Evidence map›Paper›PMID 40050722›Full record

ArticleBMC genomics2025

The case-only design is a powerful approach to detect interactions but should be used with caution.

Rui Dong, Gao T Wang, Andrew T DeWan, Suzanne M Leal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rui DongCenter for Statistical Genetics, Gertrude H. Sergievsky Center, and the Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA.
Gao T WangCenter for Statistical Genetics, Gertrude H. Sergievsky Center, and the Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA.
Andrew T DeWanDepartment of Chronic Disease Epidemiology and Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health, 1 Church Street, New Haven, CT, 06510, USA.
Suzanne M LealCenter for Statistical Genetics, Gertrude H. Sergievsky Center, and the Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA. sml3@cumc.columbia.edu.

Funding

Innovative approaches to elucidate the genetic etiology of age-related hearing impairment and tinnitusR01DC017712 · NIDCD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Paul L. Auer, Andrew DeWan · 2019 to 2026
$4.7M
NIDCD NIH HHS R01 DC017712
6 · The paper itself

Abstract

backgroundThe case-only design is a powerful approach to identify gene

methodsThrough theoretical and extensive simulation studies, we investigated type I error, power, and bias of interaction term for a wide variety of disease prevalences, main and interaction effect sizes, sample sizes, and variant and environmental exposure frequencies.

resultsFor diseases with prevalence

conclusionsWe determined the upper bound of the disease prevalence in order not to violate the rare disease assumption for the case-only design. To verify that a case-only design study does not have increased type I error rate, the bias of the interaction term should be estimated. Although the case-only design is a powerful method to detect interactions, prevalences for some complex traits are too high to implement this method without increasing type I error rates.

Indexed as

Gene-Environment InteractionCase-Control StudiesComputer SimulationHumansModels, GeneticPrevalenceResearch DesignSample SizeCase-only designComplex traitsGeneRare disease

Identifiers

PMID40050722
PMCPMC11884093

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.