Dataset Open Access

GWAS and GTEx QTL integration

Barbeira, Alvaro Numa; Bonazzola, Rodrigo; Gamazon, Eric R; Liang, Yanyu; Park, YoSon; Kim-Hellmuth, Sarah; Wang, Gao; Jiang, Zhuoxun; Zhou, Dan; Hormozdiari, Farhad; Liu, Boxiang; Rao, Abhiram; Hamel, Andrew R; Pividori, Milton D; Aguet, François; Bastarache, Lisa; Jordan, Daniel M; Verbanck, Marie; Do, Ron; Stephens, Matthew; Ardlie, Kristin; McCarthy, Mark; Montgomery, Stephen B; Segré, Ayellet V; Brown, Christopher D; Lappalainen, Tuuli; Wen, Xiaoquan; Im, Hae Kyung

Research group(s)
The GTEx Consortium

# Data usage policy

When using this data, you must acknowledge the source by citing the publication "Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits" (https://doi.org/10.1101/814350).

 

# GTEx GWAS integration

This package contains the application of several GWAS-QTL integration methods.
The results were analyzed in [this preprint](https://www.biorxiv.org/content/10.1101/814350v1)
about GTEx v8 application to several GWAS traits.
 
``` 
.
|-- colocalization
|   |-- coloc
|   |   `-- coloc_enloc_priors_eqtl.tar.gz
|   |-- enloc
|   |   |-- enloc_eqtl_eur.tar.gz
|   |   `-- enloc_sqtl_eur.tar.gz
|   `-- eur_ld.bed.gz
|-- prediction_models
|   |-- gtex_v8_expression_mashr_snp_smultixcan_covariance.txt.gz
|   |-- gtex_v8_splicing_mashr_snp_smultixcan_covariance.txt.gz
|   |-- mashr_eqtl.tar
|   `-- mashr_sqtl.tar
|-- smr
|   |-- SMR_gtex_v8_README.txt
|   `-- SMRresults_GTEx_v8_peQTL5e-08.tar.gz
|-- smultixcan
|   |-- smultixcan_eqtl.tar.gz
|   `-- smultixcan_sqtl.tar.gz
`-- spredixcan
    |-- spredixcan_eqtl.tar.gz
    `-- spredixcan_sqtl.tar.gz

 ```
 
You can uncompress gzipped tarball packages `*.tar.gz` in a UNIX command line with an instruction such as:
```bash
tar -xzvpf smultixcan_eqtl.tar.gz
```
, and the tar packages (`*.tar`) with an analogous instruction:
```bash
tar -xvpf mashr_eqtl.tar
```


## Preliminaries

**Finemapping** results are contained in a separate release due to size constraints.

GWAS summary statistics for 114 traits were harmonized and imputed to GTEx v8 variants with MAF>0.01 using only european samples.
(summary imputation software [here](https://github.com/hakyimlab/summary-gwas-imputation)). 
Some of the following analyses used the full set of 114 traits,
while some focused only on 87 traits whose imputed associations showed no deflation
(the imputation algorithm is conservative, and studies with too few available variants have a depleted distribution of association p-values after imputation).

The harmonized and imputed GWAS summary statistics are contained in a separate release due to size constraints. 
For completeness' sake, the imputed summary statistics look like:
```
variant_id panel_variant_id   chromosome position   effect_allele  non_effect_allele  current_build  frequency  sample_size    zscore pvalue effect_size    standard_error imputation_status  n_cases
rs554008981    chr1_13550_G_A_b38 chr1   13550  A  G  hg38   0.017316017316017316   336474 -2.2919929353647097    0.021906050841240293   NA NA imputed    NA
rs201055865    chr1_14671_G_C_b38 chr1   14671  C  G  hg38   0.012987012987012988   336474 -0.9559192804440632    0.33911301727494103    NA NA imputed    NA
...
```

The GWAS were split in approximately independent LD regions (Berisa-Pickrell)/
GWAS regions are defined in `eur_ld.bed.gz` (note that a few of them are ill-defined in hg38 and where ignored; only completely defined regions were used). 

## Colocalization

### Enloc

ENLOC ([see fotware here](https://github.com/xqwen/integrative))
was run for sQTLs and eQTLs using individuals of european ancestry and DAP-G QTL enrichment results on 87 traits.
Result files are included in `enloc_eqtl_eur.tar.gz` and `enloc_sqtl_eur.tar.gz`
Each file contains a particular tissue-trait combination.
Each row details colocalization between a GWAS region (Berisa-Pickrell) and gene's or intron's cis-window.

A region might overlap multiple genes/introns or viceversa.
Each ENLOC file contains the following columns:

* gwas_locus: GWAS LD region
* molecular_qtl_trait: gene or intron
* locus_gwas_pip: posterior inclusion probability of variants in the GWAS LD region
* locus_rcp: regional colocalization probability (main colocalization measure)
* lead_coloc_SNP: snp with highest RCP
* lead_snp_rcp: rcp of the lead coloc snp


### Coloc

Coloc ([see software here](https://cran.r-project.org/web/packages/coloc/index.html))
was run using prior probabilities estimated from QTL enrichment of GWAS variants (computed via ENLOC).
Results for eQTL are available in `coloc_enloc_priors_eqtl.tar.gz`. 
Each file contains results for a trait-tissue combination. Columns are:
* gene_id: gene or intron id
* p0: probability that neither QTL nor GWAS contain a causal variant
* p1: probability that only GWAS contains a causal variant
* p2: probability that only QTL has a causal variant
* p3: probability that GWAS and QTL have a causal variant and it's distinct
* p4: probability that GWAS and QTL have a causal variant and it's the same (main colocalization measure)

## PrediXcan

`mashr_eqtl.tar` and `mashr_sqtl.tar` contain prediction models 
(trained on expression or splicing data respectively, for 49 GTEx tissues) and LD compilations 
to be used with PrediXcan, S-PrediXcan, MultiXcan and S-MultiXcan.

For every tissue, the `mashr_{tissue}.db` file is a SQLite file with the prediction model definitions.
`mashr_{tissue}.txt.gz` is a gzipped-text file with the upper triangular matrices of covariance between snps
within a gene/intron prediction model.

Many variants in these models don't have an rsid. To fully leverage the information in these models, 
it is advised to at least harmonize to GTEx variants, and if possible impute as we did [here](https://github.com/hakyimlab/summary-gwas-imputation).

### S-PrediXcan

S-PrediXcan was run for the 114 harmonized and imputed traits, on eQTL and sQTL mashr prediction models.
All of the GWAS traits had the same format, so that the following format parameters were used with S-PrediXcan:

```
--snp_column panel_variant_id --effect_allele_column effect_allele --non_effect_allele_column non_effect_allele --zscore_column zscore \
--keep_non_rsid --additional_output --model_db_snp_key varID \
```

Each file is a CSV, with each row containing a gene/intron association at a given trait-tissue combination:
* gene: ENSEMBLE ID or intron id
* gene_name: HUGO name or intron id
* zscore: predicted association z-score
* effect_size: estimated effect size
* pvalue: association p-value
* var_g: estimated variance of predicted expression or splicing
* pred_perf_r2: prediction model cross-validated performance
* pred_perf_pval: prediction model cross-validated performance
* pred_perf_qval: deprecated, empty field left for compatibility
* n_snps_used: number of snps in the intersection of GWAS and model
* n_snps_in_cov: number of snps in the LD compilation
* n_snps_in_model: number of snps in the model
* best_gwas_p: smallest p-value acros GWAS snps used in this model
* largest_weight: largest prediction model weight

### S-Multixcan

S-MultiXcan results were generated from the above S-PrediXcan results. Each fiel contains multi-tissue associations for a given trait:


* gene: ENSEMBLE ID or intron id
* gene_name: HUGO name or intron id
* pvalue: multi-tissue association p-value
* n: number of models avialble for this gene/intron
* n_indep: number of independent components of variation in predicted expression/splicing (surviving principal components) 
* p_i_best: highest single-tissue p-value (S-PrediXcan) 
* t_i_best: tissue of highest p-value
* p_i_worst: lowest single-tissue p-value (S-PrediXcan)
* t_i_worst: tissue of lowest p-value
* eigen_max: maximum eigenvalue of SVD
* eigen_min: minimum eigenvalue of SVD
* eigen_min_kept: smallest eigenvalue retained after discarding smallest variations
* z_min: minimum single-tissue z-score
* z_max: maximum single-tissue z-score
* z_mean: mean single-tissue zscre
* z_sd: standard deviation of the single-tissue z-scores
* tmi: trace of M * M_i where M is predicted expression/splicing covariance across tissues for a gene, and M_i is its SVD pseudo-inverse
* status: computation status, 0 if no errors

## SMR

See `SMR_gtex_v8_README.txt` for details.

 

 

# Disclaimer

The data is provided "as is", and the authors assume no responsibility for errors or omissions.  
The User assumes the entire risk associated with its use of these data.  
The authors shall not be held liable for any use or misuse of the data described and/or contained herein.  
The User bears all responsibility in determining whether these data are fit for the User's intended use.  

The information contained in these data is not better than the original sources from which they were derived,
and both scale and accuracy may vary across the data set.  
These data may not have the accuracy, resolution, completeness, timeliness, or other characteristics
appropriate for applications that potential users of the data may contemplate.  
 
The user is responsible to comply with any data usage policy from the original GWAS studies;
refer to the list of traits described [here](https://www.biorxiv.org/content/10.1101/814350v1)
to identify their respective Consortia's requirements.


THE DATA IS PROVIDED WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE DATA OR THE USE OR OTHER DEALINGS IN THE DATA.

This data was analyzed in: https://www.biorxiv.org/content/early/2019/10/22/814350.full.pdf
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gtex_v8_expression_mashr_snp_smultixcan_covariance.txt.gz
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