Evidence map›Paper›PMID 29872216›Full record

ReviewNature reviews. Genetics2018

Using genetic data to strengthen causal inference in observational research.

Jean-Baptiste Pingault, Paul F O'Reilly, Tabea Schoeler, George B Ploubidis, Frühling Rijsdijk, Frank Dudbridge

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 272 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
272citing papers in PubMed, 12 pooled it
–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

272 citing papers in PubMed, 12 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Pooled it
  11. Pooled it
  12. Pooled it
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Polygenic scores in psychiatric research and clinical practice.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026
    Article
  20. Article

212 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

6 authors.

Jean-Baptiste PingaultDepartment of Clinical, Educational and Health Psychology, University College London, London, UK. j.pingault@ucl.ac.uk.
Paul F O'ReillySocial, Genetic, and Developmental Psychiatry, King's College London, De Crespigny Park, London, UK.
Tabea SchoelerDepartment of Clinical, Educational and Health Psychology, University College London, London, UK.
George B PloubidisCentre for Longitudinal Studies, Department of Social Science, UCL Institute of Education, University College London, London, UK.
Frühling RijsdijkSocial, Genetic, and Developmental Psychiatry, King's College London, De Crespigny Park, London, UK.
Frank DudbridgeDepartment of Health Sciences, University of Leicester, Leicester, UK.

Funding

Medical Research Council MR/N015746/1
6 · The paper itself

Abstract

Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.

Indexed as

Databases, GeneticGenotypeGenotyping TechniquesData MiningHumans

Identifiers

PMID29872216

What OpenQuestion holds

Textmetadata
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.