PredictDB Data Repository

Here you can find transcriptome and other traits prediction weights for the PrediXcan family of methods: S-PrediXcan, MultiXcan, S-MultiXcan, and BrainXcan.

Popular Downloads


Prediction weights and covariance files

The prediction weigths are saved in SQLite format, *.db, which can be easily queried using sqlit, R, python, etc. See example in R here. The summary statistics based methods need covariances (LD reference) between predictor SNPs and are saved in gzipped text files *.txt.gz. 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.

The Search option in the menu may be the fastest way to find the right models or answers to your questions. If you still have more questions, join our mailing list and post your questions there. You can also navigate through the menu options.

Mailing List

Please join our Google Group for general discussion, notification of future changes to our tools, feature requests, etc.


The models are provided “as is”, with the hope that they may be of use, 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 models or the use or other dealings in the models.


  1. PrediXcan: Gamazon ER†, Wheeler HE†, Shah KP†, Mozaffari SV, Aquino-Michaels K, Carroll RJ, Eyler AE, Denny JC, Nicolae DL, Cox NJ, Im HK. (2015) A gene-based association method for mapping traits using reference transcriptome data. Nat Genet. doi:10.1038/ng.3367 url
  2. S-PrediXcan: Barbeira, Alvaro N., Scott P. Dickinson, Rodrigo Bonazzola, Jiamao Zheng, Heather E. Wheeler, Jason M. Torres, Eric S. Torstenson, Kaanan P. Shah, Tzintzuni Garcia, Todd L. Edwards, Eli A. Stahl, Laura M. Huckins, Dan L. Nicolae, Nancy J. Cox, and Hae Kyung Im. 2018. “Exploring the Phenotypic Consequences of Tissue Specific Gene Expression Variation Inferred from GWAS Summary Statistics.” Nature Communications 9 (1). Nature Publishing Group: 1–20.
  3. Genetic Architecture of Expression traits: Heather E Wheeler, Kaanan P Shah, Jonathon Brenner, Tzintzuni Garcia, Keston Aquino-Michaels, GTEx Consortium, Nancy J Cox, Dan L Nicolae, Hae Kyung Im. (2016) Survey of the Heritability and Sparsity of Gene Expression Traits Across Human Tissues. url
  4. MultiXcan: Barbeira, Alvaro N., Milton Pividori, Jiamao Zheng, Heather E. Wheeler, Dan L. Nicolae, and Hae Kyung Im. 2019. “Integrating Predicted Transcriptome from Multiple Tissues Improves Association Detection.” PLoS Genetics 15 (1). Public Library of Science: e1007889–20.
  5. GTEx V8 models: Barbeira, Alvaro N., Rodrigo Bonazzola, Eric R. Gamazon, Yanyu Liang, Yoson Park, …, and Hae Kyung Im. 2021. “Exploiting the GTEx Resources to Decipher the Mechanisms at GWAS Loci.” Genome Biology 22 (1): 49.
  6. GTEx Consortium Main Paper: 2020. “The GTEx Consortium Atlas of Genetic Regulatory Effects across Human Tissues.” Science 369 (6509): 1318–30.
  7. Fine-mapping-based prediction more reliable: Barbeira, Alvaro N., Owen J. Melia, Yanyu Liang, Rodrigo Bonazzola, Gao Wang, Heather E. Wheeler, François Aguet, Kristin G. Ardlie, Xiaoquan Wen, and Hae K. Im. 2020. “Fine-Mapping and QTL Tissue-Sharing Information Improves the Reliability of Causal Gene Identification.” Genetic Epidemiology n/a (n/a). John Wiley & Sons, Ltd. doi:10.1002/gepi.22346.


This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Ratxcan brain prediction models

2024-02-28 Haky Im
ℙrediction models of five brain region gene expression in rats. See description in Santhanam, Sanchez Roige, et al (2024). Download link To query the database, checkout this post […] Natasha Santhanam, Sandra Sanchez-Roige, Yanyu Liang, Apurva S. Chitre, Daniel Munro, Denghui Chen, Riyan … Read more →

Alpha-Missense Pathogenicity Query

2023-11-13 Haky Im
𝕎e downloaded the pathogenicity predictions from Alpha-Missense and made this shinyapp to query. Try it out. Jun Cheng et al., Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381 (2023) … Read more →

Multivariate adaptive shrinkage improves cross-population transcriptome prediction

2023-02-28 Daniel Araujo
𝔻ownload prediction weights from: Preprint from Wheeler lab sharing multi-ancestry prediction models from TOPMED/MESA […] Multivariate adaptive shrinkage improves cross-population … Read more →

How to map introns to genes

2023-02-22 Haky Im
𝔽ind the mapping from intron ids to gene ids in this link […] Q: How should I interpret the z-score? Does a negative z-score for an intron imply that a decrease in the excision of that intron leads to an increase in the GWAS trait? … Read more →

Protein prediction models - ARIC

2022-11-14 Sabrina Mi
𝔸RIC protein prediction models in predictdb format can be downloaded from here See more details here Read more →

MetaXcan output file formats

2022-03-08 Festus
𝕋he MetaXcan Software hosts a suite of tools i.e PrediXcan, SPrediXcan, MultiXcan and SMultiXcan. This post describes the file format output from each tool. […] Individual-level data method to compute gene-trait associations. Detailed info The output is a tab delimited file which contains … Read more →

Protein prediction for trait mapping in diverse populations

2022-02-12 Ryan Schubert, Heather Wheeler
ℙrediXcan ready databases and covariance files of the paper “Protein prediction for trait mapping in diverse populations” can be downloaded from here Read more →

Error for region (...) LinAlgError('SVD did not converge')

2021-07-23 PredictDB Team
𝕆ne step of the summary imputation, the computation of snp covariance matrix inverse, is performed via singular value decomposition (SVD). Numerical solutions to the SVD algorithm are not guaranteed to converge, and fail on some regions. When this happens, unmeasured zscores will not be present in … Read more →

Error No intersection between model names in MetaXcan Results and Prediction Models

2021-07-23 Festus
𝕋his error happen when the strings captured from the metaxcan results and models don’t coincide A good example is when the metaxcan results' captured names looked like Aorta while the models' captured names looked like Artery_Aorta, this will result into this error […] … Read more →

BrainXcan Brain Feature Prediction Models

2021-07-22 Yanyu Liang
𝕊-BrainXcan takes GWAS as input and return the association between GWAS phenotype and a list of brain image-derived phenotypes. BrainXcan manuscript link. Software documentation (Quick start) link. BrainXcan database link. Analysis scripts link. […] The software is built upon both Python and … Read more →