Simulations reveal that the effects of hybridization can linger for several generations.
Understanding the relationship between genotype and phenotype remains one of the central challenges in biology. How does an organism’s genetic make-up give rise to its observable traits? One way to approach this question is by studying the regulatory networks that govern gene expression. By examining how genes interact, researchers can uncover the underlying patterns that shape biological function. These networks are often inferred from statistical correlations between the expression levels of different genes. In simple terms, we can expect the expression of transcription factors to be positively correlated with the genes they activate, and the expression of inhibitors to be negatively correlated with their targets.
However, correlation does not necessarily imply causation. Many patterns of correlated gene expression may arise from indirect or unrelated molecular processes. It is therefore important to interpret gene co-expression patterns with caution. This task becomes even more complex in the presence of hybridization, as highlighted by a recent simulation study published in the journal Genetics.
Two Scenarios
Rogini Runghen and Daniel Bolnick examined two scenarios using individual-based simulations. In the first, they modeled successive generations of hybrid matings derived from two divergent parental populations. In the second, they simulated the effect of recurrent gene flow from a source population into a larger population with distinct gene expression patterns.
The analyses revealed that “these evolutionary processes can create blocks of highly correlated gene expression that might be identified as modules in a gene expression network, but which may have no shared biological function.” In other words, hybridization can lead to statistical correlations between genes that have no functional or regulatory meaning.
Three-phase Trajectory
A closer examination of the simulations showed a three-phase trajectory. In the initial disruption phase, hybrids exhibit substantial network perturbation, marked by a dramatic increase in gene network connectivity during the F2 generation. This disruption is followed by a reorganization phase, during which recombination in subsequent generations progressively breaks down the physical linkage between blocks of correlated gene expression. Finally, the networks enter a stabilization phase, characterized by greater structural organization than in the parental genotypes. Interestingly, hybrid networks retained elevated connectivity (approximately two-fold higher) even after 20 generations.
It is relatively intuitive that hybridization can generate statistical correlations between genes located on the same chromosome through physical linkage. However, the impact of hybridization extends even further, influencing correlations among genes situated on different chromosomes.
Even the correlations between genes on different chromosomes become denser. This happens because upstream regulatory genes in the network are co-inherited with other genes on the same linkage block, so all these linked genes become correlated with the downstream targets of their neighboring regulatory gene.
Overall, these results demonstrate that hybridization can profoundly restructure gene regulatory networks, generating lasting patterns of elevated connectivity that extend both within and across chromosomes.

Evolutionary Innovation
This study provides a clear demonstration of how hybridization can shape statistical patterns of gene co-expression. Given the prevalence of hybridization across the Tree of Life, we must thus be cautious when interpreting correlations between genes. At the same time, the findings highlight the potentially creative role of hybridization: some hybridization-induced correlations may represent genuine functional connections, providing hybrids with novel expression patterns that open pathways to new phenotypes. Incorporating selection into future simulations could shed light on how such patterns of gene expression are refined (or eliminated) over evolutionary time. Ultimately, hybridization may not only act as a source of disruption, but also as a powerful engine of evolutionary innovation in gene regulatory networks.
References
Runghen, R., & Bolnick, D. I. (2025). Effects of hybridization and gene flow on gene co-expression networks. Genetics, 230(2), iyaf057.
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