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

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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.

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  1. Rather than separating by positive and negative interactions, the “innovativeness” of genetic interactions might better be described by how unexpected the fitness of the double mutants is relative to the multiplicative outcome of the singles…so the strength of their interactions in either direction. Information theoretic metrics like mutual information or KL divergence could be informative here. I’d be interested to see if the more surprising/unexpected interactions that actually confer greater information gain are perceived as interesting or impactful by the scientific community based on citations in the literature.