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 … Read more →

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, … Read more →

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”, … Read more →

𝕎e are releasing prediction models trained on GTEx Version 7 data. Download fromhere. We have updated our processing pipeline, and restricted to individuals of European ancestry to obtain more reliable LD data. This reduces false positive associations in the Summary Version of PrediXcan. Because of … Read more →

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. You can find release notes in this link. Acknowledging these models: If … Read more →

𝕋oday we are updating the tissue-wide prediction models trained from GTEx data which use 1000 Genomes SNPs. There are two main differences: […] The snp covariance files we had provided previously were built using a reference dataset with 1000 genomes data. Now the covariance data is based off … Read more →

𝔼xpression prediction models with LD reference data are available in this Zenodo repository. […] Additional support information and details are available in the Zenodo repository. Please check this link, about the GTEx v7 models, for a few caveats found in GTEx v6 models. Acknowledging these … Read more →

𝔸fter numerous requests, we have now made our prediction models using HapMap SNPs available. These are models trained on GTEx’s V6p data, but filtered to include HapMap SNPs only. These may prove beneficial to your analysis if you are working with older data or if you are having to impute much … Read more →

𝕎e are happy to announce the release of new and improved Transcriptome Prediction Models, based off GTEx expression data trained on 1000 Genomes. The models (and associated covariance matrices) can be downloaded from PredictDB Data Repository. A more comprehensive explanation of the model changes is … Read more →