More Like this

1. Genomic Architecture Predicts Tree Topology, Population Structuring, and Demographic History in Amazonian Birds

   https://doi.org/10.1093/gbe/evae002  

   Thom, Gregory; Moreira, Lucas Rocha; Batista, Romina; Gehara, Marcelo; Aleixo, Alexandre; Smith, Brian Tilston (January 2024, Genome Biology and Evolution)

   Hancock, Angela (Ed.)

   Abstract Geographic barriers are frequently invoked to explain genetic structuring across the landscape. However, inferences on the spatial and temporal origins of population variation have been largely limited to evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known as genomic architecture in producing heterogeneity in differentiation across the genome. To test how variation in genomic characteristics (e.g. recombination rate) impacts our ability to reconstruct general patterns of differentiation between species that cooccur across geographic barriers, we sequenced the whole genomes of multiple bird populations that are distributed across rivers in southeastern Amazonia. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with species tree support. Gene flow was less pervasive in genomic regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection, skewing genome-wide estimates of effective population sizes and gene flow between populations toward lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from neutral processes to elucidate spatial patterns of population differentiation. 

   more » « less
2. Extending isolation by resistance to predict genetic connectivity

   https://doi.org/10.1111/2041-210X.13975  

   Fletcher, Robert J.; Sefair, Jorge A.; Kortessis, Nicholas; Jaffe, Roldolfo; Holt, Robert D.; Robertson, Ellen P.; Duncan, Sarah I.; Marx, Andrew J.; Austin, James D. (November 2022, Methods in Ecology and Evolution)

   Abstract Genetic connectivity lies at the heart of evolutionary theory, and landscape genetics has rapidly advanced to understand how gene flow can be impacted by the environment. Isolation by landscape resistance, often inferred through the use of circuit theory, is increasingly identified as being critical for predicting genetic connectivity across complex landscapes. Yet landscape impediments to migration can arise from fundamentally different processes, such as landscape gradients causing directional migration and mortality during migration, which can be challenging to address. Spatial absorbing Markov chains (SAMC) have been introduced to understand and predict these (and other) processes affecting connectivity in ecological settings, but the relationship of this framework to landscape genetics remains unclear. Here, we relate the SAMC to population genetics theory, provide simulations to interpret the extent to which the SAMC can predict genetic metrics and demonstrate how the SAMC can be applied to genomic data using an example with an endangered species, the Panama City crayfish Procambarus econfinae , where directional migration is hypothesized to occur. The use of the SAMC for landscape genetics can be justified based on similar grounds to using circuit theory, as we show how circuit theory is a special case of this framework. The SAMC can extend circuit‐theoretic connectivity modelling by quantifying both directional resistance to migration and acknowledging the difference between migration mortality and resistance to migration. Our empirical example highlights that the SAMC better predicts population structure than circuit theory and least‐cost analysis by acknowledging asymmetric environmental gradients (i.e. slope) and migration mortality in this species. These results provide a foundation for applying the SAMC to landscape genetics. This framework extends isolation‐by‐resistance modelling to account for some common processes that can impact gene flow, which can improve predicting genetic connectivity across complex landscapes. 

   more » « less
3. Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

   https://doi.org/10.1093/molbev/msaa134  

   Cheng, Xiaoheng; DeGiorgio, Michael (October 2020, Molecular Biology and Evolution)

   Satta, Yoko (Ed.)

   Abstract Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B2, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B2 on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community. 

   more » « less
4. learnPopGen : An R package for population genetic simulation and numerical analysis

   https://doi.org/10.1002/ece3.5412  

   Revell, Liam J. (July 2019, Ecology and Evolution)

   Abstract Here, I briefly present a new R package calledlearnPopGenthat has been designed primarily for the purposes of teaching evolutionary biology, population genetics, and evolutionary theory. Functions of the package can be used to conduct simulations and numerical analyses of a wide range of evolutionary phenomena that would typically be covered in advanced undergraduate through graduate‐level curricula in population genetics or evolution. For instance,learnPopGenfunctions can be used to visualize gene frequency changes through time under multiple deterministic and stochastic processes, to compute and animate the changes in phenotypic trait values or distributions under natural selection, to numerically analyze and graph the outcome of simple game theory models, and to plot coalescence within a population experiencing genetic drift, along with a number of other things. Functions have been designed to be maximally didactic and frequently employ compelling animated visualizations. Furthermore, it is straightforward to export plots and animations from R in the form of flat or animated graphics, or as videos. For maximum flexibility, students working with the package can run functions directly in R; however, instructors may choose to guide students less adept in the R environment to one of various web interfaces that I have built for a number of the functions of the package and that are already available online. 

   more » « less
5. Demography and linked selection interact to shape the genomic landscape of codistributed woodpeckers during the Ice Age

   https://doi.org/10.1111/mec.16841  

   Moreira, Lucas Rocha; Klicka, John; Smith, Brian Tilston (January 2023, Molecular ecology)

   The influence of genetic drift on population dynamics during Pleistocene glacial cycles is well understood, but the role of selection in shaping patterns of genomic variation during these events is less explored. We resequenced whole genomes to investigate how demography and natural selection interact to generate the genomic landscapes of Downy and Hairy Woodpecker, species codistributed in previously glaciated North America. First, we explored the spatial and temporal patterns of genomic diversity produced by neutral evolution. Next, we tested (i) whether levels of nucleotide diversity along the genome are correlated with intrinsic genomic properties, such as recombination rate and gene density, and (ii) whether different demographic trajectories impacted the efficacy of selection. Our results revealed cycles of bottleneck and expansion, and genetic structure associated with glacial refugia. Nucleotide diversity varied widely along the genome, but this variation was highly correlated between the species, suggesting the presence of conserved genomic features. In both taxa, nucleotide diversity was positively correlated with recombination rate and negatively correlated with gene density, suggesting that linked selection played a role in reducing diversity. Despite strong fluctuations in effective population size, the maintenance of relatively large populations during glaciations may have facilitated selection. Under these conditions, we found evidence that the individual demographic trajectory of populations modulated linked selection, with purifying selection being more efficient in removing deleterious alleles in large populations. These results highlight that while genome-wide variation reflects the expected signature of demographic change during climatic perturbations, the interaction of multiple processes produces a predictable and highly heterogeneous genomic landscape. 

   more » « less