Evidence map›Paper›PMID 38649715›Full record

ArticleNature communications2024

An approach to identify gene-environment interactions and reveal new biological insight in complex traits.

Xiaofeng Zhu, Yihe Yang, Noah Lorincz-Comi, Gen Li, Amy R Bentley, Paul S de Vries, Michael Brown, Alanna C Morrison, Charles N Rotimi, W James Gauderman and 3 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 1 pooled it
17.6field-weighted citation impact, top 1% of its field
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

25 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

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  13. Hypothesis test of specific parametric structure in a generalized additive model.medRxiv : the preprint server for health sciences · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors at 5 institutions in 2 countries.

Xiaofeng ZhuDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA. xxz10@case.edu.ORCID http://orcid.org/0000-0003-0037-411X
Yihe YangDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.ORCID http://orcid.org/0000-0001-6563-3579
Noah Lorincz-ComiDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.ORCID http://orcid.org/0000-0002-0517-2499
Gen LiDepartment of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Amy R BentleyCenter for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0002-0827-9101
Paul S de VriesHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0003-0964-0111
Michael BrownHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0002-5235-9037
Alanna C MorrisonHuman Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID http://orcid.org/0000-0001-6381-4296
Charles N RotimiCenter for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0001-5759-053X
W James GaudermanDivision of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
Dabeeru C RaoCenter for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
Hugues AschardInstitut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015, Paris, France.ORCID http://orcid.org/0000-0002-7554-6783
CHARGE Gene-lifestyle Interactions Working Group
Case Western Reserve University · USThe University of Texas Health Science Center at Houston · USNational Institutes of Health · USHarvard University · USUniversity of Southern California · US

Funding

Genetic epidemiology of complex diseasesZIAHG200362 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ROTIMI, CHARLES · 2009 to 2025
$54.7M
Statistical Methods for Integrative Genomics in CancerP01CA196569 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI David V Conti · 2016 to 2026
$25.5M
A Multi-Ancestry Study of Gene-Lifestyle Interactions and Multi-Omics in Cardiometabolic TraitsR01HL156991 · NHLBI · WASHINGTON UNIVERSITY · PI RAO, DABEERU C · 2021 to 2024
$8.8M
A Multi-Ethnic Study of Gene-Lifestyle Interactions in Cardiovascular TraitsR01HL118305 · NHLBI · WASHINGTON UNIVERSITY · PI RAO, DABEERU C · 2014 to 2017
$8.3M
Statistical Analysis of Large Genomic Data SetsR01HG011052 · NHGRI · CASE WESTERN RESERVE UNIVERSITY · PI XIAOFENG ZHU · 2020 to 2026
$3.3M
Genetic epidemiology of complex diseasesZ01HG200362 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ROTIMI, CHARLES · 2008 to 2008
$1.7M
Intramural NIH HHS Z01 HG200362NCI NIH HHS P01 CA196569NHGRI NIH HHS R01 HG011052NHLBI NIH HHS R01 HL118305NHLBI NIH HHS R01 HL156991
6 · The paper itself

Abstract

There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.

Indexed as

Gene-Environment InteractionGenome-Wide Association StudyMultifactorial InheritanceAlcohol DrinkingCholesterol, LDLCigarette SmokingFemaleHumansMaleMiddle AgedPhenotypePolymorphism, Single NucleotideQuantitative Trait LociTriglyceridesCholesterol, LDLTriglycerides

Identifiers

PMID38649715
PMCPMC11035594
OpenAlexW4395004719

What OpenQuestion holds

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LicenceCC BY
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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.