Write a SNPRelate object from a tidy data frame

Write a SNPRelate object from a tidy data frame. Used internally in genometranslator and might be of interest for users. SNPRelate analyses commonly require biallelic SNPs and may require linkage disequilibrium pruning. Make those filtering decisions before export for the intended SNPRelate analysis.

Usage

write_snprelate(data, biallelic = TRUE, filename = NULL, verbose = TRUE)

Arguments

data: A tidy data frame object in the global environment or a tidy data frame in wide or long format in the working directory. How to get a tidy data frame ? Look into genometranslator tidy_genome. The genotypes are biallelic.
biallelic: (logical, optional) If you already know that the data is biallelic use this argument to speed up the function. Default: biallelic = TRUE.
filename: (optional) The file name of the Genomic Data Structure (GDS) file. radiator will append .gds to the filename. If filename chosen is already present in the working directory, the default radiator_snprelate_datetime.gds is chosen. Default: filename = NULL.

Value

An object in the global environment of class "SNPGDSFileClass", "gds.class" and a file in the working directory.

Dependencies

Required package dependencies are declared in DESCRIPTION and installed with genometranslator. Run genometranslator_dependencies() to inspect core packages, optional packages, and external executables.

This writer requires the optional Bioconductor package SNPRelate.

Data filtering

This writer does not silently filter markers or individuals. It may validate requirements imposed by the destination format and stop with an informative error when the input is unsuitable. It is the user's responsibility to filter and quality-control the data appropriately for the intended analysis before generating the output. Use radr or another suitable workflow when filtering is required.

References

Zheng X, Levine D, Shen J, Gogarten SM, Laurie C, Weir BS. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics. 2012;28: 3326-3328. doi:10.1093/bioinformatics/bts606

Author

Thierry Gosselin thierrygosselin@icloud.com

Examples

if (FALSE) { # \dontrun{
require(SNPRelate)
data.gds <- genometranslator::write_snprelate(data = "shark.rad")
} # }