Introduction to CoPheScan

Ichcha Manipur




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

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