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Plot SNP associations

Source: R/ggplot_snpasso.R
ggplot_snpasso.Rd

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

Usage

ggplot_snpasso(
  scan1output,
  snpinfo,
  genes = NULL,
  lodcolumn = 1,
  show_all_snps = TRUE,
  drop_hilit = NA,
  col_hilit = "violetred",
  col = "darkslateblue",
  ylim = NULL,
  gap = 25,
  minlod = 0,
  bgcolor = "gray90",
  altbgcolor = "gray85",
  ...
)

Arguments

scan1output

Output of scan1. Should contain an attribute, "snpinfo", as when scan1 are run with SNP probabilities produced by genoprob_to_snpprob.

snpinfo

Data frame with SNP information with the following columns (the last three are generally derived from with 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). One or more value(s) allowed.

show_all_snps

If TRUE, expand to show all SNPs.

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

ylim

y-axis limits

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.

bgcolor

Background color for the plot.

altbgcolor

Background color for alternate chromosomes.

...

Additional graphics parameters.

Value

object of class ggplot.

Hidden 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.

See also

ggplot_scan1, ggplot_coef

Examples

dirpath <- "https://raw.githubusercontent.com/rqtl/qtl2data/master/DOex"

# Read DOex example cross from 'qtl2data'
DOex <- subset(qtl2::read_cross2(file.path(dirpath, "DOex.zip")), chr = "2")

# Download genotype probabilities
tmpfile <- tempfile()
download.file(file.path(dirpath, "DOex_genoprobs_2.rds"), tmpfile, quiet=TRUE)
pr <- readRDS(tmpfile)
unlink(tmpfile)

# Download SNP info for DOex from web and read as RDS.
tmpfile <- tempfile()
download.file(file.path(dirpath, "c2_snpinfo.rds"), tmpfile, quiet=TRUE)
snpinfo <- readRDS(tmpfile)
unlink(tmpfile)
snpinfo <- dplyr::rename(snpinfo, pos = pos_Mbp)

# Convert to SNP probabilities
snpinfo <- qtl2::index_snps(DOex$pmap, snpinfo)
snppr <- qtl2::genoprob_to_snpprob(pr, snpinfo)

# Scan SNPs.
scan_snppr <- qtl2::scan1(snppr, DOex$pheno)

# plot results
ggplot_snpasso(scan_snppr, snpinfo, show_all_snps=FALSE, patterns="all", drop_hilit=1.5)


# \donttest{
# can also just type autoplot() if ggplot2 attached
library(ggplot2)

# plot just subset of distinct SNPs
autoplot(scan_snppr, snpinfo, show_all_snps=FALSE, drop_hilit=1.5)


# highlight SDP patterns in SNPs; connect with lines.
autoplot(scan_snppr, snpinfo, patterns="all",drop_hilit=4)


# query function for finding genes in region
gene_dbfile <- system.file("extdata", "mouse_genes_small.sqlite", package="qtl2")
query_genes <- qtl2::create_gene_query_func(gene_dbfile)
genes <- query_genes(2, 97, 98)

# plot SNP association results with gene locations
autoplot(scan_snppr, snpinfo, patterns="hilit", drop_hilit=1.5, genes=genes)

# }