GTEx v8 fine mapping on eQTL and sQTL

# 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: Finemapping

This package contains DAP-G  results on GTEx v8 eQTL and sQTL data.
See  ([DAP-G software](https://github.com/xqwen/dap)) for details.
We used only European individuals and variants with MAF>0.01, on genes that are annotated as `protein_coding` or `lncRNA`. 
DAP-G `ld_control` parameter was 0.75.

The results were analyzed in [this preprint](https://www.biorxiv.org/content/10.1101/814350v1)

## Contents

```
finemapping/
|-- README_finemapping.md
|-- dapg_eqtl.tar
`-- dapg_sqtl.tar
```
Unpack each tarball with a command like  `tar -xvpf dapg_sqtl.tar`

For every tissue:

* `{tissue}.variants_pip.txt.gz` contains the variants' posterior inclusion probabilities at being causal for every gene.
    * gene: gene id (or intron id)
    * rank: ranking of the variant according to its PIP (see below)
    * variant_id: gtex variant id
    * pip: posterior inclusion probability of the variant in the causal models
    * log10_abf: approximate Bayes factor (-log10)
    * cluster_id: id of cluster to which the variant belongs 
* `{tissue}.models_variants.txt.gz` contains, for every model contemplated by DAPG, the list of variants involved. Most of them have  single variant.
* `{tissue}.model_summary.txt.gz` contains, for every analized gene, a summary of the modes such as expected number of causal variants
    * gene: gene id (or intron id)
    * pes: posterior expected model size (i.e. number of causal variants)
    * pse_se: standard error of the above
    * log_nc: dapg undocumented statistic
    * log10_nc: dapg undocumented statistic
* `{tissue}.models.txt.gz` for every analyzed gene:
    * gene: gene id (or intron id)
    * model: number (serving as a model name)
    * n: number of variants (0 for null model)
    * pp: posterior inclusion probability of the model
    * ps: posterior score
* `{tissue}.clusters.txt.gz` for every analyzed gene:
    * gene: gene id (or intron id)
    * cluster: number (serving as cluster name)
    * n_snps: number of variants in the cluster
    * pip: posterior inclusion probability
    * average_r2: average correlation within the cluster
* `{tissue}.cluster_correlations.txt.gz`: upper triangular matrix of correlations among clusters   

# 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.