plot_snpasso: Plot SNP associations
In rqtl/qtl2plot: Data Visualization for QTL Experiments

Description Usage Arguments Hidden graphics parameters See Also Examples

View source: R/plot_snpasso.R

Plot SNP associations, with possible expansion from distinct snps to all snps.

plot_snpasso(scan1output, snpinfo, genes = NULL, lodcolumn = 1,
  show_all_snps = TRUE, add = FALSE, drop_hilit = NA,
  col_hilit = "violetred", col = "darkslateblue", gap = 25, minlod = 0,
  ...)

`scan1output`	Output of `qtl2scan::scan1()` using SNP probabilities derived by `qtl2scan::genoprob_to_snpprob()`.
`snpinfo`	Data frame with SNP information with the following columns (the last three are generally derived from with `qtl2scan::index_snps()`): `chr` - Character string or factor with chromosome `pos` - Position (in same units as in the `"map"` attribute in `genoprobs`. `sdp` - Strain distribution pattern: an integer, between 1 and 2^n - 2 where n is the number of strains, whose binary encoding indicates the founder genotypes `snp` - Character string with SNP identifier (if missing, the rownames are used). `index` - Indices that indicate equivalent groups of SNPs. `intervals` - Indexes that indicate which marker intervals the SNPs reside. `on_map` - Indicate whether SNP coincides with a marker in the `genoprobs`
`genes`	Optional data frame containing gene information for the region, with columns `start` and `stop` in Mbp, `strand` (as `"-"`, `"+"`, or `NA`), and `Name`. If included, a two-panel plot is produced, with SNP associations above and gene locations below.
`lodcolumn`	LOD score column to plot (a numeric index, or a character string for a column name). Only one value allowed.
`show_all_snps`	If TRUE, expand to show all SNPs.
`add`	If TRUE, add to current plot (must have same map and chromosomes).
`drop_hilit`	SNPs with LOD score within this amount of the maximum SNP association will be highlighted.
`col_hilit`	Color of highlighted points
`col`	Color of other points
`gap`	Gap between chromosomes.
`minlod`	Minimum LOD to display. (Mostly for GWAS, in which case using `minlod=1` will greatly increase the plotting speed, since the vast majority of points would be omittted.
`...`	Additional graphics parameters.

A number of graphics parameters can be passed via .... For example, bgcolor to control the background color and altbgcolor to control the background color on alternate chromosomes. cex for character expansion for the points (default 0.5), pch for the plotting character for the points (default 16), and ylim for y-axis limits.

plot_scan1(), plot_coef(), plot_coefCC()

# load example DO data from web
library(qtl2geno)
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/DOex.zip")
DOex <- read_cross2(file)

# subset to chr 2
DOex <- DOex[,"2"]

# calculate genotype probabilities and convert to allele probabilities
pr <- calc_genoprob(DOex, error_prob=0.002)
apr <- genoprob_to_alleleprob(pr)

# download snp info from web
tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/c2_snpinfo.rds")
download.file(file, tmpfile, quiet=TRUE)
snpinfo <- readRDS(tmpfile)
unlink(tmpfile)

# SNP association scan
library(qtl2scan)
out_snps <- scan1snps(apr, DOex$pmap, DOex$pheno, snpinfo=snpinfo, keep_all_snps=TRUE)

# plot results
plot_snpasso(out_snps$lod, out_snps$snpinfo)

# can also just type plot()
plot(out_snps$lod, out_snps$snpinfo)

# plot just subset of distinct SNPs
plot_snpasso(out_snps$lod, out_snps$snpinfo, show_all_snps=FALSE)

# highlight the top snps (with LOD within 1.5 of max)
plot(out_snps$lod, out_snps$snpinfo, drop_hilit=1.5)

# download gene info from web
tmpfile <- tempfile()
file <- paste0("https://raw.githubusercontent.com/rqtl/",
               "qtl2data/master/DOex/c2_genes.rds")
download.file(file, tmpfile, quiet=TRUE)
genes <- readRDS(tmpfile)
unlink(tmpfile)

# plot SNP association results with gene locations
plot(out_snps$lod, out_snps$snpinfo, drop_hilit=1.5, genes=genes)