Recent selective sweeps in North American Drosophila melanogaster show signatures of soft sweeps

The genetic basis of adaptation in phenology in an introduced population of Black Cottonwood (Populus trichocarpa, Torr. & Gray)

BACKGROUND

Entering and exiting winter dormancy present important trade-offs between growth and survival at northern latitudes. Many forest trees display local adaptation across latitude in traits associated with these phenology transitions. Transfers of a species outside its native range introduce the species to novel combinations of environmental conditions potentially requiring different combinations of alleles to optimize growth and survival. In this study, we performed genome wide association analyses and a selection scan in a P. trichocarpa mapping population derived from crossings between clones collected across the native range and introduced into Sweden. GWAS analyses were performed using phenotypic data collected across two field seasons and in a controlled phytotron experiment.

RESULTS

We uncovered 584 putative candidate genes associated with spring and autumn phenology traits as well as with growth. Many regions harboring variation significantly associated with the initiation of leaf shed and leaf autumn coloring appeared to have been evolving under positive selection in the native environments of P. trichocarpa. A comparison between the candidate genes identified with results from earlier GWAS analyses performed in the native environment found a smaller overlap for spring phenology traits than for autumn phenology traits, aligning well with earlier observations that spring phenology transitions have a more complex genetic basis than autumn phenology transitions.

CONCLUSIONS

In a small and structured introduced population of P. trichocarpa, we find complex genetic architectures underlying all phenology and growth traits, and identify multiple putative candidate genes despite the limitations of the study population.

Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome

Evolutionary forces like Hill-Robertson interference and negative epistasis can lead to deleterious mutations being found on distinct haplotypes. However, the extent to which these forces depend on the selection and dominance coefficients of deleterious mutations and shape genome-wide patterns of linkage disequilibrium (LD) in natural populations with complex demographic histories has not been tested. In this study, we first used forward-in-time simulations to predict how negative selection impacts LD. Under models where deleterious mutations have additive effects on fitness, deleterious variants less than 10 kb apart tend to be carried on different haplotypes relative to pairs of synonymous SNPs. In contrast, for recessive mutations, there is no consistent ordering of how selection coefficients affect LD decay, due to the complex interplay of different evolutionary effects. We then examined empirical data of modern humans from the 1000 Genomes Project. LD between derived alleles at nonsynonymous SNPs is lower compared to pairs of derived synonymous variants, suggesting that nonsynonymous derived alleles tend to occur on different haplotypes more than synonymous variants. This result holds when controlling for potential confounding factors by matching SNPs for frequency in the sample (allele count), physical distance, magnitude of background selection, and genetic distance between pairs of variants. Lastly, we introduce a new statistic HR(j) which allows us to detect interference using unphased genotypes. Application of this approach to high-coverage human genome sequences confirms our finding that nonsynonymous derived alleles tend to be located on different haplotypes more often than are synonymous derived alleles. Our findings suggest that interference may play a pervasive role in shaping patterns of LD between deleterious variants in the human genome, and consequently influences genome-wide patterns of LD.

Haplotype tagging reveals parallel formation of hybrid races in two butterfly species

Genetic variation segregates as linked sets of variants or haplotypes. Haplotypes and linkage are central to genetics and underpin virtually all genetic and selection analysis. Yet, genomic data often omit haplotype information due to constraints in sequencing technologies. Here, we present "haplotagging," a simple, low-cost linked-read sequencing technique that allows sequencing of hundreds of individuals while retaining linkage information. We apply haplotagging to construct megabase-size haplotypes for over 600 individual butterflies (Heliconius erato and H. melpomene), which form overlapping hybrid zones across an elevational gradient in Ecuador. Haplotagging identifies loci controlling distinctive high- and lowland wing color patterns. Divergent haplotypes are found at the same major loci in both species, while chromosome rearrangements show no parallelism. Remarkably, in both species, the geographic clines for the major wing-pattern loci are displaced by 18 km, leading to the rise of a novel hybrid morph in the center of the hybrid zone. We propose that shared warning signaling (Müllerian mimicry) may couple the cline shifts seen in both species and facilitate the parallel coemergence of a novel hybrid morph in both comimetic species. Our results show the power of efficient haplotyping methods when combined with large-scale sequencing data from natural populations.

Genotyping and Whole-Genome Resequencing of Welsh Sheep Breeds Reveal Candidate Genes and Variants for Adaptation to Local Environment and Socioeconomic Traits

Background

Advances in genetic tools applied to livestock breeding has prompted research into the previously neglected breeds adapted to harsh local environments. One such group is the Welsh mountain sheep breeds, which can be farmed at altitudes of 300 m above sea level but are considered to have a low productive value because of their poor wool quality and small carcass size. This is contrary to the lowland breeds which are more suited to wool and meat production qualities, but do not fare well on upland pasture. Herein, medium-density genotyping data from 317 individuals representing 15 Welsh sheep breeds were used alongside the whole-genome resequencing data of 14 breeds from the same set to scan for the signatures of selection and candidate genetic variants using haplotype- and SNP-based approaches.

Results

Haplotype-based selection scan performed on the genotyping data pointed to a strong selection in the regions of GBA3, PPARGC1A, APOB, and PPP1R16B genes in the upland breeds, and RNF24, PANK2, and MUC15 in the lowland breeds. SNP-based selection scan performed on the resequencing data pointed to the missense mutations under putative selection relating to a local adaptation in the upland breeds with functions such as angiogenesis (VASH1), anti-oxidation (RWDD1), cell stress (HSPA5), membrane transport (ABCA13 and SLC22A7), and insulin signaling (PTPN1 and GIGFY1). By contrast, genes containing candidate missense mutations in the lowland breeds are related to cell cycle (CDK5RAP2), cell adhesion (CDHR3), and coat color (MC1R).

Conclusion

We found new variants in genes with potentially functional consequences to the adaptation of local sheep to their environments in Wales. Knowledge of these variations is important for improving the adaptative qualities of UK and world sheep breeds through a marker-assisted selection.

Application of a novel haplotype-based scan for local adaptation to study high-altitude adaptation in rhesus macaques

When natural populations split and migrate to different environments, they may experience different selection pressures that can lead to local adaptation. To capture the genomic patterns of a local selective sweep, we develop XP-nSL, a genomic scan for local adaptation that compares haplotype patterns between two populations. We show that XP-nSL has power to detect ongoing and recently completed hard and soft sweeps, and we then apply this statistic to search for evidence of adaptation to high altitude in rhesus macaques. We analyze the whole genomes of 23 wild rhesus macaques captured at high altitude (mean altitude > 4000 m above sea level) to 22 wild rhesus macaques captured at low altitude (mean altitude < 500 m above sea level) and find evidence of local adaptation in the high-altitude population at or near 303 known genes and several unannotated regions. We find the strongest signal for adaptation at EGLN1, a classic target for convergent evolution in several species living in low oxygen environments. Furthermore, many of the 303 genes are involved in processes related to hypoxia, regulation of ROS, DNA damage repair, synaptic signaling, and metabolism. These results suggest that, beyond adapting via a beneficial mutation in one single gene, adaptation to high altitude in rhesus macaques is polygenic and spread across numerous important biological systems.

An overview of current population genomics methods for the analysis of whole-genome resequencing data in eukaryotes

Characterizing the population history of a species and identifying loci underlying local adaptation is crucial in functional ecology, evolutionary biology, conservation and agronomy. The constant improvement of high-throughput sequencing techniques has facilitated the production of whole genome data in a wide range of species. Population genomics now provides tools to better integrate selection into a historical framework, and take into account selection when reconstructing demographic history. However, this improvement has come with a profusion of analytical tools that can confuse and discourage users. Such confusion limits the amount of information effectively retrieved from complex genomic data sets, and impairs the diffusion of the most recent analytical tools into fields such as conservation biology. It may also lead to redundancy among methods. To address these isssues, we propose an overview of more than 100 state-of-the-art methods that can deal with whole genome data. We summarize the strategies they use to infer demographic history and selection, and discuss some of their limitations. A website listing these methods is available at www.methodspopgen.com.

Genetic and environmental modulation of transposition shapes the evolutionary potential of Arabidopsis thaliana

BACKGROUND

How species can adapt to abrupt environmental changes, particularly in the absence of standing genetic variation, is poorly understood and a pressing question in the face of ongoing climate change. Here we leverage publicly available multi-omic and bio-climatic data for more than 1000 wild Arabidopsis thaliana accessions to determine the rate of transposable element (TE) mobilization and its potential to create adaptive variation in natural settings.

RESULTS

We demonstrate that TE insertions arise at almost the same rate as base substitutions. Mobilization activity of individual TE families varies greatly between accessions, in association with genetic and environmental factors as well as through complex gene-environment interactions. Although the distribution of TE insertions across the genome is ultimately shaped by purifying selection, reflecting their typically strong deleterious effects when located near or within genes, numerous recent TE-containing alleles show signatures of positive selection. Moreover, high rates of transposition appear positively selected at the edge of the species' ecological niche. Based on these findings, we predict through mathematical modeling higher transposition activity in Mediterranean regions within the next decades in response to global warming, which in turn should accelerate the creation of large-effect alleles.

CONCLUSIONS

Our study reveals that TE mobilization is a major generator of genetic variation in A. thaliana that is finely modulated by genetic and environmental factors. These findings and modeling indicate that TEs may be essential genomic players in the demise or rescue of native populations in times of climate crises.

Inferring Adaptive Introgression Using Hidden Markov Models

Adaptive introgression-the flow of adaptive genetic variation between species or populations-has attracted significant interest in recent years and it has been implicated in a number of cases of adaptation, from pesticide resistance and immunity, to local adaptation. Despite this, methods for identification of adaptive introgression from population genomic data are lacking. Here, we present Ancestry_HMM-S, a hidden Markov model-based method for identifying genes undergoing adaptive introgression and quantifying the strength of selection acting on them. Through extensive validation, we show that this method performs well on moderately sized data sets for realistic population and selection parameters. We apply Ancestry_HMM-S to a data set of an admixed Drosophila melanogaster population from South Africa and we identify 17 loci which show signatures of adaptive introgression, four of which have previously been shown to confer resistance to insecticides. Ancestry_HMM-S provides a powerful method for inferring adaptive introgression in data sets that are typically collected when studying admixed populations. This method will enable powerful insights into the genetic consequences of admixture across diverse populations. Ancestry_HMM-S can be downloaded from https://github.com/jesvedberg/Ancestry_HMM-S/.

Genetic architecture and adaptation of flowering time among environments

The genetic basis of flowering time changes across environments, and pleiotropy may limit adaptive evolution of populations in response to local conditions. However, little information is known about how genetic architecture changes among environments. We used genome-wide association studies (GWAS) in Boechera stricta (Graham) Al-Shehbaz, a relative of Arabidopsis, to examine flowering variation among environments and associations with climate conditions in home environments. Also, we used molecular population genetics to search for evidence of historical natural selection. GWAS found 47 significant quantitative trait loci (QTLs) that influence flowering time in one or more environments, control plastic changes in phenology between experiments, or show associations with climate in sites of origin. Genetic architecture of flowering varied substantially among environments. We found that some pairs of QTLs showed similar patterns of pleiotropy across environments. A large-effect QTL showed molecular signatures of adaptive evolution and is associated with climate in home environments. The derived allele at this locus causes later flowering and predominates in sites with greater water availability. This work shows that GWAS of climate associations and ecologically important traits across diverse environments can be combined with molecular signatures of natural selection to elucidate ecological genetics of adaptive evolution.

A Likelihood Approach for Uncovering Selective Sweep Signatures from Haplotype Data

Selective sweeps are frequent and varied signatures in the genomes of natural populations, and detecting them is consequently important in understanding mechanisms of adaptation by natural selection. Following a selective sweep, haplotypic diversity surrounding the site under selection decreases, and this deviation from the background pattern of variation can be applied to identify sweeps. Multiple methods exist to locate selective sweeps in the genome from haplotype data, but none leverages the power of a model-based approach to make their inference. Here, we propose a likelihood ratio test statistic T to probe whole-genome polymorphism data sets for selective sweep signatures. Our framework uses a simple but powerful model of haplotype frequency spectrum distortion to find sweeps and additionally make an inference on the number of presently sweeping haplotypes in a population. We found that the T statistic is suitable for detecting both hard and soft sweeps across a variety of demographic models, selection strengths, and ages of the beneficial allele. Accordingly, we applied the T statistic to variant calls from European and sub-Saharan African human populations, yielding primarily literature-supported candidates, including LCT, RSPH3, and ZNF211 in CEU, SYT1, RGS18, and NNT in YRI, and HLA genes in both populations. We also searched for sweep signatures in Drosophila melanogaster, finding expected candidates at Ace, Uhg1, and Pimet. Finally, we provide open-source software to compute the T statistic and the inferred number of presently sweeping haplotypes from whole-genome data.

Three Signatures of Adaptive Polymorphism Exemplified by Malaria-Associated Genes

Malaria has been one of the strongest selective pressures on our species. Many of the best-characterized cases of adaptive evolution in humans are in genes tied to malaria resistance. However, the complex evolutionary patterns at these genes are poorly captured by standard scans for nonneutral evolution. Here, we present three new statistical tests for selection based on population genetic patterns that are observed more than once among key malaria resistance loci. We assess these tests using forward-time evolutionary simulations and apply them to global whole-genome sequencing data from humans, and thus we show that they are effective at distinguishing selection from neutrality. Each test captures a distinct evolutionary pattern, here called Divergent Haplotypes, Repeated Shifts, and Arrested Sweeps, associated with a particular period of human prehistory. We clarify the selective signatures at known malaria-relevant genes and identify additional genes showing similar adaptive evolutionary patterns. Among our top outliers, we see a particular enrichment for genes involved in erythropoiesis and for genes previously associated with malaria resistance, consistent with a major role for malaria in shaping these patterns of genetic diversity. Polymorphisms at these genes are likely to impact resistance to malaria infection and contribute to ongoing host-parasite coevolutionary dynamics.

Population genomics in the arboviral vector Aedes aegypti reveals the genomic architecture and evolution of endogenous viral elements

Horizontal gene transfer from viruses to eukaryotic cells is a pervasive phenomenon. Somatic viral integrations are linked to persistent viral infection whereas integrations into germline cells are maintained in host genomes by vertical transmission and may be co-opted for host functions. In the arboviral vector Aedes aegypti, an endogenous viral element from a nonretroviral RNA virus (nrEVE) was shown to produce PIWI-interacting RNAs (piRNAs) to limit infection with a cognate virus. Thus, nrEVEs may constitute a heritable, sequence-specific mechanism for antiviral immunity, analogous to piRNA-mediated silencing of transposable elements. Here, we combine population genomics and evolutionary approaches to analyse the genomic architecture of nrEVEs in A. aegypti. We conducted a genome-wide screen for adaptive nrEVEs and searched for novel population-specific nrEVEs in the genomes of 80 individual wild-caught mosquitoes from five geographical populations. We show a dynamic landscape of nrEVEs in mosquito genomes and identified five novel nrEVEs derived from two currently circulating viruses, providing evidence of the environmental-dependent modification of a piRNA cluster. Overall, our results show that virus endogenization events are complex with only a few nrEVEs contributing to adaptive evolution in A. aegypti.

Distinguishing between recent balancing selection and incomplete sweep using deep neural networks

Balancing selection is an important adaptive mechanism underpinning a wide range of phenotypes. Despite its relevance, the detection of recent balancing selection from genomic data is challenging as its signatures are qualitatively similar to those left by ongoing positive selection. In this study, we developed and implemented two deep neural networks and tested their performance to predict loci under recent selection, either due to balancing selection or incomplete sweep, from population genomic data. Specifically, we generated forward-in-time simulations to train and test an artificial neural network (ANN) and a convolutional neural network (CNN). ANN received as input multiple summary statistics calculated on the locus of interest, while CNN was applied directly on the matrix of haplotypes. We found that both architectures have high accuracy to identify loci under recent selection. CNN generally outperformed ANN to distinguish between signals of balancing selection and incomplete sweep and was less affected by incorrect training data. We deployed both trained networks on neutral genomic regions in European populations and demonstrated a lower false-positive rate for CNN than ANN. We finally deployed CNN within the MEFV gene region and identified several common variants predicted to be under incomplete sweep in a European population. Notably, two of these variants are functional changes and could modulate susceptibility to familial Mediterranean fever, possibly as a consequence of past adaptation to pathogens. In conclusion, deep neural networks were able to characterize signals of selection on intermediate frequency variants, an analysis currently inaccessible by commonly used strategies.

Genomic Targets of Positive Selection in Giant Mice from Gough Island

A key challenge in understanding how organisms adapt to their environments is to identify the mutations and genes that make it possible. By comparing patterns of sequence variation to neutral predictions across genomes, the targets of positive selection can be located. We applied this logic to house mice that invaded Gough Island (GI), an unusual population that shows phenotypic and ecological hallmarks of selection. We used massively parallel short-read sequencing to survey the genomes of 14 GI mice. We computed a set of summary statistics to capture diverse aspects of variation across these genome sequences, used approximate Bayesian computation to reconstruct a null demographic model, and then applied machine learning to estimate the posterior probability of positive selection in each region of the genome. Using a conservative threshold, 1,463 5-kb windows show strong evidence for positive selection in GI mice but not in a mainland reference population of German mice. Disproportionate shares of these selection windows contain genes that harbor derived nonsynonymous mutations with large frequency differences. Over-represented gene ontologies in selection windows emphasize neurological themes. Inspection of genomic regions harboring many selection windows with high posterior probabilities pointed to genes with known effects on exploratory behavior and body size as potential targets. Some genes in these regions contain candidate adaptive variants, including missense mutations and/or putative regulatory mutations. Our results provide a genomic portrait of adaptation to island conditions and position GI mice as a powerful system for understanding the genetic component of natural selection.

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.

The population genomics of adaptive loss of function

Discoveries of adaptive gene knockouts and widespread losses of complete genes have in recent years led to a major rethink of the early view that loss-of-function alleles are almost always deleterious. Today, surveys of population genomic diversity are revealing extensive loss-of-function and gene content variation, yet the adaptive significance of much of this variation remains unknown. Here we examine the evolutionary dynamics of adaptive loss of function through the lens of population genomics and consider the challenges and opportunities of studying adaptive loss-of-function alleles using population genetics models. We discuss how the theoretically expected existence of allelic heterogeneity, defined as multiple functionally analogous mutations at the same locus, has proven consistent with empirical evidence and why this impedes both the detection of selection and causal relationships with phenotypes. We then review technical progress towards new functionally explicit population genomic tools and genotype-phenotype methods to overcome these limitations. More broadly, we discuss how the challenges of studying adaptive loss of function highlight the value of classifying genomic variation in a way consistent with the functional concept of an allele from classical population genetics.

Biases in Demographic Modeling Affect Our Understanding of Recent Divergence

Testing among competing demographic models of divergence has become an important component of evolutionary research in model and non-model organisms. However, the effect of unaccounted demographic events on model choice and parameter estimation remains largely unexplored. Using extensive simulations, we demonstrate that under realistic divergence scenarios, failure to account for population size (N_e) changes in daughter and ancestral populations leads to strong biases in divergence time estimates as well as model choice. We illustrate these issues reconstructing the recent demographic history of North Sea and Baltic Sea turbots (Scophthalmus maximus) by testing 16 isolation with migration (IM) and 16 secondary contact (SC) scenarios, modeling changes in N_e as well as the effects of linked selection and barrier loci. Failure to account for changes in N_e resulted in selecting SC models with long periods of strict isolation and divergence times preceding the formation of the Baltic Sea. In contrast, models accounting for N_e changes suggest recent (<6 kya) divergence with constant gene flow. We further show how interpreting genomic landscapes of differentiation can help discerning among competing models. For example, in the turbot data, islands of differentiation show signatures of recent selective sweeps, rather than old divergence resisting secondary introgression. The results have broad implications for the study of population divergence by highlighting the potential effects of unmodeled changes in N_e on demographic inference. Tested models should aim at representing realistic divergence scenarios for the target taxa, and extreme caution should always be exercised when interpreting results of demographic modeling.

Detection of hard and soft selective sweeps from Drosophila melanogaster population genomic data

Whether hard sweeps or soft sweeps dominate adaptation has been a matter of much debate. Recently, we developed haplotype homozygosity statistics that (i) can detect both hard and soft sweeps with similar power and (ii) can classify the detected sweeps as hard or soft. The application of our method to population genomic data from a natural population of Drosophila melanogaster (DGRP) allowed us to rediscover three known cases of adaptation at the loci Ace, Cyp6g1, and CHKov1 known to be driven by soft sweeps, and detected additional candidate loci for recent and strong sweeps. Surprisingly, all of the top 50 candidates showed patterns much more consistent with soft rather than hard sweeps. Recently, Harris et al. 2018 criticized this work, suggesting that all the candidate loci detected by our haplotype statistics, including the positive controls, are unlikely to be sweeps at all and that instead these haplotype patterns can be more easily explained by complex neutral demographic models. They also claim that these neutral non-sweeps are likely to be hard instead of soft sweeps. Here, we reanalyze the DGRP data using a range of complex admixture demographic models and reconfirm our original published results suggesting that the majority of recent and strong sweeps in D. melanogaster are first likely to be true sweeps, and second, that they do appear to be soft. Furthermore, we discuss ways to take this work forward given that most demographic models employed in such analyses are necessarily too simple to capture the full demographic complexity, while more realistic models are unlikely to be inferred correctly because they require a large number of free parameters.

Population Genomics on the Fly: Recent Advances in Drosophila

Drosophila melanogaster, a small dipteran of African origin, represents one of the best-studied model organisms. Early work in this system has uniquely shed light on the basic principles of genetics and resulted in a versatile collection of genetic tools that allow to uncover mechanistic links between genotype and phenotype. Moreover, given its worldwide distribution in diverse habitats and its moderate genome-size, Drosophila has proven very powerful for population genetics inference and was one of the first eukaryotes whose genome was fully sequenced. In this book chapter, we provide a brief historical overview of research in Drosophila and then focus on recent advances during the genomic era. After describing different types and sources of genomic data, we discuss mechanisms of neutral evolution including the demographic history of Drosophila and the effects of recombination and biased gene conversion. Then, we review recent advances in detecting genome-wide signals of selection, such as soft and hard selective sweeps. We further provide a brief introduction to background selection, selection of noncoding DNA and codon usage and focus on the role of structural variants, such as transposable elements and chromosomal inversions, during the adaptive process. Finally, we discuss how genomic data helps to dissect neutral and adaptive evolutionary mechanisms that shape genetic and phenotypic variation in natural populations along environmental gradients. In summary, this book chapter serves as a starting point to Drosophila population genomics and provides an introduction to the system and an overview to data sources, important population genetic concepts and recent advances in the field.

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.

Resistance to pirimiphos-methyl in West African Anopheles is spreading via duplication and introgression of the Ace1 locus

Vector population control using insecticides is a key element of current strategies to prevent malaria transmission in Africa. The introduction of effective insecticides, such as the organophosphate pirimiphos-methyl, is essential to overcome the recurrent emergence of resistance driven by the highly diverse Anopheles genomes. Here, we use a population genomic approach to investigate the basis of pirimiphos-methyl resistance in the major malaria vectors Anopheles gambiae and A. coluzzii. A combination of copy number variation and a single non-synonymous substitution in the acetylcholinesterase gene, Ace1, provides the key resistance diagnostic in an A. coluzzii population from Côte d'Ivoire that we used for sequence-based association mapping, with replication in other West African populations. The Ace1 substitution and duplications occur on a unique resistance haplotype that evolved in A. gambiae and introgressed into A. coluzzii, and is now common in West Africa primarily due to selection imposed by other organophosphate or carbamate insecticides. Our findings highlight the predictive value of this complex resistance haplotype for phenotypic resistance and clarify its evolutionary history, providing tools to for molecular surveillance of the current and future effectiveness of pirimiphos-methyl based interventions.

Environment and pathogens shape local and regional adaptations to climate change in the chocolate tree, Theobroma cacao L

Predicting the potential fate of a species in the face of climate change requires knowing the distribution of molecular adaptations across the geographic range of the species. In this work, we analysed 79 genomes of Theobroma cacao, an Amazonian tree known for the fruit from which chocolate is produced, to evaluate how local and regional molecular signatures of adaptation are distributed across the natural range of the species. We implemented novel techniques that incorporate summary statistics from multiple selection scans to infer selective sweeps. The majority of the molecular adaptations in the genome are not shared among populations. We show that ~71.5% of genes under selection also show significant associations with changes in environmental variables. Our results support the interpretation that these genes contribute to local adaptation of the populations in response to abiotic factors. We also found strong patterns of molecular adaptation in a diverse array of disease resistance genes (6.5% of selective sweeps), suggesting that differential adaptation to pathogens also contributes significantly to local adaptations. Our results are consistent with the interpretation that local selective pressures are more important than regional selective pressures in explaining adaptation across the range of a species.

Global adaptation complicates the interpretation of genome scans for local adaptation

Spatially varying selection promotes variance in allele frequencies, increasing genetic differentiation between the demes of a metapopulation. For that reason, outliers in the genome‐wide distribution of summary statistics measuring genetic differentiation, such as F_ST, are often interpreted as evidence for alleles that contribute to local adaptation. However, theoretical studies have shown that in spatially structured populations the spread of beneficial mutations with spatially uniform fitness effects can also induce transient genetic differentiation. In recent years, numerous empirical studies have suggested that such species‐wide, or global, adaptation makes a substantial contribution to molecular evolution. In this perspective, we discuss how commonly such global adaptation may influence the genome‐wide distribution of F_ST and generate genetic differentiation patterns, which could be mistaken for local adaptation. To illustrate this, we use forward‐in‐time population genetic simulations assuming parameters for the rate and strength of beneficial mutations consistent with estimates from natural populations. We demonstrate that the spread of globally beneficial mutations in parapatric populations may frequently generate F_ST outliers, which could be misinterpreted as evidence for local adaptation. The spread of beneficial mutations causes selective sweeps at flanking sites, so in some cases, the effects of global versus local adaptation may be distinguished by examining patterns of nucleotide diversity within and between populations in addition to F_ST. However, when local adaptation has been only recently established, it may be much more difficult to distinguish from global adaptation, due to less accumulation of linkage disequilibrium at flanking sites. Through our discussion, we conclude that a large fraction of F_ST outliers that are presumed to arise from local adaptation may instead be due to global adaptation.

Contrasting signatures of genomic divergence during sympatric speciation

The transition from 'well-marked varieties' of a single species into 'well-defined species'-especially in the absence of geographic barriers to gene flow (sympatric speciation)-has puzzled evolutionary biologists ever since Darwin1,2. Gene flow counteracts the buildup of genome-wide differentiation, which is a hallmark of speciation and increases the likelihood of the evolution of irreversible reproductive barriers (incompatibilities) that complete the speciation process3. Theory predicts that the genetic architecture of divergently selected traits can influence whether sympatric speciation occurs4, but empirical tests of this theory are scant because comprehensive data are difficult to collect and synthesize across species, owing to their unique biologies and evolutionary histories5. Here, within a young species complex of neotropical cichlid fishes (Amphilophus spp.), we analysed genomic divergence among populations and species. By generating a new genome assembly and re-sequencing 453 genomes, we uncovered the genetic architecture of traits that have been suggested to be important for divergence. Species that differ in monogenic or oligogenic traits that affect ecological performance and/or mate choice show remarkably localized genomic differentiation. By contrast, differentiation among species that have diverged in polygenic traits is genomically widespread and much higher overall, consistent with the evolution of effective and stable genome-wide barriers to gene flow. Thus, we conclude that simple trait architectures are not always as conducive to speciation with gene flow as previously suggested, whereas polygenic architectures can promote rapid and stable speciation in sympatry.

Genetic Adaptation in New York City Rats

Brown rats (Rattus norvegicus) thrive in urban environments by navigating the anthropocentric environment and taking advantage of human resources and by-products. From the human perspective, rats are a chronic problem that causes billions of dollars in damage to agriculture, health, and infrastructure. Did genetic adaptation play a role in the spread of rats in cities? To approach this question, we collected whole-genome sequences from 29 brown rats from New York City (NYC) and scanned for genetic signatures of adaptation. We tested for 1) high-frequency, extended haplotypes that could indicate selective sweeps and 2) loci of extreme genetic differentiation between the NYC sample and a sample from the presumed ancestral range of brown rats in northeast China. We found candidate selective sweeps near or inside genes associated with metabolism, diet, the nervous system, and locomotory behavior. Patterns of differentiation between NYC and Chinese rats at putative sweep loci suggest that many sweeps began after the split from the ancestral population. Together, our results suggest several hypotheses on adaptation in rats living in proximity to humans.

Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps

Numerous studies of emerging species have identified genomic "islands" of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward "speciation genes" that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last ∼50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be soft sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed light on the complex manner in which natural selection shapes genome sequences during speciation.

Divergent Selection and Primary Gene Flow Shape Incipient Speciation of a Riparian Tree on Hawaii Island

A long-standing goal of evolutionary biology is to understand the mechanisms underlying the formation of species. Of particular interest is whether or not speciation can occur in the presence of gene flow and without a period of physical isolation. Here, we investigated this process within Hawaiian Metrosideros, a hypervariable and highly dispersible woody species complex that dominates the Hawaiian Islands in continuous stands. Specifically, we investigated the origin of Metrosideros polymorpha var. newellii (newellii), a riparian ecotype endemic to Hawaii Island that is purportedly derived from the archipelago-wide M. polymorpha var. glaberrima (glaberrima). Disruptive selection across a sharp forest-riparian ecotone contributes to the isolation of these varieties and is a likely driver of newellii's origin. We examined genome-wide variation of 42 trees from Hawaii Island and older islands. Results revealed a split between glaberrima and newellii within the past 0.3-1.2 My. Admixture was extensive between lineages within Hawaii Island and between islands, but introgression from populations on older islands (i.e., secondary gene flow) did not appear to contribute to the emergence of newellii. In contrast, recurrent gene flow (i.e., primary gene flow) between glaberrima and newellii contributed to the formation of genomic islands of elevated absolute and relative divergence. These regions were enriched for genes with regulatory functions as well as for signals of positive selection, especially in newellii, consistent with divergent selection underlying their formation. In sum, our results support riparian newellii as a rare case of incipient ecological speciation with primary gene flow in trees.

Genome-wide scan for selection signatures reveals novel insights into the adaptive capacity in local North African cattle

Natural-driven selection is supposed to have left detectable signatures on the genome of North African cattle which are often characterized by the fixation of genetic variants associated with traits under selection pressure and/or an outstanding genetic differentiation with other populations at particular loci. Here, we investigate the population genetic structure and we provide a first outline of potential selection signatures in North African cattle using single nucleotide polymorphism genotyping data. After comparing our data to African, European and indicine cattle populations, we identified 36 genomic regions using three extended haplotype homozygosity statistics and 92 outlier markers based on Bayescan test. The 13 outlier windows detected by at least two approaches, harboured genes (e.g. GH1, ACE, ASIC3, HSPH1, MVD, BCL2, HIGD2A, CBFA2T3) that may be involved in physiological adaptations required to cope with environmental stressors that are typical of the North African area such as infectious diseases, extended drought periods, scarce food supply, oxygen scarcity in the mountainous areas and high-intensity solar radiation. Our data also point to candidate genes involved in transcriptional regulation suggesting that regulatory elements had also a prominent role in North African cattle response to environmental constraints. Our study yields novel insights into the unique adaptive capacity in these endangered populations emphasizing the need for the use of whole genome sequence data to gain a better understanding of the underlying molecular mechanisms.

Fitness effects for Ace insecticide resistance mutations are determined by ambient temperature

BACKGROUND

Insect pest control programs often use periods of insecticide treatment with intermittent breaks, to prevent fixing of mutations conferring insecticide resistance. Such mutations are typically costly in an insecticide-free environment, and their frequency is determined by the balance between insecticide treatment and cost of resistance. Ace, a key gene in neuronal signaling, is a prominent target of many insecticides and across several species, three amino acid replacements (I161V, G265A, and F330Y) provide resistance against several insecticides. Because temperature disturbs neuronal signaling homeostasis, we reasoned that the cost of insecticide resistance could be modulated by ambient temperature.

RESULTS

Experimental evolution of a natural Drosophila simulans population at hot and cold temperature regimes uncovered a surprisingly strong effect of ambient temperature. In the cold temperature regime, the resistance mutations were strongly counter selected (s = - 0.055), but in a hot environment, the fitness costs of resistance mutations were reduced by almost 50% (s = - 0.031). We attribute this unexpected observation to the advantage of the reduced enzymatic activity of resistance mutations in hot environments.

CONCLUSION

We show that fitness costs of insecticide resistance genes are temperature-dependent and suggest that the duration of insecticide-free periods need to be adjusted for different climatic regions to reflect these costs. We suggest that such environment-dependent fitness effects may be more common than previously assumed and pose a major challenge for modeling climate change.

Genome-Wide Detection of Selection Signatures in Duroc Revealed Candidate Genes Relating to Growth and Meat Quality

With the development of high-throughput genotyping techniques, selection signatures in the genome of domestic pigs have been extensively interrogated in the last decade. The Duroc, a major commercial pig breed famous for its fast growth rate and high lean ratio, has not been extensively studied focusing on footprints of intensively artificial selection in their genomes by a lot of re-sequencing data. The goal of this study was to investigate genomic regions under artificial selection and their contribution to the unique phenotypic traits of the Duroc using whole-genome resequencing data from 97 pigs. Three complementary methods (d_i, CLR, and iHH12) were implemented for selection signature detection. In Total, 464 significant candidate regions were identified, which covered 46.4 Mb of the pig genome. Within the identified regions, 709 genes were annotated, including 600 candidate protein-coding genes (486 functionally annotated genes) and 109 lncRNA genes. Genes undergoing selective pressure were significantly enriched in the insulin resistance signaling pathway, which may partly explain the difference between the Duroc and other breeds in terms of growth rate. The selection signatures identified in the Duroc population demonstrated positive pressures on a set of important genes with potential functions that are involved in many biological processes. The results provide new insights into the genetic mechanisms of fast growth rate and high lean mass, and further facilitate follow-up studies on functional genes that contribute to the Duroc's excellent phenotypic traits.

Ancient RNA virus epidemics through the lens of recent adaptation in human genomes

Over the course of the last several million years of evolution, humans probably have been plagued by hundreds or perhaps thousands of epidemics. Little is known about such ancient epidemics and a deep evolutionary perspective on current pathogenic threats is lacking. The study of past epidemics has typically been limited in temporal scope to recorded history, and in physical scope to pathogens that left sufficient DNA behind, such as Yersinia pestis during the Great Plague. Host genomes, however, offer an indirect way to detect ancient epidemics beyond the current temporal and physical limits. Arms races with pathogens have shaped the genomes of the hosts by driving a large number of adaptations at many genes, and these signals can be used to detect and further characterize ancient epidemics. Here, we detect the genomic footprints left by ancient viral epidemics that took place in the past approximately 50 000 years in the 26 human populations represented in the 1000 Genomes Project. By using the enrichment in signals of adaptation at approximately 4500 host loci that interact with specific types of viruses, we provide evidence that RNA viruses have driven a particularly large number of adaptive events across diverse human populations. These results suggest that different types of viruses may have exerted different selective pressures during human evolution. Knowledge of these past selective pressures will provide a deeper evolutionary perspective on current pathogenic threats.

This article is part of the theme issue ‘Insights into health and disease from ancient biomolecules’.

Background Selection Does Not Mimic the Patterns of Genetic Diversity Produced by Selective Sweeps

It is increasingly evident that natural selection plays a prominent role in shaping patterns of diversity across the genome. The most commonly studied modes of natural selection are positive selection and negative selection, which refer to directional selection for and against derived mutations, respectively. Positive selection can result in hitchhiking events, in which a beneficial allele rapidly replaces all others in the population, creating a valley of diversity around the selected site along with characteristic skews in allele frequencies and linkage disequilibrium among linked neutral polymorphisms. Similarly, negative selection reduces variation not only at selected sites but also at linked sites, a phenomenon called background selection (BGS). Thus, discriminating between these two forces may be difficult, and one might expect efforts to detect hitchhiking to produce an excess of false positives in regions affected by BGS. Here, we examine the similarity between BGS and hitchhiking models via simulation. First, we show that BGS may somewhat resemble hitchhiking in simplistic scenarios in which a region constrained by negative selection is flanked by large stretches of unconstrained sites, echoing previous results. However, this scenario does not mirror the actual spatial arrangement of selected sites across the genome. By performing forward simulations under more realistic scenarios of BGS, modeling the locations of protein-coding and conserved noncoding DNA in real genomes, we show that the spatial patterns of variation produced by BGS rarely mimic those of hitchhiking events. Indeed, BGS is not substantially more likely than neutrality to produce false signatures of hitchhiking. This holds for simulations modeled after both humans and Drosophila, and for several different demographic histories. These results demonstrate that appropriately designed scans for hitchhiking need not consider BGS's impact on false-positive rates. However, we do find evidence that BGS increases the false-negative rate for hitchhiking, an observation that demands further investigation.

Evolution of the Insecticide Target Rdl in African Anopheles Is Driven by Interspecific and Interkaryotypic Introgression

The evolution of insecticide resistance mechanisms in natural populations of Anopheles malaria vectors is a major public health concern across Africa. Using genome sequence data, we study the evolution of resistance mutations in the resistance to dieldrin locus (Rdl), a GABA receptor targeted by several insecticides, but most notably by the long-discontinued cyclodiene, dieldrin. The two Rdl resistance mutations (296G and 296S) spread across West and Central African Anopheles via two independent hard selective sweeps that included likely compensatory nearby mutations, and were followed by a rare combination of introgression across species (from A. gambiae and A. arabiensis to A. coluzzii) and across nonconcordant karyotypes of the 2La chromosomal inversion. Rdl resistance evolved in the 1950s as the first known adaptation to a large-scale insecticide-based intervention, but the evolutionary lessons from this system highlight contemporary and future dangers for management strategies designed to combat development of resistance in malaria vectors.

Signatures of selection reveal candidate genes involved in economic traits and cold acclimation in five Swedish cattle breeds

Background

Thousands of years of natural and artificial selection have resulted in indigenous cattle breeds that are well-adapted to the environmental challenges of their local habitat and thereby are considered as valuable genetic resources. Understanding the genetic background of such adaptation processes can help us design effective breeding objectives to preserve local breeds and improve commercial cattle. To identify regions under putative selection, GGP HD 150 K single nucleotide polymorphism (SNP) arrays were used to genotype 106 individuals representing five Swedish breeds i.e. native to different regions and covering areas with a subarctic cold climate in the north and mountainous west, to those with a continental climate in the more densely populated south regions.

Results

Five statistics were incorporated within a framework, known as de-correlated composite of multiple signals (DCMS) to detect signatures of selection. The obtained p-values were adjusted for multiple testing (FDR < 5%), and significant genomic regions were identified. Annotation of genes in these regions revealed various verified and novel candidate genes that are associated with a diverse range of traits, including e.g. high altitude adaptation and response to hypoxia (DCAF8, PPP1R12A, SLC16A3, UCP2, UCP3, TIGAR), cold acclimation (AQP3, AQP7, HSPB8), body size and stature (PLAG1, KCNA6, NDUFA9, AKAP3, C5H12orf4, RAD51AP1, FGF6, TIGAR, CCND2, CSMD3), resistance to disease and bacterial infection (CHI3L2, GBP6, PPFIBP1, REP15, CYP4F2, TIGD2, PYURF, SLC10A2, FCHSD2, ARHGEF17, RELT, PRDM2, KDM5B), reproduction (PPP1R12A, ZFP36L2, CSPP1), milk yield and components (NPC1L1, NUDCD3, ACSS1, FCHSD2), growth and feed efficiency (TMEM68, TGS1, LYN, XKR4, FOXA2, GBP2, GBP5, FGD6), and polled phenotype (URB1, EVA1C).

Conclusions

We identified genomic regions that may provide background knowledge to understand the mechanisms that are involved in economic traits and adaptation to cold climate in cattle. Incorporating p-values of different statistics in a single DCMS framework may help select and prioritize candidate genes for further analyses.

Sequencing era methods for identifying signatures of selection in the genome

Insights into genetic loci which are under selection and their functional roles contribute to increased understanding of the patterns of phenotypic variation we observe today. The availability of whole-genome sequence data, for humans and other species, provides opportunities to investigate adaptation and evolution at unprecedented resolution. Many analytical methods have been developed to interrogate these large data sets and characterize signatures of selection in the genome. We review here recently developed methods and consider the impact of increased computing power and data availability on the detection of selection signatures. Consideration of demography, recombination and other confounding factors is important, and use of a range of methods in combination is a powerful route to resolving different forms of selection in genome sequence data. Overall, a substantial improvement in methods for application to whole-genome sequencing is evident, although further work is required to develop robust and computationally efficient approaches which may increase reproducibility across studies.

Host-parasite co-evolution and its genomic signature

Studies in diverse biological systems have indicated that host-parasite co-evolution is responsible for the extraordinary genetic diversity seen in some genomic regions, such as major histocompatibility (MHC) genes in jawed vertebrates and resistance genes in plants. This diversity is believed to evolve under balancing selection on hosts by parasites. However, the mechanisms that link the genomic signatures in these regions to the underlying co-evolutionary process are only slowly emerging. We still lack a clear picture of the co-evolutionary concepts and of the genetic basis of the co-evolving phenotypic traits in the interacting antagonists. Emerging genomic tools that provide new options for identifying underlying genes will contribute to a fuller understanding of the co-evolutionary process.

Population genetics of the coral Acropora millepora: Toward genomic prediction of bleaching

Although reef-building corals are declining worldwide, responses to bleaching vary within and across species and are partly heritable. Toward predicting bleaching response from genomic data, we generated a chromosome-scale genome assembly for the coral Acropora millepora We obtained whole-genome sequences for 237 phenotyped samples collected at 12 reefs along the Great Barrier Reef, among which we inferred little population structure. Scanning the genome for evidence of local adaptation, we detected signatures of long-term balancing selection in the heat-shock co-chaperone sacsin We conducted a genome-wide association study of visual bleaching score for 213 samples, incorporating the polygenic score derived from it into a predictive model for bleaching in the wild. These results set the stage for genomics-based approaches in conservation strategies.

Genomic, Transcriptomic and Epigenomic Tools to Study the Domestication of Plants and Animals: A Field Guide for Beginners

In the last decade, genomics and the related fields of transcriptomics and epigenomics have revolutionized the study of the domestication process in plants and animals, leading to new discoveries and new unresolved questions. Given that some domesticated taxa have been more studied than others, the extent of genomic data can range from vast to nonexistent, depending on the domesticated taxon of interest. This review is meant as a rough guide for students and academics that want to start a domestication research project using modern genomic tools, as well as for researchers already conducting domestication studies that are interested in following a genomic approach and looking for alternate strategies (cheaper or more efficient) and future directions. We summarize the theoretical and technical background needed to carry out domestication genomics, starting from the acquisition of a reference genome and genome assembly, to the sampling design for population genomics, paleogenomics, transcriptomics, epigenomics and experimental validation of domestication-related genes. We also describe some examples of the aforementioned approaches and the relevant discoveries they made to understand the domestication of the studied taxa.

Evolutionary History of the Risk of SNPs for Diffuse-Type Gastric Cancer in the Japanese Population

A genome wide association study reported that the T allele of rs2294008 in a cancer-related gene, PSCA, is a risk allele for diffuse-type gastric cancer. This allele has the highest frequency (0.63) in Japanese in Tokyo (JPT) among 26 populations in the 1000 Genomes Project database. F_ST ≈ 0.26 at this single nucleotide polymorphism is one of the highest between JPT and the genetically close Han Chinese in Beijing (CHB). To understand the evolutionary history of the alleles in PSCA, we addressed: (i) whether the C non-risk allele at rs2294008 is under positive selection, and (ii) why the mainland Japanese population has a higher T allele frequency than other populations. We found that haplotypes harboring the C allele are composed of two subhaplotypes. We detected that positive selection on both subhaplotypes has occurred in the East Asian lineage. However, the selection on one of the subhaplotypes in JPT seems to have been relaxed or ceased after divergence from the continental population; this may have caused the elevation of T allele frequency. Based on simulations under the dual structure model (a specific demography for the Japanese) and phylogenetic analysis with ancient DNA, the T allele at rs2294008 might have had high frequency in the Jomon people (one of the ancestral populations of the modern Japanese); this may explain the high T allele frequency in the extant Japanese.

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Genome Diversity and the Origin of the Arabian Horse

The Arabian horse, one of the world’s oldest breeds of any domesticated animal, is characterized by natural beauty, graceful movement, athletic endurance, and, as a result of its development in the arid Middle East, the ability to thrive in a hot, dry environment. Here we studied 378 Arabian horses from 12 countries using equine single nucleotide polymorphism (SNP) arrays and whole-genome re-sequencing to examine hypotheses about genomic diversity, population structure, and the relationship of the Arabian to other horse breeds. We identified a high degree of genetic variation and complex ancestry in Arabian horses from the Middle East region. Also, contrary to popular belief, we could detect no significant genomic contribution of the Arabian breed to the Thoroughbred racehorse, including Y chromosome ancestry. However, we found strong evidence for recent interbreeding of Thoroughbreds with Arabians used for flat-racing competitions. Genetic signatures suggestive of selective sweeps across the Arabian breed contain candidate genes for combating oxidative damage during exercise, and within the “Straight Egyptian” subgroup, for facial morphology. Overall, our data support an origin of the Arabian horse in the Middle East, no evidence for reduced global genetic diversity across the breed, and unique genetic adaptations for both physiology and conformation.

Space is the Place: Effects of Continuous Spatial Structure on Analysis of Population Genetic Data

Real geography is continuous, but standard models in population genetics are based on discrete, well-mixed populations. As a result, many methods of analyzing genetic data assume that samples are a random draw from a well-mixed population, but are applied to clustered samples from populations that are structured clinally over space. Here, we use simulations of populations living in continuous geography to study the impacts of dispersal and sampling strategy on population genetic summary statistics, demographic inference, and genome-wide association studies (GWAS). We find that most common summary statistics have distributions that differ substantially from those seen in well-mixed populations, especially when Wright's neighborhood size is < 100 and sampling is spatially clustered. "Stepping-stone" models reproduce some of these effects, but discretizing the landscape introduces artifacts that in some cases are exacerbated at higher resolutions. The combination of low dispersal and clustered sampling causes demographic inference from the site frequency spectrum to infer more turbulent demographic histories, but averaged results across multiple simulations revealed surprisingly little systematic bias. We also show that the combination of spatially autocorrelated environments and limited dispersal causes GWAS to identify spurious signals of genetic association with purely environmentally determined phenotypes, and that this bias is only partially corrected by regressing out principal components of ancestry. Last, we discuss the relevance of our simulation results for inference from genetic variation in real organisms.

Identifying and Classifying Shared Selective Sweeps from Multilocus Data

Positive selection causes beneficial alleles to rise to high frequency, resulting in a selective sweep of the diversity surrounding the selected sites. Accordingly, the signature of a selective sweep in an ancestral population may still remain in its descendants. Identifying signatures of selection in the ancestor that are shared among its descendants is important to contextualize the timing of a sweep, but few methods exist for this purpose. We introduce the statistic SS-H12, which can identify genomic regions under shared positive selection across populations and is based on the theory of the expected haplotype homozygosity statistic H12, which detects recent hard and soft sweeps from the presence of high-frequency haplotypes. SS-H12 is distinct from comparable statistics because it requires a minimum of only two populations, and properly identifies and differentiates between independent convergent sweeps and true ancestral sweeps, with high power and robustness to a variety of demographic models. Furthermore, we can apply SS-H12 in conjunction with the ratio of statistics we term [Formula: see text] and [Formula: see text] to further classify identified shared sweeps as hard or soft. Finally, we identified both previously reported and novel shared sweep candidates from human whole-genome sequences. Previously reported candidates include the well-characterized ancestral sweeps at LCT and SLC24A5 in Indo-Europeans, as well as GPHN worldwide. Novel candidates include an ancestral sweep at RGS18 in sub-Saharan Africans involved in regulating the platelet response and implicated in sudden cardiac death, and a convergent sweep at C2CD5 between European and East Asian populations that may explain their different insulin responses.

Genetic Diversity of Persian Arabian Horses and Their Relationship to Other Native Iranian Horse Breeds

The principal aims of this study were to explore genetic diversity and genome-wide selection signatures in Persian Arabian horses and to determine genetic relationship of Persian Arabians with other Iranian horse breeds. We evaluated 71 horses from 8 matrilineal strains tracing to 47 mares from the mid to late 19th century, using the equine 670k single nucleotide polymorphism (SNP) BeadChip. Mean observed and expected heterozygosity were (0.43) and (0.45), respectively, average inbreeding measures (inbreeding estimates based on runs of homozygosity and pedigree information) were low, indicating high genetic diversity in Persian Arabian horses. Analysis of population genetic structure using STRUCTURE and principal component analysis suggested that Persian Arabian horses can be divided into 3 groups, however the groups do not match traditional matrilineal strains. In total, 15 genomic regions were identified by at least 2 of the 3 implemented methods, Tajima's D, H, and H12, as potentially under selection in Persian Arabian horses. Most of these peaks were found on chromosome 9, overlapping with QTLs previously associated with horse temperament. Biological function analysis of identified candidate genes highlighted enrichment of GO term "response to lipopolysaccharide" and KEGG pathway "chemokine-mediated signaling pathway," which are associated with immune responses and may have been targets of selection in Persian Arabian horses. Independent analyses of SNP data from 30 horses of 4 other Iranian breeds suggested distinct population structure between Persian Arabian, and Turkemen and Caspian horse breeds. Overall, the results of this study suggest a rich genetic diversity in the Persian Arabian horses and a clear genetic differentiation with Turkemen and Caspian breeds.

Bioinformatic Detection of Positive Selection Pressure in Plant Pathogens: The Neutral Theory of Molecular Sequence Evolution in Action

The genomes of plant pathogenic fungi and oomycetes are often exposed to strong positive selection pressure. During speciation, shifts in host range and preference can lead to major adaptive changes. Furthermore, evolution of total host resistance to most isolates can force rapid evolutionary changes in host-specific pathogens. Crop pathogens are subjected to particularly intense selective pressures from monocultures and fungicides. Detection of the footprints of positive selection in plant pathogen genomes is a worthwhile endeavor as it aids understanding of the fundamental biology of these important organisms. There are two main classes of test for detection of positively selected alleles. Tests based on the ratio of non-synonymous to synonymous substitutions per site detect the footprints of multiple fixation events between divergent lineages. Thus, they are well-suited to the study of ancient adaptation events spanning speciations. On the other hand, tests that scan genomes for local fluctuations in allelic diversity within populations are suitable for detection of recent positive selection in populations. In this review, I briefly describe some of the more widely used tests of positive selection and the theory underlying them. I then discuss various examples of their application to plant pathogen genomes, emphasizing the types of genes that are associated with signatures of positive selection. I conclude with a discussion of the practicality of such tests for identification of pathogen genes of interest and the important features of pathogen ecology that must be taken into account for accurate interpretation.

Young JP, Andersen SU. Symbiosis genes show a unique pattern of introgression and selection within a Rhizobium leguminosarum species complex

Rhizobia supply legumes with fixed nitrogen using a set of symbiosis genes. These can cross rhizobium species boundaries, but it is unclear how many other genes show similar mobility. Here, we investigate inter-species introgression using de novo assembly of 196 Rhizobium leguminosarum sv. trifolii genomes. The 196 strains constituted a five-species complex, and we calculated introgression scores based on gene-tree traversal to identify 171 genes that frequently cross species boundaries. Rather than relying on the gene order of a single reference strain, we clustered the introgressing genes into four blocks based on population structure-corrected linkage disequilibrium patterns. The two largest blocks comprised 125 genes and included the symbiosis genes, a smaller block contained 43 mainly chromosomal genes, and the last block consisted of three genes with variable genomic location. All introgression events were likely mediated by conjugation, but only the genes in the symbiosis linkage blocks displayed overrepresentation of distinct, high-frequency haplotypes. The three genes in the last block were core genes essential for symbiosis that had, in some cases, been mobilized on symbiosis plasmids. Inter-species introgression is thus not limited to symbiosis genes and plasmids, but other cases are infrequent and show distinct selection signatures.

From Summary Statistics to Gene Trees: Methods for Inferring Positive Selection

Methods to detect signals of natural selection from genomic data have traditionally emphasized the use of simple summary statistics. Here, we review a new generation of methods that consider combinations of conventional summary statistics and/or richer features derived from inferred gene trees and ancestral recombination graphs (ARGs). We also review recent advances in methods for population genetic simulation and ARG reconstruction. Finally, we describe opportunities for future work on a variety of related topics, including the genetics of speciation, estimation of selection coefficients, and inference of selection on polygenic traits. Together, these emerging methods offer promising new directions in the study of natural selection.