Evidence map›Paper›PMID 23927518›Full record

ArticleAmerican journal of public health2013

The challenge of causal inference in gene-environment interaction research: leveraging research designs from the social sciences.

Jason M Fletcher, Dalton Conley

Abstract read
In one paragraph

Article in American journal of public health, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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

28 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Many roads to a gene-environment interaction.American journal of human genetics · 2024
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021
    Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Geographic Clustering of Polygenic Scores at Different Stages of the Life Course.The Russell Sage Foundation journal of the social sciences : RSF · 2018
    Article
  20. 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

2 authors.

Jason M FletcherAt the time of this study, Jason M. Fletcher was with the Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, and the Robert Wood Johnson Foundation Health & Society Scholar, Columbia University, New York, NY. Dalton Conley was with the Department of Sociology, the School of Medicine, and the Wagner School of Public Service at New York University, New York.
Dalton Conley

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of genetics and the social sciences will lead to a more complex understanding of the articulation between social and biological processes, although the empirical difficulties inherent in this integration are large. One key challenge is the implications of moving "outside the lab" and away from the experimental tools available for research with model organisms. Social science research methods used to examine human behavior in nonexperimental, real-world settings to date have not been fully taken advantage of during this disciplinary integration, especially in the form of gene-environment interaction research. This article outlines and provides examples of several prominent research designs that should be used in gene-environment research and highlights a key benefit to geneticists of working with social scientists.

Indexed as

CausalityGene-Environment InteractionGenetic ResearchSocial SciencesHumansResearch Design

Identifiers

PMID23927518
PMCPMC3786757

What OpenQuestion holds

Textmetadata
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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.