Evidence map›Paper›PMID 29896809›Full record

ArticleGenetic epidemiology2018

Gene-environment interactions in case-control studies with silent disease.

Iryna Lobach, Joshua Sampson, Siarhei Lobach, Li Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetic epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Iryna LobachDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California.ORCID 0000-0002-3317-7633
Joshua SampsonNational Cancer Institute, National Institutes of Health, Bethesda, Maryland.
Siarhei LobachApplied Mathematics and Computer Science Department, Belarusian State University, Minsk, Belarus.
Li ZhangDepartment of Epidemiology and Biostatistics, University of California, San Francisco, California.

Funding

Methods for Epidemiology StudiesZIACP010181 · NCI · DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS · PI ALBERT, PAUL · 2009 to 2025
$34.8M
Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) often measure gene-environment interactions (G × E). We consider the problem of accurately estimating a G × E in a case-control GWAS when a subset of the controls have silent, or undiagnosed, disease and the frequency of the silent disease varies by the environmental variable. We show that using case-control status without accounting for misdiagnosis can lead to biased estimates of the G × E. We further propose a pseudolikelihood approach to remove the bias and accurately estimate how the relationship between the genetic variant and the true disease status varies by the environmental variable. We demonstrate our method in extensive simulations and apply our method to a GWAS of prostate cancer.

Indexed as

Gene-Environment InteractionGenome-Wide Association StudyBiasCase-Control StudiesComputer SimulationDiseaseHumansMaleModels, GeneticPolymorphism, Single NucleotideProstatic Neoplasmscase-control studygene-environment interactionsprostate cancerpseudolikelihoodsilent disease

Identifiers

PMID29896809
PMCPMC6237277

What OpenQuestion holds

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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.