We have produced different families of prediction models for sQTL and eQTL, using several prediction strategies, on GTEx v8 release data.

We recommend MASHR-based models below. Elastic Net-based are a safe, robust alternative with decreased power.

MASHR-based models

Expression and splicing prediction models with LD reference data are available in this Zenodo repository.

Files:

Warning: these models are based on fine-mapped variants that may occasionally be absent in a tipical GWAS, and frequently absent in older GWAS. We have tools to address this, presented here. A tutorial is available here.

Acknowledging these models: If you use these models in your research, please cite:

"Widespread dose-dependent effects of RNA expression and splicing on complex diseases and traits", Barbeira et al, 2019, preprint
"A gene-based association method for mapping traits using reference transcriptome data", Gamazon et al, 2015, Nature Genetics
"Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics", Barbeira et al, 2018, Nature Communications

If you use S-MultiXcan, we ask you to cite:

"Integrating predicted transcriptome from multiple tissues improves association detection", Barbeira et al, 2019, PLOS Genetics

Elastic Net

Expression and splicing prediction models with LD references data are available in this Zenodo repository.

Files:

Acknowledging these models : If you use these models in your research, we ask you to cite:

"The GTEx Consortium atlas of genetic regulatory effects across human tissues", Aguet et al, 2019, preprint
"A gene-based association method for mapping traits using reference transcriptome data", Gamazon et al, 2015, Nature Genetics
"Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics", Barbeira et al, 2018, Nature Communications

If you use S-MultiXcan, we ask you to cite:

"Integrating predicted transcriptome from multiple tissues improves association detection", Barbeira et al, 2019, PLOS Genetics

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The source code is licensed under MIT.

Suggest changes

If you find any mistakes (including typos) or want to suggest changes, please feel free to edit the source file of this page on Github and create a pull request.

Citation

For attribution, please cite this work as

PredictDB Team (2021). GTEx v8 models on eQTL and sQTL. PredictDB. /post/2021/07/21/gtex-v8-models-on-eqtl-and-sqtl/

BibTeX citation

@misc{
  title = "GTEx v8 models on eQTL and sQTL",
  author = "PredictDB Team",
  year = "2021",
  journal = "PredictDB",
  note = "/post/2021/07/21/gtex-v8-models-on-eqtl-and-sqtl/"
}