Predictdb

Here you can find transcriptome prediction models for the PrediXcan family of methods: S-PrediXcan, MultiXcan and S-MultiXcan. .db files are prediction models, usable by all methods. .txt.gz files are compilations of LD reference for summary-based methods (S- prefix). S-PrediXcan is meant to use the single-tissue LD reference files (“covariances”) appropriate to each model. S-MultiXcan uses single-tissue prediction models and a cross-tissue LD reference.

GTEx v8 models on eQTL and sQTL

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:

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

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:

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

GTEx v7 Expression models

Expression prediction models with LD reference data ar available in this Zenodo repository. The underlying algorithm is Elastic Net.

Additional support information and details are available in the Zenodo repository.

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

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

GTEx v6 Expression models

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

Additional support information and details are available in the Zenodo repository.

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

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

Models from collaborators and other sources:

MESA models

Single-tissue expression prediction models with LD reference data are available in this Zenodo repository. The underlying algorithm is Elastic Net on MESA multi-ethnic cohort.

These models were presented in “Genetic architecture of gene expression traits across diverse populations”, Mogil et al, 2018, PLOS Genetics. Please cite if you find these useful.

CommonMind consortium

Single-tissue expression prediction models with LD reference data are available in this GitHub repository. The underlying algorithm is Elastic Net.

These models were presented in “Gene expression imputation across multiple brain regions provides insights into schizophrenia risk, Huckins et al, 2019, Nature Genetics. Please cite if you find these useful.

EpiXcan Models

Expression prediction models with LD reference data are available in this website. The models were trained on Common Mind Consortium, GTEx, and STARNET consortiums. The underlying algorithm is Elastic Net, informed by epigenetic data.

These models were presented in “Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits”, Zhaneg et al, 2019, Nature Communications. Please cite if you find these useful.

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.