Concurrent invasions of European starlings in Australia and North America reveal population‐specific differentiation in shared genomic regions - Natalie R. Hofmeister; Katarina C. Stuart; Wesley C. Warren; Scott J. Werner; Melissa Bateson; Gregory F. Ball; Katherine L. Buchanan; David W. Burt; Adam P. A. Cardilini; Phillip Cassey; Tim De Meyer; Julia George; et al. (Molecular Ecology, 2025)
A species' success during the invasion of new areas hinges on an interplay between the demographic processes common to invasions and the specific ecological context of the novel environment. Evolutionary genetic studies of invasive species can investigate how genetic bottlenecks and ecological conditions shape genetic variation in invasions, and our study pairs two invasive populations that are hypothesized to be from the same source population to compare how each population evolved during and after introduction. Invasive European starlings ( Sturnus vulgaris ) established populations in both Australia and North America in the 19th century. Here, we compare whole‐genome sequences among native and independently introduced European starling populations to determine how demographic processes interact with rapid evolution to generate similar genetic patterns in these recent and replicated invasions. Demographic models indicate that both invasive populations experienced genetic bottlenecks as expected based on invasion history, and we find that specific genomic regions have differentiated even on this short evolutionary timescale. Despite genetic bottlenecks, we suggest that genetic drift alone cannot explain differentiation in at least two of these regions. The demographic boom intrinsic to many invasions as well as potential inversions may have led to high population‐specific differentiation, although the patterns of genetic variation are also consistent with the hypothesis that this infamous and highly mobile invader adapted to novel selection (e.g., extrinsic factors). We use targeted sampling of replicated invasions to identify and evaluate support for multiple, interacting evolutionary mechanisms that lead to differentiation during the invasion process.

A Bayesian hierarchical approach to combine interpolation and extrapolation methods for fluvial flux calculation - Fred Worrall; Nicholas J.K. Howden; Tim P. Burt (Journal of Hydrology, 2025)

Uncovering nitrogen accumulation in a large mixed land-use catchment: Implications for national-scale budget studies and environmental management - Xiangwen Fan; Fred Worrall; Lisa M. Baldini; Tim P. Burt (CATENA, 2024)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.