plot_pxg: Plot phenotype vs genotype

Description

Plot phenotype vs genotype for a single putative QTL and a single phenotype.

Usage

plot_pxg(
  geno,
  pheno,
  sort = TRUE,
  SEmult = NULL,
  pooledSD = TRUE,
  swap_axes = FALSE,
  jitter = 0.2,
  force_labels = TRUE,
  alternate_labels = FALSE,
  omit_points = FALSE,
  ...
)

Value

(Invisibly) A matrix with rows being the genotype groups and columns for the means and (if SEmult is specified) the SEs.

Arguments

geno: Vector of genotypes, for example as produced by maxmarg() with specific chr and pos.
pheno: Vector of phenotypes.
sort: If TRUE, sort genotypes from largest to smallest.
SEmult: If specified, interval estimates of the within-group averages will be displayed, as mean +/- SE * SEmult.
pooledSD: If TRUE and SEmult is specified, calculated a pooled within-group SD. Otherwise, get separate estimates of the within-group SD for each group.
swap_axes: If TRUE, swap the axes, so that the genotypes are on the y-axis and the phenotype is on the x-axis.
jitter: Amount to jitter the points horizontally, if a vector of length > 0, it is taken to be the actual jitter amounts (with values between -0.5 and 0.5).
force_labels: If TRUE, force all genotype labels to be shown.
alternate_labels: If TRUE, place genotype labels in two rows
omit_points: If TRUE, omit the points, just plotting the averages (and, potentially, the +/- SE intervals).
...: Additional graphics parameters, passed to plot().

Hidden graphics parameters

A number of graphics parameters can be passed via .... For example, bgcolor to control the background color, and seg_width, seg_lwd, and seg_col to control the lines at the confidence intervals. Further, hlines, hlines_col, hlines_lwd, and hlines_lty to control the horizontal grid lines. (Use hlines=NA to avoid plotting horizontal grid lines.) Similarly vlines, vlines_col, vlines_lwd, and vlines_lty for vertical grid lines. These are not included as formal parameters in order to avoid cluttering the function definition.

Examples

Run this code

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
iron <- iron[,"16"]

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# inferred genotype at a 28.6 cM on chr 16
geno <- maxmarg(probs, map, chr=16, pos=28.6, return_char=TRUE)

# plot phenotype vs genotype
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)))

# include +/- 2 SE intervals
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         SEmult=2)

# plot just the means
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE)

# plot just the means +/- 2 SEs
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE, SEmult=2)