- r-geno-tools-engine
- Build r-geno-tools-engine
- How to use r-geno-tools-engine
- Functions documentation
- Tests
- Tools
- Data references
- Utils
The goal of “r-geno-tools-engine” is to provide a simple “input/output” style command line toolbox for several genomic related analysis in order to be used inside an API or other services.
The easiest way to use r-geno-tools-engine is to use the Docker image. To build the docker image simply do:
git clone https://github.com/ut-biomet/r-geno-tools-engine
cd r-geno-tools-engine
docker build -t rgenotoolsengine ./You can test the image by running:
docker run --rm --entrypoint="Rscript" rgenotoolsengine ./tests/testthat.RAll tests should pass.
Basically the command to use the engine from the docker image is as follow:
docker run --rm rgenotoolsengine <subcommand> --param1 valueOfParam1 --param2 valueOfParam2 ...The sub-command can be one of: (see: docker run --rm rgenotoolsengine --help)
gwas: to do a gwas analysis from geno and pheno filesgwas-manplot: to create an interactive or static manhathan plot from the results generated bygwasgwas-adjresults: to calculate the adjusted p-value from the results generated bygwasldplot: to get a plot showing the Linkage disequilibrium between consecutive markers.relmat-ped: to calculate the relationship matrix from a pedigree file.relmat-heatmap: to get the heatmap of a relationship matrix.pedNetwork: to get an interactive pedigree network from a pedigree file.
The parameters to use depends of the sub-command. You can have an exhaustive list of them by using:
docker run --rm rgenotoolsengine <subcommand> --helpHowever, the tools need to read/write files from the host machine. To let docker container access those files, you need to mount the the folders containing the files in the container using the option -v --volume. Then the file path must be specify relative to the place in the container. (See examples below).
docker run --rm \
-v /path/to/folder1/on/host/:/dockerFolder1 \
-v /path/to/folder2/on/host/:/dockerFolder2 \
<subcommand> \
--fileParam1 "/dockerFolder1/nameOfFile1.txt" \
--fileParam2 "/dockerFolder2/nameOfFile2.txt" \
--param3 ...This r-geno-tools-engine is written in R, therefore it can also be run directly from your computer (without docker) if you have R and the all the dependencies packages installed.
To install r-geno-tools-engine’s packages dependencies through renv you can simply run:
make depsOr in an R console:
# open the repo as an Rstudio project
# or use `setwd('path/to/r-geno-tools-engine)`
# Install `renv`
install.packages(renv)
# Install dependencies
renv::restore()To use the engine from R, move to the location of the engine and execute the R-geno-tools-Engine.R file with the Rscript command.
From a bash terminal:
cd /path/to/r-geno-tools-engine
Rscript ./r-geno-tools-engine.R <subcommand> --param1 valueOfParam1 --param2 valueOfParam2 ...See:
Rscript ./r-geno-tools-engine.R --help
and
Rscript ./r-geno-tools-engine.R <subcommand> --help
If you want to use the engine from another location, you need to set an environment variable named RGENOROOT specifying the location of the engine. For example:
export RGENOROOT='/path/to/r-geno-tools-engine'
Rscript /path/to/r-geno-tools-engine/r-geno-tools-engine.R --helpThere is a special option flag available, --json-errors, that will make this tool write errors information as json in stderr and will exit with status code of 42, this can be usefull if you integrate this tool in others.
For example:
Rscript ./r-geno-tools-engine.R relmat-ped --pedFile "this_file_do_not_exist.csv" --outFile "/out/pedRelMat.json"{
"message":"Error in calc_pedRelMat(...): file not found\nAdditional information:\n- code : FILE_NOT_FOUND\n- file : doNotExist",
"extra":{
"code":"FILE_NOT_FOUND",
"file":"this_file_do_not_exist.csv",
"error_location":"calc_pedRelMat(...)",
"trace":"calc_pedRelMat(...)"
}
}The extra field will contains elements related to the specific errors (in this case, for example, the file that have not been found).
Unexpected errors will also exit with an error code 42 and write information in stderr as json with the following informations:
{
"message":"<error message>",
"extra":{
"code":"UNEXPECTED_ERROR",
"stacktrace":"<informations on error location>"
}
}It is also possible to use this engine in other project written in R as a library. For that I suggest to include this repository as a git sub-module of your project.
Then you can load in your code the engine’s function using:
invisible(
sapply(FUN = source,
X = list.files("path/to/r-geno-tools-engine/src", pattern = ".R$",full.names = T))
)For each tool of the engine, some “main functions” are available. Those functions do essentially the same than the command lines ones. It is recommended to use those functions in your R project.
Other “utility” functions are also available (eg. for loading data, save the results…) see the functions documentation for an exhaustive list.
All the R functions of this engine are documented in ./doc/README.md.
You can generate this markdown documentation using the function writeDoc (defined in ./src/utils.R):
writeDoc(srcDir = "src",
docDir = "doc")The R package testthat is needed to run the unit tests. To run the unit tests of this engine you can use the command:
Rscript ./tests/testthat.RClick to expand
This toolbox provide several genomic analysis. To get a simple description of each of them, please
GWAS stands for Genome-Wide Association Study. It is a statistical study that aims to detect genetic markers associated with a particular phenotypic trait.
To test if a marker is associated with a particular phenotypic trait, we split individuals into different groups according to their genotype for the studied marker and we test if there is a statistical difference between the phenotypic values of the groups. The strength of the statistical difference is measured by what statisticians call the “p-value”.
Usually to consider a test “statistically significant” we check if the p-value of the test is lower than a threshold “α”. In this case the test is considered positive (“there is a statistical difference”) but we have a probability α to have a false positive. Very often α is equal to 5% or 1%.
GWAS analysis does this test individually and independently for all the genetic markers in the data-set and records the “statistical significance” (the p-values) of all the tests.
However, the number of tested genetic markers are usually very large (several hundreds of thousands or several million) therefore, the number of false-positive can be high even for a small value of α. For example, with α=1%, 500000 markers, and if none of them is associated with the phenotypic trait, we can still expect 5000 “significant test”.
To avoid that, the p-values must be adjusted according to the number of markers.
Finally, the result of a GWAS analysis is presented as a Manhattan_plot which shows the -log(p-value) on the y-axis and all the markers (order according to their position on the chromosomes) on the x-axis. A horizontal line represents the significance threshold and the points of the markers above the line can be considered as being associated with the phenotypic trait.
screenshot of the plotly graph
r-geno-tools-engine provides command line tools to do GWAS analysis.
Note: You can also check this 7min video by Nuno Carvalho that explain GWAS very well.
To do a gwas analysis and write the results in a json file.
docker run --rm -v "$PWD"/data/geno/:/geno \
-v "$PWD"/data/pheno/:/pheno \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
gwas \
--genoFile "/geno/testMarkerData01.vcf.gz" \
--phenoFile "/pheno/testPhenoData01.csv" \
--trait "Flowering.time.at.Arkansas" \
--test "score" \
--fixed 0 \
--response "quantitative" \
--thresh_maf 0.05 \
--thresh_callrate 0.95 \
--outFile "/out/gwasRes.json"To create a Manhattan plot.
Interactive plot:
docker run --rm -v "$PWD"/readmeTemp:/files rgenotoolsengine \
gwas-manplot \
--gwasFile "/files/gwasRes.json" \
--adj_method "bonferroni" \
--thresh_p 0.05 \
--interactive TRUE \
--filter_nPoints 3000 \
--outFile "/files/manPlot.html"Static plot:
docker run --rm -v "$PWD"/readmeTemp:/files rgenotoolsengine \
gwas-manplot \
--gwasFile "/files/gwasRes.json" \
--adj_method "bonferroni" \
--thresh_p 0.05 \
--interactive FALSE \
--outFile "/files/manPlot.png"To create a json file with the gwas results with adjusted p-values. It is also possible to filter the result to keep the values with the lowest p-values.
docker run --rm -v "$PWD"/readmeTemp:/files rgenotoolsengine \
gwas-adjResults \
--gwasFile "/files/gwasRes.json" \
--adj_method "bonferroni" \
--filter_nPoints 3000 \
--outFile "/files/adjRes.json"For a usage as R library one can use those functions.
gwas_results <- run_gwas(genoFile = "data/geno/testMarkerData01.vcf.gz",
phenoFile = "data/pheno/testPhenoData01.csv",
genoUrl = NULL,
phenoUrl = NULL,
trait = "Flowering.time.at.Arkansas",
test = "score",
fixed = 0,
response = "quantitative",
thresh_maf = 0.05,
thresh_callrate = 0.95,
outFile = tempfile(fileext = ".json"))
#> 2024-03-04 11:07:30.837106 - r-run_gwas(): Get data ...
#> 2024-03-04 11:07:30.904313 - r-readData(): get geno data ...
#> 2024-03-04 11:07:30.913054 - r-readGenoData(): Check file extention ...
#> 2024-03-04 11:07:30.921748 - r-readGenoData(): Read geno file ...
#> ped stats and snps stats have been set.
#> 'p' has been set.
#> 'mu' and 'sigma' have been set.
#> 2024-03-04 11:07:31.734199 - r-readGenoData(): Read geno file DONE
#> 2024-03-04 11:07:31.734325 - r-readGenoData(): DONE, return output.
#> 2024-03-04 11:07:31.73439 - r-readData(): get geno data DONE
#> 2024-03-04 11:07:31.734454 - r-readData(): get pheno data ...
#> 2024-03-04 11:07:31.750441 - r-readPhenoData(): Read phenotypic file ...
#> 2024-03-04 11:07:31.755147 - r-readPhenoData(): Read phenotypic file DONE
#> 2024-03-04 11:07:31.755227 - r-readPhenoData(): Check individuals unicity ...
#> 2024-03-04 11:07:31.75532 - r-readPhenoData(): Check individuals unicity DONE
#> 2024-03-04 11:07:31.755381 - r-readPhenoData(): Set pheno data's row names ...
#> 2024-03-04 11:07:31.755487 - r-readPhenoData(): Set pheno data's row names DONE
#> 2024-03-04 11:07:31.755553 - r-readPhenoData(): DONE, return output.
#> 2024-03-04 11:07:31.755611 - r-readData(): get pheno data DONE
#> 2024-03-04 11:07:31.75567 - r-readData(): prepare data ...
#> 2024-03-04 11:07:31.755785 - r-prepareData(): Remove from geno data individuals that are not in phenotypic data-set ...
#> 2024-03-04 11:07:32.11601 - r-prepareData(): Remove from geno data individuals that are not in phenotypic data-set DONE
#> 2024-03-04 11:07:32.116139 - r-prepareData(): reorder matrix ...
#> 2024-03-04 11:07:32.116494 - r-prepareData(): reorder matrix DONE
#> 2024-03-04 11:07:32.116562 - r-prepareData(): remove monomorphic markers ...
#> 2024-03-04 11:07:32.134077 - r-prepareData(): remove monomorphic markers DONE
#> 2024-03-04 11:07:32.134224 - r-prepareData(): DONE, return output.
#> 2024-03-04 11:07:32.134292 - r-readData(): prepare data DONE
#> 2024-03-04 11:07:32.134358 - r-readData(): DONE, return output.
#> 2024-03-04 11:07:32.134419 - r-run_gwas(): Get data DONE
#> 2024-03-04 11:07:32.1345 - r-run_gwas(): GWAS analysis ...
#> 2024-03-04 11:07:32.150414 - r-gwas(): aggregate data in bed matrix ...
#> 2024-03-04 11:07:32.150642 - r-gwas(): aggregate data in bed matrix DONE
#> 2024-03-04 11:07:32.150742 - r-gwas(): remove individuals with missing phenotypic values ...
#> 2024-03-04 11:07:32.302235 - r-gwas(): remove samples with missing phenotypic values DONE
#> 2024-03-04 11:07:32.302379 - r-gwas(): filter SNPs ...
#> 2024-03-04 11:07:32.341511 - r-gwas(): filter SNPs DONE
#> 2024-03-04 11:07:32.341655 - r-gwas(): calculate genetic relatinoal matrix ...
#> 2024-03-04 11:07:32.49034 - r-gwas(): calculate genetic relatinoal matrix DONE
#> 2024-03-04 11:07:32.490526 - r-gwas(): fit model ...
#> [Iteration 1] theta = 78.0648 78.2819
#> [Iteration 1] log L = -1002.17
#> [Iteration 1] AI-REML update
#> [Iteration 1] ||gradient|| = 0.344543
#> [Iteration 2] theta = 22.5611 163.026
#> [Iteration 2] log L = -990.943
#> [Iteration 2] AI-REML update
#> [Iteration 2] ||gradient|| = 0.798818
#> [Iteration 3] theta = 27.72 190.624
#> [Iteration 3] log L = -986.649
#> [Iteration 3] AI-REML update
#> [Iteration 3] ||gradient|| = 0.113945
#> [Iteration 4] theta = 29.2502 195.436
#> [Iteration 4] log L = -986.514
#> [Iteration 4] AI-REML update
#> [Iteration 4] ||gradient|| = 0.00580945
#> [Iteration 5] theta = 29.4021 195.213
#> [Iteration 5] log L = -986.513
#> [Iteration 5] AI-REML update
#> [Iteration 5] ||gradient|| = 0.000323296
#> [Iteration 6] theta = 29.4178 195.143
#> [Iteration 6] log L = -986.513
#> [Iteration 6] AI-REML update
#> [Iteration 6] ||gradient|| = 4.32634e-05
#> [Iteration 7] theta = 29.4199 195.133
#> [Iteration 7] log L = -986.513
#> [Iteration 7] AI-REML update
#> [Iteration 7] ||gradient|| = 5.95134e-06
#> 2024-03-04 11:07:32.919143 - r-gwas(): fit model DONE
#> 2024-03-04 11:07:32.919276 - r-gwas(): DONE, return output.
#> 2024-03-04 11:07:32.919352 - r-run_gwas(): GWAS analysis DONE
#> 2024-03-04 11:07:32.919418 - r-run_gwas(): Save metadata ...
#> 2024-03-04 11:07:32.944153 - r-run_gwas(): Save metadata DONE
#> 2024-03-04 11:07:32.944306 - r-run_gwas(): Save results ...
#> 2024-03-04 11:07:32.944441 - r-saveGWAS(): Check file ...
#> 2024-03-04 11:07:32.944581 - r-saveGWAS(): Check file DONE
#> 2024-03-04 11:07:33.157765 - r-run_gwas(): Save results DONE
gwas_results$file
#> [1] "/tmp/Rtmpf5vQTG/file112964dc003a.json"
substr(gwas_results$gwasRes, start=1, stop=500)
#> [
#> {
#> "chr": "1",
#> "pos": 9563,
#> "id": "SNP-1.8562.",
#> "A1": "A",
#> "A2": "T",
#> "freqA2": 0.1359,
#> "score": 4.395,
#> "p": 0.036
#> },
#> {
#> "chr": "1",
#> "pos": 25922,
#> "id": "SNP-1.24921.",
#> "A1": "C",
#> "A2": "T",
#> "freqA2": 0.1254,
#> "score": 1.6744,
#> "p": 0.1957
#> },
#> {
#> "chr": "1",
#> "pos": 26254,
#> "id": "SNP-1.25253.",
#> "A1": "A",
#> "A2": "T",
#> "freqA2": 0.2935,
#> "score": 3.6592,
#> "p": 0.0558
#> },
#> {
#> "chr": "1",
#> "posp <- draw_manhattanPlot(gwasFile = gwas_results$file,
gwasUrl = NULL,
adj_method = "bonferroni",
thresh_p = 0.05,
chr = NA,
interactive = TRUE,
# filter_pAdj = 1,
# filter_nPoints = Inf,
filter_quant = 0.1,
outFile = tempfile(fileext = ".html"))
#> 2024-03-04 11:07:33.299759 - r-draw_manhattanPlot(): Get data ...
#> 2024-03-04 11:07:33.299936 - r-readGWAS(): Read result file ...
#> 2024-03-04 11:07:33.408482 - r-readGWAS(): Read result file DONE
#> 2024-03-04 11:07:33.408673 - r-readGWAS(): Convert Json to data.frame ...
#> 2024-03-04 11:07:33.790282 - r-readGWAS(): Convert Json to data.frame DONE
#> 2024-03-04 11:07:33.790442 - r-readGWAS(): DONE, return output.
#> 2024-03-04 11:07:33.790514 - r-draw_manhattanPlot(): Get data DONE
#> 2024-03-04 11:07:33.79058 - r-draw_manhattanPlot(): Draw Manhattan Plot ...
#> 2024-03-04 11:07:33.820982 - r-manPlot(): Remove NAs ...
#> 2024-03-04 11:07:33.989858 - r-manPlot(): Remove NAs DONE
#> 2024-03-04 11:07:33.990012 - r-manPlot(): Adjust p-values ...
#> 2024-03-04 11:07:34.001414 - r-adjustPval(): Check adj_method ...
#> 2024-03-04 11:07:34.001529 - r-adjustPval(): Check adj_method DONE
#> 2024-03-04 11:07:34.001594 - r-adjustPval(): Check p values ...
#> 2024-03-04 11:07:34.001813 - r-adjustPval(): Check p values DONE
#> 2024-03-04 11:07:34.001875 - r-adjustPval(): Adjust p-values ...
#> 2024-03-04 11:07:34.002284 - r-adjustPval(): Adjust p-values DONE
#> 2024-03-04 11:07:34.002349 - r-adjustPval(): Adjust threshold ...
#> 2024-03-04 11:07:34.036902 - r-adjustPval(): Adjust threshold DONE
#> 2024-03-04 11:07:34.03707 - r-adjustPval(): DONE, return output
#> 2024-03-04 11:07:34.037152 - r-manPlot(): Adjust p-values DONE
#> 2024-03-04 11:07:34.076836 - r-manPlot(): Check duplicated SNP ID ...
#> 2024-03-04 11:07:34.077478 - r-manPlot(): Check duplicated SNP ID DONE
#> 2024-03-04 11:07:34.077561 - r-manPlot(): Extract significant SNP ...
#> 2024-03-04 11:07:34.077716 - r-manPlot(): Extract significant SNP DONE
#> 2024-03-04 11:07:34.095363 - r-filterGWAS(): Filter points ...
#> 2024-03-04 11:07:34.095496 - r-filterGWAS(): skip filter_pAdj
#> 2024-03-04 11:07:34.100442 - r-filterGWAS(): skip filter_nPoints
#> 2024-03-04 11:07:34.100562 - r-manPlot(): Draw plot ...
#> 2024-03-04 11:07:34.436416 - r-manPlot(): Draw plot DONE
#> 2024-03-04 11:07:34.436538 - r-manPlot(): DONE, return output
#> 2024-03-04 11:07:34.43661 - r-draw_manhattanPlot(): Draw Manhattan Plot DONE
#> 2024-03-04 11:07:34.436673 - r-draw_manhattanPlot(): Save results ...
#> 2024-03-04 11:07:35.136112 - r-draw_manhattanPlot(): Save results DONEgwas_adj <- run_resAdjustment(gwasFile = gwas_results$file,
gwasUrl = NULL,
adj_method = "bonferroni",
outFile = tempfile(fileext = ".json"))
#> 2024-03-04 11:07:35.189365 - r-run_resAdjustment(): Get data ...
#> 2024-03-04 11:07:35.197226 - r-readGWAS(): Read result file ...
#> 2024-03-04 11:07:35.288746 - r-readGWAS(): Read result file DONE
#> 2024-03-04 11:07:35.288938 - r-readGWAS(): Convert Json to data.frame ...
#> 2024-03-04 11:07:35.710556 - r-readGWAS(): Convert Json to data.frame DONE
#> 2024-03-04 11:07:35.710829 - r-readGWAS(): DONE, return output.
#> 2024-03-04 11:07:35.710904 - r-run_resAdjustment(): Get data DONE
#> 2024-03-04 11:07:35.710971 - r-run_resAdjustment(): Adjust p-values ...
#> 2024-03-04 11:07:35.711103 - r-adjustPval(): Check adj_method ...
#> 2024-03-04 11:07:35.711168 - r-adjustPval(): Check adj_method DONE
#> 2024-03-04 11:07:35.71123 - r-adjustPval(): Check p values ...
#> 2024-03-04 11:07:35.711434 - r-adjustPval(): Check p values DONE
#> 2024-03-04 11:07:35.7115 - r-adjustPval(): Adjust p-values ...
#> 2024-03-04 11:07:35.711909 - r-adjustPval(): Adjust p-values DONE
#> 2024-03-04 11:07:35.711974 - r-adjustPval(): DONE, return output
#> 2024-03-04 11:07:35.712047 - r-run_resAdjustment(): Adjust p-values DONE
#> 2024-03-04 11:07:35.71215 - r-filterGWAS(): Filter points ...
#> 2024-03-04 11:07:35.712225 - r-filterGWAS(): skip filter_pAdj
#> 2024-03-04 11:07:35.712289 - r-filterGWAS(): skip filter_quant
#> 2024-03-04 11:07:35.712356 - r-filterGWAS(): skip filter_nPoints
#> 2024-03-04 11:07:35.712417 - r-run_resAdjustment(): Save results ...
#> 2024-03-04 11:07:35.719417 - r-saveGWAS(): Check file ...
#> 2024-03-04 11:07:35.719559 - r-saveGWAS(): Check file DONE
#> 2024-03-04 11:07:35.895174 - r-run_resAdjustment(): Save results DONE
substr(gwas_adj$gwasAdjusted, start=1, stop=500)
#> [
#> {
#> "chr": "1",
#> "pos": 9563,
#> "id": "SNP-1.8562.",
#> "A1": "A",
#> "A2": "T",
#> "freqA2": 0.1359,
#> "score": 4.395,
#> "p": 0.036,
#> "p_adj": 1
#> },
#> {
#> "chr": "1",
#> "pos": 25922,
#> "id": "SNP-1.24921.",
#> "A1": "C",
#> "A2": "T",
#> "freqA2": 0.1254,
#> "score": 1.6744,
#> "p": 0.1957,
#> "p_adj": 1
#> },
#> {
#> "chr": "1",
#> "pos": 26254,
#> "id": "SNP-1.25253.",
#> "A1": "A",
#> "A2": "T",
#> "freqA2": 0.2935,
#> "score": 3.6592,
#> "p": 0.0558,
#> Click to expand
This help to calculate and visualize the linkage disequilibrium between some consecutive snps.
docker run --rm -v "$PWD"/data/geno/:/geno \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
ldplot \
--genoFile "/geno/testMarkerData01.vcf.gz" \
--from 42 \
--to 62 \
--outFile "/out/ldplot.png"Main function
imgFile <- draw_ldPlot(genoFile = "data/geno/testMarkerData01.vcf.gz",
genoUrl = NULL,
from = 42,
to = 62,
outFile = tempfile(fileext = ".png"))
#> 2024-03-04 11:07:36.105452 - r-draw_ldPlot(): Get data ...
#> 2024-03-04 11:07:36.10563 - r-readGenoData(): Check file extention ...
#> 2024-03-04 11:07:36.105862 - r-readGenoData(): Read geno file ...
#> ped stats and snps stats have been set.
#> 'p' has been set.
#> 'mu' and 'sigma' have been set.
#> 2024-03-04 11:07:36.822769 - r-readGenoData(): Read geno file DONE
#> 2024-03-04 11:07:36.822898 - r-readGenoData(): DONE, return output.
#> 2024-03-04 11:07:36.822989 - r-draw_ldPlot(): Get data DONE
#> 2024-03-04 11:07:36.823057 - r-draw_ldPlot(): Draw LD Plot ...
#> 2024-03-04 11:07:36.823187 - r-LDplot(): Compute LD ...
#> 2024-03-04 11:07:36.824136 - r-LDplot(): Compute LD DONE
#> 2024-03-04 11:07:36.824211 - r-LDplot(): Create LD plot ...
#> 2024-03-04 11:07:36.82554 - r-LDplot(): Create create file: /tmp/Rtmpf5vQTG/file1129621202e0e.png
#> 2024-03-04 11:07:37.004627 - r-LDplot(): Create LD plot DONE
#> 2024-03-04 11:07:37.004787 - r-LDplot(): DONE, return output
#> 2024-03-04 11:07:37.004855 - r-draw_ldPlot(): Draw LD Plot DONEClick to expand
Relationship matrix represents how close two individuals are to each other (share the same genes). It can be calculated using their pedigree, or their genotypes.
This engine can calculate the pedigree-based relationship matrix:
docker run --rm -v "$PWD"/data/pedigree/:/pedigree \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
relmat-ped \
--pedFile "/pedigree/testPedData_char.csv" \
--outFile "/out/pedRelMat.json"calc_pedRelMat(pedFile = 'data/pedigree/testPedData_char.csv',
pedUrl = NULL,
header = TRUE,
unknown_string = '',
outFile = tempfile(fileext = ".json"))
#> 2024-03-04 11:07:37.018051 - r-calc_pedRelMAt(): Get data ...
#> 2024-03-04 11:07:37.053553 - r-readPedData: Read pedigree file ...
#> 2024-03-04 11:07:37.058639 - r-readPedData: Read pedigree file DONE
#> 2024-03-04 11:07:37.058746 - r-readPedData: Check pedigree file ...
#> 2024-03-04 11:07:37.093595 - r-readPedData: Check pedigree file DONE
#> 2024-03-04 11:07:37.093723 - r-readPedData: DONE, return output.
#> 2024-03-04 11:07:37.093827 - r-calc_pedRelMAt(): Get data DONE
#> 2024-03-04 11:07:37.093904 - r-calc_pedRelMAt(): Calcualte pedigree relationship matrix ...
#> 2024-03-04 11:07:37.144964 - r-pedRelMat(): Check inputs ...
#> 2024-03-04 11:07:37.145123 - r-pedRelMat(): Check inputs DONE
#> 2024-03-04 11:07:37.145195 - r-pedRelMat(): Create look-up table ...
#> 2024-03-04 11:07:37.146203 - r-pedRelMat(): Create look-up table DONE
#> 2024-03-04 11:07:37.146286 - r-pedRelMat(): Calculate relationship matrix ...
#> 2024-03-04 11:07:37.146694 - r-pedRelMat(): Calculate relationship matrix DONE
#> 2024-03-04 11:07:37.146775 - r-pedRelMat(): DONE, return output.
#> 2024-03-04 11:07:37.146846 - r-calc_pedRelMAt(): Calcualte pedigree relationship matrix DONE
#> 2024-03-04 11:07:37.146923 - r-calc_pedRelMAt(): Get metadata ...
#> 2024-03-04 11:07:37.147051 - r-calc_pedRelMAt(): Get metadata DONE
#> 2024-03-04 11:07:37.147121 - r-calc_pedRelMAt(): Save results ...
#> 2024-03-04 11:07:37.167838 - r-saveRelMat(): Check relationship matrix ...
#> 2024-03-04 11:07:37.168875 - r-saveRelMat(): Check relationship matrix DONE
#> 2024-03-04 11:07:37.168965 - r-saveRelMat(): Check file ...
#> 2024-03-04 11:07:37.169084 - r-saveRelMat(): Check file DONE
#> 2024-03-04 11:07:37.169152 - r-saveRelMat(): Check file format ...
#> 2024-03-04 11:07:37.169267 - r-saveRelMat(): Check file format DONE
#> 2024-03-04 11:07:37.169333 - r-saveRelMat(): Write relationship matrix in `.json` file ...
#> 2024-03-04 11:07:37.172395 - r-saveRelMat(): Write relationship matrix in `.json` file DONE
#> 2024-03-04 11:07:37.172481 - r-calc_pedRelMAt(): Save results DONE
#> $relMat
#> Pluto Zeus Leda Dione Tantale Europe Pelos Minos
#> Pluto 1.0000 0.000000 0.000000 0.000 0.5000000 0.000000 0.2500000 0.0000
#> Zeus 0.0000 1.000000 0.000000 0.000 0.5000000 0.500000 0.2500000 0.7500
#> Pelopia Atree Catree Egiste Aerope Menelas
#> Pluto 0.2500000 0.2500000 0.0000000 0.2500000 0.0000000 0.1250000
#> Zeus 0.2500000 0.2500000 0.3750000 0.2500000 0.1875000 0.2187500
#> Agamemnon Clytemnestre Helene Electre Oreste Hyphigenie
#> Pluto 0.1250000 0.0000 0.0000 0.0625000 0.0625000 0.0625000
#> Zeus 0.2187500 0.5000 0.5000 0.3593750 0.3593750 0.3593750
#> [ reached getOption("max.print") -- omitted 18 rows ]
#>
#> $metadata
#> $metadata$info
#> [1] "R-geno-engine, Pedigree relationship matrix"
#>
#> $metadata$date
#> [1] "2024-03-04 11:07:37 JST"
#>
#> $metadata$nInds
#> [1] 20
#>
#> $metadata$pedFP
#> [1] "8a6b7f58359f42470000f4a5a2d440d7"
#>
#>
#> $file
#> [1] "/tmp/Rtmpf5vQTG/file1129652d9d9a3.json"This engine can calculate the genomic-based relationship matrix:
docker run --rm -v "$PWD"/data/geno/:/geno \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
relmat-geno \
--genoFile "/geno/breedGame_geno.vcf.gz" \
--outFile "/out/genoRelMat.json"calc_genoRelMat(genoFile = 'data/geno/breedGame_geno.vcf.gz',
genoUrl = NULL,
outFile = tempfile(fileext = ".json"))
#> 2024-03-04 11:07:37.200845 - r-calc_genoRelMAt(): Get data ...
#> 2024-03-04 11:07:37.200994 - r-readGenoData(): Check file extention ...
#> 2024-03-04 11:07:37.20124 - r-readGenoData(): Read geno file ...
#> ped stats and snps stats have been set.
#> 'p' has been set.
#> 'mu' and 'sigma' have been set.
#> 2024-03-04 11:07:37.312313 - r-readGenoData(): Read geno file DONE
#> 2024-03-04 11:07:37.312448 - r-readGenoData(): DONE, return output.
#> 2024-03-04 11:07:37.312524 - r-calc_genoRelMAt(): Calcualte genomic relationship matrix ...
#> 2024-03-04 11:07:37.312657 - r-genodRelMat(): Check inputs ...
#> 2024-03-04 11:07:37.318083 - r-genodRelMat(): Check inputs DONE
#> 2024-03-04 11:07:37.318184 - r-genodRelMat(): Calculate genomic relationship matrix ...
#> 2024-03-04 11:07:37.507568 - r-genodRelMat(): Calculate genomic relationship matrix DONE
#> 2024-03-04 11:07:37.507742 - r-genodRelMat(): DONE, return output.
#> 2024-03-04 11:07:37.507812 - r-calc_genoRelMAt(): Calcualte genomic relationship matrix DONE
#> 2024-03-04 11:07:37.507879 - r-calc_genoRelMAt(): Get metadata ...
#> 2024-03-04 11:07:37.514085 - r-calc_genoRelMAt(): Get metadata DONE
#> 2024-03-04 11:07:37.514194 - r-calc_genoRelMAt(): Save results ...
#> 2024-03-04 11:07:37.514335 - r-saveRelMat(): Check relationship matrix ...
#> 2024-03-04 11:07:37.665752 - r-saveRelMat(): Check relationship matrix DONE
#> 2024-03-04 11:07:37.665925 - r-saveRelMat(): Check file ...
#> 2024-03-04 11:07:37.666038 - r-saveRelMat(): Check file DONE
#> 2024-03-04 11:07:37.666101 - r-saveRelMat(): Check file format ...
#> 2024-03-04 11:07:37.666216 - r-saveRelMat(): Check file format DONE
#> 2024-03-04 11:07:37.666656 - r-saveRelMat(): Write relationship matrix in `.json` file ...
#> 2024-03-04 11:07:38.748591 - r-saveRelMat(): Write relationship matrix in `.json` file DONE
#> 2024-03-04 11:07:38.749569 - r-calc_genoRelMAt(): Save results DONE
#> $relMat
#> F2_0001.0001 F2_0001.0002 F2_0001.0003 F2_0001.0004
#> F2_0001.0005 F2_0001.0006 F2_0001.0007 F2_0001.0008
#> F2_0001.0009 F2_0001.0010 F2_0001.0011 F2_0001.0012
#> F2_0001.0013 F2_0001.0014 F2_0001.0015 F2_0001.0016
#> F2_0001.0017 F2_0001.0018 F2_0001.0019 F2_0001.0020
#> F2_0001.0021 F2_0001.0022 F2_0001.0023 F2_0001.0024
#> F2_0001.0025 F2_0001.0026 F2_0001.0027 F2_0001.0028
#> F2_0001.0029 F2_0001.0030 F2_0001.0031 F2_0001.0032
#> F2_0001.0033 F2_0001.0034 F2_0001.0035 F2_0001.0036
#> F2_0001.0037 F2_0001.0038 F2_0001.0039 F2_0001.0040
#> F2_0001.0041 F2_0001.0042 F2_0001.0043 F2_0001.0044
#> F2_0001.0045 F2_0001.0046 F2_0001.0047 F2_0001.0048
#> F2_0001.0049 F2_0001.0050 F2_0001.0051 F2_0001.0052
#> F2_0001.0053 F2_0001.0054 F2_0001.0055 F2_0001.0056
#> F2_0001.0057 F2_0001.0058 F2_0001.0059 F2_0001.0060
#> F2_0001.0061 F2_0001.0062 F2_0001.0063 F2_0001.0064
#> F2_0001.0065 F2_0001.0066 F2_0001.0067 F2_0001.0068
#> F2_0001.0069 F2_0001.0070 F2_0001.0071 F2_0001.0072
#> F2_0001.0073 F2_0001.0074 F2_0001.0075 F2_0001.0076
#> F2_0001.0077 F2_0001.0078 F2_0001.0079 F2_0001.0080
#> F2_0001.0081 F2_0001.0082 F2_0001.0083 F2_0001.0084
#> F2_0001.0085 F2_0001.0086 F2_0001.0087 F2_0001.0088
#> F2_0001.0089 F2_0001.0090 F2_0001.0091 F2_0001.0092
#> F2_0001.0093 F2_0001.0094 F2_0001.0095 F2_0001.0096
#> F2_0001.0097 F2_0001.0098 F2_0001.0099 F2_0001.0100
#> F2_0002.0001 F2_0002.0002 F2_0002.0003 F2_0002.0004 F2_0002.0005
#> F2_0002.0006 F2_0002.0007 F2_0002.0008 F2_0002.0009
#> F2_0002.0010 F2_0002.0011 F2_0002.0012 F2_0002.0013
#> F2_0002.0014 F2_0002.0015 F2_0002.0016 F2_0002.0017 F2_0002.0018
#> F2_0002.0019 F2_0002.0020 F2_0002.0021 F2_0002.0022 F2_0002.0023
#> F2_0002.0024 F2_0002.0025 F2_0002.0026 F2_0002.0027 F2_0002.0028
#> F2_0002.0029 F2_0002.0030 F2_0002.0031 F2_0002.0032 F2_0002.0033
#> F2_0002.0034 F2_0002.0035 F2_0002.0036 F2_0002.0037 F2_0002.0038
#> F2_0002.0039 F2_0002.0040 F2_0002.0041 F2_0002.0042
#> F2_0002.0043 F2_0002.0044 F2_0002.0045 F2_0002.0046 F2_0002.0047
#> F2_0002.0048 F2_0002.0049 F2_0002.0050 F2_0002.0051 F2_0002.0052
#> F2_0002.0053 F2_0002.0054 F2_0002.0055 F2_0002.0056 F2_0002.0057
#> F2_0002.0058 F2_0002.0059 F2_0002.0060 F2_0002.0061
#> F2_0002.0062 F2_0002.0063 F2_0002.0064 F2_0002.0065 F2_0002.0066
#> F2_0002.0067 F2_0002.0068 F2_0002.0069 F2_0002.0070 F2_0002.0071
#> F2_0002.0072 F2_0002.0073 F2_0002.0074 F2_0002.0075 F2_0002.0076
#> F2_0002.0077 F2_0002.0078 F2_0002.0079 F2_0002.0080 F2_0002.0081
#> F2_0002.0082 F2_0002.0083 F2_0002.0084 F2_0002.0085
#> F2_0002.0086 F2_0002.0087 F2_0002.0088 F2_0002.0089
#> F2_0002.0090 F2_0002.0091 F2_0002.0092 F2_0002.0093 F2_0002.0094
#> F2_0002.0095 F2_0002.0096 F2_0002.0097 F2_0002.0098
#> F2_0002.0099 F2_0002.0100 F2_0003.0001 F2_0003.0002 F2_0003.0003
#> F2_0003.0004 F2_0003.0005 F2_0003.0006 F2_0003.0007
#> F2_0003.0008 F2_0003.0009 F2_0003.0010 F2_0003.0011 F2_0003.0012
#> F2_0003.0013 F2_0003.0014 F2_0003.0015 F2_0003.0016 F2_0003.0017
#> F2_0003.0018 F2_0003.0019 F2_0003.0020 F2_0003.0021 F2_0003.0022
#> F2_0003.0023 F2_0003.0024 F2_0003.0025 F2_0003.0026
#> F2_0003.0027 F2_0003.0028 F2_0003.0029 F2_0003.0030
#> F2_0003.0031 F2_0003.0032 F2_0003.0033 F2_0003.0034
#> F2_0003.0035 F2_0003.0036 F2_0003.0037 F2_0003.0038
#> F2_0003.0039 F2_0003.0040 F2_0003.0041 F2_0003.0042 F2_0003.0043
#> F2_0003.0044 F2_0003.0045 F2_0003.0046 F2_0003.0047
#> F2_0003.0048 F2_0003.0049 F2_0003.0050 F2_0003.0051 F2_0003.0052
#> F2_0003.0053 F2_0003.0054 F2_0003.0055 F2_0003.0056
#> F2_0003.0057 F2_0003.0058 F2_0003.0059 F2_0003.0060
#> F2_0003.0061 F2_0003.0062 F2_0003.0063 F2_0003.0064 F2_0003.0065
#> F2_0003.0066 F2_0003.0067 F2_0003.0068 F2_0003.0069 F2_0003.0070
#> F2_0003.0071 F2_0003.0072 F2_0003.0073 F2_0003.0074
#> F2_0003.0075 F2_0003.0076 F2_0003.0077 F2_0003.0078 F2_0003.0079
#> F2_0003.0080 F2_0003.0081 F2_0003.0082 F2_0003.0083 F2_0003.0084
#> F2_0003.0085 F2_0003.0086 F2_0003.0087 F2_0003.0088
#> F2_0003.0089 F2_0003.0090 F2_0003.0091 F2_0003.0092
#> F2_0003.0093 F2_0003.0094 F2_0003.0095 F2_0003.0096
#> F2_0003.0097 F2_0003.0098 F2_0003.0099 F2_0003.0100
#> F3_0001.0001 F3_0001.0002 F3_0001.0003 F3_0001.0004
#> F3_0001.0005 F3_0001.0006 F3_0001.0007 F3_0001.0008
#> F3_0001.0009 F3_0001.0010 F3_0001.0011 F3_0001.0012
#> F3_0001.0013 F3_0001.0014 F3_0001.0015 F3_0001.0016
#> F3_0001.0017 F3_0001.0018 F3_0001.0019 F3_0001.0020
#> F3_0001.0021 F3_0001.0022 F3_0001.0023 F3_0001.0024
#> F3_0001.0025 F3_0001.0026 F3_0001.0027 F3_0001.0028
#> F3_0001.0029 F3_0001.0030 F3_0001.0031 F3_0001.0032
#> F3_0001.0033 F3_0001.0034 F3_0001.0035 F3_0001.0036
#> F3_0001.0037 F3_0001.0038 F3_0001.0039 F3_0001.0040
#> F3_0001.0041 F3_0001.0042 F3_0001.0043 F3_0001.0044
#> F3_0001.0045 F3_0001.0046 F3_0001.0047 F3_0001.0048
#> F3_0001.0049 F3_0001.0050 F3_0001.0051 F3_0001.0052
#> F3_0001.0053 F3_0001.0054 F3_0001.0055 F3_0001.0056
#> F3_0001.0057 F3_0001.0058 F3_0001.0059 F3_0001.0060
#> F3_0001.0061 F3_0001.0062 F3_0001.0063 F3_0001.0064
#> F3_0001.0065 F3_0001.0066 F3_0001.0067 F3_0001.0068
#> F3_0001.0069 F3_0001.0070 F3_0001.0071 F3_0001.0072
#> F3_0001.0073 F3_0001.0074 F3_0001.0075 F3_0001.0076
#> F3_0001.0077 F3_0001.0078 F3_0001.0079 F3_0001.0080
#> F3_0001.0081 F3_0001.0082 F3_0001.0083 F3_0001.0084
#> F3_0001.0085 F3_0001.0086 F3_0001.0087 F3_0001.0088
#> F3_0001.0089 F3_0001.0090 F3_0001.0091 F3_0001.0092
#> F3_0001.0093 F3_0001.0094 F3_0001.0095 F3_0001.0096
#> F3_0001.0097 F3_0001.0098 F3_0001.0099 F3_0001.0100
#> F3_0002.0001 F3_0002.0002 F3_0002.0003 F3_0002.0004
#> F3_0002.0005 F3_0002.0006 F3_0002.0007 F3_0002.0008
#> F3_0002.0009 F3_0002.0010 F3_0002.0011 F3_0002.0012
#> F3_0002.0013 F3_0002.0014 F3_0002.0015 F3_0002.0016
#> F3_0002.0017 F3_0002.0018 F3_0002.0019 F3_0002.0020
#> F3_0002.0021 F3_0002.0022 F3_0002.0023 F3_0002.0024
#> F3_0002.0025 F3_0002.0026 F3_0002.0027 F3_0002.0028
#> F3_0002.0029 F3_0002.0030 F3_0002.0031 F3_0002.0032
#> F3_0002.0033 F3_0002.0034 F3_0002.0035 F3_0002.0036
#> F3_0002.0037 F3_0002.0038 F3_0002.0039 F3_0002.0040
#> F3_0002.0041 F3_0002.0042 F3_0002.0043 F3_0002.0044
#> F3_0002.0045 F3_0002.0046 F3_0002.0047 F3_0002.0048
#> F3_0002.0049 F3_0002.0050 F3_0002.0051 F3_0002.0052
#> F3_0002.0053 F3_0002.0054 F3_0002.0055 F3_0002.0056
#> F3_0002.0057 F3_0002.0058 F3_0002.0059 F3_0002.0060
#> F3_0002.0061 F3_0002.0062 F3_0002.0063 F3_0002.0064
#> F3_0002.0065 F3_0002.0066 F3_0002.0067 F3_0002.0068
#> F3_0002.0069 F3_0002.0070 F3_0002.0071 F3_0002.0072
#> F3_0002.0073 F3_0002.0074 F3_0002.0075 F3_0002.0076
#> F3_0002.0077 F3_0002.0078 F3_0002.0079 F3_0002.0080
#> F3_0002.0081 F3_0002.0082 F3_0002.0083 F3_0002.0084
#> F3_0002.0085 F3_0002.0086 F3_0002.0087 F3_0002.0088
#> F3_0002.0089 F3_0002.0090 F3_0002.0091 F3_0002.0092
#> F3_0002.0093 F3_0002.0094 F3_0002.0095 F3_0002.0096
#> F3_0002.0097 F3_0002.0098 F3_0002.0099 F3_0002.0100
#> F3_0003.0001 F3_0003.0002 F3_0003.0003 F3_0003.0004
#> F3_0003.0005 F3_0003.0006 F3_0003.0007 F3_0003.0008
#> F3_0003.0009 F3_0003.0010 F3_0003.0011 F3_0003.0012
#> F3_0003.0013 F3_0003.0014 F3_0003.0015 F3_0003.0016
#> F3_0003.0017 F3_0003.0018 F3_0003.0019 F3_0003.0020
#> F3_0003.0021 F3_0003.0022 F3_0003.0023 F3_0003.0024
#> F3_0003.0025 F3_0003.0026 F3_0003.0027 F3_0003.0028
#> F3_0003.0029 F3_0003.0030 F3_0003.0031 F3_0003.0032
#> F3_0003.0033 F3_0003.0034 F3_0003.0035 F3_0003.0036
#> F3_0003.0037 F3_0003.0038 F3_0003.0039 F3_0003.0040
#> F3_0003.0041 F3_0003.0042 F3_0003.0043 F3_0003.0044
#> F3_0003.0045 F3_0003.0046 F3_0003.0047 F3_0003.0048
#> F3_0003.0049 F3_0003.0050 F3_0003.0051 F3_0003.0052
#> F3_0003.0053 F3_0003.0054 F3_0003.0055 F3_0003.0056
#> F3_0003.0057 F3_0003.0058 F3_0003.0059 F3_0003.0060
#> F3_0003.0061 F3_0003.0062 F3_0003.0063 F3_0003.0064
#> F3_0003.0065 F3_0003.0066 F3_0003.0067 F3_0003.0068
#> F3_0003.0069 F3_0003.0070 F3_0003.0071 F3_0003.0072
#> F3_0003.0073 F3_0003.0074 F3_0003.0075 F3_0003.0076
#> F3_0003.0077 F3_0003.0078 F3_0003.0079 F3_0003.0080
#> F3_0003.0081 F3_0003.0082 F3_0003.0083 F3_0003.0084
#> F3_0003.0085 F3_0003.0086 F3_0003.0087 F3_0003.0088
#> F3_0003.0089 F3_0003.0090 F3_0003.0091 F3_0003.0092
#> F3_0003.0093 F3_0003.0094 F3_0003.0095 F3_0003.0096
#> F3_0003.0097 F3_0003.0098 F3_0003.0099 F3_0003.0100
#> F4_0001.0001 F4_0001.0002 F4_0001.0003 F4_0001.0004
#> F4_0001.0005 F4_0001.0006 F4_0001.0007 F4_0001.0008
#> F4_0001.0009 F4_0001.0010 F4_0001.0011 F4_0001.0012
#> F4_0001.0013 F4_0001.0014 F4_0001.0015 F4_0001.0016
#> F4_0001.0017 F4_0001.0018 F4_0001.0019 F4_0001.0020
#> F4_0001.0021 F4_0001.0022 F4_0001.0023 F4_0001.0024
#> F4_0001.0025 F4_0001.0026 F4_0001.0027 F4_0001.0028
#> F4_0001.0029 F4_0001.0030 F4_0001.0031 F4_0001.0032
#> F4_0001.0033 F4_0001.0034 F4_0001.0035 F4_0001.0036
#> F4_0001.0037 F4_0001.0038 F4_0001.0039 F4_0001.0040
#> F4_0001.0041 F4_0001.0042 F4_0001.0043 F4_0001.0044
#> F4_0001.0045 F4_0001.0046 F4_0001.0047 F4_0001.0048
#> F4_0001.0049 F4_0001.0050 F4_0001.0051 F4_0001.0052
#> F4_0001.0053 F4_0001.0054 F4_0001.0055 F4_0001.0056
#> F4_0001.0057 F4_0001.0058 F4_0001.0059 F4_0001.0060
#> F4_0001.0061 F4_0001.0062 F4_0001.0063 F4_0001.0064
#> F4_0001.0065 F4_0001.0066 F4_0001.0067 F4_0001.0068
#> F4_0001.0069 F4_0001.0070 F4_0001.0071 F4_0001.0072
#> F4_0001.0073 F4_0001.0074 F4_0001.0075 F4_0001.0076
#> F4_0001.0077 F4_0001.0078 F4_0001.0079 F4_0001.0080
#> F4_0001.0081 F4_0001.0082 F4_0001.0083 F4_0001.0084
#> F4_0001.0085 F4_0001.0086 F4_0001.0087 F4_0001.0088
#> F4_0001.0089 F4_0001.0090 F4_0001.0091 F4_0001.0092
#> F4_0001.0093 F4_0001.0094 F4_0001.0095 F4_0001.0096
#> F4_0001.0097 F4_0001.0098 F4_0001.0099 F4_0001.0100
#> F4_0001.0101 F4_0001.0102 F4_0001.0103 F4_0001.0104
#> F4_0001.0105 F4_0001.0106 F4_0001.0107 F4_0001.0108
#> F4_0001.0109 F4_0001.0110 F4_0001.0111 F4_0001.0112
#> F4_0001.0113 F4_0001.0114 F4_0001.0115 F4_0001.0116
#> F4_0001.0117 F4_0001.0118 F4_0001.0119 F4_0001.0120
#> F4_0001.0121 F4_0001.0122 F4_0001.0123 F4_0001.0124
#> F4_0001.0125 F4_0001.0126 F4_0001.0127 F4_0001.0128
#> F4_0001.0129 F4_0001.0130 F4_0001.0131 F4_0001.0132
#> F4_0001.0133 F4_0001.0134 F4_0001.0135 F4_0001.0136
#> F4_0001.0137 F4_0001.0138 F4_0001.0139 F4_0001.0140
#> F4_0001.0141 F4_0001.0142 F4_0001.0143 F4_0001.0144
#> F4_0001.0145 F4_0001.0146 F4_0001.0147 F4_0001.0148 F4_0001.0149
#> F4_0001.0150 F4_0001.0151 F4_0001.0152 F4_0001.0153
#> F4_0001.0154 F4_0001.0155 F4_0001.0156 F4_0001.0157
#> F4_0001.0158 F4_0001.0159 F4_0001.0160 F4_0001.0161
#> F4_0001.0162 F4_0001.0163 F4_0001.0164 F4_0001.0165
#> F4_0001.0166 F4_0001.0167 F4_0001.0168 F4_0001.0169
#> F4_0001.0170 F4_0001.0171 F4_0001.0172 F4_0001.0173
#> F4_0001.0174 F4_0001.0175 F4_0001.0176 F4_0001.0177
#> F4_0001.0178 F4_0001.0179 F4_0001.0180 F4_0001.0181 F4_0001.0182
#> F4_0001.0183 F4_0001.0184 F4_0001.0185 F4_0001.0186
#> F4_0001.0187 F4_0001.0188 F4_0001.0189 F4_0001.0190
#> F4_0001.0191 F4_0001.0192 F4_0001.0193 F4_0001.0194
#> F4_0001.0195 F4_0001.0196 F4_0001.0197 F4_0001.0198
#> F4_0001.0199 F4_0001.0200 F4_0001.0201 F4_0001.0202
#> F4_0001.0203 F4_0001.0204 F4_0001.0205 F4_0001.0206
#> F4_0001.0207 F4_0001.0208 F4_0001.0209 F4_0001.0210
#> F4_0001.0211 F4_0001.0212 F4_0001.0213 F4_0001.0214
#> F4_0001.0215 F4_0001.0216 F4_0001.0217 F4_0001.0218
#> F4_0001.0219 F4_0001.0220 F4_0001.0221 F4_0001.0222
#> F4_0001.0223 F4_0001.0224 F4_0001.0225 F4_0001.0226
#> F4_0001.0227 F4_0001.0228 F4_0001.0229 F4_0001.0230
#> F4_0001.0231 F4_0001.0232 F4_0001.0233 F4_0001.0234
#> F4_0001.0235 F4_0001.0236 F4_0001.0237 F4_0001.0238
#> F4_0001.0239 F4_0001.0240 F4_0001.0241 F4_0001.0242
#> F4_0001.0243 F4_0001.0244 F4_0001.0245 F4_0001.0246
#> F4_0001.0247 F4_0001.0248 F4_0001.0249 F4_0001.0250
#> F4_0001.0251 F4_0001.0252 F4_0001.0253 F4_0001.0254
#> F4_0001.0255 F4_0001.0256 F4_0001.0257 F4_0001.0258
#> F4_0001.0259 F4_0001.0260 F4_0001.0261 F4_0001.0262
#> F4_0001.0263 F4_0001.0264 F4_0001.0265 F4_0001.0266
#> F4_0001.0267 F4_0001.0268 F4_0001.0269 F4_0001.0270
#> F4_0001.0271 F4_0001.0272 F4_0001.0273 F4_0001.0274
#> F4_0001.0275 F4_0001.0276 F4_0001.0277 F4_0001.0278
#> F4_0001.0279 F4_0001.0280 F4_0001.0281 F4_0001.0282
#> F4_0001.0283 F4_0001.0284 F4_0001.0285 F4_0001.0286
#> F4_0001.0287 F4_0001.0288 F4_0001.0289 F4_0001.0290
#> F4_0001.0291 F4_0001.0292 F4_0001.0293 F4_0001.0294
#> F4_0001.0295 F4_0001.0296 F4_0001.0297 F4_0001.0298
#> F4_0001.0299 F4_0001.0300 F1_0001.0001 F1_0002.0001 F1_0003.0001
#> F1_0004.0001 Coll0402 Coll0425 Coll0486 Coll0659
#> [ reached getOption("max.print") -- omitted 908 rows ]
#>
#> $metadata
#> $metadata$info
#> [1] "R-geno-engine, genomic relationship matrix"
#>
#> $metadata$date
#> [1] "2024-03-04 11:07:37 JST"
#>
#> $metadata$nInds
#> [1] 908
#>
#> $metadata$genoFP
#> [1] "cf3f595abebed5876d557867f2fd2037"
#>
#>
#> $file
#> [1] "/tmp/Rtmpf5vQTG/file11296676adb09.json"This method allow to correct a pedigree relationship matrix using a genomic relationship matrix.
2 methods are implemented:
Legarra: Legarra, A, et al. 2009 A relationship matrix including full pedigree and genomic information. Journal of Dairy Science 92, 4656–4663Martini: Martini, JW, et al. 2018 The effect of the H-1 scaling factors tau and omega on the structure of H in the single-step procedure. Genetics Selection Evolution 50(1), 16. This method use additional parameterstauandomega. Martini’s method withtau=1andomega=1is equivalent to theLegarra’s method.
docker run --rm -v "$PWD"/data/results/:/results \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
relmat-combined \
--ped-relmatFile /results/breedGame_pedRelMat.csv
--geno-relmatFile /results/breedGame_genoRelMat.csv
--combine-method Martini
--tau 1
--omega 0.5
--outFile "/out/combined_relMat.json"calc_combinedRelMat(pedRelMatFile = 'data/results/breedGame_pedRelMat.csv',
genoRelMatFile = 'data/results/breedGame_genoRelMat.csv',
method = "Martini",
tau = 1,
omega = 0.5,
outFile = tempfile(fileext = ".json"))
#> 2024-03-04 11:07:38.854305 - r-calc_combinedRelMat(): Get data ...
#> 2024-03-04 11:07:38.860328 - r-readRelMat(): Read relationship matrix `csv` file ...
#> 2024-03-04 11:07:39.538838 - r-readRelMat(): Read relationship matrix `csv` file DONE
#> 2024-03-04 11:07:39.539022 - r-readRelMat(): Check loaded relationship matrix ...
#> 2024-03-04 11:07:39.717473 - r-readRelMat(): Check loaded relationship matrix DONE
#> 2024-03-04 11:07:39.71773 - r-readRelMat(): DONE, return output.
#> 2024-03-04 11:07:39.718341 - r-readRelMat(): Read relationship matrix `csv` file ...
#> 2024-03-04 11:07:40.262082 - r-readRelMat(): Read relationship matrix `csv` file DONE
#> 2024-03-04 11:07:40.262249 - r-readRelMat(): Check loaded relationship matrix ...
#> 2024-03-04 11:07:40.291506 - r-readRelMat(): Check loaded relationship matrix DONE
#> 2024-03-04 11:07:40.291683 - r-readRelMat(): DONE, return output.
#> 2024-03-04 11:07:40.291751 - r-calc_combinedRelMat(): Get data DONE
#> 2024-03-04 11:07:40.291855 - r-calc_combinedRelMat(): Calcualte combined relationship matrix ...
#> 2024-03-04 11:07:40.313483 - r-combineRelMat(): Check inputs ...
#> 2024-03-04 11:07:40.633274 - r-combineRelMat(): Check inputs DONE
#> 2024-03-04 11:07:40.633453 - r-combineRelMat(): Calculate combined relationship matrix ...
#> 2024-03-04 11:07:40.9375 - r-combineRelMat(): Calculate combined relationship matrix DONE
#> 2024-03-04 11:07:40.937689 - r-combineRelMat(): DONE, return output.
#> 2024-03-04 11:07:40.937763 - r-calc_combinedRelMat(): Calcualte combined relationship matrix DONE
#> 2024-03-04 11:07:40.937824 - r-calc_combinedRelMat(): Get metadata ...
#> 2024-03-04 11:07:41.040536 - r-calc_combinedRelMat(): Get metadata DONE
#> 2024-03-04 11:07:41.040697 - r-calc_combinedRelMat(): Save results ...
#> 2024-03-04 11:07:41.040837 - r-saveRelMat(): Check relationship matrix ...
#> 2024-03-04 11:07:41.161059 - r-saveRelMat(): Check relationship matrix DONE
#> 2024-03-04 11:07:41.161235 - r-saveRelMat(): Check file ...
#> 2024-03-04 11:07:41.161346 - r-saveRelMat(): Check file DONE
#> 2024-03-04 11:07:41.161409 - r-saveRelMat(): Check file format ...
#> 2024-03-04 11:07:41.161535 - r-saveRelMat(): Check file format DONE
#> 2024-03-04 11:07:41.161602 - r-saveRelMat(): Write relationship matrix in `.json` file ...
#> 2024-03-04 11:07:43.608388 - r-saveRelMat(): Write relationship matrix in `.json` file DONE
#> 2024-03-04 11:07:43.609027 - r-calc_combinedRelMat(): Save results DONE
#> $relMat
#> F2_0001.0001 F2_0001.0002 F2_0001.0003 F2_0001.0004
#> F2_0001.0005 F2_0001.0006 F2_0001.0007 F2_0001.0008
#> F2_0001.0009 F2_0001.0010 F2_0001.0011 F2_0001.0012
#> F2_0001.0013 F2_0001.0014 F2_0001.0015 F2_0001.0016
#> F2_0001.0017 F2_0001.0018 F2_0001.0019 F2_0001.0020
#> F2_0001.0021 F2_0001.0022 F2_0001.0023 F2_0001.0024
#> F2_0001.0025 F2_0001.0026 F2_0001.0027 F2_0001.0028
#> F2_0001.0029 F2_0001.0030 F2_0001.0031 F2_0001.0032
#> F2_0001.0033 F2_0001.0034 F2_0001.0035 F2_0001.0036
#> F2_0001.0037 F2_0001.0038 F2_0001.0039 F2_0001.0040
#> F2_0001.0041 F2_0001.0042 F2_0001.0043 F2_0001.0044
#> F2_0001.0045 F2_0001.0046 F2_0001.0047 F2_0001.0048
#> F2_0001.0049 F2_0001.0050 F2_0001.0051 F2_0001.0052
#> F2_0001.0053 F2_0001.0054 F2_0001.0055 F2_0001.0056
#> F2_0001.0057 F2_0001.0058 F2_0001.0059 F2_0001.0060
#> F2_0001.0061 F2_0001.0062 F2_0001.0063 F2_0001.0064
#> F2_0001.0065 F2_0001.0066 F2_0001.0067 F2_0001.0068
#> F2_0001.0069 F2_0001.0070 F2_0001.0071 F2_0001.0072
#> F2_0001.0073 F2_0001.0074 F2_0001.0075 F2_0001.0076
#> F2_0001.0077 F2_0001.0078 F2_0001.0079 F2_0001.0080
#> F2_0001.0081 F2_0001.0082 F2_0001.0083 F2_0001.0084
#> F2_0001.0085 F2_0001.0086 F2_0001.0087 F2_0001.0088
#> F2_0001.0089 F2_0001.0090 F2_0001.0091 F2_0001.0092
#> F2_0001.0093 F2_0001.0094 F2_0001.0095 F2_0001.0096
#> F2_0001.0097 F2_0001.0098 F2_0001.0099 F2_0001.0100
#> F2_0002.0001 F2_0002.0002 F2_0002.0003 F2_0002.0004
#> F2_0002.0005 F2_0002.0006 F2_0002.0007 F2_0002.0008
#> F2_0002.0009 F2_0002.0010 F2_0002.0011 F2_0002.0012
#> F2_0002.0013 F2_0002.0014 F2_0002.0015 F2_0002.0016
#> F2_0002.0017 F2_0002.0018 F2_0002.0019 F2_0002.0020
#> F2_0002.0021 F2_0002.0022 F2_0002.0023 F2_0002.0024
#> F2_0002.0025 F2_0002.0026 F2_0002.0027 F2_0002.0028
#> F2_0002.0029 F2_0002.0030 F2_0002.0031 F2_0002.0032
#> F2_0002.0033 F2_0002.0034 F2_0002.0035 F2_0002.0036
#> F2_0002.0037 F2_0002.0038 F2_0002.0039 F2_0002.0040
#> F2_0002.0041 F2_0002.0042 F2_0002.0043 F2_0002.0044
#> F2_0002.0045 F2_0002.0046 F2_0002.0047 F2_0002.0048
#> F2_0002.0049 F2_0002.0050 F2_0002.0051 F2_0002.0052
#> F2_0002.0053 F2_0002.0054 F2_0002.0055 F2_0002.0056
#> F2_0002.0057 F2_0002.0058 F2_0002.0059 F2_0002.0060
#> F2_0002.0061 F2_0002.0062 F2_0002.0063 F2_0002.0064
#> F2_0002.0065 F2_0002.0066 F2_0002.0067 F2_0002.0068
#> F2_0002.0069 F2_0002.0070 F2_0002.0071 F2_0002.0072
#> F2_0002.0073 F2_0002.0074 F2_0002.0075 F2_0002.0076
#> F2_0002.0077 F2_0002.0078 F2_0002.0079 F2_0002.0080
#> F2_0002.0081 F2_0002.0082 F2_0002.0083 F2_0002.0084
#> F2_0002.0085 F2_0002.0086 F2_0002.0087 F2_0002.0088
#> F2_0002.0089 F2_0002.0090 F2_0002.0091 F2_0002.0092
#> F2_0002.0093 F2_0002.0094 F2_0002.0095 F2_0002.0096
#> F2_0002.0097 F2_0002.0098 F2_0002.0099 F2_0002.0100
#> F2_0003.0001 F2_0003.0002 F2_0003.0003 F2_0003.0004
#> F2_0003.0005 F2_0003.0006 F2_0003.0007 F2_0003.0008
#> F2_0003.0009 F2_0003.0010 F2_0003.0011 F2_0003.0012
#> F2_0003.0013 F2_0003.0014 F2_0003.0015 F2_0003.0016
#> F2_0003.0017 F2_0003.0018 F2_0003.0019 F2_0003.0020
#> F2_0003.0021 F2_0003.0022 F2_0003.0023 F2_0003.0024
#> F2_0003.0025 F2_0003.0026 F2_0003.0027 F2_0003.0028
#> F2_0003.0029 F2_0003.0030 F2_0003.0031 F2_0003.0032
#> F2_0003.0033 F2_0003.0034 F2_0003.0035 F2_0003.0036
#> F2_0003.0037 F2_0003.0038 F2_0003.0039 F2_0003.0040
#> F2_0003.0041 F2_0003.0042 F2_0003.0043 F2_0003.0044
#> F2_0003.0045 F2_0003.0046 F2_0003.0047 F2_0003.0048
#> F2_0003.0049 F2_0003.0050 F2_0003.0051 F2_0003.0052
#> F2_0003.0053 F2_0003.0054 F2_0003.0055 F2_0003.0056
#> F2_0003.0057 F2_0003.0058 F2_0003.0059 F2_0003.0060
#> F2_0003.0061 F2_0003.0062 F2_0003.0063 F2_0003.0064
#> F2_0003.0065 F2_0003.0066 F2_0003.0067 F2_0003.0068
#> F2_0003.0069 F2_0003.0070 F2_0003.0071 F2_0003.0072
#> F2_0003.0073 F2_0003.0074 F2_0003.0075 F2_0003.0076
#> F2_0003.0077 F2_0003.0078 F2_0003.0079 F2_0003.0080
#> F2_0003.0081 F2_0003.0082 F2_0003.0083 F2_0003.0084
#> F2_0003.0085 F2_0003.0086 F2_0003.0087 F2_0003.0088
#> F2_0003.0089 F2_0003.0090 F2_0003.0091 F2_0003.0092
#> F2_0003.0093 F2_0003.0094 F2_0003.0095 F2_0003.0096
#> F2_0003.0097 F2_0003.0098 F2_0003.0099 F2_0003.0100
#> F3_0001.0001 F3_0001.0002 F3_0001.0003 F3_0001.0004
#> F3_0001.0005 F3_0001.0006 F3_0001.0007 F3_0001.0008
#> F3_0001.0009 F3_0001.0010 F3_0001.0011 F3_0001.0012
#> F3_0001.0013 F3_0001.0014 F3_0001.0015 F3_0001.0016
#> F3_0001.0017 F3_0001.0018 F3_0001.0019 F3_0001.0020
#> F3_0001.0021 F3_0001.0022 F3_0001.0023 F3_0001.0024
#> F3_0001.0025 F3_0001.0026 F3_0001.0027 F3_0001.0028
#> F3_0001.0029 F3_0001.0030 F3_0001.0031 F3_0001.0032
#> F3_0001.0033 F3_0001.0034 F3_0001.0035 F3_0001.0036
#> F3_0001.0037 F3_0001.0038 F3_0001.0039 F3_0001.0040
#> F3_0001.0041 F3_0001.0042 F3_0001.0043 F3_0001.0044
#> F3_0001.0045 F3_0001.0046 F3_0001.0047 F3_0001.0048
#> F3_0001.0049 F3_0001.0050 F3_0001.0051 F3_0001.0052
#> F3_0001.0053 F3_0001.0054 F3_0001.0055 F3_0001.0056
#> F3_0001.0057 F3_0001.0058 F3_0001.0059 F3_0001.0060
#> F3_0001.0061 F3_0001.0062 F3_0001.0063 F3_0001.0064
#> F3_0001.0065 F3_0001.0066 F3_0001.0067 F3_0001.0068
#> F3_0001.0069 F3_0001.0070 F3_0001.0071 F3_0001.0072
#> F3_0001.0073 F3_0001.0074 F3_0001.0075 F3_0001.0076
#> F3_0001.0077 F3_0001.0078 F3_0001.0079 F3_0001.0080
#> F3_0001.0081 F3_0001.0082 F3_0001.0083 F3_0001.0084
#> F3_0001.0085 F3_0001.0086 F3_0001.0087 F3_0001.0088
#> F3_0001.0089 F3_0001.0090 F3_0001.0091 F3_0001.0092
#> F3_0001.0093 F3_0001.0094 F3_0001.0095 F3_0001.0096
#> F3_0001.0097 F3_0001.0098 F3_0001.0099 F3_0001.0100
#> F3_0002.0001 F3_0002.0002 F3_0002.0003 F3_0002.0004
#> F3_0002.0005 F3_0002.0006 F3_0002.0007 F3_0002.0008
#> F3_0002.0009 F3_0002.0010 F3_0002.0011 F3_0002.0012
#> F3_0002.0013 F3_0002.0014 F3_0002.0015 F3_0002.0016
#> F3_0002.0017 F3_0002.0018 F3_0002.0019 F3_0002.0020
#> F3_0002.0021 F3_0002.0022 F3_0002.0023 F3_0002.0024
#> F3_0002.0025 F3_0002.0026 F3_0002.0027 F3_0002.0028
#> F3_0002.0029 F3_0002.0030 F3_0002.0031 F3_0002.0032
#> F3_0002.0033 F3_0002.0034 F3_0002.0035 F3_0002.0036
#> F3_0002.0037 F3_0002.0038 F3_0002.0039 F3_0002.0040
#> F3_0002.0041 F3_0002.0042 F3_0002.0043 F3_0002.0044
#> F3_0002.0045 F3_0002.0046 F3_0002.0047 F3_0002.0048
#> F3_0002.0049 F3_0002.0050 F3_0002.0051 F3_0002.0052
#> F3_0002.0053 F3_0002.0054 F3_0002.0055 F3_0002.0056
#> F3_0002.0057 F3_0002.0058 F3_0002.0059 F3_0002.0060
#> F3_0002.0061 F3_0002.0062 F3_0002.0063 F3_0002.0064
#> F3_0002.0065 F3_0002.0066 F3_0002.0067 F3_0002.0068
#> F3_0002.0069 F3_0002.0070 F3_0002.0071 F3_0002.0072
#> F3_0002.0073 F3_0002.0074 F3_0002.0075 F3_0002.0076
#> F3_0002.0077 F3_0002.0078 F3_0002.0079 F3_0002.0080
#> F3_0002.0081 F3_0002.0082 F3_0002.0083 F3_0002.0084
#> F3_0002.0085 F3_0002.0086 F3_0002.0087 F3_0002.0088
#> F3_0002.0089 F3_0002.0090 F3_0002.0091 F3_0002.0092
#> F3_0002.0093 F3_0002.0094 F3_0002.0095 F3_0002.0096
#> F3_0002.0097 F3_0002.0098 F3_0002.0099 F3_0002.0100
#> F3_0003.0001 F3_0003.0002 F3_0003.0003 F3_0003.0004
#> F3_0003.0005 F3_0003.0006 F3_0003.0007 F3_0003.0008
#> F3_0003.0009 F3_0003.0010 F3_0003.0011 F3_0003.0012
#> F3_0003.0013 F3_0003.0014 F3_0003.0015 F3_0003.0016
#> F3_0003.0017 F3_0003.0018 F3_0003.0019 F3_0003.0020
#> F3_0003.0021 F3_0003.0022 F3_0003.0023 F3_0003.0024
#> F3_0003.0025 F3_0003.0026 F3_0003.0027 F3_0003.0028
#> F3_0003.0029 F3_0003.0030 F3_0003.0031 F3_0003.0032
#> F3_0003.0033 F3_0003.0034 F3_0003.0035 F3_0003.0036
#> F3_0003.0037 F3_0003.0038 F3_0003.0039 F3_0003.0040
#> F3_0003.0041 F3_0003.0042 F3_0003.0043 F3_0003.0044
#> F3_0003.0045 F3_0003.0046 F3_0003.0047 F3_0003.0048
#> F3_0003.0049 F3_0003.0050 F3_0003.0051 F3_0003.0052
#> F3_0003.0053 F3_0003.0054 F3_0003.0055 F3_0003.0056
#> F3_0003.0057 F3_0003.0058 F3_0003.0059 F3_0003.0060
#> F3_0003.0061 F3_0003.0062 F3_0003.0063 F3_0003.0064
#> F3_0003.0065 F3_0003.0066 F3_0003.0067 F3_0003.0068
#> F3_0003.0069 F3_0003.0070 F3_0003.0071 F3_0003.0072
#> F3_0003.0073 F3_0003.0074 F3_0003.0075 F3_0003.0076
#> F3_0003.0077 F3_0003.0078 F3_0003.0079 F3_0003.0080
#> F3_0003.0081 F3_0003.0082 F3_0003.0083 F3_0003.0084
#> F3_0003.0085 F3_0003.0086 F3_0003.0087 F3_0003.0088
#> F3_0003.0089 F3_0003.0090 F3_0003.0091 F3_0003.0092
#> F3_0003.0093 F3_0003.0094 F3_0003.0095 F3_0003.0096
#> F3_0003.0097 F3_0003.0098 F3_0003.0099 F3_0003.0100
#> F4_0001.0001 F4_0001.0002 F4_0001.0003 F4_0001.0004
#> F4_0001.0005 F4_0001.0006 F4_0001.0007 F4_0001.0008
#> F4_0001.0009 F4_0001.0010 F4_0001.0011 F4_0001.0012
#> F4_0001.0013 F4_0001.0014 F4_0001.0015 F4_0001.0016
#> F4_0001.0017 F4_0001.0018 F4_0001.0019 F4_0001.0020
#> F4_0001.0021 F4_0001.0022 F4_0001.0023 F4_0001.0024
#> F4_0001.0025 F4_0001.0026 F4_0001.0027 F4_0001.0028
#> F4_0001.0029 F4_0001.0030 F4_0001.0031 F4_0001.0032
#> F4_0001.0033 F4_0001.0034 F4_0001.0035 F4_0001.0036
#> F4_0001.0037 F4_0001.0038 F4_0001.0039 F4_0001.0040
#> F4_0001.0041 F4_0001.0042 F4_0001.0043 F4_0001.0044
#> F4_0001.0045 F4_0001.0046 F4_0001.0047 F4_0001.0048
#> F4_0001.0049 F4_0001.0050 F4_0001.0051 F4_0001.0052
#> F4_0001.0053 F4_0001.0054 F4_0001.0055 F4_0001.0056
#> F4_0001.0057 F4_0001.0058 F4_0001.0059 F4_0001.0060
#> F4_0001.0061 F4_0001.0062 F4_0001.0063 F4_0001.0064
#> F4_0001.0065 F4_0001.0066 F4_0001.0067 F4_0001.0068
#> F4_0001.0069 F4_0001.0070 F4_0001.0071 F4_0001.0072
#> F4_0001.0073 F4_0001.0074 F4_0001.0075 F4_0001.0076
#> F4_0001.0077 F4_0001.0078 F4_0001.0079 F4_0001.0080
#> F4_0001.0081 F4_0001.0082 F4_0001.0083 F4_0001.0084
#> F4_0001.0085 F4_0001.0086 F4_0001.0087 F4_0001.0088
#> F4_0001.0089 F4_0001.0090 F4_0001.0091 F4_0001.0092
#> F4_0001.0093 F4_0001.0094 F4_0001.0095 F4_0001.0096
#> F4_0001.0097 F4_0001.0098 F4_0001.0099 F4_0001.0100
#> F4_0001.0101 F4_0001.0102 F4_0001.0103 F4_0001.0104
#> F4_0001.0105 F4_0001.0106 F4_0001.0107 F4_0001.0108
#> F4_0001.0109 F4_0001.0110 F4_0001.0111 F4_0001.0112
#> F4_0001.0113 F4_0001.0114 F4_0001.0115 F4_0001.0116
#> F4_0001.0117 F4_0001.0118 F4_0001.0119 F4_0001.0120
#> F4_0001.0121 F4_0001.0122 F4_0001.0123 F4_0001.0124
#> F4_0001.0125 F4_0001.0126 F4_0001.0127 F4_0001.0128
#> F4_0001.0129 F4_0001.0130 F4_0001.0131 F4_0001.0132
#> F4_0001.0133 F4_0001.0134 F4_0001.0135 F4_0001.0136
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#> [ reached getOption("max.print") -- omitted 1904 rows ]
#>
#> $metadata
#> $metadata$info
#> [1] "R-geno-engine, combined relationship matrix"
#>
#> $metadata$date
#> [1] "2024-03-04 11:07:40 JST"
#>
#> $metadata$nInds
#> [1] 1904
#>
#> $metadata$geno_relMatFP
#> [1] "a9b05abfa32259b91fb4bcddc2aa47dc"
#>
#> $metadata$ped_relMatFP
#> [1] "9d809e52fcf7f9fe62421d80131d8b85"
#>
#>
#> $file
#> [1] "/tmp/Rtmpf5vQTG/file1129641451106.json"The engine include a function to generate an heatmap of a relationship matrix:
This heatmap can either be interactive (.html file) or static (.png file).
heatmap
docker run --rm -v "$PWD"/data/results/:/results \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
relmat-heatmap \
--relmatFile "/results/pedRelMat.json" \
--outFile "/out/relMat_heatmap.png"draw_relHeatmap(relMatFile = 'data/results/pedigreeRelationship.csv',
relMatUrl = NULL,
interactive = FALSE,
outFile = tempfile(fileext = ".png"))
#> 2024-03-04 11:07:43.731536 - r-draw_relHeatmap(): Get data ...
#> 2024-03-04 11:07:43.731843 - r-readRelMat(): Read relationship matrix `csv` file ...
#> 2024-03-04 11:07:43.736876 - r-readRelMat(): Read relationship matrix `csv` file DONE
#> 2024-03-04 11:07:43.736974 - r-readRelMat(): Check loaded relationship matrix ...
#> 2024-03-04 11:07:43.7373 - r-readRelMat(): Check loaded relationship matrix DONE
#> 2024-03-04 11:07:43.73737 - r-readRelMat(): DONE, return output.
#> 2024-03-04 11:07:43.737432 - r-draw_relHeatmap(): Get data DONE
#> 2024-03-04 11:07:43.737497 - r-draw_relHeatmap(): Open connexion to draw the png plot ...
#> 2024-03-04 11:07:43.73929 - r-draw_relHeatmap(): Open connexion to draw the png plot DONE
#> 2024-03-04 11:07:43.739461 - r-draw_relHeatmap(): Draw relationship heatmap ...
#> 2024-03-04 11:07:43.749123 - r-manPlot(): Check parameters ...
#> 2024-03-04 11:07:43.749696 - r-manPlot(): Check parameters DONE
#> 2024-03-04 11:07:43.749776 - r-manPlot(): Create static heatmap ...
#> 2024-03-04 11:07:43.787163 - r-manPlot(): Create static heatmap DONE
#> 2024-03-04 11:07:43.78731 - r-manPlot(): DONE, return output
#> 2024-03-04 11:07:43.787379 - r-draw_relHeatmap(): Draw relationship heatmap DONE
#> 2024-03-04 11:07:43.787441 - r-draw_relHeatmap(): Save results ...
#> 2024-03-04 11:07:43.954777 - r-draw_relHeatmap(): Save results DONE
#> NULLClick to expand
This is quite experimental
The engine contain a function to generate an interactive network of the pedigree relations.
Individuals with parental relations are linked with an arrow pointing from the parents to the offspring.
pedNet
Note: This network is not organized by generation (like a genealogical tree) can be big, messy and not very responsive if the number of individual in the pedigree is big.
docker run --rm -v "$PWD"/data/pedigree/:/pedigree \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
pedNetwork \
--pedFile "/pedigree/testPedData_char.csv" \
--outFile "/out/pedNet.html"Main function
imgFile <- draw_pedNetwork(pedFile = "data/pedigree/testPedData_char.csv",
pedUrl = NULL,
unknown_string = '',
header = TRUE,
outFile = tempfile(fileext = ".html"))
#> 2024-03-04 11:07:43.959013 - r-draw_pedNetwork(): Get data ...
#> 2024-03-04 11:07:43.959258 - r-readPedData: Read pedigree file ...
#> 2024-03-04 11:07:43.959875 - r-readPedData: Read pedigree file DONE
#> 2024-03-04 11:07:43.959952 - r-readPedData: Check pedigree file ...
#> 2024-03-04 11:07:43.96619 - r-readPedData: Check pedigree file DONE
#> 2024-03-04 11:07:43.966278 - r-readPedData: DONE, return output.
#> 2024-03-04 11:07:43.966353 - r-draw_pedNetwork(): Get data DONE
#> 2024-03-04 11:07:43.966417 - r-draw_pedNetwork(): Draw pedigree interactive network ...
#> 2024-03-04 11:07:43.983666 - r-pedNetwork(): Check parameters ...
#> 2024-03-04 11:07:43.983812 - r-pedNetwork(): Check inputs ...
#> 2024-03-04 11:07:43.983891 - r-pedNetwork(): Check inputs DONE
#> 2024-03-04 11:07:43.983952 - r-pedNetwork(): Create network data ...
#> 2024-03-04 11:07:43.984792 - r-pedNetwork(): Create network data DONE
#> 2024-03-04 11:07:43.984867 - r-pedNetwork(): Create network ...
#> 2024-03-04 11:07:43.99396 - r-pedNetwork(): Create network DONE
#> 2024-03-04 11:07:43.994056 - r-pedNetwork(): DONE, return output.
#> 2024-03-04 11:07:43.994125 - r-draw_pedNetwork(): Draw pedigree interactive network DONE
#> 2024-03-04 11:07:43.9942 - r-draw_pedNetwork(): Save results ...
#> 2024-03-04 11:07:44.021553 - r-draw_pedNetwork(): Save results DONEClick to expand
Introduction
Click to see GitHub version
This engine contain a simulation tool that can simulate the genotypes of some progeny given the phased genotypes of parental individuals.
The for the simulation, the engine needs:
- The phased genotypes of the parents
- A crossing table specifying which cross to simulate
- The SNP coordinates in Morgan
To generate the SNP genotypes of new individuals from those of the parents, the engine simulate two gametogenesis, one from each parent:
For each pair of chromosome:
We drew the number of crossing-overs
$$ n_{co} \sim \text{Pois}(l_{chr}) $$
When
$$ Y[i] = \left{ \begin{array}{ll} X[a,i] & \text{if} \quad \exists\ k \in [0, floor\left(\frac{n_{co}}{2}\right)], pos_{2k} \leq pos_i < pos_{2k+1} \ X[b,i] & \text{if} \quad \exists\ k \in [1, floor\left(\frac{n_{co}}{2}\right)], pos_{2k-1} < pos_i \leq pos_{2k} \end{array} \right. $$
where
Genotype of the offspring is obtain by merging two gametes from its parents.
Click to see GitLab version
This engine contain a simulation tool that can simulate the genotypes of some progeny given the phased genotypes of parental individuals.
The for the simulation, the engine needs:
- The phased genotypes of the parents
- A crossing table specifying which cross to simulate
- The SNP coordinates in Morgan
To generate the SNP genotypes of new individuals from those of the parents, the engine simulate two gametogenesis, one from each parent:
For each pair of chromosome:
- We drew the number of crossing-overs
$n_{co}$ for each chromosome in a Poisson distribution of rate$l_{chr}$ , which is the length of the chromosome in Morgan:$n_{co} \sim \text{Pois}(l_{chr})$ - When
$n_{co} \neq 0$ , we drew independently the positions of crossing-overs in a uniform distribution along the length of the chromosome in Morgan. We had, the sampled positions$pos_i$ ($\forall i \in \llbracket 0,n_{co}+1 \rrbracket$ ) of the crossing-overs so that$pos_j < pos_{j+1}$ ($\forall j \in \llbracket0,n_{co}\rrbracket$ ) with$pos_0 = 0$ and$pos_{n_{co}+1} = l_{chr}$ . -
$X$ was the$2 \times {n_{snp}}_k$ matrix representing the genotype of the parent for the current pair of chromosomes$k$ . Each individual represents one chromosome of the pair.$Y$ was the vector of length${n_{snp}}_k$ representing the genotype of the gamete for the chromosome pair. We set$[a,b] = [1, 2]$ or$[2, 1]$ with probability$\frac{1}{2}$ .$Y$ was then calculated as:
Y[i] = \left\{
\begin{array}{ll}
X[a,i] & \text{if} \quad \exists\ k \in [0, floor\left(\frac{n_{co}}{2}\right)], pos_{2k} \leq pos_i < pos_{2k+1} \\
X[b,i] & \text{if} \quad \exists\ k \in [1, floor\left(\frac{n_{co}}{2}\right)], pos_{2k-1} < pos_i \leq pos_{2k}
\end{array}
\right.
where
Genotype of the offspring is obtain by merging two gametes from its parents.
Command
docker run --rm -v "$PWD"/data/:/data \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
crossing-simulation \
--genoFile "/data/geno/breedGame_phasedGeno.vcf.gz" \
--crossTableFile "/data/crossingTable/breedGame_crossTable.csv" \
--SNPcoordFile "/data/SNPcoordinates/breedingGame_SNPcoord.csv" \
--nCross 10 \
--outFile "/out/crossSim.vcf.gz"Main function
crossingSimulation(genoFile = 'data/geno/breedGame_phasedGeno.vcf.gz',
crossTableFile = 'data/crossingTable/breedGame_crossTable.csv',
SNPcoordFile = 'data/SNPcoordinates/breedingGame_SNPcoord.csv',
nCross = 10,
outFile = tempfile(fileext = ".vcf.gz"))
#> 2024-03-04 11:07:44.043695 - r-crossingSimulation(): Get data ...
#> 2024-03-04 11:07:44.064659 - r-readPhasedGeno(): Check file extention ...
#> 2024-03-04 11:07:44.064997 - r-readPhasedGeno(): Read geno file ...
#> 2024-03-04 11:07:46.625616 - r-readPhasedGeno(): Read phased geno file DONE
#> 2024-03-04 11:07:46.626061 - r-readPhasedGeno(): Extract SNP information...
#> 2024-03-04 11:07:46.627621 - r-readPhasedGeno(): Extract SNP information DONE
#> 2024-03-04 11:07:46.627697 - r-readPhasedGeno(): Check pahsing ...
#> 2024-03-04 11:07:46.845951 - r-readPhasedGeno(): Check pahsing DONE
#> 2024-03-04 11:07:46.846128 - r-readPhasedGeno(): Extract haplotypes...
#> 2024-03-04 11:07:50.253242 - r-readPhasedGeno(): Extract haplotypes DONE
#> 2024-03-04 11:07:50.253401 - r-readPhasedGeno(): DONE, return output.
#> 2024-03-04 11:07:50.273762 - r-readSNPcoord(): Read snps coordinates file ...
#> 2024-03-04 11:07:50.287104 - r-readSNPcoord(): Read snps coordinates file DONE
#> 2024-03-04 11:07:50.287212 - r-readSNPcoord(): Check snps coordinates file ...
#> 2024-03-04 11:07:50.299128 - r-readSNPcoord(): Check snps coordinates file DONE
#> 2024-03-04 11:07:50.312847 - r-readCrossTable: Read crossing table file ...
#> 2024-03-04 11:07:50.313926 - r-readCrossTable: Read crossing table file DONE
#> 2024-03-04 11:07:50.314003 - r-readCrossTable: Check crossing table file ...
#> 2024-03-04 11:07:50.314749 - r-readCrossTable: Generate simulated individuals names...
#> 2024-03-04 11:07:50.315084 - r-readCrossTable: Generate simulated individuals names DONE
#> 2024-03-04 11:07:50.31516 - r-crossingSimulation(): Get data DONE
#> 2024-03-04 11:07:50.31522 - r-crossingSimulation(): Check SNP's coordinates consistency between `.vcf` and SNPcoordinate file ...
#> 2024-03-04 11:07:50.352706 - r-crossingSimulation(): Check SNP's coordinates consistency between `.vcf` and SNPcoordinate file DONE
#> 2024-03-04 11:07:50.352856 - r-crossingSimulation(): Check individuals' names consistency between `.vcf` and `.csv` file ...
#> 2024-03-04 11:07:50.36048 - r-crossingSimulation(): Check individuals' names consistency between `.vcf` and `.csv` file DONE
#> 2024-03-04 11:07:50.360622 - r-crossingSimulation(): Initialise simulation ...
#> 2024-03-04 11:07:50.379837 - r-initializeSimulation(): Extract chromosomes information ...
#> 2024-03-04 11:07:50.385029 - r-initializeSimulation(): Create specie ...
#> 2024-03-04 11:07:50.388993 - r-initializeSimulation(): Create specie DONE
#> 2024-03-04 11:07:50.389084 - r-initializeSimulation(): Create snp information ...
#> 2024-03-04 11:07:50.404275 - r-initializeSimulation(): Create snp information DONE
#> 2024-03-04 11:07:50.404382 - r-initializeSimulation(): Create parents population ...
#> 2024-03-04 11:07:52.266401 - r-initializeSimulation(): Create parents population DONE
#> 2024-03-04 11:07:52.266581 - r-crossingSimulation(): Initialise simulation DONE
#> 2024-03-04 11:07:52.266649 - r-crossingSimulation(): Crossing simulation ...
#> 2024-03-04 11:08:35.159658 - r-crossingSimulation(): Crossing simulation DONE
#> 2024-03-04 11:08:35.159838 - r-crossingSimulation(): Write output file ...
#> 2024-03-04 11:08:54.797749 - r-crossingSimulation(): Write output file DONE
#> [1] "/tmp/Rtmpf5vQTG/file112965ca7794c.vcf.gz"Click to expand
Introduction
We would like to estimate the esperance and variance of progenies’ genotypes from parents’ (phased) genotypes and recombination rates of markers.
Theory
Let’s consider 2 markers
- the maternal alleles
$x_M, y_M$ - the paternal alleles:
$x_P, y_P$ .
Let
We call
We are interested in the variance, covariance and expected value of
For one gamete, we have: $$ \begin{align} \mathbb{E}(X_i) &= \frac{1}{2}(x_M+x_P)\ Cov(X_i,Y_i) &= \frac{1}{4} (1-2r_{xy}) z_{ixy}\ Var(X_i) &= \frac{1}{4} z_{ixx}\ \text{with } z_{ixy} &= x_My_M + x_Py_P - x_My_P - x_Py_M\ \end{align} $$
And for a progeny from parent 1 and 2 we have:
$$ \begin{align} \mathbb{E}(X_1 + X_2) &= \frac{1}{2}(x_{1M} + x_{1P} + x_{2M} + x_{2P})\ Cov(X_1 + X_2, Y_1 + Y_2) &= \frac{1}{4} (1-2r) (z_{1xy} + z_{2xy})\ Var(X_1+X_2) &= \frac{1}{4}(z_{1xx} + z_{2xx})\ \end{align} $$
We can also calculate the expected value and variance of the genetic value of the progeny if we suppose an additive genetic architecture and if we know the markers effects
$$ \begin{align} \mathbb{E}(G) &= \frac{1}{2} \sum_i e_i (x_{1iM} + x_{1iP} + x_{2iM} + x_{2iP}) \ Var(G) &= \frac{1}{4} \sum_{jk} e_je_k (1-2r_{jk})(z_{1jk} + z_{2jk}) \ \end{align} $$
When considering 2 traits
With:
$$ \begin{eqnarray} \mathbb{E}(X_{1m} X_{1k}) &=& \frac{1}{4}(2x_{1mM}x_{1kM}
- 2x_{1mP}x_{1kP})
- \frac{r_{X_{1m}X_{1k}}}{2} (x_{1mM} x_{1kM} - x_{1mM} x_{1kP} - x_{1mP} x_{1kM} + x_{1mP} x_{1kP})\ \mathbb{E}(X_{2m} X_{2k}) &=& \frac{1}{4}(2x_{2mM}x_{2kM}
- 2x_{2mP}x_{2kP})
- \frac{r_{X_{2m}X_{2k}}}{2} (x_{2mM} x_{2kM} - x_{2mM} x_{2kP} - x_{2mP} x_{2kM} + x_{2mP} x_{2kP})\ \mathbb{E}(X_{1m} X_{2k}) &=& \frac{1}{4} ( x_{1mM} x_{2kM}
- x_{1mM} x_{2kP}
- x_{1mP} x_{2kM}
- x_{1mP} x_{2kP} )\ \mathbb{E}(X_{2m} X_{1k}) &=& \frac{1}{4} ( x_{2mM} x_{1kM}
- x_{2mM} x_{1kP}
- x_{2mP} x_{1kM}
- x_{2mP} x_{1kP} ) \end{eqnarray} $$
Proof
Let
We have the following formula:
$P(x_{iM}) = P(x_{iP}) = P(y_{iM}) = P(y_{iP}) = \frac{1}{2}$ $P(x_{iM}y_{iM}) = P(x_{iP}y{i_P})$ $P(x_{iM}y_{iP}) = P(x_{iP}y_{iM})$
Expected value
$$ \begin{align} \mathbb{E}(X_i) &= \sum_jx_{ij}P(x_{ij})\ \mathbb{E}(X_i) &= x_{iM}P(x_{iM}) + x_{iP}P(x_{iP})\ \mathbb{E}(X_i) &= \frac{1}{2} (x_{iM}+x_{iP})\ \end{align} $$
Covariance
By definition the recombination rate
$$ \begin{align} r_{xy} &\overset{def}{=} P(x_{iM}y_{iP} \cup x_{iP}y_{iM})\ &= P(x_{iM}y_{iP}) + P(x_{iP}y_{iM}) - P(x_{iM}y_{iP} \cap {ix}Py{iM})\ &= P(x_{iM}y_{iP}) + P(x_{iP}y_{iM}) - 0\ r_{xy} &= 2P(x_{iM}y_{iP}) = 2P(x_{iP}y_{iM})\ P(x_{iM}y_{iP}) = P(x_{iP}y_{iM}) &= \frac{r_{xy}}{2} \end{align} $$
and
$$ \begin{align} 1 - r_{xy} &= 1 - P(x_{iM}y_{iP} \cup x_{iP}y_{iM})\ &= P(x_{iM}y_{iM} \cup x_{iP}y_{iP})\ &= P(x_{iM}y_{iM}) + P(x_{iP}y_{iP}) - P(x_{iM}y_{iM} \cap x_{iP}y_{iP})\ &= P(x_{iM}y_{iM}) + P(x_{iP}y_{iP}) - 0\ 1 - r_{xy} &= 2P(x_{iM}y_{iM}) = 2P(x_{iP}y_{iP})\ P(x_{iM}y_{iM}) = P(x_{iP}y_{iP}) &= \frac{1-r_{xy}}{2} \end{align} $$
We can now calculate the formula of
$$ \begin{align} Cov(X_i, Y_i) &\overset{def}{=} \mathbb{E}[(X_i-\mathbb{E}(X_i))(Y_i-\mathbb{E}(Y_i))]\ Cov(X_i, Y_i) &= \mathbb{E}(X_iY_i) - \mathbb{E}(X_i)\mathbb{E}(Y_i) \ \text{(covariance property)}\ \end{align} $$
First let’s calculate
$$ \begin{align} \mathbb{E}(X_i)\mathbb{E}(Y_i) &= \frac{1}{2}(x_{iM}+x_{iP})\frac{1}{2}(y_{iM}+y_{iP})\ \mathbb{E}(X_i)\mathbb{E}(Y_i) &= \frac{1}{4}(x_{iM}+x_{iP})(y_{iM}+y_{iP})\ \mathbb{E}(X_i)\mathbb{E}(Y_i) &= \frac{1}{4}(x_{iM}y_{iM} + x_{iM}y_{iP} + x_{iP}y_{iM} + x_{iP}y_{iP})\ \end{align} $$
Now let’s calculate
$$ \begin{align} \mathbb{E}(X_iY_i) &= \sum_{j,k}x_{ij}y{ik} \times P(x_{ij}y_{ik})\ \mathbb{E}(X_iY_i) &= x_{iM}y_{iM} P(x_{iM}y_{iM}) + x_{iM}y_{iP} P(x_{iM}y_{iP}) + x_{iP}y_{iM} P(x_{iP}y_{iM}) + x_{iP}y_{iP} P(x_{iP}y_{iP})\ \mathbb{E}(X_iY_i) &= x_{iM}y_{iM} \frac{1-r_{xy}}{2} + x_{iM}y_{iP} \frac{r_{xy}}{2} + x_{iP}y_{iM} \frac{r_{xy}}{2} + x_{iP}y_{iP} \frac{1-r_{xy}}{2})\ \mathbb{E}(X_iY_i) &= \frac{1}{2}(x_{iM}y_{iM}) - \frac{r_{xy}}{2}(x_{iM}y_{iM}) + \frac{r_{xy}}{2} (x_{iM}y_{iP}) + \frac{r_{xy}}{2} (x_{iP}y_{iM}) + \frac{1}{2}(x_{iP}y_{iP}) - \frac{r_{xy}}{2}(x_{iP}y_{iP})\ \mathbb{E}(X_iY_i) &= \frac{1}{2}(x_{iM}y_{iM} + x_{iP}y_{iP}) + \frac{r_{xy}}{2}(-x_{iM}y_{iM} + x_{iM}y_{iP} + x_{iP}y_{iM} -x_{iP}y_{iP})\ \mathbb{E}(X_iY_i) &= \frac{1}{4}(2x_{iM}y_{iM} + 2x_{iP}y_{iP}) + \frac{r_{xy}}{2}(-x_{iM}y_{iM} + x_{iM}y_{iP} + x_{iP}y_{iM} -x_{iP}y_{iP})\ \mathbb{E}(X_iY_i) &= \frac{1}{4}(2x_{iM}y_{iM} + 2x_{iP}y_{iP}) - \frac{r_{xy}}{2}(x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} + x_{iP}y_{iP})\ \end{align} $$
We now calculate
$$ \begin{align} \mathbb{E}(X_iY_i) - \mathbb{E}(X_i)\mathbb{E}(Y_i) &= \frac{1}{4}(2x_{iM}y_{iM} + 2x_{iP}y_{iP}) - \frac{r_{xy}}{2}(x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} + x_{iP}y_{iP}) -\frac{1}{4}(x_{iM}y_{iM} + x_{iM}y_{iP} + x_{iP}y_{iM} + x_{iP}y_{iP})\ Cov(X_i,Y_i) &= \frac{1}{4}(2x_{iM}y_{iM} + 2x_{iP}y_{iP} - x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} - x_{iP}y_{iP}) - \frac{r_{xy}}{2}(x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} + x_{iP}y_{iP})\ &= \frac{1}{4}(x_{iM}y_{iM} + x_{iP}y_{iP} - x_{iM}y_{iP} - x_{iP}y_{iM} ) - \frac{r_{xy}}{2}(x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} + x_{iP}y_{iP})\ &= \frac{1}{4}z_{ixy} - \frac{r_{xy}}{2}z_{ixy} \qquad\text{with: } z_{ixy} = x_{iM}y_{iM} - x_{iM}y_{iP} - x_{iP}y_{iM} + x_{iP}y_{iP}\ Cov(X_i,Y_i) &= \frac{1}{4}(1-2r_{xy})z_{ixy}\ \end{align} $$
Variance
We can now calculate the variance
$$ \begin{align} Var(X_i) &= Cov(X_i, X_i)\ &= \frac{1}{4}(1-2r_{xx})z_{ixx}\ &= \frac{1}{4}(1-0)z_{ixx}\ Var(X_i) &= \frac{1}{4}z_{ixx} \end{align} $$
For the sum of $X_1,X_2$ and $Y_1,Y_2$
Let
Let’s calculate
$$ \begin{align} \mathbb{E}(X_1 + X_2) &= \mathbb{E}(X_1) + \mathbb{E}(X_2)\ &= \frac{1}{2} (x_{1M}+x_{1P}) + \frac{1}{2} (x_{2M}+x_{2P})\ \mathbb{E}(X_1 + X_2) &= \frac{1}{2}(x_{1M} + x_{1P} + x_{2M} + x_{2P})\ \end{align} $$
Let’s calculate
$$ \begin{align} Var(X_1 + X_2) &= Var(X_1) + Var(X_2) + 2 Cov(X_1, X_2)\ &= Var(X_1) + Var(X_2) + 0 \qquad \text{(we consider } X_1, X_2 \text{ independent)}\ &= \frac{1}{4}z_{1xx} + \frac{1}{4}z_{2xx}\ Var(X_1 + X_2) &= \frac{1}{4} (z_{1xx} + z_{2xx}) \end{align} $$
Let’s calculate
$$ \begin{align} Cov(X_1 + X_2, Y_1 + Y_2) &= Cov(X_1, Y_1) + Cov(X_1, Y_2) + Cov(X_2, Y_1) + Cov(X_2, Y_2)\ &= Cov(X_1, Y_1) + 0 + 0 + Cov(X_2, Y_2) \qquad \text{(we consider } X_1, Y_2 \text{ and } X_2, Y_1 \text{ independent)}\ &= \frac{1}{4}(1-2r_{xy})z_{1xy} + \frac{1}{4}(1-2r_{xy})z_{2xy} \ Cov(X_1 + X_2, Y_1 + Y_2) &= \frac{1}{4}(1-2r_{xy})(z_{1xy} + z_{2xy}) \ \end{align} $$
Genetic values
We will now consider all the markers.
Let
be the genotype value of the allele
Let’s consider an additive genetic architecture and let
$G = \sum_j e_j (X_{1j} + X_{2j})$
$$ \begin{align} \mathbb{E}(G) &= \mathbb{E}(\sum_j e_j (X_{1j} + X_{2j}))\ &= \sum_j e_j \mathbb{E}(X_{1j} + X_{2j})\ &= \sum_j e_j \frac{1}{2}(x_{1jM} + x_{1jP} + x_{2jM} + x_{2jP})\ \mathbb{E}(G) &= \frac{1}{2} \sum_j e_j (x_{1jM} + x_{1jP} + x_{2jM} + x_{2jP})\ \end{align} $$
Note: we can recognise that this is the mean of the genetic value of the 2 parents:
$\frac{1}{2} (\sum_j e_j (x_{1jM} + x_{1jP}) + \sum_j e_j (x_{2jM} + x_{2jP}))$
$$ \begin{align} Var(G) &= Var(\sum_j e_j (X_{1j} + X_{2j}))\ &= \sum_{jk} Cov(e_j(X_{1j} + X_{2j}), e_k(X_{1k}+X_{2k})) \qquad \text{(Bienaymé's identity)}\ &= \sum_{jk} e_je_kCov(X_{1j} + X_{2j}, X_{1k}+X_{2k})\ Var(G) &= \sum_{jk} e_je_k \frac{1}{4}(1-2r_{jk})(z_{1jk} + z_{2jk})\ \end{align} $$
in Matrix notation: with
$$ \begin{align} Cov(X) &= E(XX') - E(X)E(X') \ Cov(a'X) &= E(a'X(a'X)') - E(a'X)E((a'X)') \ &= E(aXX'a) - a'E(X)E(X')a \ &= a'E(XX')a - aE(X)E(X')a \ &= a'(E(XX') - E(X)E(X'))a \ Cov(a'X) &= a'Cov(X)a \ \end{align} $$
Covariance of the genetic values
$$\begin{eqnarray} E(G_a G_b) &=& \sum_{m}\sum_{k} E(eff_{a_m}(X_{1m} + X_{2m}) \times eff_{b_k}(X_{1k} + X_{2k}))\ &=& \sum_{m}\sum_{k} eff_{a_m} eff_{b_k} E((X_{1m} + X_{2m}) \times (X_{1k} + X_{2k})) \end{eqnarray}$$
$$\begin{eqnarray} (X_{1m} + X_{2m}) \times (X_{1k} + X_{2k}) &=& X_{1m} X_{1k} + X_{1m} X_{2k} + X_{2m} X_{1k} + X_{2m} X_{2k}\ E((X_{1m} + X_{2m}) \times (X_{1k} + X_{2k})) &=& E(X_{1m} X_{1k}) + E(X_{1m} X_{2k}) + E(X_{2m} X_{1k}) + E(X_{2m} X_{2k}) \end{eqnarray}$$
Command
Calculation
docker run --rm -v "$PWD"/data/:/data \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
progeny-blup-calculation \
--genoFile "/data/geno/breedGame_phasedGeno.vcf.gz" \
--crossTableFile "/data/crossingTable/breedGame_small_crossTable.csv" \
--SNPcoordFile "/data/SNPcoordinates/breedingGame_SNPcoord.csv" \
--markerEffectsFile "/data/markerEffects/breedGame_markerEffects.csv" \
--outFile "/out/progBlups.json"Main function
calc_progenyBlupEstimation(
genoFile = 'data/geno/breedGame_phasedGeno.vcf.gz',
crossTableFile = 'data/crossingTable/breedGame_small_crossTable.csv',
SNPcoordFile = 'data/SNPcoordinates/breedingGame_SNPcoord.csv',
markerEffectsFile = 'data/markerEffects/breedGame_markerEffects.csv',
outFile = tempfile(fileext = ".json")
)
#> 2024-03-04 11:08:54.842538 - r-progenyBlupVarExp(): Get data ...
#> 2024-03-04 11:08:54.84271 - r-readPhasedGeno(): Check file extention ...
#> 2024-03-04 11:08:54.843019 - r-readPhasedGeno(): Read geno file ...
#> 2024-03-04 11:08:56.546951 - r-readPhasedGeno(): Read phased geno file DONE
#> 2024-03-04 11:08:56.547603 - r-readPhasedGeno(): Extract SNP information...
#> 2024-03-04 11:08:56.549284 - r-readPhasedGeno(): Extract SNP information DONE
#> 2024-03-04 11:08:56.54937 - r-readPhasedGeno(): Check pahsing ...
#> 2024-03-04 11:08:56.822275 - r-readPhasedGeno(): Check pahsing DONE
#> 2024-03-04 11:08:56.822447 - r-readPhasedGeno(): Extract haplotypes...
#> 2024-03-04 11:09:00.090922 - r-readPhasedGeno(): Extract haplotypes DONE
#> 2024-03-04 11:09:00.091091 - r-readPhasedGeno(): DONE, return output.
#> 2024-03-04 11:09:00.091628 - r-readSNPcoord(): Read snps coordinates file ...
#> 2024-03-04 11:09:00.101091 - r-readSNPcoord(): Read snps coordinates file DONE
#> 2024-03-04 11:09:00.101176 - r-readSNPcoord(): Check snps coordinates file ...
#> 2024-03-04 11:09:00.112575 - r-readSNPcoord(): Check snps coordinates file DONE
#> 2024-03-04 11:09:00.112957 - r-readCrossTable: Read crossing table file ...
#> 2024-03-04 11:09:00.117645 - r-readCrossTable: Read crossing table file DONE
#> 2024-03-04 11:09:00.117741 - r-readCrossTable: Check crossing table file ...
#> 2024-03-04 11:09:00.11797 - r-readCrossTable: Generate simulated individuals names...
#> 2024-03-04 11:09:00.118061 - r-readCrossTable: Generate simulated individuals names DONE
#> 2024-03-04 11:09:00.137676 - r-readMarkerEffects_csv(): Read marker effects file ...
#> 2024-03-04 11:09:00.145284 - r-readMarkerEffects_csv(): Read marker effects file DONE
#> 2024-03-04 11:09:00.145373 - r-readMarkerEffects_csv(): Check marker effects file ...
#> 2024-03-04 11:09:00.151071 - r-readMarkerEffects_csv(): Check marker effects file DONE
#> 2024-03-04 11:09:00.151564 - r-progenyBlupVarExp(): Get data DONE
#> 2024-03-04 11:09:00.151641 - r-progenyBlupVarExp(): Check individuals' names consistency between `.vcf` and `.csv` file ...
#> 2024-03-04 11:09:00.152069 - r-progenyBlupVarExp(): Check individuals' names consistency between `.vcf` and `.csv` file DONE
#> 2024-03-04 11:09:00.152133 - r-progenyBlupVarExp(): Check SNP's coordinates consistency between `.vcf` and SNPcoordinate file ...
#> 2024-03-04 11:09:00.175646 - r-progenyBlupVarExp(): Check SNP's coordinates consistency between `.vcf` and SNPcoordinate file DONE
#> 2024-03-04 11:09:00.175744 - r-progenyBlupVarExp(): Check SNPs' ids consistency between SNPcoordinate and markerEffects file ...
#> 2024-03-04 11:09:00.175994 - r-progenyBlupVarExp(): Check SNPs' ids consistency between SNPcoordinate and markerEffects file DONE
#> 2024-03-04 11:09:00.362677 - r-progenyBlupVarExp(): BLUP variance and expected value calculation for each crosses ...
#> 2024-03-04 11:09:00.362854 - r-progenyBlupVarExp(): Calculating cross: 1/10
#> 2024-03-04 11:09:01.542105 - r-progenyBlupVarExp(): Calculating cross: 2/10
#> 2024-03-04 11:09:02.382669 - r-progenyBlupVarExp(): Calculating cross: 3/10
#> 2024-03-04 11:09:03.237386 - r-progenyBlupVarExp(): Calculating cross: 4/10
#> 2024-03-04 11:09:04.08217 - r-progenyBlupVarExp(): Calculating cross: 5/10
#> 2024-03-04 11:09:04.91902 - r-progenyBlupVarExp(): Calculating cross: 6/10
#> 2024-03-04 11:09:05.752794 - r-progenyBlupVarExp(): Calculating cross: 7/10
#> 2024-03-04 11:09:06.591046 - r-progenyBlupVarExp(): Calculating cross: 8/10
#> 2024-03-04 11:09:07.429289 - r-progenyBlupVarExp(): Calculating cross: 9/10
#> 2024-03-04 11:09:08.259246 - r-progenyBlupVarExp(): Calculating cross: 10/10
#> 2024-03-04 11:09:09.084858 - r-progenyBlupVarExp(): BLUP variance and expected value calculation for each crosses DONE
#> 2024-03-04 11:09:09.08501 - r-progenyBlupVarExp(): Save results ...
#> 2024-03-04 11:09:09.085161 - r-save_blupVarExp_as_json(): Check file ...
#> 2024-03-04 11:09:09.085237 - r-save_blupVarExp_as_json(): Check output file extention ...
#> 2024-03-04 11:09:09.085381 - r-save_blupVarExp_as_json(): Check output file extention DONE
#> 2024-03-04 11:09:09.091241 - r-progenyBlupVarExp(): Save results DONE
#> $F2_0001.0001_X_F2_0002.0059
#> $F2_0001.0001_X_F2_0002.0059$ind1
#> [1] "F2_0001.0001"
#>
#> $F2_0001.0001_X_F2_0002.0059$ind2
#> [1] "F2_0002.0059"
#>
#> $F2_0001.0001_X_F2_0002.0059$blup_exp
#> $F2_0001.0001_X_F2_0002.0059$blup_exp$trait1
#> trait1
#> 13.57
#>
#>
#> $F2_0001.0001_X_F2_0002.0059$blup_var
#> $F2_0001.0001_X_F2_0002.0059$blup_var$trait1
#> trait1
#> 6.838875
#>
#>
#> $F2_0001.0001_X_F2_0002.0059$cov
#> trait1
#> trait1 6.838875
#>
#>
#> $F2_0001.0009_X_F4_0001.0147
#> $F2_0001.0009_X_F4_0001.0147$ind1
#> [1] "F2_0001.0009"
#>
#> $F2_0001.0009_X_F4_0001.0147$ind2
#> [1] "F4_0001.0147"
#>
#> $F2_0001.0009_X_F4_0001.0147$blup_exp
#> $F2_0001.0009_X_F4_0001.0147$blup_exp$trait1
#> trait1
#> 15.83361
#>
#>
#> $F2_0001.0009_X_F4_0001.0147$blup_var
#> $F2_0001.0009_X_F4_0001.0147$blup_var$trait1
#> trait1
#> 3.831113
#>
#>
#> $F2_0001.0009_X_F4_0001.0147$cov
#> trait1
#> trait1 3.831113
#>
#>
#> $F4_0001.0092_X_F4_0001.0185
#> $F4_0001.0092_X_F4_0001.0185$ind1
#> [1] "F4_0001.0092"
#>
#> $F4_0001.0092_X_F4_0001.0185$ind2
#> [1] "F4_0001.0185"
#>
#> $F4_0001.0092_X_F4_0001.0185$blup_exp
#> $F4_0001.0092_X_F4_0001.0185$blup_exp$trait1
#> trait1
#> 16.97976
#>
#>
#> $F4_0001.0092_X_F4_0001.0185$blup_var
#> $F4_0001.0092_X_F4_0001.0185$blup_var$trait1
#> trait1
#> 7.510513
#>
#>
#> $F4_0001.0092_X_F4_0001.0185$cov
#> trait1
#> trait1 7.510513
#>
#>
#> $Coll0659_X_F2_0001.0053
#> $Coll0659_X_F2_0001.0053$ind1
#> [1] "Coll0659"
#>
#> $Coll0659_X_F2_0001.0053$ind2
#> [1] "F2_0001.0053"
#>
#> $Coll0659_X_F2_0001.0053$blup_exp
#> $Coll0659_X_F2_0001.0053$blup_exp$trait1
#> trait1
#> 20.08265
#>
#>
#> $Coll0659_X_F2_0001.0053$blup_var
#> $Coll0659_X_F2_0001.0053$blup_var$trait1
#> trait1
#> 2.088031
#>
#>
#> $Coll0659_X_F2_0001.0053$cov
#> trait1
#> trait1 2.088031
#>
#>
#> $F1_0004.0001_X_F4_0001.0049
#> $F1_0004.0001_X_F4_0001.0049$ind1
#> [1] "F1_0004.0001"
#>
#> $F1_0004.0001_X_F4_0001.0049$ind2
#> [1] "F4_0001.0049"
#>
#> $F1_0004.0001_X_F4_0001.0049$blup_exp
#> $F1_0004.0001_X_F4_0001.0049$blup_exp$trait1
#> trait1
#> 21.75336
#>
#>
#> $F1_0004.0001_X_F4_0001.0049$blup_var
#> $F1_0004.0001_X_F4_0001.0049$blup_var$trait1
#> trait1
#> 10.17769
#>
#>
#> $F1_0004.0001_X_F4_0001.0049$cov
#> trait1
#> trait1 10.17769
#>
#>
#> $F3_0001.0080_X_F4_0001.0274
#> $F3_0001.0080_X_F4_0001.0274$ind1
#> [1] "F3_0001.0080"
#>
#> $F3_0001.0080_X_F4_0001.0274$ind2
#> [1] "F4_0001.0274"
#>
#> $F3_0001.0080_X_F4_0001.0274$blup_exp
#> $F3_0001.0080_X_F4_0001.0274$blup_exp$trait1
#> trait1
#> 16.20195
#>
#>
#> $F3_0001.0080_X_F4_0001.0274$blup_var
#> $F3_0001.0080_X_F4_0001.0274$blup_var$trait1
#> trait1
#> 7.656071
#>
#>
#> $F3_0001.0080_X_F4_0001.0274$cov
#> trait1
#> trait1 7.656071
#>
#>
#> $Coll0659_X_Coll0425
#> $Coll0659_X_Coll0425$ind1
#> [1] "Coll0659"
#>
#> $Coll0659_X_Coll0425$ind2
#> [1] "Coll0425"
#>
#> $Coll0659_X_Coll0425$blup_exp
#> $Coll0659_X_Coll0425$blup_exp$trait1
#> trait1
#> 25.04259
#>
#>
#> $Coll0659_X_Coll0425$blup_var
#> $Coll0659_X_Coll0425$blup_var$trait1
#> trait1
#> 0
#>
#>
#> $Coll0659_X_Coll0425$cov
#> trait1
#> trait1 0
#>
#>
#> $F3_0002.0035_X_F4_0001.0280
#> $F3_0002.0035_X_F4_0001.0280$ind1
#> [1] "F3_0002.0035"
#>
#> $F3_0002.0035_X_F4_0001.0280$ind2
#> [1] "F4_0001.0280"
#>
#> $F3_0002.0035_X_F4_0001.0280$blup_exp
#> $F3_0002.0035_X_F4_0001.0280$blup_exp$trait1
#> trait1
#> 24.49025
#>
#>
#> $F3_0002.0035_X_F4_0001.0280$blup_var
#> $F3_0002.0035_X_F4_0001.0280$blup_var$trait1
#> trait1
#> 5.077835
#>
#>
#> $F3_0002.0035_X_F4_0001.0280$cov
#> trait1
#> trait1 5.077835
#>
#>
#> $F4_0001.0006_X_F3_0002.0097
#> $F4_0001.0006_X_F3_0002.0097$ind1
#> [1] "F4_0001.0006"
#>
#> $F4_0001.0006_X_F3_0002.0097$ind2
#> [1] "F3_0002.0097"
#>
#> $F4_0001.0006_X_F3_0002.0097$blup_exp
#> $F4_0001.0006_X_F3_0002.0097$blup_exp$trait1
#> trait1
#> 16.41541
#>
#>
#> $F4_0001.0006_X_F3_0002.0097$blup_var
#> $F4_0001.0006_X_F3_0002.0097$blup_var$trait1
#> trait1
#> 6.142879
#>
#>
#> $F4_0001.0006_X_F3_0002.0097$cov
#> trait1
#> trait1 6.142879
#>
#>
#> $F4_0001.0103_X_F4_0001.0114
#> $F4_0001.0103_X_F4_0001.0114$ind1
#> [1] "F4_0001.0103"
#>
#> $F4_0001.0103_X_F4_0001.0114$ind2
#> [1] "F4_0001.0114"
#>
#> $F4_0001.0103_X_F4_0001.0114$blup_exp
#> $F4_0001.0103_X_F4_0001.0114$blup_exp$trait1
#> trait1
#> 18.81592
#>
#>
#> $F4_0001.0103_X_F4_0001.0114$blup_var
#> $F4_0001.0103_X_F4_0001.0114$blup_var$trait1
#> trait1
#> 8.637738
#>
#>
#> $F4_0001.0103_X_F4_0001.0114$cov
#> trait1
#> trait1 8.637738Plot
1 trait
docker run --rm -v "$PWD"/data/:/data \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
progeny-blup-plot \
--progeniesBlupFile "/data/results/progenyBlupEstimation.json" \
--outFile "/out/progBlupsPlot.html"Main function
plot <- draw_progBlupsPlot(
progEstimFile = 'data/results/progenyBlupEstimation.json',
sorting = 'dec',
outFile = tempfile(fileext = ".html")
)
#> 2024-03-04 11:09:09.110855 - r-draw_progBlupsPlot(): Get data ...
#> 2024-03-04 11:09:09.125008 - r-readProgBlupEstim(): Read blup esimation file ...
#> 2024-03-04 11:09:09.129447 - r-readProgBlupEstim(): Read blup esimation file DONE
#> 2024-03-04 11:09:09.129546 - r-readProgBlupEstim(): Convert Json ...
#> 2024-03-04 11:09:09.131693 - r-readProgBlupEstim(): Convert Json DONE
#> 2024-03-04 11:09:09.131789 - r-readProgBlupEstim(): DONE, return output.
#> 2024-03-04 11:09:09.131855 - r-draw_progBlupsPlot(): Get data DONE
#> 2024-03-04 11:09:09.131948 - r-draw_progBlupsPlot(): Draw progenies' blup plot ...
#> 2024-03-04 11:09:09.158251 - r-plotBlup_1trait(): sort x axis ...
#> 2024-03-04 11:09:09.158807 - r-plotBlup_1trait(): sort x axis DONE
#> 2024-03-04 11:09:09.158881 - r-plotBlup_1trait(): draw plot ...
#> 2024-03-04 11:09:09.159965 - r-plotBlup_1trait(): draw plot DONE
#> 2024-03-04 11:09:09.160043 - r-draw_progBlupsPlot(): Draw progenies' blup plot DONE
#> 2024-03-04 11:09:09.160106 - r-draw_progBlupsPlot(): Save results ...
#> 2024-03-04 11:09:09.237544 - r-draw_progBlupsPlot(): Save results DONE
progBlupPlot
2 traits
docker run --rm -v "$PWD"/data/:/data \
-v "$PWD"/readmeTemp:/out rgenotoolsengine \
progeny-blup-plot-2-traits \
--progeniesBlupFile "/data/results/progenyBlupEstimation_2traits.json" \
--x-trait "trait1"
--y-trait "trait2"
--outFile "/out/progBlupsPlot.html"Main function
plot <- draw_progBlupsPlot_2traits(
progEstimFile = 'data/results/progenyBlupEstimation_2traits.json',
x_trait = "trait1",
y_trait = "trait2",
outFile = tempfile(fileext = ".html")
)
#> 2024-03-04 11:09:09.257763 - r-draw_progBlupsPlot_2traits(): Get data ...
#> 2024-03-04 11:09:09.257934 - r-readProgBlupEstim(): Read blup esimation file ...
#> 2024-03-04 11:09:09.258355 - r-readProgBlupEstim(): Read blup esimation file DONE
#> 2024-03-04 11:09:09.258427 - r-readProgBlupEstim(): Convert Json ...
#> 2024-03-04 11:09:09.2608 - r-readProgBlupEstim(): Convert Json DONE
#> 2024-03-04 11:09:09.2609 - r-readProgBlupEstim(): DONE, return output.
#> 2024-03-04 11:09:09.260967 - r-draw_progBlupsPlot_2traits(): Get data DONE
#> 2024-03-04 11:09:09.266384 - r-draw_progBlupsPlot_2traits(): Draw progenies' blup plot ...
#> 2024-03-04 11:09:09.286277 - r-plotBlup_2traits(): draw plot ...
#> 2024-03-04 11:09:09.299323 - r-plotBlup_2traits(): draw plot DONE
#> 2024-03-04 11:09:09.299428 - r-draw_progBlupsPlot_2traits(): Draw progenies' blup plot DONE
#> 2024-03-04 11:09:09.299498 - r-draw_progBlupsPlot_2traits(): Save results ...
#> 2024-03-04 11:09:09.631046 - r-draw_progBlupsPlot_2traits(): Save results DONE
progBlupPlot-2-traits
Some example data are available in the folder data. This folder also includes examples of output files.
These examples output files can be automatically generated using the function createResultExample (defined in ./src/utils.R):
createResultExample()The genotypic and phenotypic data used as example come from:
Keyan Zhao, Chih-Wei Tung, Georgia C. Eizenga, Mark H. Wright, M. Liakat Ali, Adam H. Price, Gareth J. Norton, M. Rafiqul Islam, Andy Reynolds, Jason Mezey, Anna M. McClung, Carlos D. Bustamante & Susan R. McCouch (2011). Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Comm 2:467 | DOI: 10.1038/ncomms1467, Published Online 13 Sep 2011.
Flutre, T., Diot, J., and David, J. (2019). PlantBreedGame: A Serious Game that Puts Students in the Breeder’s Seat. Crop Science. DOI 10.2135/cropsci2019.03.0183le
The utils folder contain miscellaneous code that can be useful for developers working on this project. This engine should NOT depend on any file of this folder.