Previous genetic studies have identified local population structure within the Netherlands; however their resolution is limited by use of unlinked markers and absence of external reference data. Here we apply advanced haplotype sharing methods (ChromoPainter/fineSTRUCTURE) to study fine-grained population genetic structure and demographic change across the Netherlands using genome-wide single nucleotide polymorphism data  with associated geography . We identify 40 haplotypic clusters exhibiting strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. North-south structure is temporally stable, with west-east differentiation more transient, potentially influenced by migrations during the middle ages. Despite superexponential population growth, regional demographic estimates reveal population crashes contemporaneous with the Black Death. Within Dutch and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than standard methods. Genetic variation in modern humans can reveal information about a population’s history and migration patterns. Here, the authors describe the ancestry and geospatial genetic structure of the Netherlands, and demonstrate the utility of haplotype-based covariates in genome-wide association studies. Despite its small geographical size, previous genetic studies of the people of the Netherlands have demonstrated coarse population structure that correlates with its geography, as well as apparent heterogeneity in effective population sizes across provinces2. These observations suggest that the demographic past of the Dutch population has left residual signatures in its present regional genetic structure; however, this has not been fully explained in the context of neighbouring populations and thus far the use of unlinked genetic markers have limited the resolution at which this structure can be described. This resolution limit also confines the extent to which the confounding effects of population structure can be controlled in genomic studies of health and disease such as genome-wide association studies (GWAS). As these studies continue to seek ever-rarer genetic variation with ever-increasing cohort sizes, intricate understanding and fine control of population structure is becoming increasingly relevant, but increasingly challenging3.The Netherlands is a densely populated country on the northwestern edge of the European continent, bounded by Germany, Belgium and the North Sea. The country is divided into twelve provinces and has a complex demographic history, with occupation by several Germanic peoples since the collapse of the Roman Empire, including the Frisians, the Low Saxons and the Franks. Over 17 million individuals now inhabit this relatively small region , Meuse (Maas), Waal and IJssel. These rivers have been speculatively linked to genetic differentiation between northern and southern Dutch subpopulations in previous work1; however the explicit relationship between Dutch genetic diversity and movement of people within the Netherlands has not been directly modelled.Recent studies have showcased the power of leveraging shared haplotypes to uncover and characterise previously unrecognised fine-grained genetic structure within populations, yielding novel insights into the demographic composition and history of Britain and Ireland2 and form a major component of substantial ongoing efforts to better understand human health, disease, demography and evolution. For example, at the time of writing, over 10% of all studies listed in the NHGRI-EBI genome-wide association study (GWAS) catalogue17 include the Netherlands in their “Country of recruitment” metadata. As well as offering insights into demography and human history, refined population genetic studies are important to identify and adequately control confounding effects in genomic studies of health and disease, especially if spatially structured environmental factors contribute substantially to variance in phenotype, which in particular impacts rare variants18.The Dutch have previously received special interest as a model population19, we demonstrate that using shared haplotypes as GWAS covariates significantly reduces this confounding over standard single-marker methods.In this study, we harness shared haplotypes to examine the fine-grained genetic structure and demography of the Netherlands. We show that Dutch population structure is more granular than previously recognised, and is ancient and persistent over time. The strength and stability of the observed structure appears to be tied to the relationship of the Netherlands to neighbouring lands and to its own internal geography, and has likely been shaped over history by migration, but preserved in recent generations by enduring sedentism of genetically similar individuals within regions. We observe genetic evidence of regional population crashes during the Black Death and a countrywide population surge in the 17th century. Finally, we show that the complex genetic structure observed demonstrably confounds GWAS; however, through analysis of the Netherlands and more extensive international data20 and clustered the resulting matrix using fineSTRUCTURE20, identifying 40 genetic clusters at the highest level of the hierarchical tree which segregated with geographical provenance. We explored the clustering from the finest (k = 40) to the coarsest level (k = 2), settling on k = 16 as it captured the major regional splits sufficiently with little redundancy. Clusters at this level were robustly defined by total variation distance  and fixation index  of coancestry followed a strong north-south trend  with PC2 generally explained by a west-east gradient . Further PCs demonstrated more complex relationships with geography  using ChromoPainter, retaining source groups that best matched Dutch individuals for at least 5% of the genome4  and spatially arranged along geographical directions S66°W, N73°E and S73°E respectively, approximately corresponding to declining ancestry gradients directed away from the German and Belgian borders and the North Sea boundary  thresholds set for IBD segment length bins, we have also provided an interactive environment in which Dutch population structure can be explored across a range of IBD segment bins (http://bioinf.gen.tcd.ie/ctg/nlibd).To explore temporal trends in Dutch population structure we called genomic segments of pairwise identity-by-descent (IBD) using RefinedIBDter Fig. , with 8 ter Fig. , corresp14 with 4514 European individuals21 representing modern proxies for admixing sources. Across the Dutch sample, significant admixture dating to 1088 CE (95% CI 1004–1111 CE) was inferred with the major contributing source best modelled by modern Germans and the minor source best modelled by southern European groups   Table  and is c2, northern provinces shared more short segments with southern provinces than southern provinces shared amongst themselves. Together, these results suggest that the north had a smaller ancestral effective population size (Ne) than the south and is probably derived from an ancient or historical founder event forming the northern population from a subset of southerners. We formally characterised ancestral trajectories in Ne between the north and the south of the Netherlands using the nonparametric method IBDNe26 for the entire Dutch sample and two subsamples representing the principal fineSTRUCTURE north/south split  of the 14th century which claimed up to 60% of Europe’s population27. Conversely, Ne estimation within individual Dutch provinces revealed a much more detectable impact of the Black Death  IBD segments, which capture recent shared ancestry, were almost always shared within genetic clusters (and provinces), and rarely between  genotypes alone, indicating latent structure captured by ChromoPainter that is stratified between cases and controls , a scalable approximate haplotype painting method based on the positional Burrows-Wheeler transform28. When run on our original Dutch dataset of 1626 individuals, the structure rendered by PBWT-paint was almost identical to ChromoPainter (As population structure confounds GWAS (for example due to stratification of cases and controls between subpopulations), we investigated the extent to which haplotype sharing captures confounding structure in a Dutch sample of 1963 cases of amyotrophic lateral sclerosis (ALS) and 2774 controls from a recent multi-population GWAS for ALSols Fig. . To estiST) than whole European countries. We have also introduced a novel use of length-binned IBD sharing combined with PCA and Gaussian mixture model-based clustering to characterise changing population structure over time, revealing transient genetic structure layered over strong and stable north-south differentiation in the Netherlands. This is contextualised by somewhat distinct demographic histories between genetic groups in the Netherlands, with consistently lower Ne in the north than the south. A potential source of the north-south differentiation is impaired migration across the east-west courses of the Rhine, Meuse and Waal, which effectively separate southern Dutch populations from the north. The population structure observed in the Netherlands is especially remarkable when considered in terms of the country’s size and extensive infrastructure; notably Denmark, which is roughly equal in geographical area, is genetically homogeneous, forming only a single cluster when interrogated using fineSTRUCTURE29, despite its island-rich geography. Both the United Kingdom and Ireland also exhibit at least one large indivisible cluster constituting a large fraction of the population6, however no extraordinarily large clusters dominate the Dutch sample. Mean FST between Dutch clusters also greatly outmeasures that observed between Irish clusters, suggesting that the extent of population differentiation is higher in the Netherlands, despite Dutch land area being less than half that of the island of Ireland.The genomes of modern humans contain a detailed record of the intricate histories that shaped them. Genomic signatures of these histories are often reflected in present-day population structure and have the potential to confound genomic studies of health and disease through stratification across phenotypic categories. Here, we have studied the Netherlands as a model population, harnessing information from shared haplotypes and recent developments in spatial modelling to gain intricate insights into the geospatial distribution and likely origin of Dutch population genetic structure. The structure identified through shared haplotypes is surprisingly strong; some Dutch genetic clusters identified this way are more mutually distinct , indicating that significant population differentiation exists even within South Holland. Migration and admixture in the highly urbanised Randstad has been proposed as a driver of genetic diversity and loss of geographic structure in this region1; the overlaid geographical distribution of regional ancestry profiles  and southern (SNL) Dutch fineSTRUCTURE populations, revealing superexponential growth in both populations with a sudden increase in rate during the 17th century 2. The apparent absence of Ne decline in 14th-century Netherlands initially hints at the possibility that the Black Death had a weaker impact in the region than elsewhere in Europe; although this agrees with the views of some historians, it is hotly debated by others31. Per province, however, most Ne estimates display a prominent dip at this time  greatly precedes different forms of Christianity  meaning the degree of structure observed is not idealised or exaggerated by sampling, but instead representative of the structure expected in any GWAS that includes Dutch data.As well as internal geography, outside populations have also played an important and significant role in the establishment of population structure in the Netherlands Fig. ; Table 112 on GWAS statistics, using the ALS study from which the Dutch data derive as an exemplar trait. Generally, population-based PCs should not predict case/control status (in the absence of any disease-ancestry interaction); if they do, this indicates that (sub)populations are stratified between cases and controls, introducing bias that artificially inflates GWAS statistics. In both Dutch-only and multi-population analyses, fine-scale genetic structure detected by haplotype sharing (ChromoPainter or PBWT-paint) explained substantially more variance in phenotype (ALS case/control status) than standard SNP-only PCA . Ongoing developments in scalable haplotype sharing algorithms such as PBWT-paint will help to address this problem by facilitating the creation of biobank-scale haplotype sharing resources, simultaneously improving studies of human health and disease and enabling large-scale, fine-grained population genetic studies of human demography. Such resources will likely be particularly useful in studies of rare variation, motivating future work exploring the efficacy of such strategies in correcting confounding where rare variation is a factor.We therefore explored the impact of fine-scale genetic structure described in this study and othersPCA Fig. . This dePCA Fig. . In the n = 1626; subset of stratum sNL3 from a GWAS for amyotrophic lateral sclerosis19) and a European reference dataset subsampled from a GWAS for multiple sclerosis21 . 1422 Dutch individuals had associated residential data (hometown at time of sampling) which were used for geographical analyses. For estimating GWAS confounding, we separately analysed the Netherlands on its own using a larger ALS case/control dataset  and the complete multi-population GWAS dataset19  from which this Dutch subset was derived. Data handling for estimating confounding is further described under “Estimating GWAS confounding” below. For population structure analyses, we applied quality control (QC) using PLINK v1.935; briefly we removed samples with high missingness (>10%), high heterozygosity (>3 median absolute deviations from median) and single-marker PCA outliers (>5 standard deviations from mean for PCs 1-20). We also filtered out A/T and G/C SNPs and SNPs with minor allele frequency <0.05, high missingness (>2%) or in Hardy Weinberg disequilibrium (p < 1 × 10−6). Before running ChromoPainter/fineSTRUCTURE we retained only one individual from any pair or group that exhibited greater than 7.5% genomic relatedness  to avoid bias generated from this association, following previous works. The final Dutch and European reference datasets contained 374,629 SNPs and 363,396 SNPs respectively at zero missingness. The merge of these datasets contained 147,097 SNPs at zero missingness. Data were phased per chromosome with the 1000 Genomes Project phase 3 reference panel36 using SHAPEIT v237 (for ChromoPainter/fineSTRUCTURE) and Beagle v4.1 (for IBD estimation). For these and all subsequent runs of SHAPEIT and ChromoPainter, we used the 1000 Genomes Project Phase 3 genetic map; IBD analyses with Beagle were carried out using the Hapmap phase 2 genetic map38 as used in the RefinedIBD and IBDNe source papers26. Both programmes were run with default settings; allele concordance was checked prior to phasing .We mapped fine-grained genetic structure in the Netherlands using a population-based Dutch ALS case-control dataset , first estimating Ne and μ (switch rate and mutation rate) with 10 expectation-maximisation (EM) iterations , then the model was finally run using these parameter estimates. The fineSTRUCTURE Markov chain Monte Carlo (MCMC) model was then run on the resulting Dutch coancestry matrix with two chains for 3,000,000 burnin and 1,000,000 sampling iterations, sampling every 10,000 iterations. To define European clusters for use in GLOBETROTTER and ancestry profile estimation we instead used 1,000,000 burnin and sampling iterations, sampling every 1000 iterations (due to large sample size). We extracted the state with the maximum posterior probability and performed an additional 10,000 burnin iterations before inferring the final trees using both the climbtree and maximum concordance methods. For all subsequent analyses the maximum concordance tree was used.We used ChromoPainter/fineSTRUCTURE4 and FST. TVD is a distance metric for assessing the distinctness of pairs of clusters, calculated from the ChromoPainter chunklength matrix. TVD is calculated as the sum of the absolute differences between copying vectors for all pairs of clusters, where the copying vector for a given cluster A is a vector of the average lengths of DNA donated to individuals in A by all clusters. Intuitively, the TVD of two clusters reflects distance between those clusters in terms of haplotype sharing amongst all clusters, and is a meaningful method for assessing the effectiveness of fineSTRUCTURE clustering. To assess whether the observed clustering performed better than chance we permuted individuals between cluster pairs (maintaining cluster size) and calculated the number of permutations that exceeded our original TVD score for that pairing of clusters. We used 1000 permutations where possible, and otherwise used the maximum number of unique permutations. P values were calculated from the number of permutations greater than or equal to the observed TVD divided by the total permutations; all p-values were less than 0.001, indicating robust clustering. We generated a TVD tree for clusters from the k = 16 fineSTRUCTURE split by merging pairs of clusters with the lowest TVD successively using methods developed in ref. 8, with the goal of providing an alternative representation of cluster relationships that is independent of sample size  and simultaneously calculating FST between European countries present in ref. 21 for comparison.To assess the robustness of clustering in the Dutch data we calculated TVD21 from Belgium, Denmark, Finland, France, Germany, Italy, Norway, Poland, Spain and Sweden. European samples were first assigned to homogeneous genetic clusters using the fineSTRUCTURE maximum concordance tree6 to reduce noise in painting profiles. We then modelled each Dutch individual’s genome as a linear mixture of the European donor groups using ChromoPainter, and applied ancestry profile estimation method developed in ref. 4 and implemented in GLOBETROTTER14 (num.mixing.iterations: 0). This method estimates the proportion of DNA which is most closely shared with each individual from each donor group calculated from a normalised ChromoPainter chunklength output matrix, and then implements a multiple linear regression of the formWe assessed the ancestral profile of Dutch samples in terms of a European reference made up of 4514 European individuals13. We ran SOURCEFIND on the ChromoPainter output described above using 50,000 burnin and 200,000 MCMC iterations, sampling every 5000 iterations. For each Dutch individual we took the weighted average (weighted by posterior probability) of ancestry estimates with the highest posterior probability taken from 50 independent runs of the algorithm. We aggregated sharing portions across donor groups from the same country to estimate total sharing between an individual and a given country to investigate the regional distribution of sharing profiles. Ancestry gradients generated by each method were regressed against one another to estimate correlation. We report both the results of both NNLS  to generate pairwise matrices of total length of IBD shared between individuals for bins of different segment lengths. To identify population structure captured by IBD sharing patterns we performed PCA on these matrices using the prcomp function in R version 3.2.339 and clustered the IBD matrices using a Gaussian mixture model implemented in the R package mclust40. Plots of model selection are shown in Supplementary Fig. 23 has shown that IBD matrices underperform the linked ChromoPainter matrix in identifying population structure, they are arguably more interpretable for visualising temporal change as they can be subdivided into cM bins corresponding to different time periods, a feature leveraged by emerging work on local population structure25. Patterns in IBD sharing that identify population subgroups in older (shorter) cM bins which are preserved in more recent (longer) bins are interpreted as persistent population structure that has been influenced by mating patterns in old and recent generations. Structure which emerges in a specific cM bin and is lost is likely to reflect transient changes in panmixia that have not necessarily persisted. We approximated the age of segments in a given cM bin using equation s19 from ref. 25, under the assumption that the population is sufficiently large:IBD segments were called in phased data using RefinedIBD14 with the Netherlands dataset as a whole and in individual cluster groups defined from the Dutch fineSTRUCTURE maximum concordance tree . This generated a copying matrix (chunklengths file) and 10 painting samples for each Dutch individual. GLOBETROTTER was run for 5 mixing iterations twice: once using the null.ind:1 setting to test for evidence of admixture accounting for unusual linkage disequilibrium (LD) patterns and once using null.ind:0 to finally infer dates and sources. We further ran 100 bootstraps for the admixture date and calculated the probability of no admixture as the proportion of nonsensical inferred dates (<1 or >400 generations). Confidence intervals were calculated from the bootstraps from the standard model (null.ind:0) using the empirical bootstrap method, and a generation time of 28 years.To infer and date admixture events from European sources we ran GLOBETROTTER41 on the combined Dutch and European samples to explore single marker-based population structure in a set of 41,675 SNPs . ADMIXTURE was run for k = 1–10 populations, using 5 EM iterations at each k value. The k value with the lowest cross-validation error was selected for further analysis using 15 fold cross-validation; where two k values had equal CV-error the lower k value was taken for parsimony . We compared estimated Ne with recorded census size (https://opendata.cbs.nl/statline/#/CBS/nl/dataset/37296ned/table?ts=1520261958200) for approximately equivalent dates (starting at 1946 CE for generation 0 and assuming 1 generation is 28 years) and found that for generations 0 - 3 our Ne estimates were approximately 1/3 of the census population  and for the complete multi-population GWAS . Haplotypes for unrelated individuals , and PBWT-paint (https://github.com/richarddurbin/pbwt) for the considerably larger multi-population GWAS dataset. PBWT-paint is a fast approximate implementation of ChromoPainter suitable for large datasets. PCs of the resulting coancestry matrices were calculated using the fineSTRUCTURE R tools (http://www.paintmychromosomes.com), removing extreme haplotype PCA outliers (>20 SD from mean on PC1-10) followed by repainting as an additional QC step. For comparison we also calculated PCs on independent markers from the SNP datasets using Plink v1.9, first removing long range LD regions44 (https://genome.sph.umich.edu/wiki/Regions_of_high_linkage_disequilibrium_(LD)) and pruning for LD (--indep-pairwise 500 50 0.8). Variance in ALS phenotype explained by ChromoPainter/PBWT-paint PCs and SNP PCs (Nagelkerke R2) was estimated using the glm function and fmsb package45 in R version 3.2.3. To estimate confounding in GWAS inflation, we implemented a logistic regression model GWAS (--logistic) in PLINK v1.9 for each dataset using a range of ChromoPainter/PBWT-paint PCs or SNP PCs  as covariates and ran LD score regression46 on the resulting summary statistics using recommended settings .To examine the contribution of observed fine-grained population structure to GWAS confounding, we estimated how well phenotype could be predicted by principal components of haplotype sharing matrices in a 2016 GWAS for ALSngs Fig. . StructuFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Cerasus serrulata (Rosaceae) is an important flowering cherry resource which is valuable for developing new cultivars of flowering cherries. It is broadly distributed and possesses abundant variations. In this study, phylogeographic analysis was conducted to reveal the evolutionary history to better understand the genetic diversity and genetic structure of C. serrulata so as to provide more accurate molecular insights into better conservation and utilization of the germplasm resources. A total of 327 individuals from 18 wild populations were collected. Three chloroplast DNA (cpDNA) fragments  and the nuclear internal transcribed spacer (ITS) were utilized. The results showed a high genetic diversity at both species level and population level of C. serrulata. High genetic differentiation and the existence of the phylogeographic structure were detected. No significant expansion events were discovered. Two geographic lineages were inferred. One was confined to the Qinling Mountains and the Taihang Mountains. The other was from the Wuling Mountains to the Jiangnan Hilly Regions and then went northeast to the coast of Asia. In addition, some taxonomic treatments of the C. serrulata complex are discussed and reconsidered. Conservation and utilization strategies of wild C. serrulata germplasm resources were recommended. Cerasus serrulata (Rosaceae) distributes in the main subtropical and temperate China as almost the most widely distributed cherry. It shows variable morphological traits, especially in ornamental traits, making it a significantly important cherry resource. We covered the major distributions and collected 18 populations to study the phylogeography of C. serrulata using three cpDNA fragments  and the nuclear ITS to see its spatiotemporal evolutionary pattern to bring insights into evolutionary history of the genus and of plants within this region. We revealed the genetic structure of C. serrulata which also brought taxonomic proofs to the controversial treatments of the C. serrulata complex, and finally found two geographical lineages and inferred the evolutionary routines of C. serrulata. Thus, making good use of this C. serrulata resource will contribute significantly to the development of new flowering cherry cultivars.Flowering cherry is an excellent ornamental flowering tree and is very popular around the world. Nowadays, most cultivars of flowering cherries are mainly developed from Japan. Based on our accumulated observation and study on the C. serrulata (Lindl.) G. Don ex London (Rosaceae) has a wide distribution range from west to east in China and is also distributed in the Korean Peninsula and Japan . These individuals are similar to C. serrulata, but are predominantly short, and the leaves are red and small. There are also controversial taxonomic relationships between C. serrulata and some other species, such as C. xueluoensis .The genomic DNA was extracted by the CTAB‐SiOhod Nan, . Three c2.3Hd/Rd) as well as nucleotide diversity (Pi) were performed using DnaSP v6.0.01  between regions clustered using BAPS 6.0 was calculated using DnaSP v6.0.01  and Cerasus (node 2). These two nodes were subsequently used to calibrate the node ages in the second tree of haplotypes of C. serrulata  trees were constructed to create consensus trees summarizing the posterior tree distribution using the results of BEAST analyses by TreeAnnotator v1.10.4. Final phylogenetic trees were edited in FigTree v. 1.4.4.CpDNA haplotypes were used for analyzing the divergence time dating. PartitionFinder 2  was 0.829 and the ribotype diversity (Rd) was 0.673; the nucleotide diversity (Pi) of cpDNA and ITS was 1.500 and 5.210, respectively. All the data suggested a high genetic diversity of C. serrulata. At population level, Hd varied from 0.000 to 0.700, while Rd varied from 0.000 to 0.733; Pi × 10–3 of cpDNA varied from 0.000 to 1.090, while that of ITS varied from 0.000 to 2.820. Population CPL showed the highest haplotype diversity both in cpDNA and in ITS regions.The total length of aligned sequences for the three chloroplast fragments  was 2,161 bp with 26 variable sites, including 5 sites with alignment gaps, 8 singleton variable sites, and 13 parsimony informative sites in 327 individuals. The total length of ITS was 661 bp with 10 variable sites in 174 individuals. Results of the diversity analyses are shown in Table Hd varied from 0.000 to 0.690, and Rd varied from 0.000 to 0.733, while Pi varied from 0.000 to 0.980 for cpDNA and from 0.000 to 2.820 for ITS  and ITS (Fst = 0.88730) in C. serrulata .Analysis of molecular variance (AMOVA) revealed a high differentiation level of both cpDNA  between different regions were estimated, and gene flow was calculated according to the equation Nm = (1/GammaSt−1)/2 , followed by Region III and Region V . Region I showed a dominantly isolated condition from others with high differentiation coefficients and low gene flow revealed by both DNA fragments. Region IV and Region V appeared extremely differentiated from each other by ITS as no gene flow was observed between them. Region VI appeared to have much less genetic exchanges with Region V through ITS gene flow than chloroplast gene flow.Pairwise genetic differentiation coefficients  . Group 1, concerning Region I in the Qinling Mountains and Taihang Mountains, began its divergence from other groups around 8.84 mya. Group 2 began to diverge out around 7.27 mya (node B). Group 3  started to separate at approximate 2.21 mya (node C). Group 4 and Group 5  started to separate away from each other around 4.31 mya (node D).The divergence time of the most recent common ancestor (TMRCA) of 3.7D and Fu's FS, along with a unimodal mismatch distribution curve, indicates a population expansion , but p > .05  and ITS  analysis proved a high genetic diversity of C. serrulata. This is higher than wild C. pseudocerasus , which is also a widely distributed Cerasus species , which is relatively widely distributed   , and the most dominant source of variations came from variation among regions , signifying the existence of a phylogeographic structure . A similar high diversity level was also detected in C. dielsiana  and Region VI in the junction of China and the Korean Peninsula (population FHS and K‐NS) was the same in both DNA fragment analyses. Grouping of the populations slightly differed in the treatment of the two populations by cpDNA sequences and ITS (nuclear) sequences: HeS and TTZ  were identified from the 18 populations of Z Tables . The twoZ Tables , 5. HighC. serrulata were also recorded in Hebei Province and Heilongjiang Province in China, as well as in Japan. But we failed to obtain samples in these regions. Thus, a lack of comprehensive sampling also caused this confusion.However, the lineage origin of Region VI concerning the junction of China and the Korean Peninsula (population FHS and K‐NS) remained unclear. Haplotypes of cpDNA within this region showed a closer relationship to Region V, and the two regions exchanged relatively sufficient gene flow Figure , Table 4C. serrulata were inferred. One is in the northwest region with group MP, BTM, and LiS. This lineage is confined to the Qinling Mountains and the Taihang Mountains. The other evolved from the southwest region (around the Wuling Mountains), eastward through the Jiangnan Hilly Region, and northward to the plain on the east coast of Asia. Populations around the junction of China and North Korea, as well as in the Korean Peninsula, remained unclear and needed more comprehensive evidence.Taken together, two lineages of the populations of Region I in the southwest region, located in the Qinling Mountains and the Taihang Mountains, appeared to have a relatively isolated condition. This region was the first to diversify out from others, at about 8.84 mya Figure . This waRegion IV  showed a very distinct differentiation pattern. The environment of this region is very special, as it is an alp, with low temperatures and high humidity. This specialized environment should have put high selection pressure on this population, and the high altitude reduced gene flow from other populations. Thus, the distinct differentiation in Region IV was generated.4.3C. serrulata appeared around 8.84 mya in the mid‐ to late Miocene, as calculated in the molecular dating analysis   Cao, . Thus, wC. serrulata, also having regarded their higher genetic diversity. All the lineage colonizations and potential refugia are generally coincident with the result of phylogeographic analysis of Cerasus  with utilization of resources. Despite detecting a high genetic diversity at the species level, diversity levels of many populations and some regions are not remarkable and some even show no diversity , is relatively isolated and ancestral, and the genetic diversity is low, which needs protection. Populations in the southwest region, especially population CPL in the Wuling Mountains (Region II), are most likely to be a current genetic diversity center as the region shows the highest genetic diversity. Furthermore, the origin is relatively ancestral. Thus, this region deserves attention regarding source collection and utilization for cultivar development. Populations in the Jiangnan Hilly Region (Region III) and the Korean Peninsula and the junction of China and North Korea (Region VI) also possess high genetic diversity, which is also worth attention. Furthermore, Region VI is located northernmost and may be valuable in developing cultivars with high cold resistance. Populations on the plain of the coast of east Asia (Region V) show relatively low diversity, which may be because of strong human interference. This resource needs practical protection measures and collection to avoid further resource loss. Habitats of HGS population in the Wuyi Mountains (Region IV) are the most special, as they are sited in alps with low temperature and high humidity. The unique environment should have pressed strong selection on this population, and the high altitude has prevented gene flow from other populations; thus, the analysis showed no genetic diversity of this population. Therefore, protection combining in situ conservation and ex situ conservation is suggested for this resource. Phenotypic characteristics of this population are also unique as the leaves are red and the plant is short and shaped like a shrub. Resources in this region have potential value for developing new cultivars with wet resistance, lower shape, and colored leaves.Flowering cherry possesses good ornamental value and is popular around the world. Nowadays, cultivars of flowering cherries are mainly developed from Japan. According to our accumulated observation and study on the cherry resources of China and Japan, we believe many of the good cultivars have some origin from y Tables , 2. In a5Cerasus serrulata. No dominant expansion events were detected. Finally, two geographic lineages were inferred. One was confined to the Qinling Mountain and the Taihang Mountains. The other was from the Wuling Mountains to the Jiangnan Hilly Regions, and then went northeast to the east coast of Asia. Taxonomic treatments of several related controversial species were reconsidered. C. laoshanensis was suggested to be merged into C. serrulata. And a new variety, C. serrulata var. huangangensis, was preferred.We found high genetic diversity and the existence of phylogeographic structure in None declared.Xian‐Gui Yi: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration (lead); Resources ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Jie Chen: Formal analysis ; Investigation ; Methodology ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Hong Zhu: Data curation ; Investigation ; Methodology . Yong‐Fu Li: Formal analysis ; Investigation . Xue‐Xia Li: Formal analysis ; Investigation . Meng Li: Formal analysis ; Investigation (supporting). Yi‐Fan Duan: Investigation (supporting); Methodology (supporting). Lin Chen: Investigation ; Methodology . Xian‐Rong Wang: Conceptualization ; Funding acquisition (lead); Methodology (lead); Project administration ; Supervision (lead).


Rutilus rutilus) is an important component of the economically and socially valuable recreational fishery and represents a well-studied member of the Cyprinidae. Microsatellite allele data were used to investigate hypothetically variant levels of microevolutionary structuring and isolation-by-distance (IBD) in in the Rivers Stour and Thames. A strong signal of IBD was found in the Stour, probably due to the limited capacity for unrestricted bidirectional dispersal in this river compared with the Thames. A weak inference of IBD in the Thames is likely erroneous and effected by a strong localised genetic signal from a recent stocking event. Whilst we found significantly genetically divergent upstream areas in the River Stour, a strong signal of IBD remained when the headwater sub-population was removed, suggesting that that the signal is not biased by non-equilibrium conditions in upstream reaches. We discuss these results with reference to the management of aquatic bioresources and emphasise the idiosyncrasy that aquatic biota and hydrological complexity may imprint upon patterns of biodiversity within any given system.The spatial distribution of organisms is maintained by a combination of in situ reproduction and dispersal of conspecifics from elsewhere within its habitable range. The determination of dispersal origin and sub-population connectivity has a vital role to play in forming effective management policies. The common roach (The online version of this article (doi:10.1007/s10592-016-0828-3) contains supplementary material, which is available to authorized users. The ability to determine the extent to which sub-populations are connected is vital to understanding, conserving and managing populations  Wright  describeIntraspecific gene flow in aquatic species is amenable to anthropogenic influences. Arterial canals and bank modification may facilitate the long-distance dispersal of fishes, potentially homogenising genetically divergent populations  is a keystone species for the angling community  reported in this species  Table ; Fig. 1 Historically both rivers were tributaries of the English Channel River system that drained most of Western Europe. They were sundered approximately 7500 years ago upon flooding of the English Channel. Recently, the EA’s River Habitat Surveys (RHS) reported that 60 % of sites along the Thames were significantly or severely modified, with only 7 % of sites described as ‘pristine’  and uncorrected (Ho) estimates of heterozygosity . The dataset was permuted 1000 times to determine statistical significance and 95 % confidence intervals (95 % CIs).DNA was extracted from scales using the CTAB method , and to determine linkage disequilibrium between pairs of loci, in all sub-populations, Fisher’s exact tests (Raymond and Rousset FST (θ), and Fisher’s exact tests of allelic differentiation in FSTAT and Genepop, respectively. 95 % CIs for all estimates of pairwise FST were calculated by jackknifing the dataset 1000 times. We assessed levels of hierarchical structuring across all sub-populations by conducting an AMOVA analysis in the program GENODIVE (Meirmans and van Tienderen FST) and genotypic frequencies (co-dominant genotypic distance (CGD)) separately in GenAlEx version 6.5 , and flow rate (m3 s−1). Oestrone concentrations were derived from data collected by the Centre for Ecology and Hydrology, UK, for all rivers in England and Wales, whereas flow rates were derived from the Environmental Agency’s network of flow gauging weirs. EDCs are suspected to increase between-population divergence by increasing reproductive variance at sites with high concentrations through the production of infertile intersex males , prior to the analysis. River distances were all derived from hydrologic data collected by Moore et al.  for the Thames sub-populations: 5, 10, 20, 30, 60, 80 and 100; and as a function of five classes for the Stour sub-populations: 5, 10, 20, 30 and 60. 95 % CIs around point estimates of r, and either side of the null hypothesis of zero genetic structure, were calculated by 999 bootstrap iterations.We estimated contemporary gene flow among sub-populations using a Bayesian method implemented in BAYESASS + version 3.0 , indicating a negligible influence of null alleles. Of the 129 locus-by-location comparisons in the Thames (Table S1), ten were found to violate the assumptions of HWE, close to the 6.45 expected by chance. This number was greater in the Stour, where 20 out of 88 locus-by-location comparisons all showed a deficit of heterozygotes (Table S2).All loci were found to be in linkage equilibrium. The FreeNA analysis found that the relationship between r  and Ho , but not for He  or the mean number of alleles . Of all the diversity metrics, only the mean number of alleles in the Thames increased with downstream distance . Similar findings were reported in other cyprinid species that inhabit similarly differently modified river habitats  and dace (Leucisucus leuciscus) where demographic instability negatively impacted levels of Ar and HO relative to those observed in non-fragmented habitats  and the Stour , respectively. Overall mean FST across all sub-populations was 0.036 (95 % CI 0.020–0.062) similar to those described by Hamilton et al. (FST = 0.028) for microsatellite loci and identical to those reported by Hänfling et al.  and the headwater population of TH in the Stour (mean FST = 0.085). These sub-populations can be visualised by PCA analysis : 76.7 %). The genotypic distances better delineated the Stour and Thames sub-populations from one another, with both rivers forming cohesive aggregates with the exception of TH. Additionally, both plots suggest MWP, and another upstream Stour site-RC-are distinct. Hierarchical structuring across all sub-populations from both rivers is low but significant, although most of the apportioning of genetic variance was found to be among and within individuals . The degree of genetic diversity apportioned between sub-populations within rivers and between rivers accounts for 5 % of the entire total variation  of a similar scale to that previously observed for UK roach . Because divergent outlier populations may overestimate the degree to which IBD is observed . Following Hänfling and Weetman , remaining borderline significant in the Thames after accounting for geography . However, the Partial Mantel test indicated a reversal of correlation in the Stour when a third variable-distance-was controlled for—an example of the so-called Simpson’s Paradox , and in the 5 km class (p = 0.010) within the Stour. Correspondingly, the point at which the plot of r intercepts with distance class is approximately twice as distant in the Thames (17.10 km) than it is in the Stour (8.85 km). These data suggest that the distance above which gene flow no longer effectively counteracts allelic frequency divergence caused by genetic drift is greater in the Thames.The distance at which genetic similarity between individuals became statistically independent was assessed by application of a genetic autocorrelation analysis Fig. . The autFST is low (<0.05) (Meirmans Contemporaneous rates of migration are high (Tables S3 and S4). Both rivers experience significant gene flow . High connectivity complicates interpretation on a system-wide scale when global Meirmans  despite Meirmans . TherefoNe was estimated for each of 22 sub-populations  to 62 (CI 95 % CI 44–94). Mean Thames Ne was 45 (95 % CI 29–77). In the Stour, Ne ranged from 22 (RC: 95 % CI 12-47) to 64 (BL: 95 % CI 45–93). Mean Stour Ne was 43 (95 % CI 30–70). Mean Ne across sub-populations between the two rivers was not significantly different, providing no support to the prediction that mean Ne in the Stour would be lower. For both the Thames and the Stour, there exists a negative linear correlation between distance upstream and the Ne of the sub-population  indicating a significant influence of genetic drift on the standing variation of upstream sub-populations. After correcting for multiple comparisons, the BOTTLENECK analysis inferred no demographic contraction in the Thames nor in the Stour, although MM and TH were borderline significant in the latter .ons Fig. . In the We investigated patterns of genetic structuring and gene flow in a very common sport fish with demonstrably high within-population genetic variability and low levels of intraspecific differentiation , consistent with non-equilibrium conditions. Further confidence in this conclusion is provided by TH receiving fewer migrants than elsewhere, with the exception of BL. The direction of net migration in both rivers was biased in the upstream direction, consonant with known migratory behaviour of roach  possibly enabling greater long-distance dispersal ; and a combination of headwater demography and periodic, anthropogenic low-flow rates (TH). Although just two rivers were compared, the results suggest that the idiosyncratic demographic, historical and natural features of individual riverine habitats will result in unique patterns of both IBD and genetic divergence within and between populations and species. A greater knowledge of population connectivity across species and lentic water bodies will greatly improve the ability to restore and maintain wild stocks of aquatic biodiversity.Supplementary material 1 (DOCX 1501 kb)Below is the link to the electronic supplementary material.


Much of the enormous phenotypic variation observed across human populations is thought to have arisen from events experienced as our ancestors peopled different regions of the world. However, little is known about the genes involved in these population-specific adaptations. Here, we explore this problem by simultaneously examining population-specific genetic and expression differentiation in four human populations. In particular, we derive a branch-based estimator of population-specific differentiation in four populations, and apply this statistic to single-nucleotide polymorphism and RNA-seq data from Italian, British, Finish, and Yoruban populations. As expected, genome-wide estimates of genetic and expression differentiation each independently recapitulate the known relationships among these four human populations, highlighting the utility of our statistic for identifying putative targets of population-specific adaptations. Moreover, genes with large copy number variations display elevated levels of population-specific genetic and expression differentiation, consistent with the hypothesis that gene duplication and deletion events are key reservoirs of adaptive variation. Further, many top-scoring genes are well-known targets of adaptation in Europeans, including those involved in lactase persistence and vitamin D absorption, and a handful of novel candidates represent promising avenues for future research. Together, these analyses reveal that our statistic can aid in uncovering genes involved in population-specific genetic and expression differentiation, and that such genes often play important roles in a diversity of adaptive and disease-related phenotypes in humans. Human phenotypes vary widely across the globe. In particular, geographically separated populations often differ in skin pigmentation , hair coDue to the abundance of whole-genome sequence and polymorphism data for many human populations , much woFST , which hFST  and to iFST . Howeverelection . To addrelection . PBS haselection  and dietelection , as wellelection .However, because natural selection acts on phenotypes, analysis of genetic data only enables assessment of its indirect effects. For this reason, it may be advantageous to study selection more directly by exploiting the recent availability of RNA-seq data for several human populations . SpecifiIn the present study, we simultaneously explore population-specific genetic and expression differentiation in four human populations: the Toscani in Italia (TSI), British in England and Scotland (GBR), Finnish in Finland (FIN), and Yoruba in Nigeria (YRI). For these analyses, we employ single-nucleotide polymorphism SNP;  and RNA-FST  and its FST , W and Y , X and Y , and X and Z . Then, we can use these distances to compute the length of each external branch by following the schematic pictured in 4 value of the gene in population W is calculated as PBS4,WWE,XWE,YWE,ZXE,YXE,Z4 and expression PBS4 of each gene in TSI, GBR, FIN, and YRI populations  . Then, wp1) , and set the threshold Genes were ranked by their genetic PBS12, 2020 , 2009, w12, 2020 , 2009. FP value of the permutation test as the proportion of times the absolute value of this difference was greater than or equal to the absolute value of the observed difference in the data. Student’s t-tests were used to assess the statistical significance of correlation coefficients shown in All statistical analyses were performed in the R software environment . Two-samIdentifying drivers of human phenotypic differentiation is crucial to understanding adaptive events that occurred in the past, as well as to developing population- and individual-targeted treatments for diseases in the future . Though 4 do not have common outlier genes (4 should thus be interpreted with caution. In spite of this issue, a handful of genes with the largest expression PBS4 are well-known candidates of adaptation, such as VDR (Yet, there are three key limitations of the data analyzed here that must be considered when interpreting our findings in the context of human evolution. The first is that there is only a single estimate of the expression level of a gene in each population, which is particularly problematic given the complex and dynamic nature of gene expression data. In contrast, there are multiple SNPs per gene in each population, and genetic data are static. Therefore, we expect our estimates derived from expression data to have lower accuracy and higher variance than those from genetic data. Indeed, we found that gene trees constructed with er genes  online, h as VDR . MoreoveMCM6 having the largest genetic PBS4 value in both TSI and GBR, which are the most closely related of the three European populations studied. Thus, though genome-wide patterns of genetic and expression differentiation are consistent with population relationships, caution needs to be taken when making inferences based on the genetic and expression PBS4 values of individual genes. Despite this limitation, several genes with the largest genetic PBS4 values, such as MCM6 and HLA-DPA1, are well-established targets of natural selection (The second caveat is that TSI, GBR, and FIN are closely related European populations. As a result, genetic distances among them are small, which can lead to noise in gene-level analyses. Moreover, due to shared ancestry and gene flow among these closely related populations, their genetic and expression differentiation are likely to be correlated. This limitation is clearly demonstrated by election , and novThe third limitation is that the RNA-seq data used in this study were obtained from lymphoblastoid cell lines. In particular, the enrichment of immune-related functions in genes with high levels of population-specific expression differentiation may be attributed to usage of this cell line, rather than reflecting widespread evolutionary patterns of immunity genes across tissues. Yet, it is important to note that associations between increased population-specific expression differentiation and immunity are consistent with previous findings. Specifically, immunity genes are among the fastest evolving genes in the human genome, likely due to adaptations to rapidly changing environments and introductions of novel pathogens . Therefo4, a novel summary statistic that can be used to estimate population-specific differentiation of a quantitative trait in four populations. PBS4 requires minimal assumptions about the data and can be used to rapidly estimate population-specific differentiation on a genome-wide scale. Further, because PBS4 utilizes data from four populations, branch lengths are more likely to represent true population-specific differentiation than differentiation that occurred ancestral to two populations, as is possible in a three-population scenario (4 can easily be applied to existing or future data sets to estimate population-specific differentiation of a wide array of genetic, expression, and other measurable traits in humans and other species. In particular, we envision that application of PBS4 to future human RNA-seq data from multiple cell lines or tissues and in many populations of varying divergence levels will shed light on complex questions about human evolutionary history and disease processes.In spite of the noted issues with the data analyzed here, a major advantage of our study is the design of PBSscenario . TherefoGenome Biology and Evolution online.evaa021_Supplementary_DataClick here for additional data file.


Ipomoea purpurea, and to determine if genetic parallelism underlies the repeated evolution of resistance across replicate resistant populations. We found no evidence for changes in 5‐enolpyruvylshikimate‐3‐phosphate synthase (EPSPS), glyphosate’s target protein, that were associated with resistance, and instead identified five genomic regions that showed evidence of selection. Within these regions, genes involved in herbicide detoxification—cytochrome P450s, ABC transporters, and glycosyltransferases—are enriched and exhibit signs of selective sweeps. One region under selection shows parallel changes across all assayed resistant populations whereas other regions exhibit signs of divergence. Thus, while it appears that the physiological mechanism of resistance in this species is likely the same among resistant populations, we find patterns of both similar and divergent selection across separate resistant populations at particular loci.The repeated evolution of herbicide resistance has been cited as an example of genetic parallelism, wherein separate species or genetic lineages utilize the same genetic solution in response to selection. However, most studies that investigate the genetic basis of herbicide resistance examine the potential for changes in the protein targeted by the herbicide rather than considering genome-wide changes. We used a population genomics screen and targeted exome re-sequencing to uncover the potential genetic basis of glyphosate resistance in the common morning glory,  Ipomoea purpurea, a noxious agricultural weed. We identified multiple regions of the genome that exhibit signs of selection, and found genes involved in herbicide detoxification to be enriched within these regions. Interestingly, while one genomic region under selection exhibited a similar haplotype among resistant populations, other regions of the genome under selection exhibited signs of divergence. Overall, our results find evidence for both parallel and nonparallel genetic changes associated with resistance, suggesting there are more genetic avenues underlying the adaptation to herbicide than previously considered.Although there are many examples of herbicide resistance among natural populations of weeds, it is unknown if the same genetic mechanism underlies its repeated evolution across the landscape. Using a population genomics screen and exome re-sequencing, we examined the genetic basis of RoundUp resistance across populations of  Due to  species ,8,9. Fur species ,10, whica priori known to produce the protein targeted by the herbicide  rather nce, TSR , which ince, TSR ,12. Intrnce, TSR , and evence, TSR . BecauseEPSPS) [Previous research on the genetic basis of resistance to the herbicide glyphosate, the active ingredient in the widely used herbicide RoundUp, has focused largely on the potential for changes at the target site, the enzyme 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) , which iEPSPS) . ConformEPSPS) ; howeverIpomoea purpurea is a common agricultural weed that shows both within- and among-population variation in the level of glyphosate resistance: while some populations of this species across its range in the southeastern and Midwest United States exhibit high survival following herbicide application (high resistance), other populations exhibit low survival (high susceptibility) [via gene flow [ibility) . The patibility) , and no ene flow . We haveene flow , suggestene flow ,19. Addiene flow . Intriguene flow . These dI. purpurea sampled from agricultural fields with a history of glyphosate exposure. We first evaluate the potential for sequence changes in the EPSPS locus and find there are no changes that correlate with resistance, providing evidence that target site resistance is not responsible for the resistance phenotype across the examined populations. We then perform a population genomics screen to identify loci that exhibit signs of selection—thus putatively responsible for the resistance phenotype—and to determine if patterns of relatedness between resistant populations suggest a single or multiple origins of resistance. We follow up on this screen with exome resequencing of candidate resistance loci, and determine if populations share a similar haplotype structure, which would suggest that a similar genetic basis was responsible for resistance across the landscape. We find regions of the genome that show evidence of selection across resistant populations to contain genes responsible for herbicide detoxification. Additionally, patterns of haplotype sharing among populations suggests both parallel and nonparallel genomic responses underlie resistance among populations. Overall, our results suggest that evolutionary constraints may underlie herbicide adaptation, but that patterns of selection across the genome indicate the potential for both parallel and divergent responses.Our overarching goal is to determine if the same genetic basis is responsible for glyphosate resistance across separate populations of EPSPS (copy A and B) from geographically separate populations of I. purpurea  and genotyped 10 individuals sampled as seed from each of four resistant populations and four susceptible populations  to do so. BayeScan identified 42 loci that were outliers while bayenv2 identified 83 loci whose allele frequencies were correlated with the level of resistance (Dataset S1). Using GO assignments (Dataset S1), we found that the top three biological processes for the resistance outlier loci were proteolysis, protein phosphorylation, and regulation of transcription. Of special note, we identified a glycosyltransferase among the outlier loci, a member of a gene family shown to be involved in herbicide detoxification in other species [We next performed a genome-wide outlier screen to identify loci exhibiting signs of selection and thus potentially involved in glyphosate resistance in  species ,22,23.STs of outliers = 0.327, 95% CI = 0.293–0.362) compared to the level of differentiation among susceptible populations . This contrasted with genome-wide patterns of FST , susceptible populations FST = 0.133 (0.128–0.137)). Further, the pattern was the same for outliers regardless of whether they were identified by BayeScan or bayenv2. This increased differentiation of outlier loci among resistant populations could be a result of drift, or could indicate that a different genetic basis underlies resistance across populations. Two resistant populations from central Tennessee (SPC and WG) exhibited substantial overlap in allele frequencies of outlier loci , previously reported differentially expressed genes associated with resistance [i.e. off-target sequences; 37%), and a few from the control probes  of which fell within the outlier enriched regions described below).We next performed target-capture re-sequencing of the genes located near (or containing) outlier SNPs identified by the population genomics screen. Using both a assembly  . Some of the five regions contained outliers identified by both bayenv2 and BayeScan while other regions had outliers primarily identified by bayenv2 . The outlier enriched regions were not located near or within the centromere for any chromosome  were less likely to be near genes from these four families than those within the regions —which are gene families hypothesized to be involved in non-target site resistance fication . Resamplfication , suggest regions .ST, which is FST generalized to multiple alleles). Although all regions showed an average GST > 0.20, the enriched region on chromosome 10, spanning ~0.28MB, displayed the highest GST . Within this region, we found higher nucleotide diversity among susceptible compared to resistant individuals . None of the non-synonymous SNPs within this region were fixed within the resistant populations, but were very close to fixation . Within the outlier-enriched region of chromosome 13, we identified an ABC transporter gene with 6 non-synonymous SNPs (shown with asterisks in Given signs of positive selection on the outlier enriched regions of chromosome 10 and (to a lesser extent) chromosome 13, we examined the genes found within these two regions in greater detail. Within the outlier-enriched region of chromosome 10, we identified 7 glycosyltransferase and 9 cytochrome P450 genes, with the 7 glycosyltransferase genes found tandemly repeated within a span of 42 kb . Sevente2 values (the correlation coefficient between each SNP pair as our estimate of LD) to be low, ranging from 0.037–0.046 . Notably, the chromosome 10 outlier-enriched region exhibited the highest r2 value  along the chromosome  to determine an expected background amount of linkage between SNPs and thus an idea of the efficacy of our RADseq followed by exome-resequencing approach for identifying the genetic basis of resistance among populations. Across each chromosome, we found the average r37–0.046 . In compue 0.88, . Becauseomosomes .via herbicide application. Using hierarchical clustering, we examined the haplotype structure among outlier-enriched regions in more depth with the idea that a similar haplotype among separate resistant populations would point to a shared genomic basis underlying at least some of the loci indicated in herbicide resistance and another indication of selection on those loci. Using each sequenced contig from the outlier-enriched regions , we assigned individuals to one of two haplotype groups based on genetic distance—either the group that contained the majority of individuals from highly susceptible populations (hereafter the ‘S’ group) or the other group (hereafter the ‘R’ group). We found that a consistently high proportion of individuals from the resistant populations had the R group haplotype (>75%) in the chromosome 10 outlier enriched region , indicating the most likely selected site. Notably, this position is within the two SNP windows that exhibited signs of selection from estimates of Tajima’s D and Fu and Way’s H. Further examination of the standing variant model at this position shows the parameters that result in the highest likelihood are very low standing allele frequency (g = 10−6) and very high selection (s = 1), with the amount of time that the beneficial allele has been standing in the populations prior to selection, or t, estimated to be 5 generations (i.e. gene flow between populations) and the five generations are interpreted as the time between gene flow between populations and the onset of selection. We ran the model with a denser grid of t (0–10 generations) and found that the likelihood value was highest when t was equal to 0, indicating that the beneficial allele was immediately advantageous after introgressing and began sweeping rapidly within populations. In comparison, for the migration model, the parameters that result in the highest likelihood are a migration rate of 1 and high selection (s = 0.65). Overall, our analyses of this region strongly supports a model where gene flow introduced the beneficial allele(s) into populations, which then began sweeping quickly and immediately. A rapid sweep like that proposed here would not allow for recombination to break down the haplotype introgressing along with the selected allele. This fits our expectations from the haplotype patterns above since there is high similarity between resistant populations over long stretches of this region.From the analysis of chromosome 10 we find the migration and standing variation models to show similarly high log-likelihood ratios . All threrations . Becausevia gene flow and then began sweeping rather quickly, but at various rates depending on the strength of selection.As with the region on chromosome 10, the migration and standing variation models exhibited similarly high log-likelihood ratios for the enriched region on chromosome 6. A very short standing time (t = 5) was predicted by the standing variant model for this region along with strong selection (s = 1) on a low-frequency variant . FurtherI. nil genome) with the standing variant model displaying the highest log-likelihood value. Unlike the other chromosomes, the standing variant model had the highest likelihood where the minimum amount of time the beneficial allele was standing prior to selection was large (t > 3000 generations) and the initial frequency of the standing variant was very small (10−6). These results suggest that there is little similarity in this region between resistant populations, but high similarity within a population due to the very high selection coefficient estimate (s = 1). There are two scenarios that could explain this pattern: either the selected allele may have been present as standing genetic variation well before the use of the herbicide  or there were independent mutations across the resistant populations. Independent mutations would leave a similar genomic signature as the standing variation model and these two models overlap in the parameter space where the standing variant is very old  and the initial frequency of the standing variant was small (10−5). Interpreting the results from this chromosome is less straight-forward than the others, and future work with a higher density of SNPs will be required to differentiate between models within this region.We found a different pattern when examining the outlier enriched regions of chromosomes 13 and 15 . For theold  in the populations tested as there are no nucleotide sequence differences in the target locus, EPSPS, that correlate with resistance. Additionally, we previously reported that transcripts of EPSPS are not overexpressed in I. purpurea [In this work, we examined the evolution of glyphosate resistance across geographically separate populations of the common morning glory, purpurea , suggestpurpurea –38. We dIpomoea purpurea is a noxious crop weed found in disturbed agricultural sites in the Southeastern and Midwest US. Our previous work examining the level of resistance among 47 populations showed that resistance appeared on the landscape in a mosaic fashion, with highly resistant populations interdigitated among highly susceptible populations. This phenotypic pattern suggested resistance was independently evolving across populations [after the introduction of the herbicide [ST = 0.127; [= 0.127; . These f= 0.127; . Thus, wEPSPS, we combined a population genomics screen and exome resequencing to identify potential candidate loci underlying resistance. This strategy identified five candidate regions of the genome that were enriched with loci exhibiting signs of selection. The pattern of genomic differentiation within these five regions was greater than that of genome-wide, background differentiation—suggesting a response to herbicide selection. None of these regions were physically located near the centromere, which has been shown in other species to be areas of reduced recombination and thus high differentiation [I. purpurea.Given the lack of structural or expression-related changes to the target-site locus, ntiation –43. We ii.e. >2 Tajima’s D and Fu and Way’s H), which in this system would most likely be driven by crop rotations leading to herbicide on and off years, i.e., a pattern of alternating selection [Intriguingly, we identified balancing selection among susceptible populations for this region on chromosome 10  ae phases ,47–49. Apurpurea , supportI. purpurea, it is important to note that we found low levels of linkage disequilibrium between SNP markers . This suggests our initial reduced representation screen, which influenced the target exons we chose for exome resequencing, likely missed portions of the genome responding to selection from the herbicide. It also suggests, however, that the positive associations we did uncover  are likely to be loci that are involved in resistance, or very tightly linked to loci involved with resistance. Importantly, linkage was strongly elevated across outlier enriched regions compared to background levels of linkage for each of the chromosomes. These areas of increased linkage (defined as r2 > 0.25) in each outlier enriched region ranged between 84 kb to 3 MB in length, and support our findings of a genomic response to herbicide selection.While our reduced representation population genomics and exome resequencing strategy has identified strong potential candidate genes associated with glyphosate resistance in via migration to new locations. This, in addition to the ‘mosaic’ appearance of resistance among populations suggested that selection on standing variation was responsible for the repeated appearance of resistance in this species across the landscape. Another likely scenario, however, is one where migration introduced beneficial allele(s) that introgressed into the local background and then rapidly increased in frequency when exposed to very strong selection. The region under selection on chromosome 10 appears to follow this pattern. We found an identical haplotype within this region in high frequency across the resistant populations , and our formal test of convergence identified a very short standing time of the variant within this region (t = 0). Thus, the most likely model is one in which gene flow shared beneficial allele(s) between populations which then started sweeping quickly and immediately, or within a few generations. This is likewise supported by our finding of low genome-wide patterns of linkage between SNPs, and evidence of a selective sweep, as indicated by low nucleotide diversity in this region and marks of positive selection indicating a high frequency of derived variants. Because this species employs a mixed mating system  = 0.5; [via outcrossing and then increase in frequency given strong selection.Our initial population genomics screen across a genome-wide panel of ~8K SNPs showed that resistant populations were not more related to one another than they were to susceptible populations, as would be expected under the simple scenario where resistance evolved once and moved ) = 0.5; ), it is via migration between the WG and SPC populations. The haplotypes of the other outlier enriched regions are in lower frequency among the other two resistant populations, BI and DW; further, pairwise FST values between resistant populations for the outlier enriched regions of chromosomes 1, 6, 13, and 15 were higher than the values among susceptible populations. Although the formal tests of convergence estimated very high selection across all regions (s = 0.4–1), the enriched regions on chromosomes 1, 6, 13, and 15 exhibited somewhat different evolutionary histories, perhaps reflected in the lower frequency of shared haplotypes among populations compared to that of the enriched region on chromosome 10. The enriched regions on chromosomes 1 and 6 both exhibited very short standing times of the allele(s) within the enriched regions, similar to chromosome 10, suggesting that beneficial alleles within these regions began sweeping quickly after introduction to the populations via gene flow. In comparison, the standing time of the variants under selection prior to the onset of selection in the enriched regions of chromosomes 13 and 15 was much longer (t ≥ 3000), indicating that the selected allele(s) likely arose independently or were present and maintained at low frequencies within these populations well before the widespread use of the herbicide in nature.Haplotypes from the other four outlier enriched regions were less consistently shared among the four highly resistant populations. The ‘resistant’ haplotype of the five outlier enriched regions were similar and in high frequency (>50%) in populations WG and SPC, and the genome-wide patterns of allele frequencies were also very similar between these two resistant populations . This suOverall, these findings suggest several non-mutually exclusive possibilities. One is that resistance in this species is mostly attributable to the region on chromosome 10 that is shared and highly similar among resistant populations, with signs of selection from the other regions attributable to other factors, such as selection for reducing the cost of resistance, or selection for other factors associated with local adaptation. In support of the cost reduction hypothesis, individuals from the resistant BI population from TN share only the haplotype found on chromosome 10 in common with the other resistant populations and yet also exhibit a higher cost of resistance than SPC and WG . This mI. purpurea, studies from other species have suggested that changes to a single step in the detoxification pathway are enough to provide some level of herbicide resistance [per se, these detoxification genes could enable the plant to survive the resulting oxidative stress after being exposed to herbicide, a mechanism that may allow for resistance to several different herbicides [i.e. the ability to handle oxidative stress—could potentially underlie glyphosate resistance in I. purpurea. Further studies will be required to first verify that detoxification underlies glyphosate resistance in this species, and second to differentiate between the direct detoxification of glyphosate or an adaptive ability to respond to oxidative stress.In support of the hypothesis that one region is largely responsible for resistance, and under the assumption that the candidate detoxification loci are responsible for resistance in sistance . Howeversistance ,52, suggrbicides . This exI. purpurea, and whether or not the same genomic features respond to selection among populations.While there is strong evidence in support of genetic parallelism from cases of target-site resistance in other species ,53, the Overall, our results suggest that genes responsible for herbicide detoxification are likely responsible for resistance in this species, with the important caveats that at this point we have yet to functionally verify any candidate loci, and that further, we cannot determine if direct detoxification of the herbicide is occurring or if the species is able to respond to subsequent oxidative damage caused by the herbicide. While we previously hypothesized that resistance across populations was due to selection on standing and shared genetic variation , the resIpomoea purpurea is a weedy annual vine that is native to the central highlands of Mexico and was introduced to the United States prior to the arrival of Columbus [summarized in [Columbus . It is aColumbus  and emplColumbus . It is crized in , 17–20.I. purpurea . Primers were used in a PCR to amplify the EPSPS coding regions using Qiagen’s Taq PCR Master Mix kit, followed by cleaning using GE’s Superfine Sephadex. Samples were then Sanger sequenced by the sequencing core at the University of Michigan. We identified two copies of EPSPS in I. purpurea (notated here as copies A and B); each individual had two distinct copies of the locus and some individuals exhibited allelic forms of one of the copies. Bases were scored using PHRED [EPSPS were aligned across all individuals using MafftWS [via Jalviewer [χ2 test to determine if allele frequencies varied between resistant and susceptible populations, and likewise determined if allele frequencies were correlated with population-level resistance values using Pearson's correlation. P-values were adjusted for multiple tests using the Benjamini and Hochberg [EPSPS, we downloaded several protein sequences from GenBank and aligned them to our translated amino acid sequences using tCoffee [From the populations collected, we chose six high resistance  and five low resistance populations  that spanned the range of the collection in the U.S [ng PHRED  followed MafftWS  via Jalvalviewer   and 4 high resistance populations  (DNA was extracted using a Qiagen Plant DNeasy kit. Genomic DNA was converted to nextRAD sequencing libraries (SNPsaurus). The nextRAD method for GBS (genotyping-by-sequencing) uses a selective PCR primer to amplify genomic loci consistently between samples; nextRAD sequences the DNA downstream of a short selective priming site. Genomic DNA (7 ng) from each sample was first fragmented using a partial Nextera reaction , which also ligates short adapter sequences to the ends of the fragments. Fragmented DNA was then amplified using PhusionÂ Hot Start Flex DNA Polymerase (NEB), with one of the Nextera primers modified to extend eight nucleotides into the genomic DNA with the selective sequence TGCAGGAG. Thus, only fragments starting with a sequence that can be hybridized by the selective sequence of the primer were amplified by PCR. The 80 dual-indexed PCR-amplified samples were pooled and the resulting libraries were purified using Agencourt AMPure XP beads at 0.7x. The purified library was then size selected to 350–800 base pairs. Sequencing was performed using two runs of an Illumina NextSeq500 . This resulted in 552 million 75 bp reads total, with an average of 6.9 m reads per individual (Genbank: PRJNA515629).i.e. above 20,000 or below 100). The remaining reads were aligned to each other using BBMap [To control for repetitive genomic material or off-target or error reads, coverage per locus was determined using reads from 16 individuals and loci with overly high or low read counts were removed (ng BBMap  with minng BBMap . The resng BBMap . SNPs weIS) were calculated via poppr [Basic population genetic statistics  and a locus specific factor. If the locus-specific factor significantly improved the model, it implies that something abnormal is occurring, which is assumed to be natural selection. We used the default settings  to identify loci that showed evidence of high FST between the resistant and susceptible populations.We used two outlier-based programs to identify potential loci under selection. We first used BayeScan (version 2.1) , which avia bowtie) with either the I. purpurea or Lycium sp. transcriptome [The second program, bayenv2, identifies correlations between locus specific allele frequencies and an environmental variable ,72; in ocriptome   for each pair of populations to calculate the average FST among resistant populations and susceptible populations. To obtain 95% confidence intervals around these estimates, we performed the same steps on 1000 randomly selected sets of loci (sampled with replacement).We compared pairwise Fusing 4P . We calcusing 4P  pairwiseI. purpurea individual. To generate the draft genome sequence, DNA from a single individual was sequenced using PacBio (11 SMRT cells) and Illumina  sequencing (Genbank: VALG00000000). PacBio reads were filtered for adaptors and to remove low quality (<0.8) and short read lengths (<500 bp). Illumina reads were trimmed of low quality sequences using trimmomatic [We annotated RAD-seq outliers by using a BLASTN analysis to align their contigs to a draft genome sequence from highly homozygous mmomatic . Illuminmmomatic . This remmomatic  was usedmmomatic . This remmomatic , FGenesHmmomatic , SNAP [8mmomatic  and tRNAmmomatic , which wI. purpurea transcriptome (GenBank PRJNA216984) [I. purpurea genomes described above and then selected the resulting coding sequences from the putative genes . Additionally, we randomly chose an even number of transcripts from the transcriptome [We next designed probes for exome sequencing of loci that were either identified from our population genomics RAD-seq screen or loci have been shown to correlate with resistance in other species. We used a variety of methods to select possible capture probe sequences. First, we used a BLASTN  analysisA216984)  and seleA216984)  which cocriptome  to serveI. trifida genome [These 436 sequences were then used to design the capture probe candidates. Candidate bait sequences were 120 nt long, with a 4x tilling density. Each bait candidate was BLASTed against the a genome , and a hTo generate material for sequencing, five seeds from each of the 8 populations used in the previous population genomics screen , Table 1de novo contigs to the probes to estimate the percentage of SNPs that were from target capture probes. Fifty-one percent of the SNPs mapped to the original probe sequences. As a control, we compared the per site nucleotide diversity (π) between Sanger sequenced EPSPS and that of the exome resequencing. We found π to be similar between the two  suggesting that our exome re-sequencing data is similar in quality. In addition to analyzing these contigs, we also examined the contigs that did not map to the probe sequences (hereafter non-probe contigs). The coverage for non-probe contigs was lower than that for probe contigs (23x average vs 33x), however because 13x coverage is sufficient to call heterozygous SNPs in a diploid [We next assembled the sequenced reads into contigs to perform SNP calls. We used trimmed sequences from one individual and default settings in Megahit  to assem diploid  we chose diploid ; e-valueWe used BayeScan and bayenv2 as above to identify putative adaptive loci from the targeted exome re-sequencing. To reduce the effect of linkage among loci, we randomly chose 1 SNP per 1000 bp using vcftools . BayeScan and bayenv2 were used as before to identify outliers. To estimate the neutral population structure for bayenv2, 2000 contigs were randomly selected from contigs that did not map to the probes designed for the outliers. To determine whether the outliers we identified were due to other selective forces and not herbicide resistance, we determined outliers for random groups of populations. We randomly assigned populations to 2 groups, with the provision that the groups were not the same as the herbicide resistance grouping. These new groupings were then used in BayeScan to identify outliers using the target capture genetic data. This procedure was repeated for a total of 100 times, and outliers were determined using a 0.05 FDR cutoff.I. nil [i.e. 71% of outlying SNPs). We delimited each of these ‘enriched regions’ by the first and last outlier of each region, and searched the regions for genes involved in non-target site resistance using the following GO terms and gene names: GO:0009635, GO:0006979, GO:0055114, glycosyltransferase, glutathione s-transferase, ABC transporters and cytochrome P450s. We randomly selected 5 regions of the same size from the I. nil genome and counted the number of genes from the above gene families to determine if outlier enriched regions contained more of these genes of interest than expected due to chance. We repeated this 1000 times to create an empirical distribution, which was then used to determine the percentile of the observed data. As a caveat, while this randomization method does simulate the region sizes, it does not account well for variation in gene density across the genome. We next determined if outliers outside of the enriched regions were more, less, or equally likely to be located near a gene family of interest . To do so, we counted the number of genes of each family within ~4MB (the largest of the 5 regions) from outliers found outside the five enriched regions and then compared the distributions of these outliers to those found within the enriched regions. We used CooVar [I. nil gene models to predict the protein level changes for each SNP that aligned to the I. nil genome and to determine if SNPs were from nonsynonymous or synonymous sites.We placed the SNPs into a genomic context by aligning them to the I. nil  genome uI. nil  and liftI. nil . A totald CooVar  with theST [πS/πR), Tajima’s D [ST estimates [ST values will occur when there is little genetic variation, and thus we set any negative value to zero. We then compared the average pairwise FST of the resistant populations versus the susceptible populations within the 5 outlier enriched regions.For estimates of genetic differentiation and diversity, we calculated GST , nucleotjima’s D , and Fu jima’s D  over 25 jima’s D . For thejima’s D , 99. Addstimates  among thi.e. those that are genetically different from the majority of the susceptible individuals and presumably have the allele that aids in resistance).We used hierarchical modeling to determine if the resistant populations had similar haplotype structure in outlier containing regions of the genome, which would potentially indicate that resistance is controlled by the same genetic basis across populations. We grouped sequenced individuals into either those that exhibited the putative susceptible allele (‘S’ group) or the putative resistant allele (‘R group’) for each contig. To do so, the pairwise genetic distance between each individual was calculated based on all SNPs in each contig using the dist.gene command from the ape package  in R 1. This ge2) between each SNP pair across each chromosome using the program GUS-LD , a likevia gene flow, or were shared among populations due to selection on standing ancestral variation. We first estimated an F matrix to account for population structure using SNPs from scaffolds on Chr 3, 7, and 14 that showed no evidence of selection from our outlier analyses  to apply the inference framework to the region on chromosome 10 that exhibited signs of selection. We estimated the maximum composite likelihood over a grid of parameters used to specify these models , NextRAD sequencing data (Genbank: PRJNA515629), genome assembly (Genbank: VALG00000000) and Exome resequencing data (Genbank: PRJNA515629) are available in GenBank.S1 FigIpomoea purpurea EPSPS protein sequence (bottom two sequences) with other reported EPSPS proteins in the NCBI database  shows no sequence variation within the region known to affect herbicide resistance (inside red outline). Non-synonymous changes (red and blue arrows) outside of this region likewise do not correlate with resistance (Comparison of amino acid sequences of sistance .(DOCX)Click here for additional data file.S2 FigOn the tree, populations are denoted by color and tip labels; values at nodes are percent bootstrap support; population level resistance is denoted by the color in the column .(DOCX)Click here for additional data file.S3 Fige.g. BI) while others are fairly homogenous (e.g. SH).At K = 2 FastStructure results for the RADseq data do not show the resistant populations (first four populations on the left) segregating into a distinct group, suggesting they are not from a single origin. FastStructure analysis suggests either K = 6 or K = 7 as the best model, both of which leads to some populations being highly admixed ((DOCX)Click here for additional data file.S4 FigBased on bayenv2 analyses using environmental variables, we identified 50 loci that correlated with minimum temperature of coldest month, only 2 of which overlapped with the resistance outliers; 27 loci correlated with precipitation of the driest month, 0 of which overlapped with the resistance outliers; 36 loci correlated with elevation, 0 of which overlapped with the resistance outliers.(DOCX)Click here for additional data file.S5 FigA screen for outliers when resistance status was randomly assigned to populations (100 randomizations) did not uncover the same pattern of enrichment across chromosomes as the original outlier screen. None of the randomizations produced 5 outlier regions (A). Few randomizations led to a greater number of outliers . The regions didn’t show an increased frequency of being identified as outliers in the randomly assigned analysis . In Fig C, the fifteen chromosomes are presented in different colors on the x-axis as in  and the (DOCX)Click here for additional data file.S6 Fig(A-D) Distributions of the number of genes within 4 mb of an outlier, either inside (blue) or outside (red) an outlier-enriched region. For each type of gene, the outliers outside of the regions show a left-skewed distribution indicating fewer close detoxification genes for (A) ABC transporters, (B) Glycosyltransferases, (C) Cytochrome P450s and (D) Glutathione S-transferases. (E) Outliers outside of the regions have lower frequencies of the resistant haplotype than those inside the regions, suggesting they are more population specific.(DOCX)Click here for additional data file.S7 FigData are shown are the ratio of susceptible to resistant individual nucleotide diversity. Grey bars indicate the outlier enriched regions identified on chromosomes 1, 6, 10, 13, and 15. Dashed lines show the 5% most extreme genome-wide values.(DOCX)Click here for additional data file.S8 Fig(A) Likelihood ratio of the following models relative to a neutral model with no selection: standing variant model (blue), migration (green) or independent mutation (red). (B) Likelihood surface for minimum frequency of the standing variant and the strength of selection holding the age of the standing variant constant; the point indicates the highest likelihood; the color indicates the likelihood (white (high) to yellow to red (low)). (C) Likelihood for the minimum age of standing variant maximizing over the other parameters.(DOCX)Click here for additional data file.S1 TableEPSPS from Convulvulus arvensis, Ho = observed heterozygosity , He = expected heterozygosity, HWE p-value = p-value for test of Hardy-Weinberg equilibrium from permutation test, Alleles = SNP alleles, Syn = whether a synonymous change , P-value chi-squared R vs S = p-value for test of allele frequency difference between resistant and susceptible populations, P-value cor with resistance = adjusted p-value for correlation with survival.SNP # = the location of the SNP after alignment with (DOCX)Click here for additional data file.S2 TablePopulation = population abbreviation. Ave N/locus = average number of individuals with high quality allele data per locus. % loci missing = average percent of the population with missing data per locus. Ho = observed heterozygosity. He = expected heterozygosity. FIS = Wright’s inbreeding coefficient.(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableI. nil, were used in analyses. r2 values were determined using all individuals regardless of population or resistance level.Only SNPs that could be mapped to the genome of the close relative, (DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 TableBI, DW, SPC, and WG are the high resistance populations.(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableMaximum log-likelihood values for the independent mutation, migration, and standing variation model, along with the position (bp) at which the maximum log-likelihood was achieved. The maximum selection coefficient (s), frequency of the variant (g), and minimum standing variation time prior to the onset of selection (t) were calculated for the standing variation model.(DOCX)Click here for additional data file.S1 Dataset(DOCX)Click here for additional data file.