Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis
Name
Genome Res.-2017-Yang-1859-71.pdf
Description
Published version
Size
941.08 KB
Format
Unknown
Checksum (MD5)
c8a72b66c39fe2b8f964a80fec1183f9
Author(s)
Yang, Fan
Wang, Jiebiao
The GTEx Consortium
Pierce, Brandon L.
Chen, Lin S.
Hou, Lei
Kellis, Manolis
Liu, Yaping
Park, YongJin
Rinaldi, Nicola
Date Issued
November 1, 2017
Journal
Genome Research
Publisher
Cold Spring Harbor Laboratory
Version
Final published version
Abstract
© 2017 Yang et al. 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution NonCommercial License 4.0
Persistent DSpace Link
DOI of Published Version
10.1101/gr.216754.116
