error about the type of the argument
`class`	use "engineError" (default) to raise an expected "engineError"
`extra`	(list, default NULL) extra information to pass to engineError
`n_skip_caller`	(int, default 2) see `engineError`

Examples

x = "a"
bad_argument("x", must_be = 42, not = x)
# will stop with the error msg: "`x` must be 42 not `a`"
bad_argument("x", must_be = "numeric", not = x, "type")
# will stop with the error msg: "`x` must be numeric not `character`"

`errorCode`

list of error codes as function so that unexpected error code raise and error

Description

list of error codes as function so that unexpected error code raise and error

Usage

errorCode(code)

`filterGWAS`

Filter gwas results

Description

Filter gwas results

Usage

filterGWAS(gwas, filter_pAdj = 1, filter_nPoints = Inf, filter_quant = 1)

Arguments

Argument	Description
`gwas`	[data.frame] output of the gwas function
`filter_pAdj`	[numeric] threshold to remove points with pAdj < filter_pAdj from the plot (default no filtering)
`filter_nPoints`	[numeric] threshold to keep only the filter_nPoints with the lowest p-values for the plot (default no filtering)
`filter_quant`	[numeric] threshold to keep only the filter_quant*100 % of the points with the lowest p-values for the plot (default no filtering)

`fit_with_gaston`

Fit a GS model using gaston package

Description

Fit a GS model using gaston package

Usage

fit_with_gaston(pheno, K)

Arguments

Argument	Description
`pheno`	1 dimensional vector of phenotypic values
`K`	list of 1 or 2 variance covariance matrices to consider for the random effect.

Details

This is a wrapper around gaston::lmm.aireml() see ?gaston::lmm.aireml() for more information

Value

list of 3 elements

logL: Value of log-likelihood of the model (cf. ?gaston::lmm.aireml())
blups: length(pheno) by length(K) matrix of the blups of the individuals (one random effects per column).
intercept: value of the intercept of the model

`fit_with_rainbowr`

Fit a GS model using RAINBOWR package

Description

Fit a GS model using RAINBOWR package

Usage

fit_with_rainbowr(pheno, K)

Arguments

Argument	Description
`pheno`	1 dimensional vector of phenotypic values
`K`	list of 1 or 2 variance covariance matrices used for the random effect.

Details

This is a wrapper around RAINBOWR::EM3.general() see ?RAINBOWR::EM3.general() for more information

Value

list of 3 elements

logL: Value of log-likelihood of the model (cf. ?RAINBOWR::EM3.general())
blups: length(pheno) by length(K) matrix of the blups of the individuals (one random effects per column).
intercept: value of the intercept of the model

`calc_mark_eff`

Calculate markers effects from the BLUPS, the genetic matrix and the variance covariance matrix of the blups (ie. genetic relationship matrices)

Description

Calculate markers effects from the BLUPS, the genetic matrix and the variance covariance matrix of the blups (ie. genetic relationship matrices)

Usage

calc_mark_eff(geno_mat, rel_mat, blups)

Arguments

Argument	Description
`geno_mat`	`n_inds` by `n_marker` genomic matrix
`rel_mat`	`n_inds` by `n_inds` relationship matrix associated to `geno_mat`
`blups`	`n_inds` vector of the individuals blups effect.

Details

Calculation is made by

$$(\frac{X}{n_{mark}})(\frac{X}{n_{mark}})'Z^{-1}B$$

with:

$X$ the genetic matrix
$n_ mark $ the number of markers (ie. number of columns of $X$)
$Z$ the relationship matrix of $X$
$B$ the blups of the individuals

Value

vector of the estimated markers effects

`train_gs_model`

Train GS model optionally with dominance

Description

Train GS model optionally with dominance

Usage

train_gs_model(pheno, geno, with_dominance = FALSE)

Arguments

Argument	Description
`pheno`	1 column data.frame of the phenotype
`geno`	bed.matrix returned by `readGenoData()`
`with_dominance`	(default = FALSE) control if the model should include dominance effects

Details

IMPORTANT ! It will return the markers effects for the genotypes encoded:

in allele dose (0, 1, 2) for the additive effects
as (0, 1, 0) for the dominance effects

Value

list of 2 elements:

intercept: the intercept of the model
eff: data.frame of 2 columns additive and dominance with their corresponding markers effects

`predict_gs_model`

Make GS predictions

Description

Make GS predictions

Usage

predict_gs_model(geno, estim_mark_eff)

Arguments

Argument	Description
`geno`	bed.matrix returned by `readGenoData()` on wich we want to make prediction
`estim_mark_eff`	(list, output of `train_gs_model()`) estimated markers effects and intercept.

Value

data.frame of one column containing the prediction

`cross_validation_evaluation`

Repeated K-Folds cross-validation of a GS model

Description

Repeated K-Folds cross-validation of a GS model

Usage

cross_validation_evaluation(
  pheno,
  geno,
  with_dominance = TRUE,
  n_folds = 10,
  n_repetitions = 5
)

Arguments

Argument	Description
`pheno`	1 column data.frame of the phenotype
`geno`	bed.matrix returned by `readGenoData()`
`with_dominance`	(default = FALSE) control if the model should include dominance effects
`n_folds`	(default `10`) number of folds for each repetition
`n_repetitions`	(default `5`) number of repetition

Value

list of 2 elements:

predictions: data.frame of the predicted values during the cross-validation. Available columns:
ind individual id
actual phenotype value in the training data
predicted predicted values
fold cross-validation fold id
repetition cross-validation repetition id
metrics: data.frame of the calculated model metrics during the cross-validation. Available columns are:
fold cross-validation fold id
repetition cross-validation repetition id
... values of the metrics returned by get_model_metrics() (one column per returned list's elements with the same name)

`get_model_metrics`

Calculate model metrics

Description

Calculate model metrics

Usage

get_model_metrics(actual, predictions)

Arguments

Argument	Description
`actual`	vector of actual values
`predictions`	vector of model's predicted values

Value

list of metrics values:

rmse root mean square error
corel_pearson Pearson's corelation coefficient
corel_spearman Spearman's corelation coefficient
r2 R squared

`check_dominance_model_is_applicable`

Check if a dominance model is applicable with the provided genetic data

Description

Check if a dominance model is applicable with the provided genetic data

Usage

check_dominance_model_is_applicable(dominance, homozygous_threshold = 0.95)

Arguments

Argument	Description
`dominance`	list returned by
`homozygous_threshold`	A threshold used to identify individuals or SNPs with a homozygosity proportion exceeding this value. These will be counted and reported in the error message if applicable. This parameter does not influence the function's behavior, only the error message it can raise.

Details

The dominance model need the dominance relationship matrix to be invertible in order to be able to calculate the dominance effects. This function will return an error if it is not the case. The error will contain information about the homozygousity of the markers and individuals as if the data have too many homozygous individuals/markers it will probably not be suited for a dominance model. The ``

Value

TRUE or raise an engineError

`gwas`

perform GWAS analysis

Description

perform GWAS analysis

Usage

gwas(
  data,
  trait,
  test,
  fixed = 0,
  response = "quantitative",
  thresh_maf,
  thresh_callrate
)

Arguments

Argument	Description
`data`	List return by `prepareDta` function
`trait`	Chraracter of length 1, name of the trait to analyze. Could be a column name of the phenotypic file
`test`	Which test to use. Either `"score"`, `"wald"` or `"lrt"`. For binary phenotypes, test = `"score"` is mandatory. For more information about this parameters see: `??gaston::association.test`
`fixed`	Number of Principal Components to include in the model with fixed effect (for test = `"wald"` or `"lrt"`). Default value is 0. For more information about this parameters see: `??gaston::association.test`
`response`	Character of length 1, Either "quantitative" or "binary". Is the trait a quantitative or a binary phenotype? Default value is "quantitative"
`thresh_maf`	Threshold for filtering markers. Only markers with minor allele frequency > `thresh_maf` will be kept.
`thresh_callrate`	Threshold for filtering markers. Only markers with a callrate > `thresh_callrate` will be kept.

Details

For the calculation, the genetic relationship matrix need to be calculated. This is done based on the genetic matrix standardized by the the genetic mean "mu" and the genetic variance "sigma", after having filtering according to the thresh_maf and thresh_callrate.

Value

data.frame

`adjustPval`

Adjust P-values for Multiple Comparisons

Description

Adjust P-values for Multiple Comparisons

Usage

adjustPval(p, adj_method, thresh_p = NULL)

Arguments

Argument	Description
`adj_method`	correction method: "holm", "hochberg", "bonferroni", "BH", "BY", "fdr", "none" (see ?p.adjust for more details)
`thresh_p`	optional value of the p value significant threshold (default NULL)
`vector`	of p-values

Details

The method "hommel" is not implemented because it is too long to calculate.

Value

list of two elements: "p_adj" vector of adjusted p values, "thresh_adj" the adjusted threshold (if thresh_p is preovided, NULL if not)

`Logger`

R6 class use to log messages in this engine's function

Description

R6 class use to log messages in this engine's function

Examples

## ------------------------------------------------
## Method `Logger$new`
## ------------------------------------------------

mylogger <- Logger$new(context = NULL)

`run_gwas`

Run GWAS analysis

Description

Run GWAS analysis

Usage

run_gwas(
  genoFile = NULL,
  phenoFile = NULL,
  genoUrl = NULL,
  phenoUrl = NULL,
  trait,
  test,
  fixed = NULL,
  response = "quantitative",
  thresh_maf,
  thresh_callrate,
  outFile = tempfile(fileext = ".json")
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoFile`	path of the phenotypic data file (`csv` file). Individuals' name should be the first column of the file and no duplication is allowed.
`genoUrl`	url of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoUrl`	url of the phenotypic data file (`csv` file) Individuals' name should be the first column of the file and no duplication is allowed.
`trait`	Chraracter of length 1, name of the trait to analyze. Must be a column name of the phenotypic file.
`test`	Which test to use. Either `"score"`, `"wald"` or `"lrt"`. For binary phenotypes, test = `"score"` is mandatory. For more information about this parameters see: `??gaston::association.test`
`fixed`	Number of Principal Components to include in the model with fixed effect (for test = `"wald"` or `"lrt"`). Default value is 0. For more information about this parameters see: `??gaston::association.test`
`response`	Character of length 1, Either "quantitative" or "binary". Is the trait a quantitative or a binary phenotype? Default value is "quantitative"
`thresh_maf`	Threshold for filtering markers. Only markers with minor allele frequency > `thresh_maf` will be kept.
`thresh_callrate`	Threshold for filtering markers. Only markers with a callrate > `thresh_callrate` will be kept.
`outFile`	path of the output file. If `NULL`, the output will not be written in any file. By default write in an tempoary `.json` file.

Value

list with 3 elements gwasRes for the results of the gwas analysis in json, metadata a list of metadata of these analysis and file path of the json file containing the results

`draw_manhattanPlot`

Draw a Manhattan Plot

Description

Draw a Manhattan Plot

Usage

draw_manhattanPlot(
  gwasFile = NULL,
  gwasUrl = NULL,
  adj_method = "bonferroni",
  thresh_p = 0.05,
  chr = NA,
  interactive = TRUE,
  filter_pAdj = 1,
  filter_nPoints = Inf,
  filter_quant = 1,
  outFile = tempfile()
)

Arguments

Argument	Description
`gwasFile`	path of the gwas result data file (json file)
`gwasUrl`	url of the gwas result data file (json file)
`adj_method`	correction method: "holm", "hochberg", "bonferroni", "BH", "BY", "fdr", "none" (see ?p.adjust for more details)
`thresh_p`	p value significant threshold (default 0.05)
`chr`	name of the chromosome to show (show all if NA)
`interactive`	[bool] should the plot be interactive (the default)
`filter_pAdj`	[numeric] threshold to remove points with pAdj < filter_pAdj from the plot (default no filtering)
`filter_nPoints`	[numeric] threshold to keep only the filter_nPoints with the lowest p-values for the plot (default no filtering)
`filter_quant`	[numeric] threshold to keep only the filter_quant*100 % of the points with the lowest p-values for the plot (default no filtering)
`outFile`	path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an tempoary file.

Details

If several filtering rules are given, the filtering process apply the filtering process sequentially (this lead to having the same result that if only the strongest rules were given). Moreover, the number of points kept for the plot will be display in the plot title.

Value

plotly graph if interactive is TRUE, or NULL if not.

`run_resAdjustment`

Adjust GWAS p-values

Description

Adjust GWAS p-values

Usage

run_resAdjustment(
  gwasFile = NULL,
  gwasUrl = NULL,
  adj_method = "bonferroni",
  filter_pAdj = 1,
  filter_nPoints = Inf,
  filter_quant = 1,
  outFile = tempfile(fileext = ".json")
)

Arguments

Argument	Description
`gwasFile`	path of the gwas result data file (json file)
`gwasUrl`	url of the gwas result data file (json file)
`adj_method`	correction method: "holm", "hochberg", "bonferroni", "BH", "BY", "fdr", "none" (see ?p.adjust for more details)
`filter_pAdj`	[numeric] threshold to remove points with pAdj < filter_pAdj from the plot (default no filtering)
`filter_nPoints`	[numeric] threshold to keep only the filter_nPoints with the lowest p-values for the plot (default no filtering)
`filter_quant`	[numeric] threshold to keep only the filter_quant*100 % of the points with the lowest p-values for the plot (default no filtering)
`outFile`	path of the output file. If `NULL`, the output will not be written in any file. By default write in an tempoary `.json` file.

Value

list with 3 elements gwasAdjusted for the results of the gwas analysis in json with adjusted p-values, metadata a list of metadata of the gwas analysis in json with adjusted p-values, and file path of the json file containing the results (if dir is not NULL)

`draw_ldPlot`

Draw an LD Plot

Description

Draw an LD Plot

Usage

draw_ldPlot(
  genoFile = NULL,
  genoUrl = NULL,
  from,
  to,
  n_max = 50,
  outFile = tempfile(fileext = ".png")
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`from`	lower bound of the range of SNPs for which the LD is computed
`to`	upper bound of the range of SNPs for which the LD is computed
`n_max`	maximum number of marker to show (to avoid unreadable plot) created. By default write in an tempoary `.png` file.
`outFile`	path of the png file to save the plot. If `NULL`, the image file will not be

Value

path of the created file (or NULL if file is NULL)

`calc_pedRelMat`

Calculate pedigree relationship matrix

Description

Calculate pedigree relationship matrix

Usage

calc_pedRelMat(
  pedFile = NULL,
  pedUrl = NULL,
  unknown_string = "",
  header = TRUE,
  outFile = tempfile(fileext = ".csv"),
  outFormat = tools::file_ext(outFile)
)

Arguments

Argument	Description
`pedFile`	path of the pedigree data file (`csv` file).
`pedUrl`	url of the pedigree data file (`csv` file).
`unknown_string`	[default: ""] a character vector of strings which are to be interpreted as "unknown parent". By default: missing value in the file.
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 will be interpreted as the individual id, column 2 as the first parent, column 3 as the second parent.
`outFile`	path of the output file. If `NULL`, the output will not be written in any file. By default write in an tempoary `.json` file.
`outFormat`	Format of the output file, either `csv` or `json`. by default it will use the file extension of `outfile`.

Details

For csv output, the file will include some metadata lines (starting by a # symbol), a header and the row ids in its first column.

Value

list with 3 elements relMat the relationship matrix, metadata a list of metadata of these analysis (pedigree fingerprint, number of individuals, creation time) and file path of the file containing the results.

`calc_genoRelMat`

Calculate genomic relationship matrix

Description

Calculate genomic relationship matrix

Usage

calc_genoRelMat(
  genoFile = NULL,
  genoUrl = NULL,
  outFile = tempfile(fileext = ".csv"),
  outFormat = tools::file_ext(outFile)
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`genoUrl`	url of the geno data file (`.vcf` or `.vcf.gz` file)
`outFile`	path of the output file. If `NULL`, the output will not be written in any file. By default write in an tempoary `.json` file.
`outFormat`	Format of the output file, either `csv` or `json`. by default it will use the file extension of `outfile`.

Details

For csv output, the file will include some metadata lines (starting by a # symbol), a header and the row ids in its first column.

Value

list with 3 elements relMat the relationship matrix, metadata a list of metadata of these analysis (pedigree fingerprint, number of individuals, creation time) and file path of the file containing the results.

`calc_combinedRelMat`

Combined (pedigree + genomic) Relationship Matrix

Description

Correct a pedigree relationship matrix by using genomic relationship matrix.

Usage

calc_combinedRelMat(
  pedRelMatFile = NULL,
  pedRelMatUrl = NULL,
  genoRelMatFile = NULL,
  genoRelMatUrl = NULL,
  method = "Legarra",
  tau = NULL,
  omega = NULL,
  outFile = tempfile(fileext = ".csv"),
  outFormat = tools::file_ext(outFile)
)

Arguments

Argument	Description
`pedRelMatFile`	path of a pedigree relationship matrix generated by the the engine.
`pedRelMatUrl`	url of a pedigree relationship matrix generated by the the engine.
`genoRelMatFile`	path of a genomic relationship matrix generated by the the engine.
`genoRelMatUrl`	url of a genomic relationship matrix generated by the the engine.
`method`	method to use, either "Legarra" or "Martini"
`tau`	tau parameter of the Martini's method
`omega`	omega parameter of the Martini's method
`outFile`	path of the output file. If `NULL`, the output will not be written in any file. By default write in an tempoary `.json` file.
`outFormat`	Format of the output file, either `csv` or `json`. by default it will use the file extension of `outfile`.

Details

This method correct the pedigree matrix with the genomic relationship matrix. Therefore, individuals in the genomic relationship matrix not in the pedigree relationship matrix will be ignored.

Using the Martini's method with tau=1, and omega=1 is equivalent of Legarra's method.

For csv output, the file will include some metadata lines (starting by a # symbol), a header and the row ids in its first column.

Value

list with 3 elements relMat the relationship matrix, metadata a list of metadata of these analysis (pedigree fingerprint, number of individuals, creation time) and file path of the file containing the results.

References

Martini, JW, et al. 2018 The effect of the H-1 scaling factors tau and omega on the structure of H in the single-step procedure. Genetics Selection Evolution 50(1), 16

Legarra, A, et al. 2009 A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663

`draw_relHeatmap`

Draw a heatmap of a relationship matrix

Description

Draw a heatmap of a relationship matrix

Usage

draw_relHeatmap(
  relMatFile = NULL,
  relMatUrl = NULL,
  format = NULL,
  interactive = TRUE,
  outFile = tempfile()
)

Arguments

Argument	Description
`relMatFile`	path of a file generated by the function `saveRelMat`
`relMatUrl`	url of a file generated by the function `saveRelMat`
`interactive`	[bool] should the plot be interactive (the default) or not
`outFile`	path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an tempoary file.

Value

plotly graph if interactive is TRUE, or NULL if not.

`draw_pedNetwork`

Draw interactive pedigree network

Description

Draw interactive pedigree network

Usage

draw_pedNetwork(
  pedFile = NULL,
  pedUrl = NULL,
  unknown_string = "",
  header = TRUE,
  outFile = tempfile(fileext = ".html")
)

Arguments

Argument	Description
`pedFile`	path of the pedigree data file (`csv` file).
`pedUrl`	url of the pedigree data file (`csv` file).
`unknown_string`	[default: ""] a character vector of strings which are to be interpreted as "unknown parent". By default: missing value in the file.
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 will be interpreted as the individual id, column 2 as the first parent, column 3 as the second parent.
`outFile`	path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an temporary file.

Value

plotly graph if interactive is TRUE, or NULL if not.

`crossingSimulation`

Simulate the genotypes of offspring given the parent genotypes.

Description

Simulate the genotypes of offspring given the parent genotypes.

Usage

crossingSimulation(
  genoFile = NULL,
  genoUrl = NULL,
  crossTableFile = NULL,
  crossTableUrl = NULL,
  SNPcoordFile = NULL,
  SNPcoordUrl = NULL,
  nCross = 30,
  outFile = tempfile(fileext = ".vcf.gz")
)

Arguments

Argument	Description
`genoFile`	phased VCF file path (ext `.vcf` or `.vcf.gz`)
`genoUrl`	url of a phased VCF file path (ext `.vcf` or `.vcf.gz`)
`crossTableFile`	path of the crossing table data file (`csv` file of 2 or 3 columns). It must contain the names of the variables as its first line. The column 1 and 2 will be interpreted as the parents ids. The optional third column will be interpreted as the offspring base name.
`crossTableUrl`	URL of a crossing table file
`SNPcoordFile`	path of the SNPs coordinates file (`csv` file). This `.csv` file should have 4 named columns: - `chr`: Chromosome holding the SNP (mandatory) - `physPos`: SNP physical position on the chromosome - `linkMapPos`: SNP linkage map position on the chromosome in Morgan (mandatory) - `SNPid`: SNP's IDs
`SNPcoordUrl`	URL of a SNP coordinate file
`nCross`	number of cross to simulate for each parent pair defined in the crossing table.
`outFile`	path of the `.vcf.gz` file containing the simulated genotypes of the offspring. It must end by `.vcf.gz`. By default write in an temporary file. For SNPcoordFile/Url, column `physPos` is optional except in some particular case (see below). If this column is provided (or contain only missing values), it should exactly match the physical positions of the SNP specified in the VCF file. If `SNPid` columns is missing or have missing values, the SNPid will be automatically imputed using the convention `chr@physPos` therefore columns `chr` and `physPos` should not have any missing values in this case.

Value

path of the .vcf.gz file containing the simulated genotypes of the offspring.

`calc_progenyBlupEstimation`

Calculate progenies BLUPs variance and expected values based on parents' genotype and markers effects

Description

Calculate progenies BLUPs variance and expected values based on parents' genotype and markers effects

Usage

calc_progenyBlupEstimation(
  genoFile = NULL,
  genoUrl = NULL,
  crossTableFile = NULL,
  crossTableUrl = NULL,
  SNPcoordFile = NULL,
  SNPcoordUrl = NULL,
  markerEffectsFile = NULL,
  markerEffectsUrl = NULL,
  outFile = tempfile(fileext = ".json")
)

Arguments

Argument	Description
`genoFile`	phased VCF file path (ext `.vcf` or `.vcf.gz`)
`genoUrl`	url of a phased VCF file path (ext `.vcf` or `.vcf.gz`)
`crossTableFile`	path of the crossing table data file (`csv` file of 2 or 3 columns). It must contain the names of the variables as its first line. The column 1 and 2 will be interpreted as the parents ids. The optional third column will be interpreted as the offspring base name.
`crossTableUrl`	URL of a crossing table file
`SNPcoordFile`	path of the SNPs coordinates file (`csv` file). This `.csv` file should have 4 named columns: - `chr`: Chromosome holding the SNP - `physPos`: SNP physical position on the chromosome - `linkMapPos`: SNP linkage map position on the chromosome in Morgan - `SNPid`: SNP's IDs
`SNPcoordUrl`	URL of a SNP coordinate file
`markerEffectsFile`	path of the marker effects file (`csv` or `json` file).
`markerEffectsUrl`	URL of a marker effect file
`outFile`	`.json` file path where to save the data. If the file already exists, it will be overwritten. For SNPcoordFile/Url, column `physPos` is optional except in some particular case (see below). If this column is provided (or contain only missing values), it should exactly match the physical positions of the SNP specified in the VCF file. If `SNPid` columns is missing or have missing values, the SNPid will be automatically imputed using the convention `chr@physPos` therefore columns `chr` and `physPos` should not have any missing values in this case.

Value

data.frame containing the calculations results

`draw_progBlupsPlot`

Draw a plot of the progenies BLUPs' expected values with error bars

Description

X axis is the crosses, and Y axis the blups. The points are located at the expected value and the error bar length is the standard deviation.

Usage

draw_progBlupsPlot(
  progEstimFile = NULL,
  progEstimUrl = NULL,
  errorBarInterval = 0.95,
  y_axisName = "Genetic values",
  sorting = "alpha",
  trait = NULL,
  outFile = tempfile(fileext = ".html")
)

Arguments

Argument	Description
`progEstimFile`	path of the progeny BLUP estimation file generated by r-geno-tools-engine containing the blup estimations of the progenies of some crosses (`json` file).
`progEstimUrl`	URL of a progeny BLUP estimation file
`errorBarInterval`	length of XX% interval of interest represented by the error bars (default=0.95)
`y_axisName`	The Y axis name (default = "genetic values")
`sorting`	method to sort the individuals (X axis) can be: - "asc": sort the BLUP expected value in ascending order (from left to right) - "dec": sort the BLUP expected value in decreasing order (from left to right) - any other value will sort the individuals in alphabetical order (from left to right)
`outFile`	outFile path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an temporary file.

Value

plotly graph

`draw_progBlupsPlot_2traits`

Draw a plotly graph of blups data for 2 traits

Description

The points are located at the expected value and the ellipses size represent the confidenceLevel prediction interval.

Usage

draw_progBlupsPlot_2traits(
  progEstimFile = NULL,
  progEstimUrl = NULL,
  x_trait,
  y_trait,
  confidenceLevel = 0.95,
  x_suffix = "",
  y_suffix = "",
  ellipses_npoints = 100,
  outFile = tempfile(fileext = ".html")
)

Arguments

Argument	Description
`progEstimFile`	path of the progeny BLUP estimation file generated by r-geno-tools-engine containing the blup estimations of the progenies of some crosses (`json` file).
`progEstimUrl`	URL of a progeny BLUP estimation file
`x_trait`	name of the trait to show on the x axis
`y_trait`	name of the trait to show on the y axis
`confidenceLevel`	level of the prediction ellipses (default 0.95, ie 95 % ellypses)
`x_suffix`	suffix to add to the x axis's name
`y_suffix`	suffix to add to the y axis's name
`ellipses_npoints`	number of points used to draw the ellipses (default 100)
`outFile`	outFile path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an temporary file.

Value

plotly graph

`train_gs_model_main`

Train GS model

Description

Train GS model

Usage

train_gs_model_main(
  genoFile = NULL,
  phenoFile = NULL,
  genoUrl = NULL,
  phenoUrl = NULL,
  trait,
  with_dominance,
  thresh_maf,
  outFile = tempfile(fileext = ".json")
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoFile`	path of the phenotypic data file (`csv` file). Individuals' name should be the first column of the file and no duplication is allowed.
`genoUrl`	url of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoUrl`	url of the phenotypic data file (`csv` file) Individuals' name should be the first column of the file and no duplication is allowed.
`trait`	Chraracter of length 1, name of the trait to analyze. Must be a column name of the phenotypic file.
`with_dominance`	should the model include dominance effects
`thresh_maf`	threshold to keep only markers with minor allele frequency greater than `thresh_maf`.
`outFile`	paht of the `.json` file where to save the model's estimated markers effects.

`predict_gs_model_main`

Make phenotypic prediction using a markers effects

Description

Make phenotypic prediction using a markers effects

Usage

predict_gs_model_main(
  genoFile = NULL,
  genoUrl = NULL,
  markerEffectsFile = NULL,
  markerEffectsUrl = NULL,
  outFile = tempfile(fileext = ".csv")
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`genoUrl`	url of the geno data file (`.vcf` or `.vcf.gz` file)
`markerEffectsFile`	path of the marker effects file (`csv` or `json` file).
`markerEffectsUrl`	URL of a marker effect file
`outFile`	`.csv` file path where to save the predictions. If the file already exists, it will be overwritten.

`cross_validation_evaluation_main`

Evaluate a model with repeated cross validation

Description

Evaluate a model with repeated cross validation

Usage

cross_validation_evaluation_main(
  genoFile = NULL,
  phenoFile = NULL,
  genoUrl = NULL,
  phenoUrl = NULL,
  trait,
  n_folds = 10,
  n_repetitions = 5,
  with_dominance,
  thresh_maf,
  outFile = tempfile(fileext = ".json")
)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoFile`	path of the phenotypic data file (`csv` file). Individuals' name should be the first column of the file and no duplication is allowed.
`genoUrl`	url of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoUrl`	url of the phenotypic data file (`csv` file) Individuals' name should be the first column of the file and no duplication is allowed.
`trait`	Chraracter of length 1, name of the trait to analyze. Must be a column name of the phenotypic file.
`n_folds`	number of fold for each cross-validation
`n_repetitions`	number of cross-validation repetitions
`with_dominance`	should the model include dominance effects
`thresh_maf`	threshold to keep only markers with minor allele frequency greater than `thresh_maf`.
`outFile`	paht of the `.json` file where to save the evaluation results

`draw_evaluation_plot`

Draw the evaluation result plot

Description

Draw the evaluation result plot

Usage

draw_evaluation_plot(
  evaluationFile = NULL,
  outFile = tempfile(fileext = ".html")
)

Arguments

Argument	Description
`evaluationFile`	path of the evaluation result file generated by `save_GS_evaluation` function
`outFile`	path of the file containing the plot. If `NULL`, the output will not be written in any file. By default write in an temporary file.

`manPlot`

create manhatan plot

Description

create manhatan plot

Usage

manPlot(
  gwas,
  adj_method,
  thresh_p = 0.05,
  chr = NA,
  title = "Manhattan Plot",
  filter_pAdj = 1,
  filter_nPoints = Inf,
  filter_quant = 1,
  interactive = TRUE
)

Arguments

Argument	Description
`gwas`	[data.frame] output of the gwas function
`adj_method`	correction method: "holm", "hochberg", "bonferroni", "BH", "BY", "fdr", "none" (see ?p.adjust for more details)
`thresh_p`	p value significant threshold (default 0.05)
`chr`	[char] name of the chromosome to show (show all if NA)
`title`	[char] Title of the plot. Default is "Manhattan Plot"
`filter_pAdj`	[numeric] threshold to remove points with pAdj < filter_pAdj from the plot (default no filtering)
`filter_nPoints`	[numeric] threshold to keep only the filter_nPoints with the lowest p-values for the plot (default no filtering)
`filter_quant`	[numeric] threshold to keep only the filter_quant*100 % of the points with the lowest p-values for the plot (default no filtering)
`interactive`	[bool] should the plot be interactive (the default) or not

Details

If several filtering rules are given, the filtering process apply the filtering process sequentially (this lead to having the same result that if only the strongest rules were given). Moreover, the number of points kept for the plot will be display in the plot title.

Value

plotly graph if interactive is TRUE, or NULL if not.

`LDplot`

writeLDplot Compute r2 Linkage Disequilibrium (LD) between given SNPs and return a plot

Description

writeLDplot Compute r2 Linkage Disequilibrium (LD) between given SNPs and return a plot

Usage

LDplot(geno, from, to, file = tempfile(fileext = ".png"))

Arguments

Argument	Description
`geno`	[bed.matrix] geno data return by function `readGenoData` or `downloadGenoData`.
`from`	lower bound of the range of SNPs for which the LD is computed
`to`	upper bound of the range of SNPs for which the LD is computed
`file`	path of the png file to save the plot. If `NULL`, the image file will not be created. By default write in an tempoary `.png` file.

Details

from should be lower than to, and the maximum ranger size is 50. (In order to get a readable image). If write is TRUE, the function will write the plot in a png file, else it will plot it.

Value

null if dir is NULL, else the path of the png file.

`relMatHeatmap`

Heatmap of a relationship matrix

Description

Heatmap of a relationship matrix

Usage

relMatHeatmap(relMat, interactive = TRUE)

Arguments

Argument	Description
`relMat`	relationship matrix return by `pedRelMat`
`interactive`	[bool] should the plot be interactive (the default) or not

Value

plotly graph if interactive is TRUE, or NULL if not.

`pedNetwork`

Draw an interactive Pedigree network

Description

Draw an interactive Pedigree network

Usage

pedNetwork(ped)

Arguments

Argument	Description
`ped`	List return by `readPedData` function

Value

a forceNetwork object (htmlwidget)

`plotBlup_1trait`

Draw a plotly graph of blups data for 1 trait

Description

X axis is the crosses, and Y axis the blups. The points are located at the expected value and the error bar length is the standard deviation.

Usage

plotBlup_1trait(
  blupDta,
  sorting = "alpha",
  y_axisName = NULL,
  errorBarInterval = 0.95,
  trait
)

Arguments

Argument	Description
`blupDta`	list of data.frame of 4 columns: "ind1", "ind2", "blup_exp", "blup_var"
`sorting`	method to sort the individuals (X axis) can be: - "asc": sort the BLUP expected value in ascending order (from left to right) - "dec": sort the BLUP expected value in decreasing order (from left to right) - any other value will sort the individuals in alphabetical order (from left to right)
`y_axisName`	Name of the Y axis (default = `trait`)
`errorBarInterval`	length of XX% interval of interest represented by the error bars (default=0.95)
`trait`	name of the trait to plot. This should be a name of the blupDta list. (optional if only one trait in `blupDta`, it will be set to the name of this trait)

Value

plotly graph

`plotBlup_2traits`

Draw a plotly graph of blups data for 2 traits

Description

The points are located at the expected value and the ellipses size represent the confidenceLevel prediction interval.

Usage

plotBlup_2traits(
  blupDta,
  x_trait,
  y_trait,
  confidenceLevel = 0.95,
  x_suffix = "",
  y_suffix = "",
  ellipses_npoints = 100
)

Arguments

Argument	Description
`blupDta`	list returned by calc_progenyBlupEstimation
`x_trait`	name of the trait to show on the x axis
`y_trait`	name of the trait to show on the y axis
`confidenceLevel`	level of the prediction ellipses (default 0.95, ie 95 % ellypses)
`x_suffix`	suffix to add to the x axis's name
`y_suffix`	suffix to add to the y axis's name
`ellipses_npoints`	number of points used to draw the ellipses (default 100)

Value

plotly graph

`evaluation_plot`

Draw a plotly graph of a GS model cross-validation evaluation

Description

This plots is composed of several subplots:

Observed vs Predicted scatter plot for all the cross-validation folds
Horizontal box plots of models metrics calculated during the cross-validation

Usage

evaluation_plot(evaluation_results)

Arguments

Argument	Description
`evaluation_results`	list returned by `cross_validation_evaluation()`

Value

plotly graph

`calcRecombRate`

Calculate the recombination rate matrix for each couple of SNP

Description

The recombination rate is alculated using the "haldane inverse" function

Usage

calcRecombRate(SNPcoord)

Arguments

Argument	Description
`SNPcoord`	SNP coordinate data.frame return by `readSNPcoord`

Value

named list of matrices. List names are the chromosomes' names. Matrices' row and columns names are the SNP ids (of the corresponding chromosome). Matrices values are the recombination rate between the corresponding SNPs.

`calcProgenyGenetCovar`

Calculate the genetic variance-covariance matrix of the progenies of 2 given parents

Description

Calculate the genetic variance-covariance matrix of the progenies of 2 given parents

Usage

calcProgenyGenetCovar(SNPcoord, r, haplo, p1.id, p2.id)

Arguments

Argument	Description
`SNPcoord`	SNP coordinate data.frame return by `readSNPcoord`
`r`	recombination rate matrices return by `calcRecombRate`
`haplo`	haplotypes of individuals ("haplotypes" element of the list return by `readPhasedGeno` function)
`p1.id`	id of the first parent
`p2.id`	id of the second parent

Value

named list of matrices. List names are the chromosomes' names. Matrices' row and columns names are the SNP ids (of the corresponding chromosome). Matrices values are the genetic covariance between the corresponding SNPs for the progeny of the given parents.

`calcProgenyBlupVariance`

Calculate the BULP variance of the progeny

Description

Calculate the BULP variance of the progeny

Usage

calcProgenyBlupVariance(SNPcoord, markerEffects, geneticCovar)

Arguments

Argument	Description
`SNPcoord`	SNP coordinate data.frame return by `readSNPcoord`
`geneticCovar`	list of the genetic variance covariance matrices return by `calcProgenyGenetCovar`
`makrerEffects`	(output of `extract_additive_effects` function) list of 2 elements: `intercept`: named vector of the intercepts `SNPeffects`: data.frame of the additive effects, 1 columns per phenotype with the marker ids as row names.

Value

numeric

`calcProgenyBlupExpected`

Calculate the BULP expected values of the progeny

Description

Calculate the BULP expected values of the progeny

Usage

calcProgenyBlupExpected(SNPcoord, haplo, p1.id, p2.id, markerEffects)

Arguments

Argument	Description
`SNPcoord`	SNP coordinate data.frame return by `readSNPcoord`
`haplo`	haplotypes of individuals ("haplotypes" element of the list return by `readPhasedGeno` function)
`p1.id`	id of the first parent
`p2.id`	id of the second parent
`makrerEffects`	(output of `extract_additive_effects` function) list of 2 elements: `intercept`: named vector of the intercepts `SNPeffects`: data.frame of the additive effects, 1 columns per phenotype with the marker ids as row names.

Value

list for each trait with a list with

sum the global expected value for the trait (taking in account the intercept)
by_chr list of the expected value for each chromosome (NOT taking in account the intercept)

`calcProgenyBlupCovariance`

Calculate the variance covariance matrix of the progeny's blups for several traits

Description

Calculate the variance covariance matrix of the progeny's blups for several traits

Usage

calcProgenyBlupCovariance(
  SNPcoord,
  r,
  haplo,
  p1.id,
  p2.id,
  markerEffects,
  blupExpectedValues
)

Arguments

Argument	Description
`SNPcoord`	SNP coordinate data.frame return by `readSNPcoord`
`r`	recombination rate matrices return by `calcRecombRate`
`haplo`	haplotypes of individuals ("haplotypes" element of the list return by `readPhasedGeno` function)
`p1.id`	id of the first parent
`p2.id`	id of the second parent
`blupExpectedValues`	output of `calcProgenyBlupExpected`
`makrerEffects`	(output of `extract_additive_effects` function) list of 2 elements: `intercept`: named vector of the intercepts `SNPeffects`: data.frame of the additive effects, 1 columns per phenotype with the marker ids as row names.

Value

matrix

`downloadGenoData`

Download geno data

Description

Download geno data

Usage

downloadGenoData(url)

Arguments

Argument	Description
`url`	url of the geno data file (.vcf.gz file)

Value

gaston::bed.matrix

`downloadPhasedGeno`

Download phased geno data

Description

Download phased geno data

Usage

downloadPhasedGeno(url)

Arguments

Argument	Description
`url`	url of the geno data file (.vcf.gz file)

Value

list of 2: haplotypes a matrix of the individuals haplotypes and SNPcoord, data frame of the SNP coordinates.

`downloadPhenoData`

Download phenotypic data

Description

Download phenotypic data

Usage

downloadPhenoData(url)

Arguments

Argument	Description
`url`	url of the phenotypic data file (csv file)

Details

The individuals' names must be on the first column. No duplication is allowed.

Value

data.frame

`downloadData`

Download and prepare data for GWAS analysis

Description

Download and prepare data for GWAS analysis

Usage

downloadData(genoUrl, phenoUrl)

Arguments

Argument	Description
`genoUrl`	url of the geno data file (.vcf.gz file)
`phenoUrl`	url of the phenotypic data file (csv file)

Value

List

`downloadGWAS`

Download a gwas reults

Description

Download a gwas reults

Usage

downloadGWAS(url)

Arguments

Argument	Description
`url`	url of the result data file (json file)

Value

data.frame

`downloadPedData`

Download pedigree data

Description

Download pedigree data

Usage

downloadPedData(url, unknown_string = "", header = TRUE)

Arguments

Argument	Description
`url`	url of the pedigree data file (`csv` file).
`unknown_string`	[default: ""] a character vector of strings which are to be interpreted as "unknown parent". By default: missing value in the file.
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 will be interpreted as the individual id, column 2 as the first parent, column 3 as the second parent.

Value

List of 2: data pedigree data, graph "igraph" object of the pedigree graph.

`downloadRelMat`

Download relationship matrix

Description

Download relationship matrix

Usage

downloadRelMat(url, format = tools::file_ext(url))

Arguments

Argument	Description
`url`	url of the result data file (csv or json file)
`format`	format of the input file. Either "csv" or "json" (optional, by default it will use "json").

Value

matrix

`downloadCrossTable`

Download crossing table

Description

Download crossing table

Usage

downloadCrossTable(url, header = TRUE)

Arguments

Argument	Description
`url`	url of the crossing table data file (`csv` file of 2 or 3 columns).
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 and 2 will be interpreted as the parents id. The optional third column will be interpreted as the offspring base name.

Value

data.frame with the crossing table information.

`downloadSNPcoord`

download SNP coordinates .csv file

Description

download SNP coordinates .csv file

Usage

downloadSNPcoord(url)

Arguments

Argument Description

url url of the SNPs coordinates file (csv file). This .csv file can have 4 named columns: - chr: Chromosome holding the SNP - physPos: SNP physical position on the chromosome - linkMapPos: SNP linkage map position on the chromosome in Morgan - SNPid: SNP's IDs If SNPid columns is missing or have missing values, the SNPid will be automatically imputed using the convention chr@physPos therefore columns chr and physPos should not have any missing values

Value

data.frame of 4 columns: 'chr', 'physPos', 'linkMapPos', 'SNPid'

`downloadMarkerEffects`

Download marker effects file

Description

Download marker effects file

Usage

downloadMarkerEffects(url)

Arguments

Argument	Description
`url`	url of the marker effects file (`csv`, or `json` file).

Value

data.frame of 1 columns named effects with the marker ids as row names.

`downloadProgBlupEstim`

Download progeny BLUP estimation file

Description

Download progeny BLUP estimation file

Usage

downloadProgBlupEstim(url)

Arguments

Argument	Description
`url`	url of the progeny BLUP estimation file generated by r-geno-tools-engine containing the blup estimations of the progenies of some crosses (`json` file).

Value

data.frame of 4 columns named "ind1", "ind2", "blup_var", "blup_exp"

`readGenoData`

Read geno data from a file

Description

Read geno data from a file

Usage

readGenoData(file)

Arguments

Argument	Description
`file`	VCF file path (ext `.vcf` or `.vcf.gz`)

Value

gaston::bed.matrix

`readPhasedGeno`

Read phased genetic data from a file

Description

Read phased genetic data from a file

Usage

readPhasedGeno(file)

Arguments

Argument	Description
`file`	phased VCF file path (ext `.vcf` or `.vcf.gz`)

Value

list of 2: haplotypes a matrix of the individuals haplotypes and SNPcoord, data frame of the SNP coordinates.

`readPhenoData`

Read phenotypic data file

Description

Read phenotypic data file

Usage

readPhenoData(file, ind.names = 1, ...)

Arguments

Argument	Description
`file`	file path
`ind.names`	[default 1] a single number giving the column of the table which contains the individuals' names.
`...`	Further arguments to be passed to `read.csv`

Details

Any duplication in the phenotypic file is forbidden.

Value

data.frame

`readData`

Read and prepare data for GWAS result

Description

Read and prepare data for GWAS result

Usage

readData(genoFile, phenoFile)

Arguments

Argument	Description
`genoFile`	path of the geno data file (`.vcf` or `.vcf.gz` file)
`phenoFile`	path of the phenotypic data file (`csv` file)

Value

List

`readPedData`

Read and prepare pedigree data

Description

Read and prepare pedigree data

Usage

readPedData(file, unknown_string = "", header = TRUE)

Arguments

Argument	Description
`file`	path of the pedigree data file (`csv` file).
`unknown_string`	[default: ""] a character vector of strings which are to be interpreted as "unknown parent". By default: missing value in the file.
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 will be interpreted as the individual id, column 2 as the first parent, column 3 as the second parent.

Details

We consider here only allo-fecundation or auto-fecundation. For auto-fecundation, use the parental individual id in both column 2 and 3. Doubles haploids can not be interpreted, please avoid them in the file.

Please be sure that all individuals id in columns 2 and 3 are defined in the column 1. If columns 2 and/or 3 contain id of individuals that are not in the first column, a warning will be raised and these individuals will be added to the pedigree with unknown parents as founder individuals.

Value

List of 2: data pedigree data, graph "igraph" object of the pedigree graph.

`readRelMat`

Read a relationship matrix file

Description

Read a relationship matrix file

Usage

readRelMat(file, format = tools::file_ext(file))

Arguments

Argument	Description
`file`	path of the file generated by the function `saveRelMat` containing relationship matrix
`format`	format of the input file. Either "csv" or "json" (optional, by default it will use the `file` extension).

Details

The metadata of the file are not kept.

Value

matrix

`readCrossTable`

Read crossing table

Description

Read crossing table

Usage

readCrossTable(file, header = TRUE)

Arguments

Argument	Description
`file`	path of the crossing table data file (`csv` file of 2 or 3 columns).
`header`	[default: TRUE] a logical value indicating whether the file contains the names of the variables as its first line. The default value is TRUE. In any cases, the column 1 and 2 will be interpreted as the parents id. The optional third column will be interpreted as the offspring base name.

Value

data.frame with the crossing table information.

`readSNPcoord`

Read SNP coordinates .csv file

Description

Read SNP coordinates .csv file

Usage

readSNPcoord(file)

Arguments

Argument Description

file path of the SNPs coordinates file (csv file). This .csv file can have 4 named columns: - chr: Chromosome holding the SNP - physPos: SNP physical position on the chromosome - linkMapPos: SNP linkage map position on the chromosome in Morgan (mandatory) - SNPid: SNP's IDs Column physPos is optional except in some particular case (see below) If SNPid columns is missing or have missing values, the SNPid will be automatically imputed using the convention chr@physPos therefore columns chr and physPos should not have any missing values

Value

data.frame of 4 columns: 'chr', 'linkMapPos', 'SNPid'

`readGWAS`

Read GWAS analysis result file (.json)

Description

Read GWAS analysis result file (.json)

Usage

readGWAS(file)

Arguments

Argument	Description
`file`	path of the json file generated by the function `gwas` containing GWAS result

Value

list of 2 elements gwas (data.frame) and metadata (list)

`readMarkerEffects`

Read marker effects file

Description

Read marker effects file

Usage

readMarkerEffects(file)

Arguments

Argument	Description
`file`	path of the marker effects file (`csv`, or `json` file)

Details

For .csv, the file file should have 2 named columns:

SNPid: Marker id
effects: effect of the corresponding marker one can specify the intercept using a "--INTERCEPT--" as SNPid.

For .json, the file should have 2 Key-value pairs:

intercept: a number with the value of the intercept.
coefficient: a nested object with SNPids as keys and their corresponding effects as values. For example : list("\n", " "intercept": 100,\n", " "coefficients": ", list("\n", " "SNP01": 1.02e-06,\n", " "SNP02": 0.42,\n", " "SNP03": 0.0\n", " "), "\n")

Value

list of 2 elements: intercept: the value of the intercept, effects: data.frame of 1 columns named SNPeffects with the marker ids as row names.

`readMarkerEffects_csv`

Read marker effects CSV file

Description

Read marker effects CSV file

Usage

readMarkerEffects_csv(file)

Arguments

Argument	Description
`file`	path of the marker effects file (`csv` file). This `.csv` file should have 2 named columns: - `SNPid`: Marker id - `effects`: effect of the corresponding marker one can specify the intercept using a "--INTERCEPT--" as SNPid.

Value

list of 2 elements: intercept: the value of the intercept, effects: data.frame of 1 columns named SNPeffects with the marker ids as row names.

`readMarkerEffects_json`

Read marker effects JSON file

Description

Read marker effects JSON file

Usage

readMarkerEffects_json(file)

Arguments

Argument Description

file path of the marker effects file (json file). This .json file should have 2 Key-value pairs: - intercept: a number with the value of the intercept. - coefficient: a nested object with SNPids as keys and their corresponding effects as values. For example : list("\n", " "intercept": 100,\n", " "coefficients": ", list("\n", " "SNP01": 1.02e-06,\n", " "SNP02": 0.42,\n", " "SNP03": 0.0\n", " "), "\n")

Value

list of 2 elements: intercept: the value of the intercept, effects: data.frame of 1 columns named SNPeffects with the marker ids as row names.

`readProgBlupEstim`

Read progeny BLUP estimation file

Description

Read progeny BLUP estimation file

Usage

readProgBlupEstim(file)

Arguments

Argument	Description
`file`	path of the progeny BLUP estimation file generated by r-geno-tools-engine containing the blup estimations of the progenies of some crosses (`json` file).

Value

data.frame of 4 columns named "ind1", "ind2", "blup_var", "blup_exp"

`saveGWAS`

saveGWAS save gwas result in a temporary file

Description

saveGWAS save gwas result in a temporary file

Usage

saveGWAS(gwasRes, metadata, dir = NULL, file = NULL)

Arguments

Argument	Description
`gwasRes`	data.frame return by `gwas` function
`metadata`	list of metadata of the gwas results
`dir`	if `filename` is NULL, directory where to save the data, by default it is a temporary directory
`file`	file path where to save the data. If the file already exists, it will be overwritten. Default NULL

Value

path of the created file

`saveRelMat`

Save relationship matrix in file

Description

Save relationship matrix in file

Usage

saveRelMat(
  relMat,
  metadata = NULL,
  dir = NULL,
  file = NULL,
  format = tools::file_ext(file)
)

Arguments

Argument	Description
`relMat`	relationship matrix created with `pedRelMat`
`metadata`	list of metadata of the relationship matrix (optional).
`dir`	if `file` is NULL, directory where to save the data, by default it is a temporary directory
`file`	file path where to save the data. If the file already exists, it will be overwritten. Default NULL (it will create a new "csv" file)
`format`	format of the output file. Either "csv" or "json". (optional, by default it will use the `file` extension, if file is NULL, "csv").

Value

path of the created file

`prepareData`

Filter individuals and remove monomorphic markers

Description

Filter individuals and remove monomorphic markers

Usage

prepareData(gDta, pDta)

Arguments

Argument	Description
`gDta`	output of `downloadGenoData` or `readGenoData` functions
`pDta`	output of `downloadPhenoData` or `readPhenoData` functions

Details

The function remove the monomorphic markers and

Value

List of 2 elements: genoData (a bed matrix), phenoData (a data.frame)

`saveVcf`

Save phased genotypes of simulatied population to vcf.gz file

Description

Save phased genotypes of simulatied population to vcf.gz file

Usage

saveVcf(file, pop, SNPcoord)

Arguments

Argument	Description
`file`	file path where to save the data. If the file already exists, it will be overwritten.
`pop`	simulated population (`breedSimulatR`'s population)
`SNPcoord`	snp coordinate of the genotypes. (data.frame with `chr`, `physPos`, and `SNPid` columns)

`save_dataFrame_as_json`

Save a R data.frame as json file

Description

Save a R data.frame as json file

Usage

save_dataFrame_as_json(df, file)

Arguments

Argument	Description
`df`	data.frame
`file`	file path where to save the data. If the file already exists, it will be overwritten.

Value

path of the created file

`save_blupVarExp_as_json`

Save a R blupVarExp as json file

Description

Save a R blupVarExp as json file

Usage

save_blupVarExp_as_json(blupVarExp, file)

Arguments

Argument	Description
`blupVarExp`	list (cf. `calc_progenyBlupEstimation()`)
`file`	file path where to save the data. If the file already exists, it will be overwritten.

Value

path of the created file

`save_plotly`

Save a plotly graph

Description

Save a plotly graph

Usage

save_plotly(plot, file)

Arguments

Argument	Description
`plot`	graph generated with plotly
`file`	file path where to save the html graph. If the file already exists, it will be overwritten.

Value

path of the created file

`save_GS_model`

Save a GS model

Description

Save a GS model

Usage

save_GS_model(model, trait_name, file)

Arguments

Argument	Description
`model`	GS model generated with
`trait_name`	name of the phenotypic trait predicted by this model
`file`	file path where to save the model. If the file already exists, it will be overwritten.

Value

path of the created file

`extract_additive_effects`

Format markerEffects to be used by calcProgenyBlupCovariance function.

Description

Format markerEffects to be used by calcProgenyBlupCovariance function.

Usage

extract_additive_effects(markerEffects)

Arguments

Argument	Description
`markerEffects`	list

Value

list of 2 elements: intercept: named vector of the intercepts SNPeffects: data.frame of the additive effects, 1 columns per phenotype with the marker ids as row names.

`save_GS_evaluation`

Save evaluation result in .json file

Description

Save evaluation result in .json file

Usage

save_GS_evaluation(evaluation, trait_name, file)

Arguments

Argument	Description
`evaluation`	output of `cross_validation_evaluation` function
`trait_name`	trait name
`file`	file path where to save the evaluations resutls. If the file already exists, it will be overwritten.

`read_GS_evaluation`

read evaluation results file

Description

read evaluation results file

Usage

read_GS_evaluation(file)

Arguments

Argument	Description
`file`	path of the evaluation result file generated by `save_GS_evaluation` function

`pedRelMat`

Pedigree Relationship Matrix calculation

Description

Pedigree Relationship Matrix calculation

Usage

pedRelMat(ped)

Arguments

Argument	Description
`ped`	List return by `readPedData` function

Value

matrix

Author

Hiroyoshi Iwata, Julien Diot

`calc_additive_geno`

Additive Genomic Matrix

Description

Additive Genomic Matrix

Usage

calc_additive_geno(geno, standardized = TRUE)

Arguments

Argument	Description
`geno`	`gaston::bed.matrix` return by `readGenoData` function
`standardized`	`boolean` (default TRUE) control if the returned genetic matrix should be standardized.

Details

The standardization is made with: (x - 2p) / sqrt(2p*(1 - p)) with x the genetic values in alleles dose and p the allelic frequency.

Value

matrix

`calc_additive_rel_mat`

Additive Genomic Relationship Matrix calculation

Description

Additive Genomic Relationship Matrix calculation

Usage

calc_additive_rel_mat(geno, standardized = TRUE)

Arguments

Argument	Description
`geno`	`gaston::bed.matrix` return by `readGenoData` function
`standardized`	`boolean` (default TRUE) control if the calculation should be done on the standardized additive genetic matrix.

Value

list of 2 elements:

geno_mat: the genetic matrix used for calculating the relationship matrix
rel_mat: the relationship matrix

`calc_dominance_geno`

Dominance Genomic Matrix

Description

Dominance Genomic Matrix

Usage

calc_dominance_geno(geno, standardized = TRUE)

Arguments

Argument	Description
`geno`	`gaston::bed.matrix` return by `readGenoData` function
`standardized`	`boolean` (default TRUE) control if the returned genetic matrix should be standardized.

Details

The standardization is made with the matrix with entries p/(1 - p), -1, (1-p)/p according to the values 0, 1, 2 in the genetic matrix, with p the allele frequency (cf. ?gaston::DM)

Value

matrix

`calc_dominance_rel_mat`

Dominance Genomic Relationship Matrix calculation

Description

Dominance Genomic Relationship Matrix calculation

Usage

calc_dominance_rel_mat(geno, standardized = TRUE)

Arguments

Argument	Description
`geno`	`gaston::bed.matrix` return by `readGenoData` function
`standardized`	`boolean` (default TRUE) control if the calculation should be done on the standardized dominance genetic matrix.

Value

list of 2 elements:

geno_mat: the genetic matrix used for calculating the relationship matrix
rel_mat: the relationship matrix

`combinedRelMat`

Combined (pedigree + genomic) Relationship Matrix

Description

Correct a pedigree relationship matrix by using genomic relationship matrix.

Usage

combinedRelMat(ped_rm, geno_rm, method = "Legarra", tau = NULL, omega = NULL)

Arguments

Argument	Description
`ped_rm`	pedigree relationship matrix (matrix from function pedRelMat)
`geno_rm`	genomic relationship matrix (matrix from function genoRelMat)
`method`	method to use, either "Legarra" or "Martini"
`tau`	tau parameter of the Martini's method
`omega`	omega parameter of the Martini's method

Value

matrix

Author

Hiroyoshi Iwata, Julien Diot

`supThisWarning`

Do not show a specific warning message

Description

Do not show a specific warning message

Usage

supThisWarning(expr, warnMessage)

Arguments

Argument	Description
`expr`	expression to evaluate.
`warnMessage`	warning message to catch

Details

This is based on the base::suppressWarnings function.

Files

README.md

Latest commit

History

README.md

File metadata and controls

checkAndFilterSNPcoord

Description

Usage

Arguments

Value

checkIndNamesConsistency

Description

Usage

Arguments

Value

initializeSimulation

Description

Usage

Arguments

Value

engineError

Description

Usage

Arguments

bad_argument

Description

Usage

Arguments

Examples

errorCode

Description

Usage

filterGWAS

Description

Usage

Arguments

fit_with_gaston

Description

Usage

Arguments

Details

Value

fit_with_rainbowr

Description

Usage

Arguments

Details

Value

calc_mark_eff

Description

Usage

Arguments

Details

Value

train_gs_model

Description

Usage

Arguments

Details

Value

predict_gs_model

Description

Usage

Arguments

Value

cross_validation_evaluation

Description

Usage

Arguments

Value

get_model_metrics

Description

Usage

Arguments

Value

check_dominance_model_is_applicable

Description

Usage

Arguments

`checkAndFilterSNPcoord`

`checkIndNamesConsistency`

`initializeSimulation`

`engineError`

`bad_argument`

`errorCode`

`filterGWAS`

`fit_with_gaston`

`fit_with_rainbowr`

`calc_mark_eff`

`train_gs_model`

`predict_gs_model`

`cross_validation_evaluation`

`get_model_metrics`

`check_dominance_model_is_applicable`

`gwas`

`adjustPval`

`Logger`

`run_gwas`

`draw_manhattanPlot`

`run_resAdjustment`

`draw_ldPlot`

`calc_pedRelMat`

`calc_genoRelMat`

`calc_combinedRelMat`

`draw_relHeatmap`

`draw_pedNetwork`