Bayesian colocalisation analysis with detailed output

Bayesian colocalisation analysis, detailed output

coloc.detail(
  dataset1,
  dataset2,
  MAF = NULL,
  p1 = 1e-04,
  p2 = 1e-04,
  p12 = 1e-05
)

Arguments

dataset1: a list with specifically named elements defining the dataset to be analysed. See check_dataset for details.
dataset2: as above, for dataset 2
MAF: Common minor allele frequency vector to be used for both dataset1 and dataset2, a shorthand for supplying the same vector as parts of both datasets
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

a list of three data.tabless:

summary is a vector giving the number of SNPs analysed, and the posterior probabilities of H0 (no causal variant), H1 (causal variant for trait 1 only), H2 (causal variant for trait 2 only), H3 (two distinct causal variants) and H4 (one common causal variant)
df is an annotated version of the input data containing log Approximate Bayes Factors and intermediate calculations, and the posterior probability SNP.PP.H4 of the SNP being causal for the shared signal
df3 is the same for all 2 SNP H3 models

This function replicates coloc.abf, but outputs more detail for further processing using coloc.process

Intended to be called internally by coloc.signals

Chris Wallace