ld_prune: Prune variants for LD
In jean997/cause: CAUSE: Causal Analysis Using Summary Effect Estimates

Get an LD pruned list of variants optionally prioritizing variants with low p-values

ld_prune(
  variants,
  ld,
  total_ld_variants,
  pval_cols,
  pval_thresh,
  r2_thresh = 0.1,
  variant_name = "snp"
)

`variants`	Either a vector of SNP names or a data.frame. If a data.frame, provide pval_cols containing the names of pvalue columns to use for pruning. Results will contain one list per p-value column. If variants is a vector, LD pruning will be random.
`ld`	data.frame with three columns: colsnp, rowsnp, and r2. Each row corresponds to an entry of the LD matrix. If a pair of variants does not appear, they are assumed to have LD of 0 (or below the threshold).
`total_ld_variants`	vector of variant names used to construct 'ld'.
`pval_cols`	Vector of names of pval_cols. If not missing, variants must be a data.frame. pval_cols may contain NA entries. NA's indicate that you desire a pruned set that is not prioritized by p-value.
`pval_thresh`	A vector the same length as 'pval_cols'. Only variants with p-value below the threshold will be retained. Not required if variants is a vector. Entries of pval_thresh may be Inf.
`r2_thresh`	r^2 threshold for pruning. The final list will contain no pairs with r2 > r2_thresh
`variant_name`	If variants is a data.frame, the column name containing the variant id.

ld_prune will generate any desired number of LD pruned variant lists. At minimum, it requires three arguments: 'variants' is the set of variants to be pruned, 'ld' is a data frame giving estimated LD from a reference panel and 'total_ld_variants' is the list of variants used to construct the ld data. Only variants ovlapping between 'variants' and 'total_ld_variants' will be considered.

If only these three arguments are given, the returned object will be a vector of LD pruned variants. These will be pruned randomly as no p-value information has been supplied.

Another option is to prune variants prefentially retaining those with small p-values. For this option, the 'variants' argument should be a data.frame that includes at minimum a column containing the variant name and a column containing the pvalue. You can prune on multiple p-values at once which can save time.

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