---
title: "Introduction to CoPheScan"
author: "Ichcha Manipur"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to CoPheScan}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "Intro-"
)
```

```{r setup, message=FALSE, warning=FALSE}
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](https://doi.org/10.1101/2023.06.29.546856)

2.  coloc: [Giambartolomei et al (2013)](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004383)

3.  coloc with SuSIE: [Wallace et al (2021)](https://doi.org/10.1371/journal.pgen.1009440), [github](https://github.com/chr1swallace/coloc)

4.  ABF: [Wakefield (2008)](https://doi.org/10.1002/gepi.20359)

5.  SuSIE: [Wang et al (2020)](https://doi.org/10.1111/rssb.12388), [github](https://github.com/stephenslab/susieR)

------------------------------------------------------------------------