Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits

Abstract

Many methods have been developed to leverage expression quantitative trait loci (eQTL) data to nominate candidate genes from genome-wide association studies. These methods, including colocalization, transcriptome-wide association studies (TWAS) and Mendelian randomization-based methods; however, all suffer from a key problem—when assessing the role of a gene in a trait using its eQTLs, nearby variants and genetic components of other genes’ expression may be correlated with these eQTLs and have direct effects on the trait, acting as potential confounders. Our extensive simulations showed that existing methods fail to account for these ‘genetic confounders’, resulting in severe inflation of false positives. Our new method, causal-TWAS (cTWAS), borrows ideas from statistical fine-mapping and allows us to adjust all genetic confounders. cTWAS showed calibrated false discovery rates in simulations, and its application on several common traits discovered new candidate genes. In conclusion, cTWAS provides a robust statistical framework for gene discovery.

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

“…Associations that are causal may not be captured by cTWAS due to the conservative significance threshold, particularly in gene sets with a low proportion of variance explained. 34 Additionally, preprocessing using the cTWAS analytical pipeline restricted gene expression models to protein-coding genes. Regardless, our fine-mapping results suggest that results from TWAS must be interpreted with caution.…”

“…Identifying the most likely causal genes requires accounting for LD and eQTL sharing among genes in cis -regions. We performed genome-wide statistical fine-mapping for whole blood and prostate tissue using causal TWAS (cTWAS), 34 which jointly models the effects of imputed gene expression and genetic variants to derive posterior inclusion probabilities (PIPs). Cross-tissue fine-mapping is currently unavailable within the cTWAS analysis framework.…”

“…A standard threshold of PIP of more than 0.8 was used for declaring statistically significant genes. 34 …”

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

“…It is increasingly recognized that these variants may exert their effects through the modulation of gene expression, which is a key molecular phenotype [2] . In this context, transcriptome-wide association studies (TWAS) have emerged as a successful strategy, uncovering genes associated with a range of diseases, including cancer [3] , [4] , [5] . This approach is grounded in the hypothesis that genetic variants influence gene expression, which in turn affects the phenotype.…”