# Colocalization Pipeline

### Colocalization 

Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. One of the next challenges is to assess whether two association signals are consistent with a shared causal variant. These can be accomplished by using statistical method that can use simply single variant summary statistics to test for colocalization of GWAS signals.

#### Methodology

A statistical analysis that computes posterior probability using Bayes Factor to see whether a causal variant colocalizes in GWAS (*trait 1*) and GWAS (*trait 2*) or GWAS and eQTL in a given region. However, Bayes Factor will only be calculated if regression coefficients and variances are available (each SNP). If these are not available then the software will then use p-value and MAF as an approximation.

### Workflow

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MERuCj53izHfLerxgRB%2F-MESSZrJ30eEGGoZOlXC%2Fflow.png?alt=media\&token=07d2c2c9-280f-4165-8380-a331a99d18c6)

#### User Journey

A few simple steps are needed to run this pipeline:

1. choose the dataset using a therapeutic tag of interest in the *Search Data* page;
2. then, select the *dataset* and *Coloc tab*;
3. user provides GWAS parameters;
4. user selects the Colocalization tab and uploads a GWAS summary file/eQTL file;
5. user provides other parameters for the colocalization and the job name and finally runs the analysis;

then, you will be redirected to the results page.

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MDyQIbCG21Oggw-HGM7%2F-MDyQWnim5pvM4y1_PZR%2Fimage.png?alt=media\&token=b6b01a3a-5230-4599-8370-a4073fe78a70)

The design is always the same as the previous pipelines since they share all the same characteristics.

You can find an in-depth explanation of GWAS parameters [here](https://app.gitbook.com/@shivom/s/documentation/~/drafts/-M5SP1CfCeje9exOpQgZ/pipelines/gwas-pipeline).

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MDyQIbCG21Oggw-HGM7%2F-MDyQh4DrQ43Vv9FV3Zz%2Fimage.png?alt=media\&token=a39692b1-fcf5-40e1-8c29-a6ed5b1984b8)

The first thing to do is providing a *Summary statistics file* corresponding to the dataset you have already chosen to use: the **input files** must be:

1. **VCF files** selected on the *Search Data* page and subsequent **GWAS summary statistics file**;
2. **Summary Statistics file** uploaded on the *Colocalization* configuration page**.**

The input parameters for both traits from each *Summary Statistics* file should be (as a List):

* **N** = Number of individuals in sample;
* **snp** = SNP id’s;
* **p-Value** = p-value for each SNP;
* **beta** = regression coefficient per SNP;
* **var beta** = Variance of beta;
* **type** = “quant” = quantitative data, “cc” = case-control data (Our platform will always use “cc”);
* **s** = proportion of samples in “Trait 1” that are cases.

The **platform parameters** to be inserted from the user interface regard the prior probability a SNP is associated with:

* trait 1, default value: 1e-4;  
* trait 2, default value: 1e-4;  
* both traits, default value: 1e-5. 

### Results

Once you have chosen the pipeline to be used, uploaded the data file and set all the parameters, you can start your analysis using the *Run Analysis* box; at this point you will be redirected to this page, where you can keep an eye on which works are *In Progress*, which are *Completed*, and choose to carry out a new analysis. 

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-ME3RTcdn0x8-jxQVEbq%2F-ME3S3HQbJfzDdiKFdRV%2FLight%20-%20Dashboard%402x.png?alt=media\&token=0092011a-43fb-4673-9d66-0b9942c4ab74)

By clicking on your *JobName*, you will have access to this page, where you can monitor all the processes involved in your analysis:

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-ME3RTcdn0x8-jxQVEbq%2F-ME3Rj_M3uekxa18wcRx%2FLight%20-%20Pipeline%20Job%402x.png?alt=media\&token=1ec23184-4d03-4aee-9030-ed55bb200481)

Now, selecting the *Results* box on the right, let's take a look at the demo results obtained using the Default Parameters Set:

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-M9XPEqus5Ro-4btTKu_%2F-M9YjOLocxJkNDjD-7Qh%2Fimage.png?alt=media\&token=85c48a26-e7a4-44ea-a746-d4928a28176c)

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-M9XPEqus5Ro-4btTKu_%2F-M9YkDpxpVylDFSROIcD%2Fimage.png?alt=media\&token=e26ef84e-6004-4d54-bb95-d91724224a59)

#### Scatter Plot

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-M9XPEqus5Ro-4btTKu_%2F-M9YkLwF01gvlhKZMi5t%2Fimage.png?alt=media\&token=97fe93d1-5abd-4362-b874-5b3b929d9b92)

By clicking on the Interactive Graphs option, you can also view your results like this:

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MA1LvbnBx1j9bd1CvRh%2F-MA1_UeMYE9vqmCBy4F2%2Fimage.png?alt=media\&token=26b24a05-cc10-46ee-a106-bf1904a7d13f)

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MA1LvbnBx1j9bd1CvRh%2F-MA1_cGV2b4YgLDnRGmJ%2Fimage.png?alt=media\&token=cbc5fb19-7e92-4944-a51f-4d328b92b254)

![](https://335305010-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-M4Sm7H8vvZ4Rwarnfru%2F-MA1LvbnBx1j9bd1CvRh%2F-MA1a34fyYm6IuspS1Ly%2Fimage.png?alt=media\&token=24c1c117-27b8-4a86-9744-10f28378f3b2)

Finally, using the *Export* box, you will be able to download the results of your analysis in a *.pdf* format file.

#### Reference

1. [**Statistical Independence of the Colocalized Association Signals for Type 1 Diabetes and RPS26 Gene Expression on Chromosome 12q13**](https://pubmed.ncbi.nlm.nih.gov/19039033/), Plagnol V et al, 2009
2. [**Statistical Testing of Shared Genetic Control for Potentially Related Traits**](https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.21765), Wallace C, 2013
3. [**Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics**](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004383), Giambartolomei C et al, 2014
4. [**Coloc: a package for colocalisation analyses**](https://cran.r-project.org/web/packages/coloc/vignettes/vignette.html), Wallace C, 2019