EGRET (Estimating Genome-wide Regulatory Effects on the Transcriptome)

EGRET is a multivariate linear model designed to identify genome-wide loci that are predictive of gene expression levels. EGRET integrates predictions from existing trans-eQTL mapping approaches (Matrix eQTL, GBAT, and trans-PCO) and determines the optimal weighted combination of regulatory variants that best explains gene expression. Finally, EGRET utilizes a genome-wide summary statistics-based TWAS to identify novel gene-disease associations.

Installation

git clone https://github.com/krishnasbrunton/EGRET.git
cd EGRET

Setting up the environment

Install dependences

Setup environment using yml file.

conda env create -f environment.yml
conda activate egret_env

R packages Required

• data.table
• optparse
• plink2R
• Matrix eQTL

To download plink go to: https://www.cog-genomics.org/plink/1.9/

Recomended

• xtune

To install xtune you wiil need to use devtools.

library(devtools)
devtools::install_github("JingxuanH/xtune", build_vignettes = TRUE)

Setup

To setup expression and genotype files in the format that EGRET requires, we have created a wrapper script to carry out preprocessing of genoytype and expression data. All outputs are stored in the ouput_dir.

Argument	Description
genotypes_file_path	Path to PLINK .bed/.bim/.fam files containing genotype data.
expression_file_path	Gene expression matrix (rows = genes, columns = individuals). Must include a gene_id column in ENSG format.
covariates_file_path	Covariate file (rows = individuals, columns = covariates to regress out).
tissue	Name of tissue for analysis. Used for labeling and output organization.
individuals_file_path	File containing list of individual IDs (one per line, no header).
LD_prune_r2	LD pruning threshold (default: 0.9).
plink_path	Path to PLINK2 executable.
genotype_output_prefix	Prefix for pruned genotype output files.
folds	Number of cross-validation folds (e.g., 5).
gene_info_file_path	File containing gene metadata (gene ID, name, chromosome, start position).
FDR	False discovery rate (FDR) that will be used to select significant variants
output_dir	Directory to store all processed outputs.

./setup_genotype_and_expression.sh \
    $genotypes_file_path \
    $expression_file_path \
    $covariates_file_path \
    $tissue \
    $individuals_file_path \
    $LD_prune_r2 \
    $plink_path \
    $genotype_output_prefix \
    $folds \
    $gene_info_file_path \
    $output_dir

Running Matrix eQTL, GBAT, and trans-PCO

In EGRET, we implement Matrix eQTL, GBAT, and trans-PCO methods to identify trans-variants with potential to enhance gene expression models. Each one is run separatly and the outputs of each is used in the final training of the genome-wide model.

Running Matrix eQTL

We utilize the R package, Matrix eQTL, to conduct pairwise association between all genetic variants and gene expression. As input this script takes the tissue you wish to carry this analysis on. It is assumed that the setup scripts were run so it looks for their respective outputs.

./MatrixeQTL_scripts.sh \
    $tissue \
    $FDR \
    $output_dir

Running GBAT

GBAT utilizes previously trained cis-expression models to find genome-wide regulators. This code is designed to take as input models fitted by FUSION.

• If FUSION models have not already been generated, model weights can be downloaded from http://gusevlab.org/projects/fusion/

./GBAT_scripts.sh \
    $FDR \
    $folds \
    $gene_info \
    $output_dir

Running trans-PCO

transPCO finds the association between a module of genes and a variant.

./transPCO_scripts.sh \
    $tissue \
    $folds \
    $FDR \
    $num_PCs \
    $output_dir

Training EGRET models

This script takes the input from the training of the models above and trains the genome-wide EGRET model and outputs the weights and model R2. We recommend using at least Matrix eQTL and GBAT.

In addition this script, will filter cross-mappable reads from alignment errors. As default we supply bed files containing cross-mappable beds for each gene in the hg38 genome build. If you want to filter cross-mappable regions in a different genome build, you can run the script 12.0_make_crossmappable_gene_beds.R with gene-gene mappabality scores provided by https://doi.org/10.12688/f1000research.17145.2.

./train_EGRET_models.sh \
    $tissue \
    $folds \
    $models \
    $output_dir

Running EGRET-TWAS

To conduct gene-trait association, we have provided the following script which takes as input gwas summary statistics and the models trained by EGRET to produce gene-trait associations.

./run_TWAS_scripts.sh \
    $tissue \
    $trait \
    $gwas_sumstat_dir \
    $output_dir