Footprints Of Selection Research Articles

How does urbanization affect natural selection?

Abstract Urbanization is one of the most significant contributors to the Anthropocene, and urban evolutionary ecology has become an important field of research. While it is commonly assumed that cities impose new and stronger selection, the contradictory assertion that selection may be relaxed in cities is also frequently mentioned, and overall, our understanding of the effects of urbanization on natural selection is incomplete. In this review, we first conduct a literature search to find evidence for patterns of natural selection on phenotypic traits including morphology, physiology, behaviour and life history, in urban and non‐urban populations of animals and plants. This search reveals that coefficients of natural selection in the context of urbanization are scarce (n = 8 studies providing selection gradients/differentials that include a total of n = 200 coefficients) and a lack of standardized methods hinders quantitative comparisons across studies (e.g. with meta‐analysis). These studies, however, provide interesting insight on the agents shaping natural selection in cities and improve our mechanistic understanding of selection processes at different spatial scales. We then perform a second literature search to review genomic studies assessing selection intensity in cities, on the genome of non‐human natural populations. While this search returns 383 articles, only 34 of these truly investigate footprints of selection associated with urbanization, and only one study provides urban genetic selection coefficients. Here again, we find highly heterogeneous approaches, yet some studies provide strong evidence of genomic footprints of urban adaptation. In neither the phenotypic nor genomic literature review were we able to quantitatively assess natural selection across urban versus non‐urban habitats. Thus, we propose a roadmap of how future studies should provide standardized metrics to facilitate mega‐ or meta‐analyses and explore generalized effects of urbanization on selection. Read the free Plain Language Summary for this article on the Journal blog.

Deep estimation of the intensity and timing of natural selection from ancient genomes.

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

A Whole-Genome Scan Revealed Genomic Features and Selection Footprints of Mengshan Cattle.

(1) Background: Mengshan cattle from the Yimeng mountainous region in China stand out as a unique genetic resource, known for their adaptive traits and environmental resilience. However, these cattle are currently endangered and comprehensive genomic characterization remains largely unexplored. This study aims to address this gap by investigating the genomic features and selection signals in Mengshan cattle. (2) Methods: Utilizing whole-genome resequencing data from 122 cattle, including 37 newly sequenced Mengshan cattle, we investigated population structure, genetic diversity, and selection signals. (3) Results: Our analyses revealed that current Mengshan cattle primarily exhibit European taurine cattle ancestry, with distinct genetic characteristics indicative of adaptive traits. We identified candidate genes associated with immune response, growth traits, meat quality, and neurodevelopment, shedding light on the genomic features underlying the unique attributes of Mengshan cattle. Enrichment analysis highlighted pathways related to insulin secretion, calcium signaling, and dopamine synapse, further elucidating the genetic basis of their phenotypic traits. (4) Conclusions: Our results provide valuable insights for further research and conservation efforts aimed at preserving this endangered genetic resource. This study enhances the understanding of population genetics and underscores the importance of genomic research in informing genetic resources and conservation initiatives for indigenous cattle breeds.

Genomic Reconstruction of the Successful Establishment of a Feralized Bovine Population on the Subantarctic Island of Amsterdam.

The feral cattle of the subantarctic island of Amsterdam provide an outstanding case study of a large mammalian population that was established by a handful of founders and thrived within a few generations in a seemingly inhospitable environment. Here, we investigated the genetic history and composition of this population using genotyping and sequencing data. Our inference showed an intense but brief founding bottleneck around the late 19th century and revealed contributions from European taurine and Indian Ocean Zebu in the founder ancestry. Comparative analysis of whole-genome sequences further revealed a moderate reduction in genetic diversity despite high levels of inbreeding. The brief and intense bottleneck was associated with high levels of drift, a flattening of the site frequency spectrum and a slight relaxation of purifying selection on mildly deleterious variants. Unlike some populations that have experienced prolonged reductions in effective population size, we did not observe any significant purging of highly deleterious variants. Interestingly, the population's success in the harsh environment can be attributed to preadaptation from their European taurine ancestry, suggesting no strong bioclimatic challenge, and also contradicting evidence for insular dwarfism. Genome scan for footprints of selection uncovered a majority of candidate genes related to nervous system function, likely reflecting rapid feralization driven by behavioral changes and complex social restructuring. The Amsterdam Island cattle offers valuable insights into rapid population establishment, feralization, and genetic adaptation in challenging environments. It also sheds light on the unique genetic legacies of feral populations, raising ethical questions according to conservation efforts.

Detecting the ecological footprint of selection

Detecting the ecological footprint of selection

Range‐wide intraspecific variation reflects past adaptation to climate in a gypsophile Mediterranean shrub

Abstract Phenotypic differences among populations stem from the interaction between neutral and adaptive processes, and phenotypic plasticity. Although clinal trait variation along climatic gradients often evolves in widely distributed species, it is unknown whether substrate specialization, such as that of Mediterranean gypsum plants, has constrained adaptation to climate. Using a common garden experiment with two contrasting watering treatments, we quantified phenotypic plasticity, assessed evidence for footprints of selection using FST – QST comparisons, and evaluated the ecological factors driving genetically based phenotypic differentiation of 11 populations encompassing the full environmental range of the gypsum shrub Lepidium subulatum. We found evidence for genetic differentiation among populations related to climatic differences, with populations from warmer and drier sites showing lower specific leaf area and leaf N, earlier phenology, greater water use efficiency and greater fitness. Multiple lines of evidence suggest that this differentiation was driven by past divergent selection rather than neutral processes. All populations showed high phenotypic plasticity, indicating that plasticity has not been selected against, even in populations from sites with harsher climatic conditions. Synthesis. Our results indicate that despite strong substrate specialization, adaptive differentiation related to climatic gradients occurs in this species. However, we also found that populations from mesic sites may be particularly vulnerable to future climate change given their relatively lower fitness under both wet and dry conditions.

Genomic variability of the MHC region: Empirical evidence from five horse breeds

The purpose of this study was to analyse the level of variability in the autosomal genome, especially in the equine major histocompatibility complex region, in five horse breeds and identify heterozygosity-rich regions and potential footprints of balancing selection. Depending on data quality control, the dataset consisted of 51,168 or 53,874 single nucleotide polymorphism markers, available for 514 individuals (89 Lipizzan, 238 Old Kladruber, 47 Shagya Arabian, 61 Czech Warmblood and 81 Slovak Warmblood horses). Genomic variability within and between breeds was examined based on levels of heterozygosity (observed and expected), genomic inbreeding, Wright's FIS index and linkage disequilibrium. Subsequently, the screening of heterozygosity-rich regions and balancing selection signals derived from Tajima's D positive values was performed. As expected, due to the polymorphic nature of the major histocompatibility complex, the genomic variability level was generally higher when analysing only markers located in this area (mainly around genes belonging to class I). The Slovak and Czech Warmblood horses, as breeds with open herdbook, showed higher average values of heterozygosity indices than Lipizzan, Old Kladruber or Shagya Arabian breeds. Concerning only markers in the major histocompatibility complex region in complete or very high linkage disequilibrium, common patterns were found close to EQMHCB2, MHCB3 and EQMHCC1 genes belonging to class I and DQA1, DRB2, DRB3 and HLA-DOB genes from class II. Genome-wide, the number of heterozygosity-rich regions per animal ranged from 345.25 (Old Kladruber) to 603.33 (Czech Warmblood). Across all breeds, 254 heterozygosity-rich regions were detected directly in the major histocompatibility complex region (194 in class I and 60 in class II). Among them, the highest overlap showed regions found in the genomes of historically connected Czech and Slovak Warmblood breeds. The results suggested that the frequency of markers in heterozygosity-rich regions increased in Lipizzan, Old Kladruber and Shagya Arabian breeds in the genomic region of EQMCE1 gene (class I) and in Czech and Slovak Warmblood horses in DQB1, DQA2, DQB2, DQA3 and DRB2 genes (class II). Although the identified heterozygosity-rich regions formed 330 islands across the genomes of tested breeds, these islands were outside the major histocompatibility complex region. On the other hand, four of 425 balancing selection signals detected across breeds were located directly in the major histocompatibility complex region, close to DRA, DRB1, DQA1, DQB1 and DQB2 genes (class II). Shared islands and balancing selection footprints among breeds were found mainly on chromosomes 7 and 11.

Artificial selection footprints in indigenous and commercial chicken genomes

BackgroundAlthough many studies have been done to reveal artificial selection signatures in commercial and indigenous chickens, a limited number of genes have been linked to specific traits. To identify more trait-related artificial selection signatures and genes, we re-sequenced a total of 85 individuals of five indigenous chicken breeds with distinct traits from Yunnan Province, China.ResultsWe found 30 million non-redundant single nucleotide variants and small indels (< 50 bp) in the indigenous chickens, of which 10 million were not seen in 60 broilers, 56 layers and 35 red jungle fowls (RJFs) that we compared with. The variants in each breed are enriched in non-coding regions, while those in coding regions are largely tolerant, suggesting that most variants might affect cis-regulatory sequences. Based on 27 million bi-allelic single nucleotide polymorphisms identified in the chickens, we found numerous selective sweeps and affected genes in each indigenous chicken breed and substantially larger numbers of selective sweeps and affected genes in the broilers and layers than previously reported using a rigorous statistical model. Consistent with the locations of the variants, the vast majority (~ 98.3%) of the identified selective sweeps overlap known quantitative trait loci (QTLs). Meanwhile, 74.2% known QTLs overlap our identified selective sweeps. We confirmed most of previously identified trait-related genes and identified many novel ones, some of which might be related to body size and high egg production traits. Using RT-qPCR, we validated differential expression of eight genes (GHR, GHRHR, IGF2BP1, OVALX, ELF2, MGARP, NOCT, SLC25A15) that might be related to body size and high egg production traits in relevant tissues of relevant breeds.ConclusionWe identify 30 million single nucleotide variants and small indels in the five indigenous chicken breeds, 10 million of which are novel. We predict substantially more selective sweeps and affected genes than previously reported in both indigenous and commercial breeds. These variants and affected genes are good candidates for further experimental investigations of genotype-phenotype relationships and practical applications in chicken breeding programs.

The Narrow Footprint of Ancient Balancing Selection Revealed by Heterokaryon Incompatibility Genes in Aspergillus fumigatus.

In fungi, fusion between individuals leads to localized cell death, a phenomenon termed heterokaryon incompatibility. Generally, the genes responsible for this incompatibility are observed to be under balancing selection resulting from negative frequency-dependent selection. Here, we assess this phenomenon in Aspergillus fumigatus, a human pathogenic fungus with a very low level of linkage disequilibrium as well as an extremely high crossover rate. Using complementation of auxotrophic mutations as an assay for hyphal compatibility, we screened sexual progeny for compatibility to identify genes involved in this process, called het genes. In total, 5/148 (3.4%) offspring were compatible with a parent and 166/2,142 (7.7%) sibling pairs were compatible, consistent with several segregating incompatibility loci. Genetic mapping identified five loci, four of which could be fine mapped to individual genes, of which we tested three through heterologous expression, confirming their causal relationship. Consistent with long-term balancing selection, trans-species polymorphisms were apparent across several sister species, as well as equal allele frequencies within A. fumigatus. Surprisingly, a sliding window genome-wide population-level analysis of an independent dataset did not show increased Tajima's D near these loci, in contrast to what is often found surrounding loci under balancing selection. Using available de novo assemblies, we show that these balanced polymorphisms are restricted to several hundred base pairs flanking the coding sequence. In addition to identifying the first het genes in an Aspergillus species, this work highlights the interaction of long-term balancing selection with rapid linkage disequilibrium decay.

Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens.

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.

Genomic signatures of climate adaptation in bank voles.

Evidence for divergent selection and adaptive variation across the landscape can provide insight into a species' ability to adapt to different environments. However, despite recent advances in genomics, it remains difficult to detect the footprints of climate-mediated selection in natural populations. Here, we analysed ddRAD sequencing data (21,892 SNPs) in conjunction with geographic climate variation to search for signatures of adaptive differentiation in twelve populations of the bank vole (Clethrionomys glareolus) distributed across Europe. To identify the loci subject to selection associated with climate variation, we applied multiple genotype-environment association methods, two univariate and one multivariate, and controlled for the effect of population structure. In total, we identified 213 candidate loci for adaptation, 74 of which were located within genes. In particular, we identified signatures of selection in candidate genes with functions related to lipid metabolism and the immune system. Using the results of redundancy analysis, we demonstrated that population history and climate have joint effects on the genetic variation in the pan-European metapopulation. Furthermore, by examining only candidate loci, we found that annual mean temperature is an important factor shaping adaptive genetic variation in the bank vole. By combining landscape genomic approaches, our study sheds light on genome-wide adaptive differentiation and the spatial distribution of variants underlying adaptive variation influenced by local climate in bank voles.

Immune targeting of HIV-1 reservoir cells: a path to elimination strategies and cure.

Successful approaches for eradication or cure of HIV-1 infection are likely to include immunological mechanisms, but remarkably little is known about how human immune responses can recognize and interact with the few HIV-1-infected cells that harbour genome-intact viral DNA, persist long term despite antiretroviral therapy and represent the main barrier to a cure. For a long time regarded as being completely shielded from host immune responses due to viral latency, these cells do, on closer examination with single-cell analytic techniques, display discrete footprints of immune selection, implying that human immune responses may be able to effectively engage and target at least some of these cells. The failure to eliminate rebound-competent virally infected cells in the majority of persons likely reflects the evolution of a highly selected pool of reservoir cells that are effectively camouflaged from immune recognition or rely on sophisticated approaches for resisting immune-mediated killing. Understanding the fine-tuned interplay between host immune responses and viral reservoir cells will helpto design improved interventions thatexploit the immunological vulnerabilities of HIV-1 reservoir cells.

Secondary Contact, Introgressive Hybridization, and Genome Stabilization in Sticklebacks.

Advances in genomic studies have revealed that hybridization in nature is pervasive and raised questions about the dynamics of different genetic and evolutionary factors following the initial hybridization event. While recent research has proposed that the genomic outcomes of hybridization might be predictable to some extent, many uncertainties remain. With comprehensive whole-genome sequence data, we investigated the genetic introgression between 2 divergent lineages of 9-spined sticklebacks (Pungitius pungitius) in the Baltic Sea. We found that the intensity and direction of selection on the introgressed variation has varied across different genomic elements: while functionally important regions displayed reduced rates of introgression, promoter regions showed enrichment. Despite the general trend of negative selection, we identified specific genomic regions that were enriched for introgressed variants, and within these regions, we detected footprints of selection, indicating adaptive introgression. Geographically, we found the selection against the functional changes to be strongest in the vicinity of the secondary contact zone and weaken as a function of distance from the initial contact. Altogether, the results suggest that the stabilization of introgressed variation in the genomes is a complex, multistage process involving both negative and positive selection. In spite of the predominance of negative selection against introgressed variants, we also found evidence for adaptive introgression variants likely associated with adaptation to Baltic Sea environmental conditions.

Positive selection footprints and haplotype distribution in the genome of dromedary camels

Dromedary camels are a domestic species characterized by various adaptive traits. Limited efforts have been employed toward identifying genetic regions and haplotypes under selection that might be related to such adaptations. These genetic elements are considered valuable sources that should be conserved to maintain the dromedaries’ adaptability. Here, we have analyzed whole genome sequences of 40 dromedary camels from different Arabian Peninsula populations to assess their genetic relationship and define regions with signatures of selection. Genetic distinction based on geography was observed, classifying the populations into four groups: (1) North and Central, (2) West, (3) Southwest, and (4) Southeast, with substantial levels of genetic admixture. Using the de-correlated composite of multiple signal approach, which combines four intra-population analyses (Tajima’s D index, nucleotide diversity, integrated haplotype score, and number of segregating sites by length), a total of 36 candidate regions harboring 87 genes were identified to be under positive selection. These regions overlapped with 185 haplotype blocks encompassing 1 340 haplotypes, of which 30 (∼2%) were found to be approaching fixation. The defined candidate genes are associated with different biological processes related to the dromedaries’ adaptive physiologies, including neurological pathways, musculoskeletal development, fertility, fat distribution, immunity, visual development, and kidney physiology. The results of this study highlight opportunities for further investigations at the whole-genome level to enhance our understanding of the evolutionary pressures shaping the dromedary genome.

Two-speed genomes of Epichloe fungal pathogens show contrasting signatures of selection between species and across populations.

Antagonistic selection between pathogens and their hosts can drive rapid evolutionary change and leave distinct molecular footprints of past and ongoing selection in the genomes of the interacting species. Despite an increasing availability of tools able to identify signatures of selection, the genetic mechanisms underlying coevolutionary interactions and the specific genes involved are still poorly understood, especially in heterogeneous natural environments. We searched the genomes of two species of Epichloe plant pathogen for evidence of recent selection. The Epichloe genus includes highly host-specific species that can sterilize their grass hosts. We performed selection scans using genome-wide SNP data from seven natural populations of two co-occurring Epichloe sibling species specialized on different hosts. We found evidence of recent (and ongoing) selective sweeps across the genome in both species. However, selective sweeps were more abundant in the species with a larger effective population size. Sweep regions often overlapped with highly polymorphic AT-rich regions supporting the role of these genome compartments in adaptive evolution. Although most loci under selection were specific to individual populations, we could also identify several candidate genes targeted by selection in sweep regions shared among populations. The genes encoded small secreted proteins typical of fungal effectors and cell wall-degrading enzymes. By investigating the genomic signatures of selection across multiple populations and species, this study contributes to our understanding of complex adaptive processes in natural plant pathogen systems.

A cosmopolitan inversion facilitates seasonal adaptation in overwintering Drosophila.

Fluctuations in the strength and direction of natural selection through time are a ubiquitous feature of life on Earth. One evolutionary outcome of such fluctuations is adaptive tracking, wherein populations rapidly adapt from standing genetic variation. In certain circumstances, adaptive tracking can lead to the long-term maintenance of functional polymorphism despite allele frequency change due to selection. Although adaptive tracking is likely a common process, we still have a limited understanding of aspects of its genetic architecture and its strength relative to other evolutionary forces such as drift. Drosophila melanogaster living in temperate regions evolve to track seasonal fluctuations and are an excellent system to tackle these gaps in knowledge. By sequencing orchard populations collected across multiple years, we characterized the genomic signal of seasonal demography and identified that the cosmopolitan inversion In(2L)t facilitates seasonal adaptive tracking and shows molecular footprints of selection. A meta-analysis of phenotypic studies shows that seasonal loci within In(2L)t are associated with behavior, life history, physiology, and morphological traits. We identify candidate loci and experimentally link them to phenotype. Our work contributes to our general understanding of fluctuating selection and highlights the evolutionary outcome and dynamics of contemporary selection on inversions.

On convolutional neural networks for selection inference: Revealing the effect of preprocessing on model learning and the capacity to discover novel patterns.

A central challenge in population genetics is the detection of genomic footprints of selection. As machine learning tools including convolutional neural networks (CNNs) have become more sophisticated and applied more broadly, these provide a logical next step for increasing our power to learn and detect such patterns; indeed, CNNs trained on simulated genome sequences have recently been shown to be highly effective at this task. Unlike previous approaches, which rely upon human-crafted summary statistics, these methods are able to be applied directly to raw genomic data, allowing them to potentially learn new signatures that, if well-understood, could improve the current theory surrounding selective sweeps. Towards this end, we examine a representative CNN from the literature, paring it down to the minimal complexity needed to maintain comparable performance; this low-complexity CNN allows us to directly interpret the learned evolutionary signatures. We then validate these patterns in more complex models using metrics that evaluate feature importance. Our findings reveal that preprocessing steps, which determine how the population genetic data is presented to the model, play a central role in the learned prediction method. This results in models that mimic previously-defined summary statistics; in one case, the summary statistic itself achieves similarly high accuracy. For evolutionary processes that are less well understood than selective sweeps, we hope this provides an initial framework for using CNNs in ways that go beyond simply achieving high classification performance. Instead, we propose that CNNs might be useful as tools for learning novel patterns that can translate to easy-to-implement summary statistics available to a wider community of researchers.

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data.

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

Weak seed banks influence the signature and detectability of selective sweeps.

Seed banking (or dormancy) is a widespread bet-hedging strategy, generating a form of population overlap, which decreases the magnitude of genetic drift. The methodological complexity of integrating this trait implies it is ignored when developing tools to detect selective sweeps. But, as dormancy lengthens the ancestral recombination graph (ARG), increasing times to fixation, it can change the genomic signatures of selection. To detect genes under positive selection in seed banking species it is important to (1) determine whether the efficacy of selection is affected, and (2) predict the patterns of nucleotide diversity at and around positively selected alleles. We present the first tree sequence-based simulation program integrating a weak seed bank to examine the dynamics and genomic footprints of beneficial alleles in a finite population. We find that seed banking does not affect the probability of fixation and confirm expectations of increased times to fixation. We also confirm earlier findings that, for strong selection, the times to fixation are not scaled by the inbreeding effective population size in the presence of seed banks, but are shorter than would be expected. As seed banking increases the effective recombination rate, footprints of sweeps appear narrower around the selected sites and due to the scaling of the ARG are detectable for longer periods of time. The developed simulation tool can be used to predict the footprints of selection and draw statistical inference of past evolutionary events in plants, invertebrates, or fungi with seed banks.

Population genetic analysis and scans for adaptation and contemporary selection footprints provide genomic insight into aus, indica and japonica rice cultivars diversification

Population genetic analysis and scans for adaptation and contemporary selection footprints provide genomic insight into aus, indica and japonica rice cultivars diversification