Unknown

Dataset Information

0

Exploring regulation in tissues with eQTL networks.


ABSTRACT: Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

SUBMITTER: Fagny M 

PROVIDER: S-EPMC5604022 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

altmetric image

Publications

Exploring regulation in tissues with eQTL networks.

Fagny Maud M   Paulson Joseph N JN   Kuijjer Marieke L ML   Sonawane Abhijeet R AR   Chen Cho-Yi CY   Lopes-Ramos Camila M CM   Glass Kimberly K   Quackenbush John J   Platig John J  

Proceedings of the National Academy of Sciences of the United States of America 20170829 37


Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly mod  ...[more]

Similar Datasets

| S-EPMC9324452 | biostudies-literature
| S-EPMC5648503 | biostudies-literature
| S-EPMC4903044 | biostudies-literature
| S-EPMC3649995 | biostudies-literature
| S-EPMC4929061 | biostudies-literature
| S-EPMC3937098 | biostudies-literature
| S-EPMC5560814 | biostudies-literature
| S-EPMC9142682 | biostudies-literature
| S-EPMC10976914 | biostudies-literature
| S-EPMC2290938 | biostudies-literature