Visualize longitudinal and cross-sectional variant effects

One of the projects from the 2021 GP2/IPDGC Hackathon. The related manuscript can be found on [biorxiv](https://www.biorxiv.org/content/10.1101/2022.05.04.490670v1)
Contributers: Michael Ta, Alejandro Martinez-Carrasco, Clodagh Towns, Regina Reynolds, María Teresa Periñán, Nikita Pillay

Table of Contents

1. About The Project
   • Quick Description
   • Background/motivation
   • Workflow Summary
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Acknowledgments

About The Project

Quick Description

The goal for this project was to visualize longitudinal and cross-sectional variant effects from GWAS.

Background/motivation

Longitudinal biomarker data is needed to understand the progression of disease and allow for easier testing and diagnosis. This tool takes results from biomarker GWAS to allow researchers to be able to query for a particular biomarker and get a visualisation of the effect on all cohorts or a set of cohorts, as well as the associated meta-analysis. It also adds the ability to display longitudinal information alongside cross-sectional results.

Workflow Summary

1. Download/clone repository.
2. Install necessary packages.
3. Open app.R in R Studio.
4. Click 'Run App' button in R Studio.
5. Visualize!

(back to top)

Getting Started

Prerequisites

• R and R studio
• R Shiny

Installation

1. Clone the repo

   git clone https://github.com/ipdgc/GP2-visualise-longitudinal-variant-effects.git

(back to top)

Usage

There is already example data from biomarker GWAS conducted with the PPMI cohort. However you can upload your own GWAS statistics using the upload tab on the app!

Make sure the necessary packages are installed, they are listed in app.R. All packages except 'ggforestplot' can be installed with install.packages(). 'ggforestplot' can be installed like this:

install.packages('devtools')
devtools::install_github("NightingaleHealth/ggforestplot")

If you wish to visualize your own variant effects, input data should look like this:

#CHROM	POS	ID	REF	ALT	A1	A1_FREQ	TEST	OBS_CT	BETA	SE	T_STAT	P	OBS_CT_rep
22	15226216	chr22:15226216:GC:G	GC	G	G	0.00852273	ADD_REP	176	-0.0723873	0.25866336	-9	0.77959159	687
22	15279243	chr22:15279243:A:T	A	T	T	0.00568182	ADD_REP	176	0.11891265	0.30542448	-9	0.69702783	687
22	15284730	chr22:15284730:C:G	C	G	G	0.00568182	ADD_REP	176	-0.1478226	0.31534816	-9	0.63924118	687
22	15306199	chr22:15306199:A:T	A	T	T	0.01420455	ADD_REP	176	-0.0959635	0.19529632	-9	0.62316195	687
22	15308575	chr22:15308575:A:T	A	T	T	0.01136364	ADD_REP	176	-0.2816581	0.2177098	-9	0.19575805	687

(back to top)

Acknowledgments

• PPMI