Dataset Information

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.

SUBMITTER: Yang F

PROVIDER: S-EPMC5668943 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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

Yang Fan F Wang Jiebiao J Pierce Brandon L BL Chen Lin S LS

Genome research 20171011 11

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-wi ...[more]

PMID: 29021290

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Project description:BackgroundMapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators.ResultsWe proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators.ConclusionsTrans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.

Project description:A large fraction of human genes are regulated by genetic variation near the transcribed sequence (cis-eQTL, expression quantitative trait locus), and many cis-eQTLs have implications for human disease. Less is known regarding the effects of genetic variation on expression of distant genes (trans-eQTLs) and their biological mechanisms. In this work, we use genome-wide data on SNPs and array-based expression measures from mononuclear cells obtained from a population-based cohort of 1,799 Bangladeshi individuals to characterize cis- and trans-eQTLs and determine if observed trans-eQTL associations are mediated by expression of transcripts in cis with the SNPs showing trans-association, using Sobel tests of mediation. We observed 434 independent trans-eQTL associations at a false-discovery rate of 0.05, and 189 of these trans-eQTLs were also cis-eQTLs (enrichment P<0.0001). Among these 189 trans-eQTL associations, 39 were significantly attenuated after adjusting for a cis-mediator based on Sobel P<10-5. We attempted to replicate 21 of these mediation signals in two European cohorts, and while only 7 trans-eQTL associations were present in one or both cohorts, 6 showed evidence of cis-mediation. Analyses of simulated data show that complete mediation will be observed as partial mediation in the presence of mediator measurement error or imperfect LD between measured and causal variants. Our data demonstrates that trans-associations can become significantly stronger or switch directions after adjusting for a potential mediator. Using simulated data, we demonstrate that this phenomenon is expected in the presence of strong cis-trans confounding and when the measured cis-transcript is correlated with the true (unmeasured) mediator. In conclusion, by applying mediation analysis to eQTL data, we show that a substantial fraction of observed trans-eQTL associations can be explained by cis-mediation. Future studies should focus on understanding the mechanisms underlying widespread cis-mediation and their relevance to disease biology, as well as using mediation analysis to improve eQTL discovery.

Dataset Information

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

Publications

Identifying <i>cis</i>-mediators for <i>trans</i>-eQTLs across many human tissues using genomic mediation analysis.

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