# Introduction to CoPheScan

#### Ichcha Manipur

#### 2024-06-11

    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