Biology subjects

Payseur, B. A.

Publications and source records attributed to Payseur, B. A..

4 recordsLinked to original sources

bioRxiv2021

Background selection under evolving recombination rates

Background selection (BGS), the effect that purifying selection exerts on sites linked to deleterious alleles, is expected to be ubiquitous across eukaryotic genomes. The effects of BGS reflect the interplay of the rates and fitness effects of deleterious mutations with recombination. A fundamental assumption of BGS models is that recombination rates are invariant over time. However, in some lineages recombination rates evolve rapidly, violating this central assumption. Here, we investigate how recombination rate evolution affects genetic variation under BGS. We show that recombination rate evolution modifies the effects of BGS in a manner similar to a localised change in the effective population size, potentially leading to an underestimation of the genome-wide effects of selection. Furthermore, we find evidence that recombination rate evolution in the ancestors of modern house mice may have impacted inferences of the genome-wide effects of selection in that species.

evolutionary biology↗

bioRxiv2021

Statistical inference in population genomics

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population-genetic insights out-paced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous non-adaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model-fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

evolutionary biology↗

bioRxiv2020

Evolution of Boldness and Exploratory Behavior in Giant Mice from Gough Island

Island populations are hallmarks of extreme phenotypic evolution. Radical changes in resource availability and predation risk accompanying island colonization drive changes in behavior, which Darwin likened to tameness in domesticated animals. Although many examples of animal boldness are found on islands, the heritability of observed behaviors, a requirement for evolution, remains largely unknown. To fill this gap, we profiled anxiety and exploration in island and mainland inbred strains of house mice raised in a common laboratory environment. The island strain was descended from mice on Gough Island, the largest wild house mice on record. Experiments utilizing open environments across two ages showed that Gough Island mice are bolder and more exploratory, even when a shelter is provided. Concurrently, Gough Island mice retain an avoidance response to predator urine. F1 offspring from crosses between these two strains behave more similarly to the mainland strain for most traits, suggesting recessive mutations contributed to behavioral evolution on the island. Our results provide a rare example of novel, inherited behaviors in an island population and demonstrate that behavioral evolution can be specific to different forms of perceived danger. Our discoveries pave the way for a genetic understanding of how island populations evolve unusual behaviors.

evolutionary biology↗

bioRxiv2019

Disrupted gene networks in subfertile hybrid house mice

The Dobzhansky-Muller model provides a widely accepted mechanism for the evolution of reproductive isolation: incompatible substitutions disrupt interactions between genes. To date, few candidate incompatibility genes have been identified, leaving the genes driving speciation mostly uncharacterized. The importance of interactions in the Dobzhansky-Muller model suggests that gene coexpression networks provide a powerful framework to understand disrupted pathways associated with postzygotic isolation. Here, we perform Weighted Gene Coexpression Network Analysis (WGCNA) to infer gene interactions in hybrids of two recently diverged European house mouse subspecies, Mus mus domesticus and M. m. musculus, which commonly show hybrid male sterility or subfertility. We use genome-wide testis expression data from 467 hybrid mice from two mapping populations: F2s from a laboratory cross between wild-derived pure subspecies strains and offspring of natural hybrids captured in the Central Europe hybrid zone. This large data set enabled us to build a robust consensus network using hybrid males with fertile phenotypes. We identify several expression modules, or groups of coexpressed genes, that are disrupted in subfertile hybrids, including modules functionally enriched for spermatogenesis, cilium and sperm flagellum organization, chromosome organization and DNA repair, and including genes expressed in spermatogonia, spermatocytes and spermatids. Our network-based approach enabled us to hone in on specific hub genes likely to be influencing module-wide gene expression and hence potentially driving Dobzhansky-Muller incompatibilities. A total of 69 (24.6%) of these genes lie in sterility loci identified previously in these mapping populations, and represent promising candidate barrier genes and targets for future functional analysis.

evolutionary biology↗