GWAS and GTEx QTL Integration: Results from Multiple Methods on 114 Traits

114 GWAS traits were harmonized and imputed to GTEx v8 variants using only European samples. This package contains results from several GWAS-QTL integration methods, including colocalization, prediction models, SMR, S-MultiXcan, and S-PrediXcan, as analyzed in a related preprint. The data was produced by Alvaro Barbeira at the University of Chicago.

Use Cases

• Prioritizing causal genes for complex traits based on colocalization results between GWAS and eQTL/sQTL signals.
• Building genetic prediction models for gene expression and splicing using the provided mashr covariance matrices.
• Performing summary-data-based Mendelian randomization (SMR) analysis to test for pleiotropic associations between genetic variants and traits.

Strengths

• Results from multiple established integration methods (coloc, enloc, SMR, S-MultiXcan, S-PrediXcan) are provided in one package.
• Analyses are based on harmonized and imputed GWAS summary statistics for 114 traits, increasing comparability.
• Data is derived from the GTEx v8 resource, a major reference for human tissue-specific gene expression.

Limitations

• Row count and specific file sizes are unknown, which may limit suitability assessment for large-scale processing.
• Column-level documentation is absent; field semantics must be inferred after download from file contents.
• The description notes a focus on 87 traits for some analyses due to imputation quality, indicating potential data completeness variation.

Provenance

Source

University of Chicago, Alvaro Barbeira

Collection Method

Integration of harmonized GWAS summary statistics with GTEx v8 QTL data using various bioinformatics methods.

Geography

Data imputation was performed using only European samples from the GTEx cohort.