TWAS

Co-expression-wide association study (COWAS) extends TWAS/PWAS by testing pairs of genes or proteins whose genetically regulated co-expression or interaction is associated with a trait; includes implemented R software and trained imputation weights for summary-statistic follow-up.

TWAS, PWAS, co-expression, gene-gene interaction, GWAS summary statistics

Transcriptome- and proteome-wide association studies (TWAS/PWAS) have proven successful in prioritizing genes and proteins whose genetically regulated expression modulates disease risk, but they ignore potential co-expression and interaction effects. To address this limitation, we introduce the co-expression-wide association study (COWAS) method, which can identify pairs of genes or proteins whose genetically regulated co-expression is associated with complex traits. COWAS first trains models to predict expression and co-expression from genetic variation, and then tests for association between imputed co-expression and the trait of interest while also accounting for direct effects from each exposure. We applied our method to plasma proteomic concentrations from the UK Biobank, identifying dozens of interacting protein pairs associated with cholesterol levels, Alzheimer's disease, and Parkinson's disease. Notably, our results demonstrate that co-expression between proteins may affect complex traits even if neither protein is detected to influence the trait when considered on its own. We also show how COWAS can help to disentangle direct and interaction effects, providing a richer picture of the molecular networks that mediate genetic effects on disease outcomes.

Statistical framework for cell-type-resolved transcriptome-wide association using single-cell RNA-seq: models sparsity and technical noise via latent variables and moment-based estimation to improve genetically regulated expression prediction and gene–trait discovery.

TWAS, single-cell, cell-type-specific, latent variable, GReX

Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most have been performed using bulk gene expression data, which aggregate signals across heterogeneous cell types. Population-scale single-cell RNA sequencing data now make it possible to perform TWAS at the cell-type resolution, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identifies substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer's disease also reveals cell-subtype-specific associations, including MS4A6A in the disease-associated microglial subtype and PPP1R37 in the inflammatory microglial subtype.

Multivariate TWAS approach that prioritizes causal gene–tissue pairs and candidate causal variants from GWAS summary data while explicitly controlling for genome-wide infinitesimal (polygenic) effects that can otherwise inflate false gene discoveries.

multivariate TWAS, infinitesimal model, causal gene-tissue, eQTL, sQTL

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariate TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariate TWAS method named tissue-gene pairs, direct causal variants, and infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS.