Evidence map›Paper›PMID 40667081›Full record

ArticlebioRxiv : the preprint server for biology2025

Meta-Research: Positive genetic interactions have greater scientific impact but are under-represented in the literature.

Mengyi Sun, David M McCandlish

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

2 authors.

Mengyi SunSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.ORCID 0000-0003-0842-8098
David M McCandlishSimons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724.

Funding

Computational analysis of complex genetic interactionsR35GM133613 · NIGMS · COLD SPRING HARBOR LABORATORY · PI David Martin McCandlish · 2019 to 2026
$3.5M
NIGMS NIH HHS R35 GM133613
6 · The paper itself

Abstract

Genetic interactions, where the combined effect of perturbing two genes leads to a phenotype that deviates from the expectation based on the effects of the individual perturbations, often provide important information about the functional architecture of biological systems. These interactions are commonly classified as negative or positive based on whether the phenotype of the double mutant is less than or greater than what would be expected based on the single mutant effects. When the trait in question is fitness, systematic studies of pairwise deletions have shown that negative interactions typically link genes with similar functional annotations, while positive interactions typically link genes that are less obviously related and thus often viewed as less informative. However, research in the sociology of science suggests that transformative discoveries often arise from unexpected, cross-domain linkages. To evaluate these competing perspectives, we integrated large-scale genetic interaction data in yeast with literature annotations from the Saccharomyces Genome Database and citation data from iCite. We found that positive genetic interactions are associated with greater scientific impact, as measured by citation metrics, contrary to prevailing assumptions. Nevertheless, despite their greater impact, positive interactions are significantly underrepresented in the scientific literature, suggesting they are frequently overlooked. These findings reveal the underappreciated value of positive genetic interactions and demonstrate the potential of applying sociology-of-science insights to foster biological innovation.

Identifiers

PMID40667081
PMCPMC12262331

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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.