Adjusting for genetic confounders in transcriptome-wide association studies leads to reliable detection of causal genes

Abstract

Expression QTLs (eQTLs), provide valuable information on the functional effects of variants. Many methods have been developed to leverage eQTLs to nominate candidate genes of complex traits. These methods include colocalization analysis, transcriptome-wide association studies (TWAS) that correlate genetic components of expression with traits, and Mendelian Randomization (MR)-based methods. A fundamental problem of all these methods is that, when assessing the role of one gene in a trait using its eQTLs, nearby variants and nearby genetic components of expression of other genes can be correlated with the eQTLs of the test gene, while affecting the trait directly. These "genetic confounders" may lead to false discoveries. To address this challenge, we introduced a novel statistical framework that generalizes TWAS to adjust all genetic confounders. In our simulations, we found that existing methods based on TWAS, colocalization or MR all suffered from high false positive rates, often greater than 50%. Our method, causal-TWAS (cTWAS), in contrast, showed calibrated false positive rates while maintaining power. Application of cTWAS on several common traits highlighted the weakness of existing methods and discovered novel candidate genes. In conclusion, cTWAS is a robust statistical framework to integrate eQTL and GWAS data for gene discovery, and is extendable to other molecular QTLs.

regionalpcs: improved discovery of DNA methylation associations with complex traits

“…Consistent with prior findings (79), we found that PT-WAS identified a greater number of genes associated with AD (n = 43) compared to colocalization (n = 6) at a genomewide significance threshold (p < 5 × 10 −8 , Supplementary Table 8). In contrast to the colocalized genes we identified, approximately 32% of the genes identified by PTWAS through rPCs (and 44% using averages) were located within 1 cM of APOE, which can likely be attributed partly to "LD hitchhiking" (80). LD hitchhiking can occur when some variants that are strong meQTLs for genes near APOE but are not risk variants for AD are in LD with AD risk variants near APOE that are not meQTLs for those nearby genes.…”

Multiomics-based causal inference identifies novel therapeutic targets for inflammatory bowel disease in East Asians

“…Causal-PWAS is a novel extension of PWAS that directly adjust for genetic confounding 74 . PWAS in its original form suffers from confounding by LD; that is, purported cis-genetic effect may in fact be originated from nearby variants or genes, which independently affect a trait of interest.…”

Integrative analysis of the genome, transcriptome, and proteome identifies causal mechanisms of complex traits

“…However, most identified associated variants lie in noncoding genome regions [1, 2], often obscuring their target genes and complicating the therapeutic target identification. Recent efforts to address this problem have resulted in the emergence of genome-scale molecular quantitative trait loci (QTL) annotations [3, 4] and the development of statistical methods bridging genetic variants with molecular phenotypes of gene candidates and complex traits [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. These mechanism-aware putative causal gene (PCG) implication methods [8] focus on molecular phenotypes that can be unambiguously linked to specific genes.…”