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Run Bayesian enumeration colocalization using Coloc

Source: R/coloc_run.R
coloc_run.Rd

This function is a wrapper around coloc::coloc.abf() that takes a dataframe as input, and performs colocalization under the single-causal-variant assumption. Coloc was described in Giambartolomei et al. (PLOS Genetics 2014; https://doi.org/10.1371/journal.pgen.1004383).

Usage

coloc_run(
  df,
  trait_col = trait,
  variant_col = rsid,
  beta_col = beta,
  se_col = se,
  samplesize_col = samplesize,
  maf_col = maf,
  type_col = type,
  case_prop_col = case_prop,
  p1 = 1e-04,
  p2 = 1e-04,
  p12 = 1e-05,
  ...
)

Arguments

df

Dataframe containing summary statistics at a single locus for two traits in a "long" format, with one row per variant per trait.

trait_col

Column containing trait names

variant_col

Column containing unique variant identifiers (Eg. rsids, chr:pos)

beta_col

Column containing effect estimates

se_col

Column containing standard errors

samplesize_col

Column containing sample sizes

maf_col

Column containing minor allele frequencies

type_col

Column containing the type of each trait ("quant" for quantitative traits, "cc" for binary traits)

case_prop_col

Column containing the proportion of cases for case control studies; this column is ignored for quantitative traits

p1

Prior probability a SNP is associated with trait 1, default 1e-4

p2

Prior probability a SNP is associated with trait 2, default 1e-4

p12

Prior probability a SNP is associated with both traits, default 1e-5

...

Arguments passed to coloc::coloc.abf()

Value

A list containing coloc results.

  • summary is a named vector containing the number of snps, and the posterior probabilities of the 5 colocalization hypotheses

  • results is an annotated version of the input data containing log approximate Bayes Factors and posterior probability of each SNP being causal if H4 is true.

See also

Other colocalization: hyprcoloc_df()

Examples

if (FALSE) {
coloc_run(locus_df)
}