Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis
Year: 2016
Type: preprint
Abstract: 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 multi-tissue 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.
DOI: 10.1101/078683
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funders: University of Miami, University of Pennsylvania, Harvard University, University of Chicago, Broad Institute, Université de Genève, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute on Drug Abuse, National Human Genome Research Institute, NIH Office of the Director, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Neurological Disorders and Stroke, Common Fund
Awards: Methods for high-resolution analysis of genetic effects on gene expression, G1206703408, G1217436835, G1534570039, G1561323845, G1650773652, Identification and validation of cell specific eQTLs by Bayesian modeling, G1883398070, Using the Transcriptome for SNP and Gene Annotation, G2158443041, G2651523388, G2835668146, G2877753951, G3128394570, G3221727586, G32319252, Epigenetic Marks of Cocaine Addiction, G3387558946, G3443372292, G347407575, G3503792436, G3547560438, G3793778294, Integrative multivariate association and genomic analyses, G386607036, G4124418613, Telomere length and chromosomal instability across various tissue types, G4395118462, Systems approaches to link tissue-specific expression to disease, Harnessing GTEx to Create Transcriptome Knowledge and Inform Disease Biology, G4478373029, G4606556408, G4715193760, G4729234894, G5097752872, G5286804338, G563538711, G5737582129, G5865458551, Statistical analysis of gene expression quantitative trait loci (eQTL), G6317516893, G633025773, G634178657, G6619812508, G664140718, G7075004892, G7215801407, G7274654584, G7353525908, CNS Mechanisms in Cocaine-Related Sudden Death, G7665525972, G7761642487, G7924626839, G7937240049, G7953026188, G7958282288, G8308451750, G8602100876, G8644024335, G8719970897, G8788848806, G8810271873, G8906494432
OpenAlex ID: W2528807305