Biology subjects

O'Brien, N. L. V.

Publications and source records attributed to O'Brien, N. L. V..

3 recordsLinked to original sources

bioRxiv2026

Directional heritability and the geometry of multivariate constraint

Evolutionary responses to selection depend on how additive genetic variance is distributed across trait combinations. We focus on directional heritability--the fraction of phenotypic variance that is additive genetic along a given selection gradient--and treat its distribution across directions as a central object for describing multivariate constraint. Using a geometric transformation that rescales trait space so that phenotypic variance is isotropic, we show that directional heritability becomes a quadratic form in a whitened genetic matrix G* = P-1/2GP-1/2. Under uniformly distributed selection directions, the squared coefficient of variation of directional heritability satisfies CV2(h2) = (2/(p + 2)) Vrel(G*), where Vrel(G*) is the relative eigenvalue variance of the whitened matrix and p is the number of traits. Simulations show that alignment between the eigenvector systems of G and P has a larger effect on the spread of directional heritability than correlation strength. Analyses of 55 empirical G-P matrix pairs from 11 studies reveal wide variation across biological systems in how often selection encounters low-heritability directions: over two thirds of the populations examined had more than 25% of phenotypic directions with h2 < 0.25. The eigenvalue spectrum of G* provides a sufficient summary for characterising how matrix geometry shapes evolutionary constraint on the phenotypic scale.

evolutionary biology↗

bioRxiv2025

The genetic architecture of polygenic adaptation under a network-derived trait

The genetic architecture of adaptation varies across species, populations, and traits. While existing models capture aspects like the number of loci, effect sizes, and allele frequencies, they often overlook the molecular processes underlying trait expression. We investigated how gene regulatory networks influence quantitative variation during adaptation by examining the negative autoregulation (NAR) motif in two configurations: K+, with four evolving network components, and K-, with two components. Using forward-time simulations, we tracked populations adapting to a shifted phenotypic optimum under varying genetic architectures. We found that K+ populations maintained rapid adaptation despite low recombination, preserving high genetic variance through positive epistasis and stronger linkage disequilibrium. Under low recombination, K+ populations reached the optimum through diverse molecular configurations, while responses were more uniform under high recombination. In contrast, K- and additive models showed impaired adaptation under low recombination. Our findings demonstrate that network structure fundamentally influences the distribution of variation within the molecular architecture of traits, with certain networks providing unexpected robustness against low recombination rates. This suggests that molecular complexity may confer evolutionary advantages in natural populations.

evolutionary biology↗

bioRxiv2023

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

evolutionary biology↗