Posts

FAQ

What do i do with these models?

2021-07-21 PredictDB Team
𝕋hese models allow prediction of gene expression or alternative splicing on a GWAS study. The predicted levels can be associated to a complex trait such as a disease’s susceptibility. PrediXcan implementation uses these models and individual-level data. MetaXcan repository contains an … Read more →
FAQ

What does “TW_” mean in the GTEx v6 file names?

2021-07-21 PredictDB Team
𝕋his is an abbreviation for “Tissue-Wide.” We have historically created a Cross-Tissue model, which is a measure of the expression common across all tissues, and Tissue-Specific models (TS), which is modeled as an orthogonal component to the shared cross tissue component. Details can be found … Read more →
FAQ

Why are there fewer available genes/introns in the prediction models than in the GTEx dataset?

2021-07-21 PredictDB Team
𝕋he prediction model databases only contain models that pass a stringent criteria (each family of models has its own criteria, e.g. MASHR models require at least one snp with high posterior probability of being an eQTL). Our model training algorithms are complex and conservative. Sometimes, a good … Read more →
FAQ

Error 0% SNPs used

2021-07-20 Festus Nyasimi
𝕋his error usually occurs when there is a mismatch between the SNP ids used in the model and those used in the genotype file or the summary stats file. A few examples to mention; 1. Using rsids in the models against variant ids (chr_pos_ref_alt_build) in the genotype and summary stats file and vice … Read more →

GTEx V8 Cross Tissue Imputation (UTMOST) Prediction Models

2020-09-10 PredictDB team - uploaded by Yanyu Liang
ℙrediction models trained with GTEx V8 data using the cross tissue imputation approach (see UTMOST paper). Trained by Alvaro Barbeira for the paper Fine‐mapping and QTL tissue‐sharing information improves the reliability of causal gene identification Download in Zenodo here Read more →

PsychENCODE Brain Expression Models

2020-07-19 Sabrina Mi, Michael Gandal
𝔾andal et al analyzed autism spectrum disorder, schizophrenia, and bipolar disorder across multiple levels of transcriptomic organization—gene expression, local splicing, transcript isoform expression, and coexpression networks for both protein-coding and noncoding genes to produce a quantitative, … Read more →

GTEx V8 Model Release

2019-12-11 PredictDB Team
𝕎e have recently published a new set of prediction models trained on GTEx v8 data (as part of efforts detailed in this preprint. We have overhauled the model construction, incorporating posterior inclusion probabilities and global patterns of tissue sharing, while also benefiting from larger sample … Read more →

EpiXcan Models - Integrated Epigenetics

2019-08-23 PredictDB Team
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 →

CommonMind consortium - Brain Dorsolateral Prefrontal Cortex

2019-07-21 PredictDB Team
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 →

MESA models - non European

2018-07-21 PredictDB Team
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 →