Introduction to GWAS

Genome-wide association study (GWAS) is a hypotheses-free method for identifying associations between genetic regions and traits (incl. diseases). GWAS typically searches for small variations, known as single-nucleotide polymorphisms (SNPs), that occur more frequently in people with a particular disease than those without the disease.

Researchers use two groups of participants: people with the disease being studied (case group) and similar people without the disease (control group). Strategically selected SNPs are then compared between groups. If certain variations are found to be significantly more frequent in people with the disease compared to people without the disease, the variations are said to be associated with the disease.

Results are typically displayed in a Manhattan plot with -log10(p-value) plotted against the position in the genome. Two lines are added to indicate the genome-wide significance threshold (p=5.0×10−8) and the cut-off level for selecting SNPs for replication study (p=1.0×10−5).

About GWAS App

The app has five tabs:

1. An interactive Manhattan plot

2. Circular Manhattan plot

3. Quantile-quantile (QQ) plot

4. SNP density plot

5. Help

This R Shiny mini-app reads the files allocated in the data folder, you can add your own data and use it in the app. The app expects a csv derived from the PLINK .map file, this file should contain the following columns:

1. CHR - chromosome number (integer)
2. BP - genomic base-pair position (integer)
3. P - p-value (integer)
4. SNP - snp identifier (character)
5. ZSCORE - z-score (numeric)
6. EFFECTSIZE - effect size (numeric)
7. GENE - nearest gene to SNP (character)
8. DISTANCE - distance between the SNP and GENE (integer)

The example data is the R example dataset HapMap, from the manhattanly packages.

Checkout and run

You can clone this repository by using the command:

git clone https://github.com/aridhia/gwas-app

Open the .Rproj file in RStudio, source the script dependencies.R to install all the packages required by the app, and run runApp() to start the app.

Workspace deployment

1. Create a new mini-app in the workspace called "gwas-app" and delete the folder created for it
2. Download this GitHub repo as a ZIP file, or clone it and zip all the files
3. Upload the zip file to the workspace and unzip inside a folder called "gwas-app"
4. Run the dependencies.R script to install all the packages the app requires
5. Run the app in your workspace

For more information visit https://knowledgebase.aridhia.io/article/how-to-upload-your-mini-app/