gwas_merge: Format data for CAUSE
In jean997/cause: CAUSE: Causal Analysis Using Summary Effect Estimates

Description Usage Arguments Details Value

Format GWAS summary statistics for CAUSE

gwas_merge(
  X1,
  X2,
  X1_formatted = FALSE,
  X2_formatted = FALSE,
  snp_name_cols = c("snp", "snp"),
  beta_hat_cols = c("beta_hat", "beta_hat"),
  se_cols = c("se", "se"),
  A1_cols = c("A1", "A1"),
  A2_cols = c("A2", "A2"),
  pval_cols = c(NA, NA),
  compute_pvals = TRUE
)

`X1`	data.frame with data for GWAS 1
`X2`	data.frame with data for GWAS 2
`snp_name_cols`	A vector of length 2 specifying the name of the snp column in X1 and X2 respectively. This is the column on which the data will be merged.
`beta_hat_cols`	A vector of length 2 specifying the name of the snp column in X1 and X2 respectively. If effect sizes are provided as odds ratios they must be converted back into coeffecient estimates by taking the log.
`se_cols`	A vector of length 2 specifying the name of the snp column in X1 and X2 respectively.
`A1_cols`	Column names for effect allele
`A2_cols`	Column names for other allele
`pval_cols`	Column names for p-values. Can be ommitted or either element can be NA.
`compute_pvals`	If p-values are missing in one or both studies, should the function compute them. If true, p-values will be computed as '2*pnorm(-abs(beta_hat/se))'.

This function will try to merge data sets X1 and X2 on the specified columns. Where necessary, it will flip the sign of effects so that the effect allele is the same in both data sets. It will remove variants with ambiguous alleles or where the alleles (G/C or A/T) or with alleles that do not match between data sets (e.g A/G in one data set and A/C in the other). It will not remove variants that are simply strand flipped between the two data sets (e. g. A/C in one data set, T/G in the other).

An object of class cause_data and data.frame.

jean997/cause documentation built on Dec. 25, 2021, 10 p.m.