coloc.abf: Fully Bayesian colocalisation analysis using Bayes Factors

View source: R/claudia.R

coloc.abfR Documentation

Fully Bayesian colocalisation analysis using Bayes Factors

Description

Bayesian colocalisation analysis

Usage

coloc.abf(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 calculates posterior probabilities of different causal variant configurations under the assumption of a single causal variant for each trait.

If regression coefficients and variances are available, it calculates Bayes factors for association at each SNP. If only p values are available, it uses an approximation that depends on the SNP's MAF and ignores any uncertainty in imputation. Regression coefficients should be used if available.

Value

a list of two data.frames:

  • 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)

  • results 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 if H4 is true. This is only relevant if the posterior support for H4 in summary is convincing.

Author(s)

Claudia Giambartolomei, Chris Wallace


chr1swallace/coloc documentation built on June 26, 2022, 4 a.m.