
Donyavi et al. (2026) provide an empirical demonstration of how evolutionary adaptation proceeds through a small number of phenotypic modules
Published today in Molecular Biology and Evolution.
It caught my eye right away as I've been (on and off) reading Homology, Genes, and Evolutionary Innovation (GP Wagner, 2014).
An example of modularity is our digit 1 (the thumb). Digits 2 to 5 are correlated, developmentally and evolutionarily, whereas digit 1 has become its own module in primates, facilitating uncorrelated adaptation:
> Specifically, digit 1, the thumb, expresses only two Hox genes, HoxA13 and HoxD13, whereas digits 2 to 5 additionally express HoxD12 to HoxD9. Reno and colleagues showed that the morphological correlations among digits 2 to 5 were much higher than the correlations between digit 1 and all other digits both in terms of population variation and evolutionary differences. In primates, the thumb shows more independent evolutionary change relative to the other four digits than each of digits 2 to 5 relative to each other. Hence, Reno et al. identified two variational modules in the primate hand: digit 1 and one module comprising digits 2 to 5.
(GP Wagner, 2014)
How modularity comes about is interesting; for example it can be a consequence of selection on robustness: Robustness (evolution) - Wikipedia. This is something Andreas Wagner's (another Wagner) lab has been working on, and on my reading list is his Arrival of the Fittest (A Wagner, 2014).
You can find his lecture on a related topic, given at the Royal Institution, here: https://www.youtube.com/watch?v=aD4HUGVN6Ko
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Back to today's paper:
Structured abstract:
Background
> Understanding how the myriad molecular impacts of mutation percolate to influence higher-order traits and ultimately fitness requires compressing a many-to-many mapping into something tractable. Decades of theoretical work suggest this may be possible because biological systems are modular: effects of perturbation are often funneled through particular pathways or subsystems rather than propagating freely through the organism. Yet, few empirical systems have been able to demonstrate such modularity at scale.
Results
> Here, we show that, even across 774 diverse yeast lineages, fitness variation across 12 drug environments is organized by a strikingly low-dimensional structure defined by only a few inferred phenotypic axes that capture the main patterns of variation. Lineages drawn from multiple evolutionary histories reveal more of these phenotypic axes than those derived from a single selection pressure. Consistent with many of these lineages having evolved under strong selection pressure, their mutations often exhibit broad pleiotropy, affecting nearly all inferred phenotypic axes.
However, fitness in any given drug depends on a much sparser subset of the phenotypic modules these axes reflect. By compressing many-to-many relationships, this low-dimensional framework exposes the modular phenotypic space, as well as the context-dependent contribution of each phenotypic module to fitness that can constrain the pleiotropic effects of adaptive mutations. It also highlights that the apparent complexity of genotype–phenotype–fitness maps depends not only on environmental context but also on the diversity of mutations through which they are observed, laying the groundwork for identifying the key phenotypic modules that matter for fitness.
and from the discussion:
> Our results echo ideas from the omnigenic perspective on complex traits, which holds that mutations in many genes can shape trait variation through dense regulatory networks (Boyle et al. 2017; Liu et al. 2019). In our case, mutational effects did not scatter unpredictably but instead collapsed onto a small set of shared axes. A recent quantitative omnigenic model formalized this principle, showing that network-level complexity can be summarized in low-dimensional, interpretable structures (Ružičková et al. 2024). The key difference is that while the quantitative omnigenic framework is a bottom-up approach deriving axes from regulatory network topology, our top-down SVD approach infers them from observed fitness patterns alone. Yet, both converge on the idea that molecular complexity funnels through a limited set of modules that enable prediction.
(all emphasis mine)
- Mohammad Hossein Donyavi, Reza Ghelich, Kara Schmidlin, Grant Kinsler, Kerry Geiler-Samerotte
Evolutionary adaptation proceeds through a small number of phenotypic modules
Molecular Biology and Evolution, Volume 43, Issue 8, August 2026, msag183, https://doi.org/10.1093/molbev/msag183