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PvalComb

Source: R/GXwasR_main_functions.R
PvalComb.Rd

This function combines the p-values of two separate GWAS summary statistics (for instance male and female populations), merges them, and then applies various statistical methods (like Stouffer's method, Fisher's method) to integrate the p-values. It also includes functionality for generating plots like Manhattan plots and Q-Q plots.

Usage

PvalComb(
  SumstatMale,
  SumstatFemale,
  combtest,
  MF.p.corr = "none",
  MF.zero.sub = 1e-05,
  MF.na.rm = TRUE,
  MF.mc.cores = 1,
  B = 1000,
  plot.jpeg = TRUE,
  plotname = "GXwas.plot",
  PlotDir = tempdir(),
  snp_pval,
  annotateTopSnp = FALSE,
  suggestiveline = 5,
  genomewideline = 7.3,
  ncores = 0
)

Arguments

SumstatMale

R dataframe object of summary statistics of male GWAS with five mandatory columns:

  • CHR (numeric chromosome code)

  • SNP (variant id)

  • A1 (allele)

  • POS (base-pair position)

  • P (p-value).

Other columns may present.

SumstatFemale

R dataframe object of summary statistics of female GWAS with five mandatory columns:

  • SNP

  • A1

  • TEST

  • POS

  • P

Other columns may present.

combtest

Character vector specifying method for combining p-values for stratified GWAS models. Choices are “stouffer.method”, "fisher.method" and "fisher.method.perm". For fisher.method the function for combining p-values uses a statistic, \(S = -2 ∑^k /log p\), which follows a \(χ^2\) distribution with 2k degrees of freedom (Fisher 1925) . For fisher.method.perm, using p-values from stratified tests, the summary statistic for combining p-values is \(S = -2 ∑ /log p\). A p-value for this statistic can be derived by randomly generating summary statistics (Rhodes 2002) . Therefore, a p-value is randomly sampled from each contributing study, and a random statistic is calculated. The fraction of random statistics greater or equal to S then gives the final p-value.

MF.p.corr

Character vector specifying method for correcting the summary p-values for FMfcomb and FMscomb models. Choices are "bonferroni", "BH" and "none" for Bonferroni, Benjamini-Hochberg and none, respectively. The default is "none".

MF.zero.sub

Small numeric value for substituting p-values of 0 in GWAS summary statistics. The default is 0.00001. As \(log(0)\) results in Inf this replaces p-values of 0 by default with a small float.

MF.na.rm

Boolean value, TRUE or FALSE for removing p-values of NA in stratified GWAS summary satistics in case of using Fisher’s and Stouffer’s methods. The default is TRUE.

MF.mc.cores

Number of cores used for fisher.method.perm for combining p-values. The default is 1.

B

Integer value specifying the number of permutation in case of using fisher.method.perm method. The default is 10000.

plot.jpeg

Boolean value, TRUE or FALSE for saving the plots in .jpeg file. The default is TRUE.

plotname

A character string specifying the prefix of the file for plots. This file will be saved in DataDir. The default is "GXwas.plot".

PlotDir

A character string specifying the path of the directory where the plots will be saved. The default is tempdir().

snp_pval

Numeric value as p-value threshold for annotation. SNPs below this p-value will be annotated on the plot. The default is 1e-08.

annotateTopSnp

Boolean value, TRUE or FALSE. If TRUE, it only annotates the top hit on each chromosome that is below the snp_pval threshold. The default is FALSE.

suggestiveline

Numeric value for suggestive cut-off line in GWAS manhattan plot. The default is 5 (for p-value 1e-05).

genomewideline

Numeric value for genome-wide significant cut-off line in GWAS manhattan plot. The default is 7.3 (for p-value 5e-08).

ncores

Integer value, specifying the number of cores for parallel processing. The default is 0 (no parallel computation).

Value

A dataframe with GWAS summary statistics (with XWAS for X-chromosomal variants) along with Manhattan and Q-Q plots.

References

Fisher RA (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh.

Rhodes DR (2002). “Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.” Cancer Research, 62(15), 4427–4433.

Examples

data("Mfile", package = "GXwasR")
data("Ffile", package = "GXwasR")
SumstatMale <- Mfile
colnames(SumstatMale)[3] <- "POS"
SumstatFemale <- Ffile
colnames(SumstatFemale)[3] <- "POS"
PvalComb_Result <- PvalComb(
    SumstatMale = SumstatMale, SumstatFemale = SumstatFemale,
    combtest = "fisher.method", MF.mc.cores = 1, snp_pval = 0.001, plot.jpeg = FALSE,
    suggestiveline = 3, genomewideline = 5.69897, ncores = 1
)
#> ℹ Saving plot to /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpO7c0S8/Stratified_GWAS.png

#> ℹ Saving plot to /var/folders/d6/gtwl3_017sj4pp14fbfcbqjh0000gp/T//RtmpO7c0S8/Stratified_XWAS.png