Gene flow as a simple cause for an excess of high-frequency-derived alleles

Boundary Effects Cause False Signals of Range Expansions in Population Genomic Data

Studying range expansions is central for understanding genetic variation through space and time as well as for identifying refugia and biological invasions. Range expansions are characterized by serial founder events causing clines of decreasing genetic diversity away from the center of origin and asymmetries in the two-dimensional allele frequency spectra. These asymmetries, summarized by the directionality index (ψ), are sensitive to range expansions and persist for longer than clines in genetic diversity. In continuous and finite meta-populations, genetic drift tends to be stronger at the edges of the species distribution in equilibrium populations and populations undergoing range expansions alike. Such boundary effects are expected to affect geographic patterns in genetic diversity and ψ. Here we demonstrate that boundary effects cause high false positive rates in equilibrium meta-populations when testing for range expansions. In the simulations, the absolute value of ψ (|ψ|) in equilibrium data sets was proportional to the fixation index (FST). By fitting signatures of range expansions as a function of ɛ |ψ|/FST and geographic clines in ψ, strong evidence for range expansions could be detected in data from a recent rapid invasion of the cane toad, Rhinella marina, in Australia, but not in 28 previously published empirical data sets from Australian scincid lizards that were significant for the standard range expansion tests. Thus, while clinal variation in ψ is still the most sensitive statistic to range expansions, to detect true signatures of range expansions in natural populations, its magnitude needs to be considered in relation to the overall levels of genetic structuring in the data.

Interpreting the pervasive observation of U-shaped Site Frequency Spectra

The standard neutral model of molecular evolution has traditionally been used as the null model for population genomics. We gathered a collection of 45 genome-wide site frequency spectra from a diverse set of species, most of which display an excess of low and high frequency variants compared to the expectation of the standard neutral model, resulting in U-shaped spectra. We show that multiple merger coalescent models often provide a better fit to these observations than the standard Kingman coalescent. Hence, in many circumstances these under-utilized models may serve as the more appropriate reference for genomic analyses. We further discuss the underlying evolutionary processes that may result in the widespread U-shape of frequency spectra.

The genomic origins of the world's first farmers

The precise genetic origins of the first Neolithic farming populations in Europe and Southwest Asia, as well as the processes and the timing of their differentiation, remain largely unknown. Demogenomic modeling of high-quality ancient genomes reveals that the early farmers of Anatolia and Europe emerged from a multiphase mixing of a Southwest Asian population with a strongly bottlenecked western hunter-gatherer population after the last glacial maximum. Moreover, the ancestors of the first farmers of Europe and Anatolia went through a period of extreme genetic drift during their westward range expansion, contributing highly to their genetic distinctiveness. This modeling elucidates the demographic processes at the root of the Neolithic transition and leads to a spatial interpretation of the population history of Southwest Asia and Europe during the late Pleistocene and early Holocene.

Fast and accurate estimation of multidimensional site frequency spectra from low-coverage high-throughput sequencing data

BACKGROUND

The site frequency spectrum summarizes the distribution of allele frequencies throughout the genome, and it is widely used as a summary statistic to infer demographic parameters and to detect signals of natural selection. The use of high-throughput low-coverage DNA sequencing data can lead to biased estimates of the site frequency spectrum due to high levels of uncertainty in genotyping.

RESULTS

Here we design and implement a method to efficiently and accurately estimate the multidimensional joint site frequency spectrum for large numbers of haploid or diploid individuals across an arbitrary number of populations, using low-coverage sequencing data. The method maximizes a likelihood function that represents the probability of the sequencing data observed given a multidimensional site frequency spectrum using genotype likelihoods. Notably, it uses an advanced binning heuristic paired with an accelerated expectation-maximization algorithm for a fast and memory-efficient computation, and can generate both unfolded and folded spectra and bootstrapped replicates for haploid and diploid genomes. On the basis of extensive simulations, we show that the new method requires remarkably less storage and is faster than previous implementations whilst retaining the same accuracy. When applied to low-coverage sequencing data from the fungal pathogen Neonectria neomacrospora, results recapitulate the patterns of population differentiation generated using the original high-coverage data.

CONCLUSION

The new implementation allows for accurate estimation of population genetic parameters from arbitrarily large, low-coverage datasets, thus facilitating cost-effective sequencing experiments in model and non-model organisms.

The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America

Understanding the genetic basis of environmental adaptation in natural populations is a central goal in evolutionary biology. The conditions at high elevation, particularly the low oxygen available in the ambient air, impose a significant and chronic environmental challenge to metabolically active animals with lowland ancestry. To understand the process of adaptation to these novel conditions and to assess the repeatability of evolution over short timescales, we examined the signature of selection from complete exome sequences of house mice (Mus musculus domesticus) sampled across two elevational transects in the Andes of South America. Using phylogenetic analysis, we show that house mice colonized high elevations independently in Ecuador and Bolivia. Overall, we found distinct responses to selection in each transect and largely nonoverlapping sets of candidate genes, consistent with the complex nature of traits that underlie adaptation to low oxygen availability (hypoxia) in other species. Nonetheless, we also identified a small subset of the genome that appears to be under parallel selection at the gene and SNP levels. In particular, three genes (Col22a1, Fgf14, and srGAP1) bore strong signatures of selection in both transects. Finally, we observed several patterns that were common to both transects, including an excess of derived alleles at high elevation, and a number of hypoxia-associated genes exhibiting a threshold effect, with a large allele frequency change only at the highest elevations. This threshold effect suggests that selection pressures may increase disproportionately at high elevations in mammals, consistent with observations of some high-elevation diseases in humans.

Population Genomics Reveals Molecular Determinants of Specialization to Tomato in the Polyphagous Fungal Pathogen Botrytis cinerea in France

Many fungal plant pathogens encompass multiple populations specialized on different plant species. Understanding the factors underlying pathogen adaptation to their hosts is a major challenge of evolutionary microbiology, and it should help to prevent the emergence of new specialized pathogens on novel hosts. Previous studies have shown that French populations of the gray mold pathogen Botrytis cinerea parasitizing tomato and grapevine are differentiated from each other, and have higher aggressiveness on their host of origin than on other hosts, indicating some degree of host specialization in this polyphagous pathogen. Here, we aimed at identifying the genomic features underlying the specialization of B. cinerea populations to tomato and grapevine. Based on whole genome sequences of 32 isolates, we confirmed the subdivision of B. cinerea pathogens into two genetic clusters on grapevine and another, single cluster on tomato. Levels of genetic variation in the different clusters were similar, suggesting that the tomato-specific cluster has not recently emerged following a bottleneck. Using genome scans for selective sweeps and divergent selection, tests of positive selection based on polymorphism and divergence at synonymous and nonsynonymous sites, and analyses of presence and absence variation, we identified several candidate genes that represent possible determinants of host specialization in the tomato-associated population. This work deepens our understanding of the genomic changes underlying the specialization of fungal pathogen populations.

Adaptive Evolution in Cities: Progress and Misconceptions

Current narratives suggest that urban adaptation - the adaptive evolution of organisms to cities - is pervasive across taxa and cities. However, in reviewing hundreds of studies, we find only six comprehensive examples of species adaptively evolving to urbanization. We discuss the utility and shortcomings of methods for studying urban adaptation. We then review diverse systems offering preliminary evidence for urban adaptation and outline a research program for advancing its study. Urban environments constitute diverse, interacting selective agents that test the limits of adaptation. Understanding urban adaptation therefore offers unique opportunities for addressing fundamental questions in evolutionary biology and for better conserving biodiversity in cities. However, capitalizing on these opportunities requires appropriate research methods and dissemination of accurate narratives.