scholarly journals Functional partitioning of local and distal gene expression regulation in multiple human tissues

2016 ◽  
Author(s):  
Xuanyao Liu ◽  
Hilary K. Finucane ◽  
Alexander Gusev ◽  
Gaurav Bhatia ◽  
Steven Gazal ◽  
...  

AbstractStudies of the genetics of gene expression have served as a key tool for linking genetic variants to phenotypes. Large-scale eQTL mapping studies have identified a large number of local eQTLs, but the molecular mechanism of how genetic variants regulate expression is still unclear, particularly for distal eQTLs, which these studies are not well-powered to detect. In this study, we use a heritability partitioning approach to dissect the functional components of gene regulation. We make use of an existing method, stratified LD score regression, that leverages all variants (not just those that pass stringent significance thresholds) to partition heritability across functional categories, and we extend this method to partition local and distal gene expression heritability in 15 human tissues. The top enriched functional categories in local regulation of peripheral blood gene expression included super enhancers (5.18x), coding regions (3.73x), conserved regions (2.33x) and four histone marks (p<3x10-7 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral blood gene expression: super enhancers (1.91x), coding regions (4.47x), conserved regions (4.51x) and two histone marks (p<3x10-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues showed that local gene expression regulation is largely shared across tissues, but distal gene expression regulation is highly tissue-specific. Our results elucidate the functional components of the genetic architecture of local and distal gene expression regulation.

2021 ◽  
Vol 16 ◽  
Author(s):  
Min Yao ◽  
Caiyun Jiang ◽  
Chenglong Li ◽  
Yongxia Li ◽  
Shan Jiang ◽  
...  

Background: Mammalian genes are regulated at the transcriptional and post-transcriptional levels. These mechanisms may involve the direct promotion or inhibition of transcription via a regulator or post-transcriptional regulation through factors such as micro (mi)RNAs. Objective: This study aimed to construct gene regulation relationships modulated by causality inference-based miRNA-(transition factor)-(target gene) networks and analyze gene expression data to identify gene expression regulators. Methods: Mouse gene expression regulation relationships were manually curated from literature using a text mining method which was then employed to generate miRNA-(transition factor)-(target gene) networks. An algorithm was then introduced to identify gene expression regulators from transcriptome profiling data by applying enrichment analysis to these networks. Results: A total of 22,271 mouse gene expression regulation relationships were curated for 4,018 genes and 242 miRNAs. GEREA software was developed to perform the integrated analyses. We applied the algorithm to transcriptome data for synthetic miR-155 oligo-treated mouse CD4+ T-cells and confirmed that miR-155 is an important network regulator. The software was also tested on publicly available transcriptional profiling data for Salmonella infection, resulting in the identification of miR-125b as an important regulator. Conclusion: The causality inference-based miRNA-(transition factor)-(target gene) networks serve as a novel resource for gene expression regulation research, and GEREA is an effective and useful adjunct to the currently available methods. The regulatory networks and the algorithm implemented in the GEREA software package are available under a free academic license at website : http://www.thua45.cn/gerea.


Sign in / Sign up

Export Citation Format

Share Document