Transcriptome-wide association study (TWAS) is introduced to identify significant expression-trait associations through imputations. It has been widely used to analyze tissue-specific associations with the reference expression quantitative trait loci (eQTL) panel. To increase the statistical power of TWAS results, meta-analysis methods aggregating TWAS results across multiple tissues are developed. However, most existing meta-analysis methods lose interpretation of disease etiology and have limited power to identify weaker associations when only a few tissues are weakly activated. Therefore, we developed the cross-tissue subset-based meta-analysis method, also called cross-tissue subset-based transcriptome-wide association study (CSTWAS). In this package, we aggregate the TWAS results across tissues and perform meta-analysis through the subset-based test. R functions are provided for researchers to integrate TWAS results across multiple tissues and visualize the result.
Use the following codes to install the CSTWAS package
library(devtools)
install_github("Thewhey-Brian/CSTWAS")For details about how to install a R package directly from GitHub: https://rdrr.io/cran/remotes/man/install_github.html.
In order to integrate TWAS results across multiple tissues, the ideal format of the TWAS results shown as following:
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TWAS results for each tissue should contain the following variables:
ID,CHR,P0,P1,TWAS.Z,TWAS.P. The additional variables will be ignored.ID CHR P0 P1 TWAS.Z TWAS.P ALOX5 10 45869661 45941561 0.573537 0.566281 COX15 10 101471601 101491857 1.539744 0.123623 ZDHHC6 10 114190058 114206672 0.834107 0.404221 ABCC2 10 101542489 101611949 1.540492 0.123440 VIM. 10 17270258 17279592 0.484861 0.627775 FAS 10 90750414 90775542 -0.925617 0.354645 ID: Feature/gene identifierCHR: ChromosomeP0: Gene startP1: Gene endTWAS.Z: TWAS Z-scoreTWAS.P: TWAS P-value
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TWAS results for each tissue are stored in a single file, named as "all.tissue_name.alldat". And all tissue-specific TWAS results files are saved in the same folder/path.
For example, TWAS results for Adipose Coronary should be saved as "all.Adipose_Coronary.alldat".
The resutls files structure looks like:
TWAS results generated from the FUSION software.
Here is an example of formating TWAS results from the FUSION software. Other TWAS software such as PrediXcan can also be used.
Inputs:
- GWAS summary statistics (file: dise.sumstats)
SNP: SNP identifier (rsID)A1: first allele (effect allele)A2: second allele (other allele)Z: Z-scores, sign with respect toA1
- Expression weights (stored in: ./WEIGHTS/) (Download)
- LD reference data--1000 Genomes (stored in: ./LDREF/1000G.EUR.) (Download)
- Tissue list (file: tissue_list.txt)
- A list of tissue names from expression weights (one tissue name per line)
Adipose_Subcutaneous
Brain_Amygdala
Esophagus_Mucosa
Heart_Left_Ventricle
Lung
Nerve_Tibial
Performing TWAS with FUSION:
dise=$1 # Disease name
tiss=$2 # File stores a list of tissues
module load R
values=$(cat $tiss)
mkdir /outcomes/$dise # create outcomes path
for tissue in $values # loop through all tissues
do
for i in $(seq 1 22) # loop through all 22 chromosomes
do # create .sh files for all combinations
echo "module load R;
Rscript /Tools/fusion_twas-master/FUSION.assoc_test.R \ # the location of installed FUSION software
--sumstats /data/$dise.sumstats \ # the GWAS summary statistics
--weights /reference/WEIGHTS/$tissue.pos \ # tissue-specific reference expression weight
--weights_dir /reference/WEIGHTS/ \ # the location of reference expression weight
--ref_ld_chr /reference/LDREF/1000G.EUR. \ # LD reference data
--chr $i \ # specify chromosome
--out /outcomes/$dise/$dise_${tissue}_${i}.dat" > $dise_${tissue}_${i}.sh
qsub -cwd -l mem_free=20G,h_vmem=30G $dise_${tissue}_${i}.sh # submit jobs
sleep 1
done
doneFormatting Outpus:
Since FUSION can only perform chromosome-specific analysis, we need to combine the results from all 22 chromosomes for each tissue.
out_dir=$1 # Output direction
file=$2 # File stores a list of tissues
tissues=$(cat $file)
for tissue in $tissues
do
head -n 1 ${out_dir}/${tissue}_1.dat | awk '{print $1, $3, $4, $5, $6, $19, $20}' > ${out_dir}/all.${tissue}.alldat
tail -q -n +2 ${out_dir}/${tissue}*.dat | awk '{print $1, $3, $4, $5, $6, $19, $20}' >> ${out_dir}/all.${tissue}.alldat
echo "Finish ${tissue}, length: $(wc -l ${out_dir}/all.${tissue}.alldat)"
doneWith the formatted TWAS results, we are ready to perform the CSTWAS anslysis.
!Note: as weights labeled in FUSION TWAS website, cov_matrix_GTEx_v7 uses gene symble while cov_matrix_GTEx_v8 uses Ensembl ID. Please be careful about the ID consistency for TWAS results and gene_list (if needed.)
If using cov_matrix_GTEx_v8 as reference panel and want gene ID in the CSTWAS results as gene symble, simply set run_CSTWAS(..., gene_symble = TRUE).
Inputs:
path: A string of the direction for TWAS results.cov_matrix: A string indicating list of matrix of the gene expression covariance matrix across tissues from the reference panel (default using cov_matrix_GTEx_v7, can also change to cov_matrix_GTEx_v8 or use your own matrix list.) This parameter is omitted if cov_matrix_path is specified.cov_matrix_path: Path for downloaded reference gene expression covariance matrix across tissues (the reference matrix can be downloaded from: https://github.com/Thewhey-Brian/CSTWAS) If NULL, the function will automatically download the reference panel indicated by cov_matrix from the GitHub repository.percent_act_tissue: A decimal of the minimum percent of activated tissues for each gene regulated expression.n_more: Simulation times for small p-values (default 1e+04; Caution: a very large number may lead to long calculation time; a very small number may lead to inaccurate p-value estimation).gene_list: An array of the list of interested genes (default NULL; if NULL, it will go over all genes in the TWAS results; if not NULL, percent_act_tissue will be ignored).pattern: A string of the file pattern for TWAS results (default ".alldat").
Outpus:
A list containing following dataframes:
cstwas_res: A dataframe for the CSTWAS results.Gene: Feature/gene identifierSubset_Tissue: Set of potential gene-expression-specific activated tissuesNumber_of_Tissues: Number of potential gene-expression-specific activated tissuesP_value: P-value for the cross-tissue subset-based test
meta_data: A dataframe for the tissue-specific TWAS results across multiple tissues.ID: Feature/gene identifierZ: Z-value transformed from TWAS p-value (qnorm(TWAS.P, lower.tail = F))TWAS.P: TWAS p-valueCHR: ChromosomeBP: Middle point of gene ((Gene_start + Gene_end)/2)Tissue: Tissue of specific gene expression
Examples:
res_CSTWAS = run_CSTWAS("path_to_TWAS_resutls",
cov_matrix = "cov_matrix_GTEx_v7",
percent_act_tissue = 0.6,
n_more = 1e+03)cstwas_res:
| Gene | Subset_Tissue | Number_of_Tissues | P_value |
|---|---|---|---|
| ALOX5 | Brain_Hippocampus, Small_Intestine_Terminal_Ileum, Spleen, Esophagus_Mucosa | 4 | 0.995 |
| ZDHHC6 | Vagina, Brain_Hypothalamus, Brain_Nucleus_accumbens_basal_ganglia, Brain_Cerebellum, Spleen, Esophagus_Mucosa, Whole_Blood, Cells_Transformed_fibroblasts, Brain_Frontal_Cortex_BA9, Adrenal_Gland | 10 | 0.409 |
| ABCC2 | Brain_Substantia_nigra, Vagina, Cells_EBV.transformed_lymphocytes, Minor_Salivary_Gland, Adipose_Subcutaneous, Heart_Left_Ventricle, Cells_Transformed_fibroblasts, Brain_Amygdala, Testis, Brain_Hippocampus, Artery_Tibial, Whole_Blood | 12 | 0.526 |
meta_data:
| ID | Z | TWAS.P | CHR | BP | Tissue |
|---|---|---|---|---|---|
| EXOC3L2 | 17.699108 | 2.13e-70 | 19 | 45726674 | Adipose_Subcutaneous |
| PVRL2 | 17.060110 | 1.47e-65 | 19 | 45370958 | Adipose_Subcutaneous |
| CEACAM19 | 8.166297 | 1.59e-16 | 19 | 45178929 | Adipose_Subcutaneous |
Inputs:
meta_data: meta_data from run_CSTWAS results.anot_index: An integer indicating how significant results are to be annotated. (-log10(TWAS.P) > anot_index) This parameter will be ignored if anno_gene is not NULL.ceiling_ctf: An integer indicating how significant results are to be cut by the ceiling. (-log10(TWAS.P) > ceiling_ctf). If is NULL, it will automatically adjust based on the data.floor_ctf: An integer indicating how insignificant results are to be cut by the floor (-log10(TWAS.P) < floor_ctf). Default 0.pts_size: An integer indicating the point size.anno_gene: A list of genes that need to be annotated.path: Path for saving the plot.
Outpus:
A Manhattan plot of tissue-specific TWAS resutls.
Examples:
mhp_twas(res_CSTWAS$meta_data, ceiling_ctf = 30)
Inputs:
meta_data: meta_data from run_CSTWAS results.cstwas_res: cstwas_res from run_CSTWAS results.anot_index: An integer indicating how significant results are to be annotated. (-log10(TWAS.P) > anot_index) This parameter will be ignored if anno_gene is not NULL.ceiling_ctf: An integer indicating how significant results are to be cut by the ceiling. (-log10(TWAS.P) > ceiling_ctf). If is NULL, it will automatically adjust based on the data.floor_ctf: An integer indicating how insignificant results are to be cut by the floor (-log10(TWAS.P) < floor_ctf). Default 0.pts_size: An integer indicating the point size.anno_gene: A list of genes that need to be annotated.path: Path for saving the plot.
Outpus:
A Manhattan plot of the CSTWAS results.
Examples:
mhp_cstwas(res_CSTWAS$meta_data, res_CSTWAS$cstwas_res, anot_index = 6)
Inputs:
meta_data: meta_data from run_SCTWAS results.sctwas_res: sctwas_res from run_SCTWAS results.merge_range: An integer indicating how wide (in base pairs) should be considered to merge nearby genes. Default +/- 1000bp..path: Path for saving the plot.
Outpus:
A Venn diagram showing overlapping conditions of GReX called between Subset-based Cross-tissue TWAS and tissue-specific TWAS.
Examples:
venn_diagram(res_CSTWAS$meta_data, res_CSTWAS$sctwas_res)