Evidence map›Paper›PMID 42025156›Full record

ArticleCell genomics2026

Regulatory network topology and the genetic architecture of gene expression.

Matthew Aguirre, Jeffrey P Spence, Guy Sella, Jonathan K Pritchard

Abstract read
In one paragraph

Article in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Matthew AguirreDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA. Electronic address: aguirm18@gene.com.
Jeffrey P SpenceDepartment of Genetics, Stanford University, Stanford, CA, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA; Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA, USA. Electronic address: jeff.spence@ucsf.edu.
Guy SellaDepartment of Biological Sciences, Columbia University, New York, NY, USA; Program for Mathematical Genomics, Columbia University, New York, NY, USA. Electronic address: gs2747@columbia.edu.
Jonathan K PritchardDepartment of Genetics, Stanford University, Stanford, CA, USA; Department of Genetics, Stanford University, Stanford, CA, USA. Electronic address: pritch@stanford.edu.

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Integration of genetic association mapping and functional data to elucidate genetic mechanisms of diseaseR01HG008140 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2016 to 2026
$7.3M
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory codeU01HG012069 · NHGRI · STANFORD UNIVERSITY · PI Anshul Kundaje · 2021 to 2026
$3.9M
The population genetics of disease risk and other quantitative traitsR01GM115889 · NIGMS · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI SELLA, GUY · 2015 to 2025
$2.9M
Bayesian estimation of gene effects on traits from coding variantsR01HG014005 · NHGRI · STANFORD UNIVERSITY · PI JONATHAN K PRITCHARD · 2025 to 2026
$1.3M
NHGRI NIH HHS R01 HG008140NHGRI NIH HHS R01 HG014005NHGRI NIH HHS U01 HG012069NIGMS NIH HHS R01 GM115889NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

Most genetic variance in gene expression is due to trans-acting expression quantitative trait loci (eQTLs) spread across the genome. However, these loci are generally hard to map due to limited discovery power. Here, we simulate how local properties of expression regulation and global properties of regulatory networks alter the genome-wide proportions of cis- and trans-heritability. We find that network motifs and modular groups can reduce or enhance the effects of trans-eQTLs and that hub regulators shorten paths across the network and act as key sources of trans-acting variance. Critically, networks with all these features best recapitulate the observed distribution of cis- and trans-heritability. Taken together, our results suggest that the genome-wide genetic architecture of gene expression involves fewer regulators for each gene but implicates the same regulators more often across genes (i.e., is less polygenic and more pleiotropic) than previously anticipated.

Indexed as

Gene Expression RegulationGene Regulatory NetworksHumansModels, GeneticQuantitative Trait Locicomplex traitseQTLexpression quantitative trait locigene expressiongene regulatory networkheritabilityhuman genetics

Identifiers

PMID42025156
PMCPMC13261694

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.