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SexDiff: Sex difference in effect size for each SNP using t-test.

Source: R/GXwasR_main_functions.R
SexDiff.Rd

This function uses the GWAS summary statistics from sex-stratified tests like "FMstratified", to evaluate the difference in effect size between males and females at each SNP using a t-test.

The input dataframes should only include X-chromosome in order to obtain results for sex differences based solely on X-linked loci.

Usage

SexDiff(Mfile, Ffile)

Arguments

Mfile

R dataframe of summary statistics of GWAS or XWAS of male samples with six mandatory columns, SNP(Variant),CHR(Chromosome number), BP(Base pair position),A1(Minor allele),BETA_M(Effect size) and SE_M(Standard error). This can be generated by running FM01comb or "FMstratified" model with GXWAS function.

Ffile

R dataframe of summary statistics of GWAS or XWAS of male samples with six mandatory columns, SNP(Variant),CHR(Chromosome number), BP(Base pair position),A1(Minor allele),BETA_F(Effect size) and SE_F(Standard error). This can be generated by running FM01comb or "FMstratified" model with GXWAS function.

Value

R dataframe with seven columns:

  • SNP (Variant)

  • CHR (Chromosome number)

  • BP (Base pair position)

  • A1 (Minor allele)

  • tstat (t-statistics for effect-size test)

  • P (p-value) and

  • adjP (Bonferroni corrected p-value)

Author

Banabithi Bose

Examples

data("Mfile", package = "GXwasR")
data("Ffile", package = "GXwasR")
Difftest <- SexDiff(Mfile, Ffile)

significant_snps <- Difftest[Difftest$adjP < 0.05, ]