cophescan (version 1.4.2)

cophe.single: Bayesian cophescan analysis using Approximate Bayes Factors

Description

Bayesian cophescan analysis under single causal variant assumption

Usage

cophe.single(
  dataset,
  querysnpid,
  querytrait,
  MAF = NULL,
  pa = 3.82e-05,
  pc = 0.00182,
  p1 = NULL,
  p2 = NULL,
  p12 = NULL
)

Value

a list of two data.frames:

• summary is a vector giving the number of SNPs analysed, and the posterior probabilities of Hn (no shared causal variant), Ha (two distinct causal variants) and Hc (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.Hc of the SNP being causal for the shared signal if Hc is true. This is only relevant if the posterior support for Hc in summary is convincing.

Arguments

dataset

a list with specifically named elements defining the query trait dataset to be analysed.

querysnpid

Id of the query variant, (id in dataset$snp)

querytrait

Query trait name

MAF

Minor allele frequency vector

pa

prior probability that a non-query variant is causally associated with the query trait (cophescan prior), default 3.82e-5

pc

prior probability that the query variant is causally associated with the query trait (cophescan prior), default 1.82e-3 (cophescan prior)

p1

prior probability a SNP is associated with trait 1, (coloc prior), pc derived by using \(pc = p12/p1+p12\); use p1, p2, p12 only when pa and pc are unavailable (See vignettes)

p2

prior probability a SNP is associated with trait 2, (coloc prior), pa derived by using \(pa = p2\)

p12

prior probability a SNP is associated with both traits, (coloc prior), pc derived by using \(pc = p12/p1+p12\)

Author

Ichcha Manipur

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. Find more input data structure details in the coloc package

Examples

library(cophescan)
data(cophe_multi_trait_data)
query_trait_1 <- cophe_multi_trait_data$summ_stat[['Trait_1']]
querysnpid <- cophe_multi_trait_data$querysnpid
res.single <- cophe.single(query_trait_1, querysnpid = querysnpid, querytrait='Trait_1')
summary(res.single)