Evidence map›Paper›PMID 41044620›Full record

ArticleGenome biology2025

Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE.

Zihan Dong, Wei Jiang, Jiangnan Shen, Hongyu Li, Yuhan Xie, Andrew T DeWan, Hongyu Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zihan Dong *Department of Biostatistics, Yale School of Public Health, 300 George Street, 5 Floor, New Haven, CT, 06511, USA.
Wei Jiang *Department of Mathematics, University of Texas at Arlington, Arlington, TX, 76019, USA.
Jiangnan ShenDepartment of Biostatistics, Yale School of Public Health, 300 George Street, 5 Floor, New Haven, CT, 06511, USA.
Hongyu LiDepartment of Biostatistics, Yale School of Public Health, 300 George Street, 5 Floor, New Haven, CT, 06511, USA.
Yuhan XieDepartment of Biostatistics, Yale School of Public Health, 300 George Street, 5 Floor, New Haven, CT, 06511, USA.
Andrew T DeWan *Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, 1 Church Street, 6 Floor, New Haven, CT, 06520, USA. andrew.dewan@yale.edu.
Hongyu Zhao *Department of Biostatistics, Yale School of Public Health, 300 George Street, 5 Floor, New Haven, CT, 06511, USA. hongyu.zhao@yale.edu.

Funding

Statistical Methods for Genetic Risk Predictions across Diverse PopulationsR01HG012735 · NHGRI · YALE UNIVERSITY · PI ZHAO, HONGYU · 2022 to 2025
$2.4M
NHGRI NIH HHS R01 HG012735
6 · The paper itself

Abstract

Uncovering environmental factors interacting with genetic factors to influence complex traits is important in genetic epidemiology and disease etiology. We introduce BiVariate Linkage-Disequilibrium Eigenvalue Regression for Gene-Environment interactions (BV-LDER-GE), a statistical method that detects the overall contributions of G × E interactions in the genome using summary statistics of complex traits. In comparison to existing methods which either ignore correlations with additive effects or use partial information of linkage disequilibrium (LD), BV-LDER-GE harnesses correlations with additive genetic effects and full LD information to enhance the statistical power to detect genome-scale G × E interactions.

Indexed as

Gene-Environment InteractionLinkage DisequilibriumModels, GeneticHumansGene-environment interactionGenetic covarianceGenome-wide association studyGenome-wide interaction scanLinkage disequilibriumStatistical power

Identifiers

PMID41044620
PMCPMC12492645

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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.