Sarre, S. D. | Search bioRxiv

bioRxiv2021

Diversity of reptile sex chromosome evolution revealed by cytogenetic and linked-read sequencing

Reptile sex determination is attracting much attention because the great diversity of sex-determination and dosage compensation mechanisms permits us to approach fundamental questions about sex chromosome turnover and evolution. However, reptile sex chromosome variation remains largely uncharacterized and no reptile master sex determination genes have yet been identified. Here we describe a powerful and cost-effective "chromosomics" approach, combining probes generated from the microdissected sex chromosomes with transcriptome sequencing to explore this diversity in non-model Australian reptiles with heteromorphic or cryptic sex chromosomes. We tested the pipeline on a turtle, a gecko, and a worm-lizard, and we also identified sequences located on sex chromosomes in a monitor lizard using linked-read sequencing. Genes identified on sex chromosomes were compared to the chicken genome to identify homologous regions among the four species. We identified candidate sex determining genes within these regions, including conserved vertebrate sex-determining genes pdgfa, pdgfra amh and wt1, and demonstrated their testis or ovary-specific expression. All four species showed gene-by-gene rather than chromosome-wide dosage compensation. Our results imply that reptile sex chromosomes originated by independent acquisition of sex-determining genes on different autosomes, as well as translocations between different ancestral macro- and micro-chromosomes. We discuss the evolutionary drivers of the slow differentiation, but rapid turnover, of reptile sex chromosomes.

genomics↗

bioRxiv2021

slimr: An R package for integrating data and tailor-made population genomic simulations over space and time

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between simulation software and software designed for working with empirical data is currently not well developed. Here we present slimr, an R package designed to create a seamless link between standalone software SLiM 3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning, and manipulation, proceed to constructing empirically-based parameters and initial conditions for simulations, then to running numerical simulations, and finally to retrieving simulation results in a format suitable for comparisons with empirical data - aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SliM 3.0 directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes.

evolutionary biology↗