Biology subjects

Barroso, G. V.

Publications and source records attributed to Barroso, G. V..

7 recordsLinked to original sources

bioRxiv2026

Strong correlations between mutation rates and selective constraint in the human genome?

The 21st century has seen repeated challenges to the premise that de novo mutations occur independently from their fitness effects. So far, these attempts have been dismissed as technical artifacts, restoring confidence in the null hypothesis. Yet the development of high-resolution genomic resources enables investigating the relationship between mutation rate variation and selective constraint with unprecedented detail. Here, we use state-of-the art genomic maps to test whether predicted mutation rates differ within and outside putatively constrained sites in humans. Contrary to the hypothesis that is typically put forward, but in agreement with another recent study, we find that protein-coding genes exhibit higher rates of mutation than the neutral genomic background. Conversely, mutation rates are lower in non-coding elements identified through sequence conservation. This can be attributed either to biases in the estimation of mutation rates in sites under selection, biases in scoring constraint in sites with low rates of mutation, or a genuine signal that would merit further investigation. Regardless of the underlying cause, our findings have broad implications for evolutionary genetics. Several studies rely on accurate mutation maps and identification of selection targets across the genome, which inherently confound each other. Caution is therefore warranted when interpreting results from currently available non-coding scores.

evolutionary biology↗

bioRxiv2026

Causal inference clarifies the roles of background selection and mutation rate variation in shaping human genetic diversity

Decades of theoretical and empirical work have struggled to reconcile competing views on how evolution shapes patterns of genetic variation. We now understand the genome as a mosaic molded by both neutral and selective forces, but quantifying their relative contributions remains an open challenge. A major obstacle has been the tendency to analyze each evolutionary process in isolation. But different processes may leave similar signatures on genetic variation, making it challenging to draw conclusions from correlations alone. To address this gap, we make predictions of the landscape of diversity based on background selection and mutation rate variation. We then develop structural equation models describing how mutation, recombination and selection jointly shape the genomic landscape of diversity. This approach offers a more realistic representation of biological interactions and enables rigorous evaluation of hypothesized causal structures, marking both conceptual and practical improvements over previous studies. Analyses of human data reveal large variation in the explanatory power of candidate models across chromosomes. We find that previous studies likely overestimated the predictive accuracy of background selection; although it emerges as the overall main driver of diversity, in some chromosomes mutation rate variation has a comparable impact. We also show that recombination increases diversity more strongly through its influence on background selection than through its mutagenic effect, resolving a longstanding debate. This work demonstrates that modeling variation inherent in genome biology substantially improves our ability to explain human genetic diversity. At the same time, evidence for unmodeled covariance between mutation rates and density of constrained sites reinvigorates an ongoing discussion about the evolution of the mutation landscape, although additional work is needed to determine its origins.

evolutionary biology↗

bioRxiv2025

A model for background selection in non-equilibrium populations

In many taxa, levels of genetic diversity are observed to vary along their genome. The framework of background selection models this variation in terms of linkage to constrained sites, and recent applications have been able to explain a large portion of the variation in human genomes. However, these studies have also yielded conflicting results, stemming from two key limitations. First, existing models are inaccurate in a critical region of parameter space (Nes ~ -1), where the local reduction in diversity is sharpest. Second, they assume a constant population size over time. Here, we develop predictions for diversity under background selection based on the Hill-Robertson system of two-locus statistics, which allows for population size changes. We treat the joint effect of multiple selected loci independently, but we show that interference among them is well captured through local rescaling of mutation, recombination and selection in an iterative procedure that converges quickly. We further accommodate existing background selection theory to non-equilibrium demography, bridging the gap between weak and strong selection. Simulations show that our predictions are accurate across the entire range of selection coefficients. We characterize the temporal dynamics of linked selection under population size changes and demonstrate that patterns of diversity can be misinterpreted by other models. Specifically, biases due to the incorrect assumption of equilibrium carry over to downstream inferences of the distribution of fitness effects and deleterious mutation rate. Jointly modeling demography and linked selection therefore improves our understanding of the genomic landscape of diversity, which will help refine inferences of linked selection in humans and other species.

evolutionary biology↗

bioRxiv2021

Inferring the mode and strength of ongoing selection

Genome sequence data is no longer scarce. The UK Biobank alone comprises 200,000 individual genomes, with more on the way, leading the field of human genetics towards sequencing entire populations. Within the next decades, other model organisms will follow suit, especially domesticated species such as crops and livestock. Having sequences from most individuals in a population will present new challenges for using these data to improve health and agriculture in the pursuit of a sustainable future. Existing population genetic methods are designed to model hundreds of randomly sampled sequences, but are not optimized for extracting the information contained in the larger and richer datasets that are beginning to emerge, with thousands of closely related individuals. Here we develop a new method called TIDES (Trio-based Inference of Dominance and Selection) that uses data from tens of thousands of family trios to make inferences about natural selection acting in a single generation. TIDES further improves on the state-of-the-art by making no assumptions regarding demography, linkage or dominance. We discuss how our method paves the way for studying natural selection from new angles.

evolutionary biology↗

bioRxiv2021

Mutation rate variation shapes genome-wide diversity in Drosophila melanogaster

What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. We then formalize a causal model of how these microevolutionary mechanisms interact, and cast it as a linear regression to estimate their individual contributions to levels of diversity along the genome. Our analyses reclaim the well-established signature of linked selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the genome-wide distribution of diversity in this species. Furthermore, our simulation results suggest that in many evolutionary scenarios the mutation landscape will be a crucial factor shaping diversity, depending notably on the genomic window size. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more realistic inferences in population genomics.

evolutionary biology↗

bioRxiv2020

Population genomics of the maize pathogen Ustilago maydis: demographic history and role of virulence clusters in adaptation

The tight interaction between pathogens and their hosts results in reciprocal selective forces that impact the genetic diversity of the interacting species. The footprints of this selection differ between pathosystems because of distinct life-history traits, demographic histories, or genome architectures. Here, we studied the genome-wide patterns of genetic diversity of 22 isolates of the causative agent of the corn smut disease, Ustilago maydis, originating from five locations in Mexico, the presumed center of origin of this species. In this species, many genes encoding secreted effector proteins reside in so-called virulence clusters in the genome, an arrangement that is so far not found in other filamentous plant pathogens. Using a combination of population genomic statistical analyses, we assessed the geographical, historical and genome-wide variation of genetic diversity in this fungal pathogen. We report evidence of two partially admixed subpopulations that are only loosely associated with geographic origin. Using the multiple sequentially Markov coalescent model, we inferred the demographic history of the two pathogen subpopulations over the last 0.5 million years. We show that both populations experienced a recent strong bottleneck starting around 10,000 years ago, coinciding with the assumed time of maize domestication. While the genome average genetic diversity is low compared to other fungal pathogens, we estimated that the rate of non-synonymous adaptive substitutions is three times higher in genes located within virulence clusters compared to non-clustered genes, including non-clustered effector genes. These results highlight the role that these singular genomic regions play in the evolution of this pathogen. Significance statementThe maize pathogen Ustilago maydis is a model species to study fungal cell biology and biotrophic host-pathogen interactions. Population genetic studies of this species, however, were so far restricted to using a few molecular markers, and genome-wide comparisons involved species that diverged more than 20 million years ago. Here, we sequenced the genomes of 22 Mexican U. maydis isolates to study the recent evolutionary history of this species. We identified two co-existing populations that went through a recent bottleneck and whose divergence date overlaps with the time of maize domestication. Contrasting the patterns of genetic diversity in different categories of genes, we further showed that effector genes in virulence clusters display a high rate of adaptive mutations, highlighting the importance of these effector arrangements for the adaptation of U. maydis to its host.

genomics↗

bioRxiv2017

Selection at the pathway level drives the evolution of gene-specific transcriptional noise

Biochemical reactions within individual cells result from the interactions of molecules, often in small numbers. Consequently, the inherent stochasticity of binding and diffusion processes generate noise along the cascade that leads to the synthesis of a protein from its encoding gene. As a result, isogenic cell populations display phenotypic variability even in homogeneous environments. The extent and consequences of this stochastic gene expression have only recently been assessed on a genome-wide scale, in particular owing to the advent of single cell transcriptomics. However, the evolutionary forces shaping this stochasticity have yet to be unraveled. We took advantage of two recently published data sets of the single-cell transcriptome of the domestic mouse Mus musculus in order to characterize the effect of natural selection on gene-specific transcriptional stochasticity. We showed that noise levels in the mRNA distributions (a.k.a. transcriptional noise) significantly correlate with three-dimensional nuclear domain organization, evolutionary constraint on the encoded protein and gene age. The position of the encoded protein in biological pathways, however, is the main factor that explains observed levels of transcriptional noise, in agreement with models of noise propagation within gene networks. Because transcriptional noise is under widespread selection, we argue that it constitutes an important component of the phenotype and that variance of expression is a potential target of adaptation. Stochastic gene expression should therefore be considered together with mean expression level in functional and evolutionary studies of gene expression.

evolutionary biology↗