Evidence map›Paper›PMID 30925861›Full record

ArticleBMC bioinformatics2019

Identification of trans-eQTLs using mediation analysis with multiple mediators.

Nayang Shan, Zuoheng Wang, Lin Hou

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Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

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25citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

  1. Article
  2. Article
  3. Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025
    Article
  4. Article
  5. Quantitative omnigenic model discovers interpretable genome-wide associations.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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  7. Review
  8. bioRxiv : the preprint server for biology · 2023
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Nayang ShanCenter for Statistical Science, Tsinghua University, Beijing, 100084, China.
Zuoheng WangDepartment of Biostatistics, Yale School of Public Health, New Haven, CT, 06510, USA. zuoheng.wang@yale.edu.
Lin HouCenter for Statistical Science, Tsinghua University, Beijing, 100084, China. houl@tsinghua.edu.cn.

Funding

Novel Methods for Longitudinal Study of Gene-Environment Interplay in AlcoholismK01AA023321 · NIAAA · YALE UNIVERSITY · PI WANG, ZUOHENG · 2015 to 2019
$863k
NIAAA NIH HHS K01 AA023321
6 · The paper itself

Abstract

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.

Indexed as

Chromosome MappingComputer SimulationDatabases, GeneticGene Expression RegulationHaplotypesHumansPolymorphism, Single NucleotideQuantitative Trait LociMediation analysisMultiple mediatorsTrans-eQTL

Identifiers

PMID30925861
PMCPMC6440281

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.