coloc.abf: Fully Bayesian colocalisation analysis using Bayes Factors

Description Usage Arguments Details Value Author(s)

View source: R/claudia.R

Description

Bayesian colocalisation analysis

Usage

1
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:

Author(s)

Claudia Giambartolomei, Chris Wallace


coloc documentation built on June 14, 2021, 5:09 p.m.