PWAS

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This tutorial is created for Proteome-wide association study (PWAS) and further performing a bivariate conditional analysis to interpret PWAS findings in the context of those from Transcriptome-wide association studies (TWAS).

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# PWAS
This tutorial is created for Proteome-wide association study (PWAS) and further performing a bivariate conditional analysis to interpret PWAS findings in the context of those from Transcriptome-wide association studies (TWAS).

Introduction
============

This tutorial is created for Proteome-wide association study (PWAS) and
further performing a bivariate conditional analysis to interpret PWAS
findings in the context of those from Transcriptome-wide association
studies (TWAS).

The pipeline of PWAS is mainly from
[TWAS/FUSION](http://gusevlab.org/projects/fusion/) with some
modifications. The bivariate conditional analysis was described in our
recent paper. Please cite both the manuscript for primary TWAS methods
and the recent pQTL and PWAS paper:

> - Zhang, Jingning, et al. “Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies.” Nature Genetics 54.5 (2022): 593-602.
> - Gusev, et al. “Integrative approaches for large-scale transcriptome-wide association studies” 2016 Nature Genetics

Note that we stored the trained models and required data for two ethnic groups separately (PWAS\_[EA/AA]). [EA/AA] in file or directory names represents the ancestry where we trained these models (EA represents for European Americans and AA represents for African Americans). We recommend matching the ancestry to your GWAS summary data.

Installation
============

Launch R and install required libraries: 
``` {.r language="R"}
install.packages(c('optparse','RColorBrewer','readr','stringr','dplyr'))
install.packages('plink2R-master/plink2R/',repos=NULL)
```

Note: We noticed some people may have issues with the installation of
plink2R. Pleas try the command below in R if the above one does not
work.

devtools::install_github("carbocation/plink2R/plink2R", ref="carbocation-permit-r361")

Download and unzip the scripts:

mkdir PWAS
    cd PWAS
	
    wget https://jhupwas.s3.amazonaws.com/scripts.zip
    unzip scripts.zip && rm scripts.zip

Download and unzip the required data:

wget https://jhupwas.s3.amazonaws.com/LDref.zip
    unzip LDref.zip && rm LDref.zip

## choose to download models for EA / AA
    wget https://jhupwas.s3.amazonaws.com/PWAS_EA.zip
    unzip PWAS_EA.zip && rm PWAS_EA.zip
    wget https://jhupwas.s3.amazonaws.com/PWAS_AA.zip
    unzip PWAS_AA.zip && rm PWAS_AA.zip
    
    ## GTEx V7 list for performing conditional analysis
    wget https://jhupwas.s3.amazonaws.com/GTEx_V7_list.zip
    unzip GTEx_V7_list.zip && rm GTEx_V7_list.zip
    
    
We also provides some example output results:

wget https://jhupwas.s3.amazonaws.com/Results.zip
    unzip Results.zip && rm Results.zip

PWAS – analysis and output
==========================

Required data to perform PWAS
-----------------------------

LD reference data (LDref in our data directory), weights
(e.g. Plasma\_Protein\_weights\_[EA/AA] in our data directory), and
weight-loading files (e.g. Plasma\_Protein\_[EA/AA]\_hg19.pos in our data
directory).

Input 1: GWAS summary statistics
--------------------------------

Same format of input of the summary statistics: A white-space separated
table with a header row containing SNP (rsid), A1 (effect allele), A2 (other allele), and Z (z-score).

Input 2: Weigths
----------------

Imputation models were available for 1,318 and 1,368 significant cis-heritable plasma proteins which have significant non-zero cis-heritability estimated by GCTA (p<0.01) in EA and AA, respectively. Currently, only enet (elastic-net) and top1 (best single SNP) models are available. We recommend using enet models because they have better prediction accuracy.

Inside PWAS\_[EA/AA], weights are stored in Plasma\_Protein\_weights\_[EA/AA]. Weights are loaded from Plasma\_Protein\_[EA/AA]\_[hg19/hg38].pos. [hg19/hg38] is the genome build which is used to locate those proteins. We recommend using the [hg19] to match TWAS models based on GTEx V7 from FUSION.

Input 3: LD reference data 
--------------------------

LD reference data for European (EUR) and African (AFR) ancestry from 1000 Genomes Project (1000G) are stored in LDref.

Example for performing association test
---------------------------------------

The association test was performed by chromosome. Below is an example
using data on chromosome 22:
    
    CHR=22
    #until [ $CHR -lt 1 ]
    #do
    Rscript ./scripts/PWAS.assoc_test.R \
    --sumstats ./sumstats.txt \
    --weights ./PWAS_EA/Plasma_Protein_EA_hg19.pos \
    --weights_dir ./PWAS_EA/Plasma_Protein_weights_EA/ \
    --ref_ld_chr ./LDref/EUR/chr \
    --force_model enet \
    --chr ${CHR} \
    --out ./Results/Example_PWAS_by_chr/chr${CHR}.out
    #let CHR-=1
    #done

Output: Gene-disease association
--------------------------------

The output file has the same format as
[TWAS/FUSION](http://gusevlab.org/projects/fusion/#typical-analysis-and-output).
(The only differences are eQTL &#10230; pQTL; TWAS &#10230; PWAS)

Bivariate conditional analysis for TWAS and PWAS
======================================

We provide a framework for interpreting results from PWAS in conjunction
with analogous results from TWAS. As demonstrated in our paper, genetic correlation of gene-expression in tissues and protein level in plasma due to *cis* genetic regulation is moderate. As a result, it is possible to explore whether the PWAS signals could be explained by cis-genetic regulation of the expression of nearby (1Mb region around) genes and vice versa using a bivariate conditional analysis framework.

Required data to perform conditional analysis
---------------------------------------------

TWAS output for only **[significant](http://gusevlab.org/projects/fusion/weights/GTEX7.txt)** cis-heritable gene expressions performed using **enet** models, PWAS output from our pipeline performed using **enet** models, and pre-imputed *cis*-regulated plasma proteins and gene expressions for 1000G reference individuals which are stored in 1000G\_imputed\_[EA/AA] inside PWAS\_[EA/AA]).

Input 1: PWAS and TWAS output tables
------------------------------------

The output tables in the FUSION format are required. Here we also require **enet** models for both PWAS and TWAS (trained with the option --force\_model enet), because the imputed plasma protein and gene expression levels for reference individuals were pre-computed using enet models (stored in 1000G\_imputed\_[EA/AA] inside PWAS\_[EA/AA]).

Output tables for all chromosomes from TWAS/PWAS are needed to be merged together. Example R code:

``` {.r language="R"}
library(dplyr)
library(readr)
results <- tibble()
for (chr in 1:22) {
 results <- rbind(results, read_tsv(paste0("./Results/Example_PWAS_by_chr/chr", chr, ".out")))
 if(chr==6){
 	results <- rbind(results, read_tsv(paste0("./Results/Example_PWAS_by_chr/chr6.out.MHC")))
 }
}
write_tsv(results, "./Results/PWAS.out")
```

TWAS output tables for different tissues should be stored separately with the name of *tissue*.out in a same folder. For example, the TWAS in whole blood should be stored with name of Whole\_Blood.out.

Input 2: list of TWAS tissues to be analyzed
--------------------------------------------

The full list of TWAS tissues to be analyzed for conditional analysis
should be stored in a text file. One tissue per line. An example is the
full list of all GTEx V7 tissues, GTEx\_V7\_tissue\_list.txt.

Input 3: imputed plasma protein and gene expression reference data
--------------------------------------------------------

*Cis*-genetic regulated plasma proteins and gene expressions were
pre-imputed on publicly available 1000G individuals. This data is stored in 1000G\_imputed\_[EA/AA] inside PWAS\_[EA/AA].

Example for performing conditional analysis of TWAS and PWAS
-------------------------------------------------------------

Here we give an example for performing the conditional analysis of TWAS and
PWAS. In this example, we stored the PWAS output table in ./Results/PWAS.out, and
all-tissue TWAS output tables inside the folder of ./Results/TWAS.out/.
 
 Rscript ./scripts/PWAS.conditional.R \
 --PWAS ./Results/PWAS.out \
 --TWAS ./Results/TWAS.out/ \
 --tissue_list ./GTEx_V7_tissue_list.txt \
 --tissue_n_gene ./GTEx_V7_n_gene.rds \
 --imputed_P ./PWAS_EA/1000G_imputed_EA/1000G_imputed_Plasma_Protein.txt \
 --imputed_T ./PWAS_EA/1000G_imputed_EA/1000G_imputed_FUSION/ \
 --out ./Results/ConditionalAnalysis/

Output: bivariate conditional analysis of PWAS and TWAS in all tissues
-----------------------------------------------------------------------

This code will first generate a tissue.RDat file for each tissue. These RDat files contain
the flowing variables: (below we use T to represent gene expressions,
and P to represent plasma proteins)

- dat.sentinel.pwas: The regional sentinel PWAS genes (+/- 500Kb).

- PcT.z: z-score of P conditional on T.

- PcT.p: p-value of P conditional on T.

- TcP.z: z-score of T conditional on P.

- TcP.p: p-value of T conditional on P.

- twas.hit: the most significant nearby TWAS gene (+/- 500Kb) for each
 regional sentinel PWAS gene.

- twas.p: p-value of the most significant nearby TWAS gene regional
 sentinel PWAS gene.

- dist: distance (bp) between each sentinel PWAS gene and its most
 significant nearby TWAS gene.

- corr: cis-regulated genetic correlation between each sentinel PWAS gene and its most significant nearby TWAS gene.

It will then generate summary tables for tissue-specific conditional analysis and an all-tissue conditional analysis. The columns are:

- PWAS\_hit : PWAS significant gene

- PWAS\_p : p-value o PWAS significant gene

- TWAS\_hit : the most significant nearby TWAS gene (+/- 500Kb) for each regional sentinel PWAS gene.

- TWAS\_p : p-value of the most significant nearby TWAS gene regional sentinel PWAS gene.

- Dist\_of\_hits : distance (bp) between each sentinel PWAS gene and its most significant nearby TWAS gene.

- Corr\_of\_hits : cis-regulated genetic correlation between each sentinel PWAS gene and its most significant nearby TWAS gene.

- PcT\_p : p-value of P conditional on T.

- TcP\_p : p-value of T conditional on P.

- min\_TWAS\_hit : the most significant nearby TWAS gene (+/- 500Kb) for each regional sentinel PWAS gene in all-tissue analysis.

- min\_TWAS\_Tissue : the tissue with the most significant nearby TWAS gene (+/- 500Kb) for each regional sentinel PWAS gene in all-tissue analysis.

- min\_TWAS\_p : the p-value of the most significant nearby TWAS gene (+/- 500Kb) for each regional sentinel PWAS gene in all-tissue analysis.

- N\_significant\_tissues\_in\_TWAS : the number of significant TWAS tissues in all-tissue analysis.

- all\_significant\_tissues\_in\_TWAS : all significant TWAS tissues in all-tissue analysis.

PWAS

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