Converts a genlight object into eigenstrat format

The output of this function are three files:

genotype file: contains genotype data for each individual at each SNP with an extension 'eigenstratgeno.'
snp file: contains information about each SNP with an extension 'snp.'
indiv file: contains information about each individual with an extension 'ind.'

gl2eigenstrat(
  x,
  outfile = "gl_eigenstrat",
  outpath = tempdir(),
  snp_pos = 1,
  snp_chr = 1,
  pos_cM = 0,
  sex_code = "unknown",
  phen_value = "Case",
  verbose = NULL
)

Arguments

x: Name of the genlight object containing the SNP data [required].
outfile: File name of the output file [default 'gl_eigenstrat'].
outpath: Path where to save the output file [default tempdir(), mandated by CRAN]. Use outpath=getwd() or outpath='.' when calling this function to direct output files to your working directory.
snp_pos: Field name from the slot loc.metrics where the SNP position is stored [default 1].
snp_chr: Field name from the slot loc.metrics where the chromosome of each is stored [default 1].
pos_cM: A vector, with as many elements as there are loci, containing the SNP position in morgans or centimorgans [default 1].
sex_code: A vector, with as many elements as there are individuals, containing the sex code ('male', 'female', 'unknown') [default 'unknown'].
phen_value: A vector, with as many elements as there are individuals, containing the phenotype value ('Case', 'Control') [default 'Case'].
verbose: Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log ; 3, progress and results summary; 5, full report [default 2 or as specified using gl.set.verbosity].

Value

returns no value (i.e. NULL)

Details

Eigenstrat only accepts chromosomes coded as numeric values, as follows: X chromosome is encoded as 23, Y is encoded as 24, mtDNA is encoded as 90, and XY is encoded as 91. SNPs with illegal chromosome values, such as 0, will be removed.

References

Patterson, N., Price, A. L., & Reich, D. (2006). Population structure and eigenanalysis. PLoS genetics, 2(12), e190.
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature genetics, 38(8), 904-909.

Author

Custodian: Luis Mijangos (Post to https://groups.google.com/d/forum/dartr)

Examples

# \donttest{
require("dartR.data")
gl2eigenstrat(platypus.gl,snp_pos='ChromPos_Platypus_Chrom_NCBIv1',
snp_chr = 'Chrom_Platypus_Chrom_NCBIv1')
#> Starting gl2eigenstrat 
#>   Processing genlight object with SNP data
#>   Warning: data include loci that are scored NA across all individuals.
#>   Consider filtering using gl <- gl.filter.allna(gl)
#> Completed: gl2eigenstrat 
#> 
# }