Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Abstract

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2 , known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

Leveraging biobank-scale rare and common variant analyses to identify ASPHD1 as the main driver of reproductive traits in the 16p11.2 locus

“…Akin to GWAS loci, CNVs present a defined interval associated to phenotype, but often offer no distinct causative genes [64]. Whereas MR has become a popular tool for post-GWAS prioritization of causally relevant genes, authors of these initial studies have refrained from functional validation of their findings de facto acknowledging that insufficient eQTL data from the target tissues might be an insurmountable limitation [28,30]. Here, we exploited the benefits of large-scale MR and overcame its tissue-specific limitation by agnostically assessing the effect of single gene dosage alteration on patterning of GnRH-expressing neurons in the context of zebrafish development.…”

“…One recently proposed solution to this challenge involves intersection of expression quantitative trait loci (eQTL) data with GWAS risk loci [26,27], a strategy called Mendelian Randomization (MR). This approach aims to resolve gene-trait associations and offer the ability to estimate the strength of the causal effects in GWAS loci [28][29][30].…”

Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLs

“…Expression quantitative trait locus (eQTL) studies using genotype and gene expression data have demonstrated that the genetic regulation of gene expression is pervasive [1,2,3,4,5]. Additionally, numerous studies have leveraged eQTLs to characterize the molecular basis of complex phenotypic variation [6,7,8,9,10].…”

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

“…To address this issue, several approaches that attempt to either detect or allow for 52 pleiotropy in the context of MR, or to investigate more complex networks of 53 relationships between variables, have been proposed [15][16][17][18][19][20][21][22][23]. There has also been 54 considerable interest in using MR in a "bi-directional"' or "reciprocal" fashion to 55 determine the direction of causation between two variables, say X and Y [12,24].…”

“…A naive application of MR, such as 486 testing a causal effect of each "omics" variable on the disease outcome one at a time, 487 may violate the no-pleiotropy assumption. Thus, recent developments in MR [16][17][18][19][20][21][22][23] 488 have focussed on considering several instruments and/or mediators simultaneously, in 489 principle better accounting for horizontal pleiotropy. However, in most of these 490 approaches, an underlying hypothesised graphical structure representing the 491 relationships between variables must be assumed (rather than being learned from the 492 data).…”