Evidence map›Paper›PMID 29511166›Full record

ArticleNature communications2018

Gene-by-environment interactions in urban populations modulate risk phenotypes.

Marie-Julie Favé, Fabien C Lamaze, David Soave, Alan Hodgkinson, Héloïse Gauvin, Vanessa Bruat, Jean-Christophe Grenier, Elias Gbeha, Kimberly Skead, Audrey Smargiassi and 3 more

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed
7.2field-weighted citation impact, top 2% 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

67 citing papers in PubMed, 130 citations in OpenAlex.

  1. Article
  2. Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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  10. Regulatory genomics at biobank scales.Nature reviews. Genetics · 2025
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7 more citing papers are in PubMed but not listed here.

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

13 authors at 6 institutions in 3 countries.

Marie-Julie FavéOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
Fabien C LamazeOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
David SoaveOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
Alan HodgkinsonSainte-Justine Research Center, Faculty of Medicine, University of Montreal, Montreal, QC, H3T 1C5, Canada.
Héloïse GauvinSainte-Justine Research Center, Faculty of Medicine, University of Montreal, Montreal, QC, H3T 1C5, Canada.
Vanessa BruatOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
Jean-Christophe GrenierOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.ORCID http://orcid.org/0000-0001-7937-5160
Elias GbehaOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
Kimberly SkeadOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada.
Audrey SmargiassiDepartment of Environmental Health and Occupational Health, University of Montreal, Montreal, QC, H3N 1X9, Canada.
Markey JohnsonHealth Canada, Air Health Science Division, Ottawa, ON, K1A 0K9, Canada.
Youssef IdaghdourNYU Abu Dhabi, Abu Dhabi, UAE.
Philip AwadallaOntario Institute for Cancer Research, Toronto, ON, M5G 0A3, Canada. Philip.Awadalla@oicr.on.ca.
Ontario Institute for Cancer Research · CAGuy's Hospital · GBHealth Canada · CANew York University Abu Dhabi · AEStatistics Canada · CAUniversité de Montréal · CA

Funding

Medical Research Council MR/L016311/1
6 · The paper itself

Abstract

Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual's response to environmental challenges.

Indexed as

Gene-Environment InteractionAdultAgedAir PollutionEnvironmental ExposureFranceGene ExpressionGene FlowHumansMiddle AgedPenetrancePolymorphism, GeneticQuantitative Trait LociQuebecTranscriptome

Identifiers

PMID29511166
PMCPMC5840419
OpenAlexW2793171360

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.