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Neural Regulation

Neural Regulation

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Related Experiment Video

Updated: Feb 23, 2026

Quantifying Tissue-Specific Proteostatic Decline in Caenorhabditis elegans

Quantifying Tissue-Specific Proteostatic Decline in Caenorhabditis elegans

Published on: September 7, 2021

Exploring regulation in tissues with eQTL networks.

Maud Fagny^1,2, Joseph N Paulson^1,2, Marieke L Kuijjer^1,2

¹Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115.

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

|August 31, 2017

Summary

This summary is machine-generated.

Understanding genetic variants

Keywords:

GTEx GWAS bipartite networks eQTL expression quantitative trait locus

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Last Updated: Feb 23, 2026

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Published on: September 7, 2021

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Area of Science:

Genomics
Systems Biology
Bioinformatics

Background:

Mapping genotype to phenotype is challenging.
Genetic variants collectively influence complex traits.
Understanding gene regulation is key.

Purpose of the Study:

To investigate the regulatory impact of genetic variants.
To explore network structures of gene expression.
To link genetic variation to biological function.

Main Methods:

Expression quantitative trait locus (eQTL) analysis.
Construction of bipartite networks.
Analysis of network modularity and node centrality across 13 tissues.

Main Results:

eQTL networks exhibit dense, modular communities.
Communities represent shared and tissue-specific biological processes.
Tissue-specific communities link to active chromatin regions.
Network hubs (global and community) show differential regulatory potential and disease association.

Conclusions:

eQTL network structure reveals insights into gene regulation.
Network organization reflects biological processes across tissues.
Node centrality in eQTL networks predicts regulatory roles and disease links.