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https://github.com/szcf-weiya/mtwas
R package for "Multi-tissue Transcriptome-Wide Association Studies (MTWAS)"
https://github.com/szcf-weiya/mtwas
eqtl multi-tissue twas
Last synced: about 2 months ago
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R package for "Multi-tissue Transcriptome-Wide Association Studies (MTWAS)"
- Host: GitHub
- URL: https://github.com/szcf-weiya/mtwas
- Owner: szcf-weiya
- License: other
- Created: 2023-10-06T16:02:07.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-07-15T00:42:13.000Z (6 months ago)
- Last Synced: 2024-07-15T01:42:05.861Z (6 months ago)
- Topics: eqtl, multi-tissue, twas
- Language: R
- Homepage: https://hohoweiya.xyz/MTWAS/
- Size: 6.97 MB
- Stars: 6
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MTWAS: Multi-tissue Transcriptome-Wide Association Studies
MTWAS is an R package for the paper
> Song, S., Wang, L., Hou, L., & Liu, J. S. (2024). Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations. Nature Communications, 15(1), 5769. https://doi.org/10.1038/s41467-024-49924-4
:bulb: We provide pre-trained eQTL weights with MTWAS on 47 **GTEx version 8** tissues, 13 types of immune cells and 2 activation conditions of the **DICE** dataset, and 14 immune cell types from the single-cell RNA-seq **OneK1K** dataset.
:smiley: You could either run MTWAS with the pre-trained weights (:star:easy and recommended), or train your own models with the expression data!
## Table of contents
* [Prerequisites](#white_check_mark-prerequisites)
* [Installation](#hammer_and_wrench-installation)
* [Prepare GWAS summary statistics](#scroll-prepare-gwas-summary-statistics)
* [Run MTWAS with pre-trained GTEx v8 weights](#rocket-run-mtwas-with-pre-trained-gtex-v8-weights)
* [Run MTWAS with pre-trained DICE weights](#rocket-run-mtwas-with-pre-trained-dice-weights)
* [Run MTWAS with pre-trained OneK1K weights](#rocket-run-mtwas-with-pre-trained-onek1k-weights)
* [Train MTWAS with your own datasets](#key-train-your-own-weights)
## :white_check_mark: Prerequisites
The software is developed and tested in Linux and Windows environments.
- R (>=3.6)
- GNU Scientific Library (GSL) (>=2.3)
## :hammer_and_wrench: Installation
```r
devtools::install_github("szcf-weiya/MTWAS")
```
## :scroll: Prepare GWAS summary statistics
Please prepare the GWAS summary statistics in the following format (including the header line):
```
chr rsid ref alt z
1 rs4040617 G A -0.199
1 rs4075116 C T 0.646
1 rs9442385 T G -0.016
...
```
**chr**: chromosome
**rsid**: SNP rsid
**ref**: reference allele
**alt**: alternative allele
**z**: GWAS z score
## :rocket: Run MTWAS with pre-trained GTEx v8 weights
### Download the pre-trained files:
```bash
wget -O gtex_v8_mtwas_eqtls.tar.gz https://cloud.tsinghua.edu.cn/f/5633911d7c39431b8be8/?dl=1 --no-check-certificate
tar -zxvf gtex_v8_mtwas_eqtls.tar.gz
```
If you are working on UKBB phenotypes, please download from the following weights instead (We restricted eQTLs within UKBB SNPs):
```bash
wget -O gtex_v8_mtwas_eqtls.tar.gz https://cloud.tsinghua.edu.cn/f/4d2186f782024ace80f5/?dl=1 --no-check-certificate
tar -zxvf gtex_v8_mtwas_eqtls.tar.gz
```
The result table for UKBB phenotypes (including 84 self-reported cancer and non-cancer illness phenotypes with effective sample sizes larger than 5,000) can also be directly downloaded from the supplementary files of the paper.
### MTWAS analysis:
We use chromosome 22 on whole blood as an example. The list of tissues is detailed in `ct_use.RData`.
```r
library(MTWAS)
data("summary_stats") ## EXAMPLE GWAS summary stats (could be specified by users, format: a data.frame with colnames: chr, rsid, a1, a2, z)
chr = 22
cell_type = 'Whole_Blood'
### remember to change the path to the downloaded folder!!!
## load twas bim files (downloaded)
load(paste0('./gtex_v8_mtwas_eqtls/twas_bim_chr', chr, '.RData'))
## load twas eqtl files (downloaded)
load(paste0('./gtex_v8_mtwas_eqtls/', cell_type, '/twas_eqtl_chr', chr, '.RData'))
## Run mtwas and derive the gene-trait association test statistics
results = run_mtwas_easy(summary_stats, twas_bim, twas_eqtl, pred_res)
head(results)
```
The output `results` is a data.frame with the following format:
```
gene MTWAS_Z MTWAS_P pred_r2 pred_pv
PLA2G3 -1.87 0.062 0.01 0.02
PANX2 -1.83 0.066 0.01 0.08
...
```
**gene**: gene name
**MTWAS_Z**: gene-trait association Z score derived by MTWAS
**MTWAS_P**: gene-trait association P value derived by MTWAS
**pred_r2**: prediction accuracy of the gene expression evaluated by $r^2$
**pred_pv**: prediction accuracy of the gene expression evaluated by an F-test
Note that we output results of all genes. Users could specify the criteria of the outputs, e.g.,`results[results$pred_pv < 0.05 & results$MTWAS_P < 5e-6, ]`.
## :rocket: Run MTWAS with pre-trained DICE weights
### Download the pre-trained files:
```bash
wget -O dice_mtwas_eqtls.tar.gz https://cloud.tsinghua.edu.cn/f/c51dcb6a7bbb417b80be/?dl=1 --no-check-certificate
tar -zxvf dice_mtwas_eqtls.tar.gz
```
### MTWAS analysis:
We use chromosome 22 on B naive cell line as an example. The list of cell types is detailed in `ct_use.RData` (remember to change the path to the downloaded folder).
```r
library(MTWAS)
data("summary_stats") ## EXAMPLE GWAS summary stats (could be specified by users, format: a data.frame with colnames: chr, rsid, a1, a2, z)
chr = 22
cell_type = 'B_NAIVE'
### remember to change the path to the downloaded folder!!!
## load twas bim files (downloaded)
load(paste0('./dice_mtwas_eqtls/twas_bim_chr', chr, '.RData'))
## load twas eqtl files (downloaded)
load(paste0('./dice_mtwas_eqtls/', cell_type, '/twas_eqtl_chr', chr, '.RData'))
## Run mtwas and derive the gene-trait association test statistics
results = run_mtwas_easy(summary_stats, twas_bim, twas_eqtl, pred_res)
head(results)
```
The output `results` is a data.frame with the following format:
```
gene MTWAS_Z MTWAS_P pred_r2 pred_pv
ENSG00000232754 3.08 0.002 0.04 0.99
ENSG00000100225 2.46 0.014 0.07 0.01
...
```
**gene**: gene name
**MTWAS_Z**: gene-trait association Z score derived by MTWAS
**MTWAS_P**: gene-trait association P value derived by MTWAS
**pred_r2**: prediction accuracy of the gene expression evaluated by $r^2$
**pred_pv**: prediction accuracy of the gene expression evaluated by an F-test
Note that we output results of all genes. Users could specify the criteria of the outputs, e.g.,`results[results$pred_pv < 0.05 & results$MTWAS_P < 5e-6, ]`.
## :rocket: Run MTWAS with pre-trained OneK1K weights
### Download the pre-trained files:
```bash
wget -O onek1k_mtwas_eqtls.tar.gz https://cloud.tsinghua.edu.cn/f/de07d968dfb94897b814/?dl=1 --no-check-certificate
tar -zxvf onek1k_mtwas_eqtls.tar.gz
```
### MTWAS analysis:
We use chromosome 22 on CD4 NC cell line as an example. The list of cell types is detailed in `ct_use.RData` (remember to change the path to the downloaded folder).
```r
library(MTWAS)
data("summary_stats") ## EXAMPLE GWAS summary stats (could be specified by users, format: a data.frame with colnames: chr, rsid, a1, a2, z)
chr = 22
cell_type = 'CD4_NC'
### remember to change the path to the downloaded folder!!!
## load twas bim files (downloaded)
load(paste0('./onek1k_mtwas_eqtls/twas_bim_chr', chr, '.RData'))
## load twas eqtl files (downloaded)
load(paste0('./onek1k_mtwas_eqtls/', cell_type, '/twas_eqtl_chr', chr, '.RData'))
## Run mtwas and derive the gene-trait association test statistics
results = run_mtwas_easy(summary_stats, twas_bim, twas_eqtl, pred_res)
head(results)
```
The output `results` is a data.frame with the following format:
```
gene MTWAS_Z MTWAS_P pred_r2 pred_pv
APOL6 1.80 0.072 0.001 0.65
RP6-109B7.3 -1.77 0.076 0.038 7.4e-09
...
```
**gene**: gene name
**MTWAS_Z**: gene-trait association Z score derived by MTWAS
**MTWAS_P**: gene-trait association P value derived by MTWAS
**pred_r2**: prediction accuracy of the gene expression evaluated by $r^2$
**pred_pv**: prediction accuracy of the gene expression evaluated by an F-test
Note that we output results of all genes. Users could specify the criteria of the outputs, e.g.,`results[results$pred_pv < 0.05 & results$MTWAS_P < 5e-6, ]`.
## :key: Train your own weights
We also provide functions to train MTWAS with your own datasets!
### Step 1: Data preparation
In order to derive your own MTWAS weights, three types of data are necessary. There is a demo dataset built in our R package:
```r
library(MTWAS)
data('demo')
# demo$dat
# demo$E.info
# demo$E
```
#### (1) Genotype files (dat)
Format: a **list** of plink bfiles, including bim, fam, bed
One could use the R function `read_plink` in package `EBPRS` to read the plink files:
```r
library(EBPRS)
dat <- read_plink('PATH_TO_PLINK_BFILE')
```
#### (2) Gene information (E.info)
Genes that we are interested in to derive the gene-trait associations.
Format: a **data.frame** in the following format (including the header line):
```
chr start end gene
22 15528192 15529139 OR11H1
22 15690026 15721631 POTEH
...
```
#### (3) Gene expression data (E)
A **list** including all the **gene expression data**, the length of the list is the number of tissues. Each element is a sample*gene matrix.
Each matrix should have **rownames** (overlapped with `dat$fam$V2`), and **colnames** (overlapped with `E.info$gene`)
The orders of the columns and rows of the matrices are not necessary to be the same. The matrices can have NAs.
:exclamation: Please NOTE that the position of `dat$bim$V4` and `E.info` should be the same build (e.g., both are hg19, or hg38, or etc.)
### Step 2: Data imputation and formatting
```r
library(MTWAS)
data('demo')
### substitute the input with your own dataset
twas_dat <- format_twas_dat(E=demo$E, E.info=demo$E.info, dat=demo$dat)
names(twas_dat)
```
### Step 3: Model training
```r
# load TWAS data
# select cross-tissue eQTLs
ct.eQTL = select.ct.eQTL(twas_dat, verbose = F, ncores = 1)
# select tissue-specific eQTLs
list.eQTL = select.ts.eQTL(twas_dat, ct.eQTL = ct.eQTL, ncores = 1)
```
### Step 4: Extract cross-tissue eQTLs
```r
gene_name = 'IL17RA' ## gene name
gene_index = which(names(ct.eQTL)==gene_name)
## ct-eQTLs for gene IL17RA
print(list.eQTL[[1]][[gene_index]]$`common.snp`)
```
### Step 5: Extract tissue-specific eQTLs
```r
gene_name = 'CCT8L2' ## gene name
gene_index = which(names(ct.eQTL)==gene_name)
tissue_index = 1 ## tissue specific
## ts-eQTLs for gene CCT8L2 on tissue 1
print(list.eQTL[[tissue_index]][[gene_index]]$`single.snp`)
```
### Step 6: Gene-trait association tests
```r
# load GWAS summary statistics (data.frame, colnames: rsid, a1, a2, chr, z)
data("summary_stats")
# association test
twas.single.trait(summary_stats, twas_dat, list.eQTL)
```
The details of output and data formats can be found in the auto-generated vignette: https://hohoweiya.xyz/MTWAS/articles/mtwas.html
## References
**MTWAS software**
> Song, S., Wang, L., Hou, L., & Liu, J. S. (2024). Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations. Nature Communications, 15(1), 5769. https://doi.org/10.1038/s41467-024-49924-4
**The GTEx dataset**
> https://gtexportal.org/home/
**The DICE dataset**
> https://dice-database.org/
> Schmiedel, Benjamin J., et al. "Impact of genetic polymorphisms on human immune cell gene expression." Cell 175.6 (2018): 1701-1715.
**The OneK1K dataset**
> https://onek1k.org/
> Yazar, Seyhan, et al. "Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease." Science 376.6589 (2022): eabf3041.