Biology subjects

Holmes, C. M.

Publications and source records attributed to Holmes, C. M..

7 recordsLinked to original sources

bioRxiv2026

Laboratory yeast crosses reveal limited epistasis in the genetic basis of complex traits

Mapping the genetic basis of complex traits is complicated by the presence of epistatic interactions between loci. While work in molecular genetics identifies numerous specific genetic interactions, statistical analyses of quantitative traits frequently conclude that additive (nonepistatic) models explain most heritable variation. However, these conclusions are typically limited by the narrow range of genetic relatedness(e.g. in F1 offspring of a biparental or circular cross). Here, we use a barcoded panel of Saccharomyces cerevisiae genotypes with a broad range of relatedness to quantify the effects of epistasis on the genetic architecture of seven complex traits. We find limited contributions of epistasis to the genetic basis of these traits. These results indicate that epistasis beyond that detected in standard yeast crosses may exist, yet it contributes little to phenotypic variance in these systems.

genetics↗

bioRxiv2025

Inferring genotype-phenotype maps using attention models

Predicting phenotype from genotype is a central challenge in genetics. Traditional approaches in quantitative genetics typically analyze this problem using methods based on linear regression. These methods generally assume that the genetic architecture of complex traits can be parameterized in terms of an additive model, where the effects of loci are independent, plus (in some cases) pair-wise epistatic interactions between loci. However, these models struggle to analyze more complex patterns of epistasis or subtle gene-environment interactions. Recent advances in machine learning, particularly attention-based models, offer a promising alternative. Initially developed for natural language processing, attention-based models excel at capturing context-dependent interactions and have shown exceptional performance in predicting protein structure and function. Here, we apply attention-based models to quantitative genetics. We analyze the performance of this attention-based approach in predicting phenotype from genotype using simulated data across a range of models with increasing epistatic complexity, and using experimental data from a recent quantitative trait locus mapping study in budding yeast. We find that our model demonstrates superior out-of-sample predictions in epistatic regimes compared to standard methods. We also explore a more general multi-environment attention-based model to jointly analyze genotype-phenotype maps across multiple environments and show that such architectures can be used for "transfer learning" - predicting phenotypes in novel environments with limited training data.

genetics↗

bioRxiv2023

Stimulus invariant aspects of the retinal code drive discriminability of natural scenes

Everything that the brain sees must first be encoded by the retina, which maintains a reliable representation of the visual world in many different, complex natural scenes while also adapting to stimulus changes. This study quantifies whether and how the brain selectively encodes stimulus features about scene identity in complex naturalistic environments. While a wealth of previous work has dug into the static and dynamic features of the population code in retinal ganglion cells, less is known about how populations form both flexible and reliable encoding in natural moving scenes. We record from the larval salamander retina responding to five different natural movies, over many repeats, and use these data to characterize the population code in terms of single-cell fluctuations in rate and pairwise couplings between cells. Decomposing the population code into independent and cell-cell interactions reveals how broad scene structure is encoded in the retinal output. while the single-cell activity adapts to different stimuli, the population structure captured in the sparse, strong couplings is consistent across natural movies as well as synthetic stimuli. We show that these interactions contribute to encoding scene identity. We also demonstrate that this structure likely arises in part from shared bipolar cell input as well as from gap junctions between retinal ganglion cells and amacrine cells.

neuroscience↗

bioRxiv2023

Fast or slow? Clock readout sets internal periods

Circadian clocks are essential for the function of a wide array of organisms, from cyanobacteria to humans. Despite decades of productive study of these systems, mysteries remain, including why many biological clocks have non-24-hour internal periods. We take a new approach to circadian clocks by focusing on downstream readout of the clocks state to answer physiologically relevant questions, such as when will the sun rise? Using this framework, we show that systematic errors arising from sunrise and sunset prediction can be compensated by having non-24 hour internal periods. We show that this prediction holds in models of cyanobacterial circadian clocks. Finally, we predict latitude-dependant qualitative changes in circadian clock structure and the performance of different clock phenotypes in common laboratory experimental setups.

biophysics↗

bioRxiv2022

Heavy-tailed neuronal connectivity arises from Hebbian self-organization

In networks of neurons, the connections are heavy-tailed, with a small number of neurons connected much more strongly than the vast majority of pairs.1-6 Yet it remains unclear whether, and how, such heavy-tailed connectivity emerges from simple underlying mechanisms. Here we propose a minimal model of synaptic self-organization: connections are pruned at random, and the synaptic strength rearranges under a mixture of Hebbian and random dynamics. Under these generic rules, networks evolve to produce scale-free distributions of connectivity strength, with a power-law exponent [Formula] that depends only on the probability p of Hebbian (rather than random) growth. By extending our model to include correlations in neuronal activity, we find that clustering--another ubiquitous feature of neuronal networks6-9--also emerges naturally. We confirm these predictions in the connectomes of several animals, suggesting that heavy-tailed and clustered connectivity may arise from general principles of self-organization, rather than the biophysical particulars of individual neural systems.

neuroscience↗

bioRxiv2019

Estimation of mutual information for real-valued data with error bars and controlled bias

Estimation of mutual information between (multidimensional) real-valued variables is used in analysis of complex systems, biological systems, and recently also quantum systems. This estimation is a hard problem, and universally good estimators provably do not exist. Kraskov et al. (PRE, 2004) introduced a successful mutual information estimation approach based on the statistics of distances between neighboring data points, which empirically works for a wide class of underlying probability distributions. Here we improve this estimator by (i) expanding its range of applicability, and by providing (ii) a self-consistent way of verifying the absence of bias, (iii) a method for estimation of its variance, and (iv) a criterion for choosing the values of the free parameter of the estimator. We demonstrate the performance of our estimator on synthetic data sets, as well as on neurophysiological and systems biology data sets.

bioinformatics↗

bioRxiv2018

Increased adaptability to rapid environmental change can more than make up for the two-fold cost of males

The famous \"two-fold cost of sex\" is really the cost of anisogamy - why should females mate with males who do not contribute resources to offspring, rather than isogamous partners who contribute equally? In typical anisogamous populations, a single very fit male can have an enormous number of offspring, far larger than is possible for any female or isogamous individual. If the sexual selection on males aligns with the natural selection on females, anisogamy thus allows much more rapid adaptation via super-successful males. We show via simulations that this effect can be sufficient to overcome the two-fold cost and maintain anisogamy against isogamy in populations adapting to environmental change. The key quantity is the variance in male fitness - if this exceeds what is possible in an isogamous population, anisogamous populations can win out in direct competition by adapting faster.

evolutionary biology↗