𝔸1: GTEx v8 MASHR-based models are parsimonious and have been shown to yield more reliable set of putative causal variants. These are the best option. However, they require GWAS preprocessing on older GWAS as detailed here. A2: GTEx v8 UTMOST models are a robust but with lower performance. These are … Read more →

𝕋his error raise when the multixcan tool does get gwas files which should be concatenated. The error is common when you are provoding multiple gwas files in a directory. The common mistake that brings the issue rises from; […] –gwas_file_pattern “(.*).txt” #selects all gwas … Read more →

𝕋his error happens when the data bases cannot be loaded into the tool and the main cause might be; […] –models_name_pattern “en_(.*).db” –models_name_filter “en_Brain_(.*).db” Read more →

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 →

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 →

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 →

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 →

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 →

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 →

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 →