Note: This project is currently under development.

GiNi-GWAS post-GWAS process:

Python-based, post-GWAS processing and analysis pipeline

GiNi is a automated pipeline built for post-GWAS analysis. With GWAS summary statistics as input, the pipeline processes and perform a set of comprehensive analyses. Uses brain gene-expression and splicing data resources for analysis. GiNi includes the following modules:

• Global Genetic Correlation
• Local Genetic Correlation
• Mendelian Randomization
• Heritability
• Transcriptome-Wide Associations

Overview of the pipeline

How to use the package:

Clone the github directory using:

https://github.com/A2U8C/GiNi_post_GWAS_processing.git

Virtual environment:

Navigate to the "smacc" folder and then create a virtual environment using the requirements.txt file:

conda create -n GiNi_Env python== 3.8 -y
conda activate GiNi_Env
pip install -r requirements.txt
pip install .

Prepare underlying tools:

## Run the following python script, to create environments and other tools which will be required by GiNi
python setup_tools_Gini.py
wget -O output_filename.ext https://www.dropbox.com/s/GiNi_underlying_data?dl=1

Input file fomat:

GiNi expect input_txt argument which is path to the input file which contains all GWAS files path. Example of input_txt input

/path/to/GWAS/stats/studyName__TraitName

/path/to/GWAS/stats/ukbb_regenie/ukbb__Total_MeanThickness.regenie
/path/to/GWAS/stats/ukbb_regenie/ukbb__Witelson5_Genu_Area.regenie
/path/to/GWAS/stats/ukbb_regenie/ukbb__Witelson5_Isthmus_MeanThickness.regenie
/path/to/GWAS/stats/abcd_regenie/abcd__Total_MeanThickness.regenie
/path/to/GWAS/stats/abcd_regenie/abcd__Witelson5_Genu_Area.regenie
/path/to/GWAS/stats/abcd_regenie/abcd__Witelson5_Isthmus_MeanThickness.regenie

Test the tool:

python gini_main.py input-module-wrapper \
	--input_txt /path/to/GWAS/stats/ABCD_UKBB_input.txt \
	--n_studies 1 \
	--ethnicity European \
	--analysis_list Heritability \
	--tissues_cells Astrocytes \
	--meta random \
	--gwas_format regenie \
	--lava_control_cases /path/to/Case_Controls/COPC_Case_control.txt

--input_txt : To the input file

--n_studies : Number of studies, to verify if Meta-Analysis is required

--ethnicity : Ethnicity for selecting the appropriate reference

--analysis_list : Comma seperated analysis list or 'ALL' for all analysis

--tissues_cells : Brain tissue and cells to performe TWAS and heritability

--meta : Analysis type for meta-analysis

--gwas_format : Format of the input GWAS

--control_cases : File containing count of control/cases for each trait

--computing_options : If HPC is available, else tests will run sequentially

If you use this code, please cite the following:

• Willer, C. J., et al. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
• Bulik-Sullivan, B. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
• Werme, J., et al. An integrated framework for local genetic correlation analysis. Nat. Genet. 54, 274–282 (2022).
• Morrison J, et al. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet. 2020.
• Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).
• de Leeuw, C., et al. On the interpretation of transcriptome-wide association studies. PLoS Genet. 19, e1010921 (2023).