knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "Intro-" )
library(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 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
Description of the CoPheScan method:
CoPheScan: phenome-wide association studies accounting for linkage disequilibrium
coloc: Giambartolomei et al (2013)
coloc with SuSIE: Wallace et al (2021), github
ABF: Wakefield (2008)
SuSIE: Wang et al (2020), github
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