coloc.detail: Bayesian colocalisation analysis with detailed output

View source: R/split.R

coloc.detailR Documentation

Bayesian colocalisation analysis with detailed output

Description

Bayesian colocalisation analysis, detailed output

Usage

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

Details

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

Intended to be called internally by coloc.signals

Value

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

Author(s)

Chris Wallace

See Also

coloc.process, coloc.abf


chr1swallace/coloc documentation built on May 6, 2022, 1:13 p.m.