Introduction to CoPheScan

Ichcha Manipur

2026-03-23

library(cophescan)

CoPheScan

The cophescan package implements Coloc adapted Phenome-wide Scan (CoPheScan), a Bayesian method to perform Phenome-wide association studies (PheWAS) that identifies causal associations between genetic variants and phenotypes while simultaneously accounting for confounding due to linkage disequilibrium.

Given a query variant and genomic region with Q SNPs for a query trait, cophescan discriminates between 3 hypotheses:

$H_n$ : No association with the query trait (1 configuration)

$H_a$ : Association of a variant other than the query variant with the query trait (Q-1 configurations)

$H_c$ : Association of the query variant with the query trait (1 configuration)

with $p_n$, $p_a$ and $p_c$ being their corresponding priors.

CoPheScan approaches

cophescan can be run in different ways depending on the size and type of dataset.

First, choosing the method for Bayes factor computation:

	Single causal variant	Multiple causal variants	Requires LD matrix
ABF	✔	x	No
SuSIE	✔	✔	Yes

Whenever LD matrices are available (preferably in-sample LD), cophe.susie is the recommended method as it accounts for multiple causal variants in the tested region.

Next, depending upon the size of the dataset we choose the method to specify priors:

	Dataset	Inclusion of covariates
Fixed priors	Small	-
Hierarchical priors	Large	✔

The different combinations that can be run are:

ABF/Fixed priors: cophe.single

SuSIE BF/Fixed priors: cophe.susie

ABF/Hierarchical priors: cophe.single.lbf + run_metrop_priors

SuSIE BF/Hierarchical priors: cophe.susie.lbf + run_metrop_priors

Further reading

1. Description of the CoPheScan method:

   CoPheScan: phenome-wide association studies accounting for linkage disequilibrium

2. coloc: Giambartolomei et al (2013)

3. coloc with SuSIE: Wallace et al (2021), github

4. ABF: Wakefield (2008)

5. SuSIE: Wang et al (2020), github

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