View source: R/gl.ld.distance.r
| gl.ld.distance | R Documentation | 
The function creates a plot showing the pairwise LD measure against distance in number of base pairs pooled over all the chromosomes and a red line representing the threshold (R.squared = 0.2) that is commonly used to imply that two loci are unlinked (Delourme et al., 2013; Li et al., 2014).
gl.ld.distance(
  ld_report,
  ld_resolution = 1e+05,
  pop_colors = NULL,
  plot_theme = NULL,
  plot.out = TRUE,
  save2tmp = FALSE,
  plot_title = " ",
  verbose = NULL
)
ld_report | 
 Output from function   | 
ld_resolution | 
 Resolution at which LD should be reported in number of base pairs [default NULL].  | 
pop_colors | 
 A color palette for box plots by population or a list with as many colors as there are populations in the dataset [default NULL].  | 
plot_theme | 
 User specified theme [default NULL].  | 
plot.out | 
 Specify if plot is to be produced [default TRUE].  | 
save2tmp | 
 If TRUE, saves any ggplots and listings to the session temporary directory (tempdir) [default FALSE].  | 
plot_title | 
 Title of tyh plot [default " "].  | 
verbose | 
 Verbosity: 0, silent or fatal errors; 1, begin and end; 2, progress log; 3, progress and results summary; 5, full report [default 2, unless specified using gl.set.verbosity].  | 
A dataframe with information of LD against distance by population.
Custodian: Luis Mijangos – Post to https://groups.google.com/d/forum/dartr
Delourme, R., Falentin, C., Fomeju, B. F., Boillot, M., Lassalle, G., André, I., . . . Marty, A. (2013). High-density SNP-based genetic map development and linkage disequilibrium assessment in Brassica napusL. BMC genomics, 14(1), 120.
Li, X., Han, Y., Wei, Y., Acharya, A., Farmer, A. D., Ho, J., . . . Brummer, E. C. (2014). Development of an alfalfa SNP array and its use to evaluate patterns of population structure and linkage disequilibrium. PLoS One, 9(1), e84329.
Other ld functions: 
gl.ld.haplotype()
if ((requireNamespace("snpStats", quietly = TRUE)) & (requireNamespace("fields", quietly = TRUE))) {
require("dartR.data")
x <- platypus.gl
x <- gl.filter.callrate(x,threshold = 1)
x <- gl.filter.monomorphs(x)
x$position <- x$other$loc.metrics$ChromPos_Platypus_Chrom_NCBIv1
x$chromosome <- as.factor(x$other$loc.metrics$Chrom_Platypus_Chrom_NCBIv1)
ld_res <- gl.report.ld.map(x,ld_max_pairwise = 10000000)
ld_res_2 <- gl.ld.distance(ld_res,ld_resolution= 1000000)
}
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