Evidence map›Paper›PMID 25998948›Full record

ArticleHuman genetics2015

Confronting the missing epistasis problem: on the reproducibility of gene-gene interactions.

William Murk, Michael B Bracken, Andrew T DeWan

Abstract read
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In one paragraph

Article in Human genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.0field-weighted citation impact, top 13% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Deciphering epistatic genetic regulation of cardiac hypertrophy.medRxiv : the preprint server for health sciences · 2025
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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

3 authors at 1 institution in 1 country.

William MurkDepartment of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, 06510, USA.
Michael B Bracken
Andrew T DeWan
Yale University · US

Funding

Canadian Institutes of Health Research DFS-129311
6 · The paper itself

Abstract

Epistasis (gene-gene interaction) is thought to play an integral role in the genetic basis of complex traits, and a significant amount of research has been invested into identifying this phenomenon in human disease. However, the overall success of empirical studies of epistasis in humans is unclear, as such studies are rarely systematically evaluated. Here, we have selected asthma as an example of a well-studied, complex human disease, and provide a critical analysis and replication attempt of nearly all prior reports of epistasis for this disease. Of 191 previously reported interactions, we find that 39.8% were not originally identified using an explicit test for interaction and thus may not have been true epistatic effects to begin with. Moreover, directions of effect were not described for 46.1% of the interactions, which prevents their rigorous replication. In the original studies, attempts at replication were made for 15.2% of the interactions, and 7.3% were actually replicated. In the current study, we were able to evaluate 85.9% of the interactions using a large asthma dataset from the GABRIEL Consortium. None of these interactions could be replicated based on strict criteria. However, we found nominally significant (p < 0.05) evidence in support of 23.8% of the evaluated interactions. Although many reports of epistasis are not robustly supported in the published literature, our results suggest that at least some of these reports may have been true-positive examples of epistasis. In general, improvements in empirical studies of epistasis are called for, in order to better understand the importance of this phenomenon in human disease.

Indexed as

Databases, GeneticEpistasis, GeneticAsthmaHumans

Identifiers

PMID25998948
OpenAlexW318062556

What OpenQuestion holds

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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.