Gene prioritization

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.

R framework that links GWAS signals to single-cell-defined cell types via a cell-type gene specificity score (expression magnitude and consistency) and regression on gene-level association statistics, with influential-gene follow-up for interpretability.

GWAS, scRNA-seq, cell type, MAGMA, post-GWAS interpretation

Integrating single-cell RNA sequencing with Genome-Wide Association Studies (GWAS) can uncover cell types involved in complex traits and disease. However, current methods often lack scalability, interpretability, and robustness. We present seismic, a framework that computes a novel specificity score capturing both expression magnitude and consistency across cell types and introduces influential gene analysis, an approach to identify genes driving each cell type-trait association. Across over 1000 cell-type characterizations at different granularities and 28 polygenic traits, seismic corroborates known associations and uncovers trait-relevant cell groups not apparent through other methodologies. In Parkinson's and Alzheimer's, seismic unveils both cell- and brain-region-specific differences in pathology. Analyzing a pathology-based Alzheimer's GWAS with seismic enables the identification of vulnerable neuron populations and molecular pathways implicated in their neurodegeneration. In general, seismic is a computationally efficient, powerful, and interpretable approach for mapping the relationships between polygenic traits and cell-type-specific expression, offering new insights into disease mechanisms.

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.