OTTERS: Omnibus Transcriptome Test using Expression Reference Summary data

OTTERS Framework

A powerful TWAS framework leveraging summary-level reference data.

We provide an integrated tool that can

perform LD-clumping
prepare the required inputs (LD reference and eQTL summary statistics) in the required formats for P+T, lassosum, SDPR, and PRS-CS
perform TWAS Stage I: run P+T, lassosum, SDPR, and PRS-CS to train eQTL weights.
perform TWAS Stage II: gene-based association test using GWAS summary statistics.

In this tool, we

integrate TABIX and PLINK 1.9 tools to extract input data per target gene more efficiently
integrate PRS-CS software, lassosum R package, and SDPR software to train eQTL weights
enable parallel computation to train GReX imputation models / perform gene-based association test simultaneously for multiple genes.

Getting Started

Download and install following required tools, modules, and packages:

Download OTTERS using:

git clone https://github.com/daiqile96/OTTERS.git

BGZIP and TABIX

Here is my code to download and install BGZIP and TABIX:

 wget https://sourceforge.net/projects/samtools/files/tabix/tabix-0.2.6.tar.bz2
 tar jxvf tabix-0.2.6.tar.bz2
 cd tabix-0.2.6
 make

 # copy the binary bgzip and tabix to my path: ~/projects/bin
 cp bgzip tabix ~/projects/bin

 # test if BGZIP and TABIX are successfully installed
 export PATH=~/projects/bin:$PATH 
 bgzip
 tabix

PLINK 1.9

Here is my code to download and install PLINK 1.9:

# Download the latest binary of PLINK 1.9
wget https://s3.amazonaws.com/plink1-assets/plink_linux_x86_64_20220402.zip

# Unzip the archive
unzip plink_linux_x86_64_20220402.zip -d ~/projects/bin

# Remove archive
rm plink2_linux_x86_64_latest.zip

# Test if PLINK 1.9 is successfully installed
export PATH=~/projects/bin:$PATH 
plink

To permanently store the path, please add export PATH=~/projects/bin:$PATH to your ~/.bash_profile (or ~/.bashrc).

Then reload file .bash_profile from the command line:
```
source ~/.bash_profile
```
Python modules/libraries:
If you're new to python, here is an example to set up the Python Environment to use OTTERS.
To apply SDPR and lassosum as imputation models, please install:
- SDPR to perform SDPR
  
  Here is my code to download SDPR
```
cd ~/projects/bin
git clone https://github.com/eldronzhou/SDPR.git

# make sure that dynamic libraries gsl/lib/libgsl.so and MKL/lib/libmkl_rt.so are not changed
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/projects/bin/SDPR/MKL/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/projects/bin/SDPR/gsl/lib
```
  Please make sure that dynamic libraries are not changed every time when use SDPR.
- R packages:
  - lassosum to perform lassosum (lassosum requires several R packages, please follow the lassosum installation guidance to install all of them).
  - fdrtool to perform pseudo-validation implemented in lassosum.

Example:

  # activate the environment
  conda activate otters

  # set number of threads to be used
  N_THREADS=1

  # set up my OTTERS directory and SDPR directory
  OTTERS_DIR=/home/qdai8/projects/bin/OTTERS
  SDPR_DIR=/home/qdai8/projects/bin/SDPR

  # make sure the dynamic libraries of SDPR are not changed (For SDPR)
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${SDPR_DIR}/MKL/lib
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${SDPR_DIR}/gsl/lib

  # prevent automatically using  all available cores on a compute node (For SDPR and PRS-CS)
  export MKL_NUM_THREADS=$N_THREADS
  export NUMEXPR_NUM_THREADS=$N_THREADS
  export OMP_NUM_THREADS=$N_THREADS

  # Load R to perform lassosum
  module load R

  # Start to run OTTERS
  cd ${OTTERS_DIR}/Example

  # Input for OTTERS STAGE I 
  # Annotation File 
  exp_anno=exp_anno.txt
  # Genotype data from LD reference panel
  geno_dir=Exp_geno
  # eQTL summary statistics 
  sst_file=Exp_eQTLSumStats.txt
  # Input for OTTERS STAGE II
  # GWAS summary statistics 
  gwas_sst_file=Exp_GWASSumStats.txt

  # Set chromosome number (The example is for chromosome 4)
  chr=4
  # Set LD-clumping threshold in STAGE I
  clump_r2=0.99
  # Set output directory for STAGE I
  out_dir=Results

  # STAGE I
  # train eQTL weights using P+T, lassosum, SDPR and PRS-CS. 
  # It may take several minutes to complete.
  python3 ${OTTERS_DIR}/training.py \
  --OTTERS_dir=${OTTERS_DIR} \
  --SDPR_dir=${SDPR_DIR} \
  --anno_dir=${exp_anno} \
  --geno_dir=${geno_dir} \
  --sst_file=${sst_file} \
  --out_dir=${out_dir} \
  --chrom=${chr} \
  --r2=${clump_r2} \
  --models=PT,lassosum,SDPR,PRScs \
  --thread=$N_THREADS

  # Set output directory for STAGE II
  twas_dir=TWAS

  # STAGE II
  # gene-based association test using eQTL-weight trained from P+T, lassosum, SDPR and PRS-CS.
  python3 ${OTTERS_DIR}/testing.py \
  --OTTERS_dir=${OTTERS_DIR} \
  --weight_dir=${OTTERS_DIR}/Example/Results \
  --models=P0.001,P0.05,lassosum,SDPR,PRScs \
  --anno_dir=${exp_anno} \
  --geno_dir=${geno_dir} \
  --out_dir=${twas_dir} \
  --gwas_file=${gwas_sst_file} \
  --chrom=${chr} \
  --thread=$N_THREADS

  # get imputed genetically regulated gene expression
  impute_dir=GReX
  # samples to perform imputation
  samples=Exp_samples.txt
  # imputation
  python3 ${OTTERS_DIR}/imputing.py \
  --OTTERS_dir=${OTTERS_DIR} \
  --weight_dir=${OTTERS_DIR}/Example/Results \
  --models=P0.001,P0.05,lassosum,SDPR,PRScs \
  --anno_dir=${exp_anno} \
  --geno_dir=${geno_dir} \
  --out_dir=${impute_dir} \
  --chrom=${chr} \
  --samples=${samples} \
  --thread=$N_THREADS

Required Inputs

Annotation File should contain following columns with the same name in same order (text file):

CHROM GeneStart GeneEnd TargetID

1 100 200 ENSG0000

Example: Example/exp_anno.txt
Genotype data from LD reference panel in PLINK binary files :

Example: Example/Exp_geno.bed; Example/Exp_geno.bim; Example/Exp_geno.fam
eQTL summary statistics should contain following columns with the same name in same order (text file):

Please sort by chromosome and then by SNP position in ascending order

CHROM POS A1 A2 Zscore TargetID N

1 100 C T 3 ENSG0000 200

To convert a two-sided pvalue and beta (coefficients) to Zscore, in python, we can do:
```
import numpy as np
from scipy.stats import norm
Z = np.sign(beta) * abs(norm.ppf(pvalue/2.0))
```
Example: Example/Exp_eQTLSumStats.txt
GWAS summary statistics should contain following columns with the same name in same order (text file):

Not required to be sorted

CHROM POS A1 A2 Zscore

1 100 C T 2

Example: Example/Exp_GWASSumStats.txt

CHROM	GeneStart	GeneEnd	TargetID
1	100	200	ENSG0000

CHROM	POS	A1	A2	Zscore	TargetID	N
1	100	C	T	3	ENSG0000	200

CHROM	POS	A1	A2	Zscore
1	100	C	T	2

Notes:

OTTERS is based on PLINK 1.9, PRS-CS software, lassosum R package, and SDPR software. We really appreciate the authors of these softwares.

daiqile96 / OTTERS

readme

OTTERS: Omnibus Transcriptome Test using Expression Reference Summary data

Getting Started

Example:

Required Inputs

Notes: