The First Steps of Adaptation of Escherichia coli to the Gut Are Dominated by Soft Sweeps

The accumulation of adaptive mutations is essential for survival in novel environments. However, in clonal populations with a high mutational supply, the power of natural selection is expected to be limited. This is due to clonal interference - the competition of clones carrying different beneficial mutations - which leads to the loss of many small effect mutations and fixation of large effect ones. If interference is abundant, then mechanisms for horizontal transfer of genes, which allow the immediate combination of beneficial alleles in a single background, are expected to evolve. However, the relevance of interference in natural complex environments, such as the gut, is poorly known. To address this issue, we have developed an experimental system which allows to uncover the nature of the adaptive process as Escherichia coli adapts to the mouse gut. This system shows the invasion of beneficial mutations in the bacterial populations and demonstrates the pervasiveness of clonal interference. The observed dynamics of change in frequency of beneficial mutations are consistent with soft sweeps, where different adaptive mutations with similar phenotypes, arise repeatedly on different haplotypes without reaching fixation. Despite the complexity of this ecosystem, the genetic basis of the adaptive mutations revealed a striking parallelism in independently evolving populations. This was mainly characterized by the insertion of transposable elements in both coding and regulatory regions of a few genes. Interestingly, in most populations we observed a complete phenotypic sweep without loss of genetic variation. The intense clonal interference during adaptation to the gut environment, here demonstrated, may be important for our understanding of the levels of strain diversity of E. coli inhabiting the human gut microbiota and of its recombination rate.     

Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.     

Soft Shoulders Ahead: Spurious Signatures of Soft and Partial Selective Sweeps Result from Linked Hard Sweeps

Characterizing the nature of the adaptive process at the genetic level is a central goal for population genetics. In particular, we know little about the sources of adaptive substitution or about the number of adaptive variants currently segregating in nature. Historically, population geneticists have focused attention on the hard-sweep model of adaptation in which a de novo beneficial mutation arises and rapidly fixes in a population. Recently more attention has been given to soft-sweep models, in which alleles that were previously neutral, or nearly so, drift until such a time as the environment shifts and their selection coefficient changes to become beneficial. It remains an active and difficult problem, however, to tease apart the telltale signatures of hard vs. soft sweeps in genomic polymorphism data. Through extensive simulations of hard- and soft-sweep models, here we show that indeed the two might not be separable through the use of simple summary statistics. In particular, it seems that recombination in regions linked to, but distant from, sites of hard sweeps can create patterns of polymorphism that closely mirror what is expected to be found near soft sweeps. We find that a very similar situation arises when using haplotype-based statistics that are aimed at detecting partial or ongoing selective sweeps, such that it is difficult to distinguish the shoulder of a hard sweep from the center of a partial sweep. While knowing the location of the selected site mitigates this problem slightly, we show that stochasticity in signatures of natural selection will frequently cause the signal to reach its zenith far from this site and that this effect is more severe for soft sweeps; thus inferences of the target as well as the mode of positive selection may be inaccurate. In addition, both the time since a sweep ends and biologically realistic levels of allelic gene conversion lead to errors in the classification and identification of selective sweeps. This general problem of “soft shoulders” underscores the difficulty in differentiating soft and partial sweeps from hard-sweep scenarios in molecular population genomics data. The soft-shoulder effect also implies that the more common hard sweeps have been in recent evolutionary history, the more prevalent spurious signatures of soft or partial sweeps may appear in some genome-wide scans.     

Soft Selective Sweeps in Complex Demographic Scenarios

Adaptation from de novo mutation can produce so-called soft selective sweeps, where adaptive alleles of independent mutational origin sweep through the population at the same time. Population genetic theory predicts that such soft sweeps should be likely if the product of the population size and the mutation rate toward the adaptive allele is sufficiently large, such that multiple adaptive mutations can establish before one has reached fixation; however, it remains unclear how demographic processes affect the probability of observing soft sweeps. Here we extend the theory of soft selective sweeps to realistic demographic scenarios that allow for changes in population size over time. We first show that population bottlenecks can lead to the removal of all but one adaptive lineage from an initially soft selective sweep. The parameter regime under which such “hardening” of soft selective sweeps is likely is determined by a simple heuristic condition. We further develop a generalized analytical framework, based on an extension of the coalescent process, for calculating the probability of soft sweeps under arbitrary demographic scenarios. Two important limits emerge within this analytical framework: In the limit where population-size fluctuations are fast compared to the duration of the sweep, the likelihood of soft sweeps is determined by the harmonic mean of the variance effective population size estimated over the duration of the sweep; in the opposing slow fluctuation limit, the likelihood of soft sweeps is determined by the instantaneous variance effective population size at the onset of the sweep. We show that as a consequence of this finding the probability of observing soft sweeps becomes a function of the strength of selection. Specifically, in species with sharply fluctuating population size, strong selection is more likely to produce soft sweeps than weak selection. Our results highlight the importance of accurate demographic estimates over short evolutionary timescales for understanding the population genetics of adaptation from de novo mutation.     

Detecting Selective Sweeps in Naturally Occurring Escherichia Coli

The nucleotide sequences of the gapA and pabB genes (separated by approximately 32.5 kb) were determined in 12 natural isolates of Escherichia coli. Three analyses were performed on the data. First, the levels of polymorphism at the loci were compared within and between E. coli and Salmonella strains relative to their degrees of constraint. Second, the gapA and pabB loci were analyzed by the Hudson-Kreitman-Aguade (HKA) test for selective neutrality. Four additional dispersed genes (crr, putP, trp and gnd) were added to the analysis to provide the necessary frame of reference. Finally, the gene genealogies of gapA and pabB were examined for topological consistency within and between the loci. These lines of evidence indicate that some evolutionary event has recently purged the variability in the region surrounding the gapA and pabB loci in E. coli. This can best be explained by the spread of a selected allele through the global E. coli population by directional selection and the resulting loss in variability in the surrounding regions due to genetic hitchhiking.     

Classic Selective Sweeps Revealed by Massive Sequencing in Cattle

Human driven selection during domestication and subsequent breed formation has likely left detectable signatures within the genome of modern cattle. The elucidation of these signatures of selection is of interest from the perspective of evolutionary biology, and for identifying domestication-related genes that ultimately may help to further genetically improve this economically important animal. To this end, we employed a panel of more than 15 million autosomal SNPs identified from re-sequencing of 43 Fleckvieh animals. We mainly applied two somewhat complementary statistics, the integrated Haplotype Homozygosity Score (iHS) reflecting primarily ongoing selection, and the Composite of Likelihood Ratio (CLR) having the most power to detect completed selection after fixation of the advantageous allele. We find 106 candidate selection regions, many of which are harboring genes related to phenotypes relevant in domestication, such as coat coloring pattern, neurobehavioral functioning and sensory perception including KIT, MITF, MC1R, NRG4, Erbb4, TMEM132D and TAS2R16, among others. To further investigate the relationship between genes with signatures of selection and genes identified in QTL mapping studies, we use a sample of 3062 animals to perform four genome-wide association analyses using appearance traits, body size and somatic cell count. We show that regions associated with coat coloring significantly (P<0.0001) overlap with the candidate selection regions, suggesting that the selection signals we identify are associated with traits known to be affected by selection during domestication. Results also provide further evidence regarding the complexity of the genetics underlying coat coloring in cattle. This study illustrates the potential of population genetic approaches for identifying genomic regions affecting domestication-related phenotypes and further helps to identify specific regions targeted by selection during speciation, domestication and breed formation of cattle. We also show that Linkage Disequilibrium (LD) decays in cattle at a much faster rate than previously thought.     

Limited Evidence for Classic Selective Sweeps in African Populations

While hundreds of loci have been identified as reflecting strong-positive selection in human populations, connections between candidate loci and specific selective pressures often remain obscure. This study investigates broader patterns of selection in African populations, which are underrepresented despite their potential to offer key insights into human adaptation. We scan for hard selective sweeps using several haplotype and allele-frequency statistics with a data set of nearly 500,000 genome-wide single-nucleotide polymorphisms in 12 highly diverged African populations that span a range of environments and subsistence strategies. We find that positive selection does not appear to be a strong determinant of allele-frequency differentiation among these African populations. Haplotype statistics do identify putatively selected regions that are shared across African populations. However, as assessed by extensive simulations, patterns of haplotype sharing between African populations follow neutral expectations and suggest that tails of the empirical distributions contain false-positive signals. After highlighting several genomic regions where positive selection can be inferred with higher confidence, we use a novel method to identify biological functions enriched among populations’ empirical tail genomic windows, such as immune response in agricultural groups. In general, however, it seems that current methods for selection scans are poorly suited to populations that, like the African populations in this study, are affected by ascertainment bias and have low levels of linkage disequilibrium, possibly old selective sweeps, and potentially reduced phasing accuracy. Additionally, population history can confound the interpretation of selection statistics, suggesting that greater care is needed in attributing broad genetic patterns to human adaptation.     

Small Mutations in Bordetella pertussis Are Associated with Selective Sweeps

Bordetella pertussis is the causative agent of pertussis, a highly contagious disease of the human respiratory tract. Despite high vaccination coverage, pertussis has resurged and has become one of the most prevalent vaccine-preventable diseases in developed countries. We have proposed that both waning immunity and pathogen adaptation have contributed to the persistence and resurgence of pertussis. Allelic variation has been found in virulence-associated genes coding for the pertussis toxin A subunit (ptxA), pertactin (prn), serotype 2 fimbriae (fim2), serotype 3 fimbriae (fim3) and the promoter for pertussis toxin (ptxP). In this study, we investigated how more than 60 years of vaccination has affected the Dutch B. pertussis population by combining data from phylogeny, genomics and temporal trends in strain frequencies. Our main focus was on the ptxA, prn, fim3 and ptxP genes. However, we also compared the genomes of 11 Dutch strains belonging to successful lineages. Our results showed that, between 1949 and 2010, the Dutch B. pertussis population has undergone as least four selective sweeps that were associated with small mutations in ptxA, prn, fim3 and ptxP. Phylogenetic analysis revealed a stepwise adaptation in which mutations accumulated clonally. Genomic analysis revealed a number of additional mutations which may have a contributed to the selective sweeps. Five large deletions were identified which were fixed in the pathogen population. However, only one was linked to a selective sweep. No evidence was found for a role of gene acquisition in pathogen adaptation. Our results suggest that the B. pertussis gene repertoire is already well adapted to its current niche and required only fine tuning to persist in the face of vaccination. Further, this work shows that small mutations, even single SNPs, can drive large changes in the populations of bacterial pathogens within a time span of six to 19 years.     

Sweeps in time: leveraging the joint distribution of branch lengths

Current methods of identifying positively selected regions in the genome are limited in two key ways: the underlying models cannot account for the timing of adaptive events and the comparison between models of selective sweeps and sequence data is generally made via simple summaries of genetic diversity. Here, we develop a tractable method of describing the effect of positive selection on the genealogical histories in the surrounding genome, explicitly modeling both the timing and context of an adaptive event. In addition, our framework allows us to go beyond analyzing polymorphism data via the site frequency spectrum or summaries thereof and instead leverage information contained in patterns of linked variants. Tests on both simulations and a human data example, as well as a comparison to SweepFinder2, show that even with very small sample sizes, our analytic framework has higher power to identify old selective sweeps and to correctly infer both the time and strength of selection. Finally, we derived the marginal distribution of genealogical branch lengths at a locus affected by selection acting at a linked site. This provides a much-needed link between our analytic understanding of the effects of sweeps on sequence variation and recent advances in simulation and heuristic inference procedures that allow researchers to examine the sequence of genealogical histories along the genome.     

Adaptation, redundancy or resilience

Diversity creates resilience both in ecosystems and living organism. Yet, although genetic diversity protects organisms from many diseases and disorders, it also makes it much harder for geneticist to identify the risk factors that lead to common diseases.The study of the natural environment teaches us that ecological systems rich in biodiversity have greater resilience than less diverse systems, and that resource-poor ecosystems tend to have greater biodiversity to buffer against environmental change. The African savannah, a huge ecosystem, contains an abundance of grasses and other plants, herbivores and their predators. The loss of one species might be compensated for by the presence of others, but if species are relentlessly removed, one after another, the continuing loss will weaken the system until it changes its steady state and eventually collapses.To use another illustrative example of the protection conferred by diversity: modern agriculture uses only six cereal crops as the main basic staples of the human diet. If even one crop were threatened—perhaps by a plant virus or other pathogen—the consequences for humanity would probably be catastrophic. To avoid such a scenario, breeders have created hundreds of cultivars, each with minor phenotypic changes that confer resistance to a biotic or abiotic stressor. Thus, humans too create resilience by increasing biodiversity.In order to improve our understanding of complex diseases, we can extend this notion of diverse ecosystems to organisms. Similarly to the disappearance of one species in an ecosystem with abundant biodiversity, the loss of one gene function might not be immediately apparent, because many such changes can be compensated for, at least partly, by changes in other genes. However, a series of small, cumulative changes in many genes could lead to the breakdown of the phenotype of the organism, rendering it less resilient and more susceptible to disease, especially when it is under environmental or infectious stress. It is like throwing a stone in a pond, which generates small waves; throwing many stones at once causes a more complex disturbance, whereby waves combine to create bigger waves or attenuate each other by interference. Thus, even inherited disorders such as hypertrophic cardiomyopathy show several phenotypes as other genes modify the action of the affected gene.Geneticists have found many genes or whole genomic regions that have multiplied throughout the genome by duplication (Eisenstein, 2010). The repeated sequences might be identical, nearly identical or related, and they can be functional or non-functional, as is the case with pseudogenes. In terms of diversity, repeats have apparently given rise to multigene families, such as the collagens, which encode several structural proteins. Even microorganisms, such as Mycobacterium tuberculosis, have extended gene families or several insertions.It was assumed previously that pseudogenes are unnecessary gene copies and therefore inactivated. Yet, there is increasing evidence that they perform a regulatory role, by influencing the function of the parent gene. The variation in copy number also seems to be as, or even more, important than the number of polymorphisms, particularly in complex diseases or phenotypic traits. One negative example is the gene that codes for glutathione transferase, GSTM1. Roughly half of the population carries a deletion of GSTM1, which reduces their ability to neutralize isothiocyanates. Clearly then, many individuals will have two null alleles and an increased risk of xenobiotic-induced disease. Another fascinating example is that preference for a high-starch diet is associated with multiple copies of the salivary amylase gene, which increases production of this enzyme.Humans show a range of vulnerabilities to complex or infectious diseases, such as pulmonary tuberculosis. Despite an exhaustive search, no obvious, major resistance or susceptibility genes for tuberculosis have been found, although many genes—each with minor effects—have a role in disease susceptibility. Further support for the argument that resilience comes from diversity is found in the confusion around genetic association studies in many complex diseases, in which a given gene might be significantly associated with a condition in one population, but not in others. I suspect that many of these reports can be explained by the fact that susceptibility is caused by cumulative functional changes in many genes along different routes in different groups of humans or animals. In fact, susceptibility to a common disease conferred by a single, major locus would make the organism extremely vulnerable—which is exactly what we see with autosomal-dominant inherited diseases. Thus, it is unlikely that complex diseases are caused by a solitary gene defect, as evolution would select against the high risk of a single dominant effect. Instead, we see a range of conditions and phenotypes, owing to the large number of genes involved.This diversity of genetic factors is a blessing for humanity, as it has equipped us with enormous resilience against many common diseases, from cancer to coronary heart disease, to infectious diseases. But, it is also a bane for the geneticist and the clinical scientists who search for genetic factors that can be used to predict disease susceptibility, or the condition or progress of disease. Complexity and diversity make things far more unpredictable and messy—and therefore more difficult for scientific analysis—but both also ensure our survival against a daily assault of biotic and abiotic stressors.     

Predicting Carriers of Ongoing Selective Sweeps without Knowledge of the Favored Allele

Methods for detecting the genomic signatures of natural selection have been heavily studied, and they have been successful in identifying many selective sweeps. For most of these sweeps, the favored allele remains unknown, making it difficult to distinguish carriers of the sweep from non-carriers. In an ongoing selective sweep, carriers of the favored allele are likely to contain a future most recent common ancestor. Therefore, identifying them may prove useful in predicting the evolutionary trajectory—for example, in contexts involving drug-resistant pathogen strains or cancer subclones. The main contribution of this paper is the development and analysis of a new statistic, the Haplotype Allele Frequency (HAF) score. The HAF score, assigned to individual haplotypes in a sample, naturally captures many of the properties shared by haplotypes carrying a favored allele. We provide a theoretical framework for computing expected HAF scores under different evolutionary scenarios, and we validate the theoretical predictions with simulations. As an application of HAF score computations, we develop an algorithm (PreCIOSS: Predicting Carriers of Ongoing Selective Sweeps) to identify carriers of the favored allele in selective sweeps, and we demonstrate its power on simulations of both hard and soft sweeps, as well as on data from well-known sweeps in human populations.     

Detecting Selective Sweeps: A New Approach Based on Hidden Markov Models

Detecting and localizing selective sweeps on the basis of SNP data has recently received considerable attention. Here we introduce the use of hidden Markov models (HMMs) for the detection of selective sweeps in DNA sequences. Like previously published methods, our HMMs use the site frequency spectrum, and the spatial pattern of diversity along the sequence, to identify selection. In contrast to earlier approaches, our HMMs explicitly model the correlation structure between linked sites. The detection power of our methods, and their accuracy for estimating the selected site location, is similar to that of competing methods for constant size populations. In the case of population bottlenecks, however, our methods frequently showed fewer false positives.     

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC’s performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics.     

Bifurcation, Bursting, and Spike Frequency Adaptation

Many neural systems display adaptive properties that occur on timescales that are slower than the time scales associated withrepetitive firing of action potentials or bursting oscillations. Spike frequency adaptation is the name givento processes thatreduce the frequency of rhythmic tonic firing of action potentials,sometimes leading to the termination of spiking and the cell becomingquiescent. This article examines these processes mathematically,within the context of singularly perturbed dynamical systems.We place emphasis on the lengths of successive interspikeintervals during adaptation. Two different bifurcation mechanisms insingularly perturbed systems that correspond to the termination offiring are distinguished by the rate at which interspike intervalsslow near the termination of firing. We compare theoreticalpredictions to measurement of spike frequency adaptation in a modelof the LP cell of the lobster stomatogastric ganglion.     

Distinguishing between Selective Sweeps from Standing Variation and from a De Novo Mutation

An outstanding question in human genetics has been the degree to which adaptation occurs from standing genetic variation or from de novo mutations. Here, we combine several common statistics used to detect selection in an Approximate Bayesian Computation (ABC) framework, with the goal of discriminating between models of selection and providing estimates of the age of selected alleles and the selection coefficients acting on them. We use simulations to assess the power and accuracy of our method and apply it to seven of the strongest sweeps currently known in humans. We identify two genes, ASPM and PSCA, that are most likely affected by selection on standing variation; and we find three genes, ADH1B, LCT, and EDAR, in which the adaptive alleles seem to have swept from a new mutation. We also confirm evidence of selection for one further gene, TRPV6. In one gene, G6PD, neither neutral models nor models of selective sweeps fit the data, presumably because this locus has been subject to balancing selection.