Create a quantile-quantile plot with ggplot2.

Assumptions for this quantile quantile plot: Expected P values are uniformly distributed. Confidence intervals assume independence between tests. We expect deviations past the confidence intervals if the tests are not independent. For example, in a genome-wide association study, the genotype at any position is correlated to nearby positions. Tests of nearby genotypes will result in similar test statistics.

pvdiv_qqplot(
  ps,
  effects = NULL,
  ind = NULL,
  ci = 0.95,
  lambdaGC = FALSE,
  tol = 1e-08
)

Arguments

ps

Numeric vector of p-values.

effects

a gwas effects FBM object created using 'pvdiv_standard_gwas'. Saved under the name "gwas_effects_suffix.rds" and can be loaded into R using the bigstatsr function "big_attach".

ind

If effects is a FBM object, this should be the row number of the phenotype from the associated metadata for the FBM object.

ci

Numeric. Size of the confidence interval, 0.95 by default.

lambdaGC

Logical. Add the Genomic Control coefficient as subtitle to the plot?

tol

Numeric. Tolerance for optional Genomic Control coefficient.

Value

A ggplot2 plot.