Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis

Abstract

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.

Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation

“…Mediation analysis has recently been used with genomic data, including in humans (e.g., [10,15]) and rodents (e.g., [51,81]), to identify and refine potential intermediates of causal paths underlying phenotypes. We use a similar genome-wide mediation analysis as used with the DO [22, 33] to detect mediators of eQTL effects on gene expression.…”

“…In human data, co-occurence of QTL across various multi-omic data has been used to assess potentially related and connected biological processes; examples include gene expression with chromatin accessibility [7] or regulatory elements [8], and ribosome occupancy with protein abundances [9]. More formal integration through statistical mediation analyses has also been used to investigate relationships between levels of human biological data, such as distal genetic regulation through local gene expression [10,11], and eQTL with regulatory elements [12][13][14] and physiological phenotypes, such as cardiometabolic traits [15].…”

eQTLMAPT: Fast and Accurate eQTL Mediation Analysis With Efficient Permutation Testing Approaches

“…Recent studies have shown that trans-eQTLs are likely involved in indirect regulations, where the trans-eGene can be mediated by the cis-eGene, which is known as the mediation effect (Pierce et al, 2014;Brynedal et al, 2017;Yang et al, 2017;Yao et al, 2017). These studies provide evidence of a cismediated mechanism that explains distal regulation of trans-eGenes by trans-eQTLs (Yao et al, 2017).…”

MOSTWAS: Multi-Omic Strategies for Transcriptome-Wide Association Studies

“…Expression prediction in DePMA hinges on assessing distal-eSNPs for inclusion in the design matrix via mediation analysis, adopting methods from previous studies [10,11,13]. We first split data for gene expression, SNP dosages, and any potential mediators into k training-testing splits.…”

“…This result accords with Boyle et al's omnigenic model, proposing that regulatory networks are so interconnected that a majority of genetic variants in the genome, local or distal, have indirect effects on transcription of any particular gene [6,7]. Many groups have leveraged this model to identify distal expression quantitative trait loci (eQTLs) by testing the effect of a distal-eSNP on an eGene mediated through a set of genes local to the SNP, concluding that many distal-eQTLs are often eQTLs for many local genes [8][9][10][11][12][13]. It has been shown previously that distal-eQTLs found in regulatory hotspots are generally cell-type specific [8,12,14].…”