Evidence map›Paper›PMID 38464142›Full record

ArticlebioRxiv : the preprint server for biology2024

The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution.

Katherine Stone, John Platig, John Quackenbush, Maud Fagny

Open access · greenAbstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 2 countries.

Katherine StoneDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-9126-5850
John PlatigCenter for Public Health Genomics, University of Virginia, Charlottesville, Virginia, USA.ORCID 0000-0002-4446-8516
John QuackenbushDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-2702-5879
Maud FagnyDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.ORCID 0000-0002-7740-2521
Brigham and Women's Hospital · USCentre National de la Recherche Scientifique · FRHarvard University · USUniversity of Virginia · US

Funding

VectorP30CA006516 · NCI · DANA-FARBER CANCER INSTITUTE · PI Irene M. Ghobrial · 1985 to 2026
$330.6M
Respiratory Computational Discovery CoreP01HL114501 · NHLBI · WEILL MEDICAL COLL OF CORNELL UNIV · PI CHOI, MARY E · 2013 to 2025
$24.9M
Systems Biology, Bioinformatics, and BiostatisticsP01HL105339 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI SILVERMAN, EDWIN K · 2011 to 2015
$12.2M
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer ResearchR35CA197449 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI XIHONG LIN · 2015 to 2026
$10.9M
Chemical Inhibitors to Define an Essential M. Tuberculosis Signaling NetworkR01AI099204 · NIAID · BOSTON CHILDREN'S HOSPITAL · PI HUSSON, ROBERT N · 2012 to 2016
$6.1M
Unraveling the Complexities of Risk and Mechanism in CancerR35CA220523 · NCI · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI QUACKENBUSH, JOHN · 2018 to 2024
$6.0M
Using Networks to Assign Gene Function in Lung DiseaseR01HL111759 · NHLBI · DANA-FARBER CANCER INST · PI QUACKENBUSH, JOHN, SILVERMAN, EDWIN K · 2012 to 2015
$3.4M
WebMeV: A Robust Platform for Intuitive Genomic Data AnalysisU24CA231846 · NCI · HARVARD SCHOOL OF PUBLIC HEALTH · PI QUACKENBUSH, JOHN · 2019 to 2023
$3.2M
Networks Tools to Understand Sex- and Gender-Specific Drivers of DiseaseR01HG011393 · NHGRI · BRIGHAM AND WOMEN'S HOSPITAL · PI DEMEO, DAWN L, QUACKENBUSH, JOHN · 2021 to 2024
$2.1M
Context matters: Network modeling of COPD regulatory variants across tissues and exposuresK25HL140186 · NHLBI · UNIVERSITY OF VIRGINIA · PI PLATIG, JOHN · 2018 to 2022
$926k
NCI NIH HHS P30 CA006516NCI NIH HHS R35 CA197449NCI NIH HHS R35 CA220523NCI NIH HHS U24 CA231846NHGRI NIH HHS R01 HG011393NHLBI NIH HHS K25 HL140186NHLBI NIH HHS P01 HL105339NHLBI NIH HHS P01 HL114501NHLBI NIH HHS R01 HL111759NIAID NIH HHS R01 AI099204
6 · The paper itself

Abstract

Complex traits are determined by many loci-mostly regulatory elements-that, through combinatorial interactions, can affect multiple traits. Such high levels of epistasis and pleiotropy have been proposed in the omnigenic model and may explain why such a large part of complex trait heritability is usually missed by genome-wide association studies while raising questions about the possibility for such traits to evolve in response to environmental constraints. To explore the molecular bases of complex traits and understand how they can adapt, we systematically analyzed the distribution of SNP heritability for ten traits across 29 tissue-specific Expression Quantitative Trait Locus (eQTL) networks. We find that heritability is clustered in a small number of tissue-specific, functionally relevant SNP-gene modules and that the greatest heritability occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. We confirm that this structure has allowed complex traits to evolve in response to environmental constraints, with the local "hubs" being the preferential targets of past and ongoing directional selection. Together, these results provide a conceptual framework for understanding complex trait architecture and evolution.

Indexed as

Bipartite NetworksComplex TraitseQTLGTExGWASHeritabilityPOlygenic selection

Identifiers

PMID38464142
PMCPMC10925220
OpenAlexW4392364595

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.