Y-STRs have emerged as important forensic and population genetic markers for human identification and population differentiation studies. Therefore, population databases for these markers have been developed for almost all major populations around the world. The Iraqi population encompasses several ethnic groups that need to be genetically characterised and evaluated for possible substructures. Previous studies on the Iraqi population based on Y-STR markers were limited by a restricted number of markers. A larger database for Iraqi Arab population needed to be developed to help study and compare the population with other Middle Eastern populations. Twenty-three Y-STR loci included in the PowerPlex Y23  were typed in 254 males from the Iraqi Arab population. Global and regional Y-STR analysis demonstrated regional genetic continuity among the populations of Iraq, the Arabian Peninsula and the Middle East. The Iraqi Arab haplotypes were used to allocate samples to their most likely haplogroups using Athey’s Haplogroup Predictor tool. Prediction indicated predominance (36.6%) of haplogroup J1 in Iraqi Arabs. The migration rate between other populations and the Iraqis was inferred using coalescence theory in the Migrate-n program. Y-STR data were used to test different out-of-Africa migration models as well as more recent migrations within the Arabian Peninsula. The migration models demonstrated that gene flow to Iraq began from East Africa, with the Levantine corridor the most probable passageway out of Africa. The data presented here will enrich our understanding of genetic diversity in the region and introduce a PowerPlex Y23 database to the forensic community. This fertile land witnessed probably the first human settlement and cultural shift processes. It attracted the ancient hunter-gatherer people to settle down around 10,000 BC and initiate the agricultural society, which then developed to become a trading society3.The location of ancient Iraq corresponds to an area known as MesopotamiaThe Arabs were tribal people who inhabited the central Arabian Peninsula under the protection of many empires .4. Supplementary Figure 5. There are five ethnic groups in Iraq but there is little published data about the diversity of the Iraqi population. In this context the major ethnic groups are Arabs and Kurds6. Our data represents the Arabs, the largest ethnic group.Modern Iraq is an Arabian country with a population of ~ 40 million, bordered by the Arabian Gulf, Kuwait, and Saudi Arabia to the south, Jordan and Syria to the west, Turkey to the north, and Iran to the east7; SNPs therefore have little diversity and weak discrimination for individual identification (unless used in large multiplexes). Therefore, in forensic practice, a combination of SNPs is used to determine haplogroups. This information also aids in studying human migration and evolutionary patterns8. In comparison, Y-STRs have an average mutation frequency of 0.2% per generation, with high levels of diversity and strong powers of discrimination between unrelated males, and can aid individual identification as well as our understanding of population structure and issues of consanguinity.SNP-markers are stable due to low mutation rates10 and these haplotypes can then be used to predict a haplogroup and the population of origin12. Using this approach, Y-STRs can address internal diversity in the population by providing information on more recent events in the history of a haplogroup13. There is little published data about genetic diversity in the Iraqi population and its ethnic groups. This study utilises Y-STRs to shed light on the genetic makeup of this population, the relationship to its close neighbours and the effect of its colonisation history.Recently, alleles at STR loci have been used to generate haplotypesThe PowerPlex Y23 loci showed more discriminating haplotypes than the Y-Filer kit. Supplementary Table Allele frequency distributions of the 23-STR loci and the most frequent allele for each locus are presented in Supplementary Table Duplicated alleles were found in three Iraqi individuals at the locus DYS19. The three haplotypes show the same duplicated alleles  and were predicted to belong to haplogroup G2a. These duplicated alleles were found in the same haplotypes that contain variant alleles at the locus (DYS385a/b). A null allele was found in two Iraqi samples at the locus DYS576 and these were predicted to belong to haplogroup J2.The 254 Iraqi Arab males carried 244 distinct haplotypes, eight identical pairs, and one trio, providing a discrimination capacity of 96%. However, when the sub-set of Yfiler haplotype was considered, the shared haplotypes increased to 25, with a discrimination capacity of 85%. The summary statistics of diversity for PowerPlex Y-23 and Y-Filer kits for the 254 haplotypes of the Iraqi Arab population in this study are listed in Supplementary Table To study the microvariant alleles at the locus DYS458 in the Middle Eastern populations, the Middle Eastern data were compared to African, European, and southeast Asian countries. The presence of the microvariant alleles at the locus DYS458 was highest in the Middle Eastern populations Table .Table 1MThe total percentage of microvariant alleles in the Iraqi population was 36.6% (93/254). Most were observed at the locus DYS458 ; 87 of these are predicted to belong to haplogroup J1, in particular the 0.2 variant which, was observed for alleles 17, 18, 19, 20 and 21. One individual with microvariant 15.1 was predicted to belong to haplogroup N. The rest of the microvariant alleles were distributed as follows: one copy of the duplicated STR DYS385a/b carrying a 0.2 variant  was observed in four haplotypes and predicted to belong to haplogroup G2a. One haplotype carrying a 0.4 variant for allele 17 at locus DYS448 was predicted to belong to haplogroup J2a1b.23 with the use of 10,000 permutations and 0.05 as the significance level. The population pairwise genetic distances (Rst) were calculated between the Iraqi population and neighbouring Arab, Asian, African and European populations. The results are shown in Supplementary Table We compared the Iraqi population with other populations using Arlequin 3.5.2.2st pairwise differences were significant between the compared populations. The closest populations to the Iraqi Arabs were the Iraqi (Kurds) (Rst = 0.01081), then the Yemeni (Rst = 0.01215) and the Kuwaiti (Rst = 0.03986). The furthest were the Djiboutian (Rst = 0.24004), the Ethiopian (Rst = 0.22156) and the Turkish (Rst = 0.16422). Among the Middle Eastern populations Lebanon showed the highest genetic difference from the Iraqi Arabs (Rst = 0.14748).The Rst = 0.25351) and the lowest was between Moroccan and Eritrean populations (Rst = 0.00714).The highest genetic difference was between Djiboutian and Iraqi (Kurds)  and within populations (PiX), in addition to the corrected average pairwise difference between populations (PiXY − (PiX + PiY)/2). The results are shown in Supplementary Table Different groupings of Iraqis were compared with other populations and are shown in Supplementary Table st values using the R statistical software24, to display the relationships among the 23 populations; see Supplementary Fig. Dendrogram clustering was illustrated based on Rst distances using the R statistical software24. Supplementary Figure The Iraqi and several Middle Eastern populations Y-STR data was analysed, using multi-dimensional scaling based on RIn the first dimension of the plot, four Middle Eastern populations lie in the lower left quadrant (Iraq (Arab), Iraq (Kurd), Yemen and Kuwait). Three European countries  are clustered with India and China in the upper left quadrant of the plot. All the other populations are clustered on the left side of the plot. The second dimension of the plot shows two clusters: the Middle Eastern occupies the lower two quadrants with some of the African countries and two Far Eastern countries (Japan and South Korea). All the European countries are clustered with some African and the South East Asian countries in the upper quadrants.Whit Athey’s tool analysis showed that the Iraqi Arab population had seven major haplogroups; J1, E1b1b, J2a1b, J2, R1a, R1b and J2b. The most common haplogroup was J1 which represented 36.6% (93/254) of the population. The complete haplogroups for Iraqi Arabs are shown in Supplementary Table 26. Based on Whit Athey’s Haplogroup Predictor, haplogroups were assigned to the Arab haplotypes within the network The complete Iraqi Arab median-joining tree contains seven major clusters, each corresponding to a major haplogroup found in the Iraqi Arab population. All the predicted haplogroups form coherent clusters and create an accurate picture of the Y-STR dataset’s relation to the haplogroups. The most coherent clusters are J1, E1b1b and R1a, followed by J2, J2a, J2b and R1b which are the most spread-out.27 showed 10 clusters; and the HapMap of the Y-STR using 19 STR markers from 134 populations 17 showed 9 clusters.Two HapMaps were generated using the program STRUCTURE which allows individuals to be clustered by their genetic information. The Kidd Ancestry Informative SNPs (AISNPs) using 55 SNPs from 140 populations The HapMap of the Kidd Ancestry Informative SNPs (AISNPs) showed an overlap between the North African and the South West Asian populations which include the Middle Eastern populations; and there was another overlap between the South West Asian and European populations. There was, however, poor sub-grouping of the countries within each population . The African pool comprised populations from Eritrea, Ethiopia, Djibouti and Kenya19. Figure The gene flow was studied at three levels. At level one, the out-of-Africa migration to the Arabian Peninsula, three routes were investigated: Morocco → Egypt → Iraq; Africa → Egypt → Iraq; and Africa → Yemen → Iraq. Published data were used to design the migration models: MoroccanThe Y chromosome migration pattern analysis showed that the best model was model 2 (the divergence model) for the route Africa → Egypt → Iraq; it has the highest log marginal likelihood , Bayes factor (0) and a probability of 1. The results are shown in Table Level two examined population movements inside the Arabian Peninsula. Four routes were investigated, two from Yemen to Iraq, through Saudi Arabia and vice versa, and two from Yemen to Iraq through the UAE and vice versa. The most probable migration route was from Yemen to Iraq through the UAE model 2) which shows the highest log marginal likelihood , Bayes factor (0) and probability of 1. The least probable route was from Yemen to Iraq, models 1 and 3. Level two results are shown in Table  which shThe gene flow from Egypt across the Sinai Peninsula was examined in two directions, to the east towards Iraq and to the south towards Yemen. The results show that the most probable route was from Egypt to Yemen with the highest log marginal likelihood , Bayes factor (0) and probability of 1 Table , Fig. 2.30.The final picture combining the outcomes of levels one and two and according to the most probable routes show that the gene flow to Iraq began from East Africa to Egypt then around the Arabian Peninsula to the south reaching Yemen, and then to the north through the UAE before reaching Iraq. Figure The level three gene flow examined the effect of Iraq and Saudi Arabia on Kuwait. All four migration models in Supplementary Fig. 31 was intended to increase the discriminative power and therefore it is a popular kit in forensic casework and population studies. Y-STR haplotypes comprising the Y STRs included in the PowerPlex Y-23 kit were evaluated for their diversity in Iraqi Arab population.The inclusion of a larger number of Y-STR loci such as those included in the PowerPlex Y-23 kit17. The comparison of the databases revealed that two loci (DYS389I and DYS392) were less variable in the Iraqi population than in the other populations. Another characteristic feature of the Iraqi database was that the highest genetic diversities were for the dual marker DYS385a/b and a single-locus marker DYS458 at 0.93 and 0.85 respectively, unlike the other populations which showed the highest genetic diversities for the markers DYS385a/b and DYS48117.Each population has its own unique genetic structure that can be characterised by its Y-STR haplotype databases for studying variation within, and between, population groups. Such databases are of great value in ascertaining the forensic value of Y-STR evidence. This study shows that the Iraqi Arab population has its own distinctive characteristics which differ from other populations17. PowerPlex Y-23 with its 23 loci proved to be more forensically informative and discriminating for the Iraqi population than the Y-Filer kit, which contained fewer loci.Four of the six newly introduced markers, namely DYS481, DYS570, DYS576 and DYS643, ranked near the top in terms of genetic diversity, with GD values exceeding 0.70. This observation was consistent with a published global study32 that showed this microvariant allele to overlap with the M267 marker; this has arisen as result of a combination of drift and founder effects, followed by rapid population expansion, in North Africa and the Middle East during human evolution.It is notable that, the high incidence of microvariant alleles, in particular as reported at DYS458 (34.6%), is characteristic of the Middle Eastern populations. Microvariant alleles add to the discriminatory power and the evidential value of a DNA profile, and can further aid in determining haplogroups. We noticed that 98.8% of the Y-chromosomes carrying these DYS458 microvariants were located within haplogroup J1. This agrees with another study17 as having the second-highest level of null alleles following DYS448 in an Asian population: 28% of the total reported null allele cases. The YHRD (release 62) contained a total of 31 null allele observations in the locus DYS576 out of a total 126,443 haplotypes (0.024%).In this study we noted two null alleles at the locus DYS576; both samples belonged to haplogroup J2. DYS576 has been reported33 and it is thought to be because the duplicated region, mutating at a rate of approximately 10−3 times per generation in a single-step fashion, gives rise to a new allele usually different from the original by a single repeated unit34. The three haplotypes that show duplicated alleles 15, 16 were predicted to belong to haplogroup G2a35.In this study, the duplication of 15, 16 at locus DYS19 was observed in three individuals (1.16%). In the YHRD (release 62) this duplication was at a frequency of 0.053%. Many studies have reviewed and addressed such duplications36. Haplogroup J1 (M267) is one of two major sub-haplogroups from the major haplogroup J (M304) found among modern West Asian, North African, Horn of Africa, Southern European, Central Asian and South Asian populations, essentially delineating the Middle East and associated with speakers of Semitic languages, especially Arabic38. The frequency of the J1 haplogroup is directly proportional to aridity in the Middle East and it increases toward the periphery of the Arabian Peninsula39.Y-haplogroups were inferred through using Whit Athey’s Haplogroup Predictor; the results showed that the most common haplogroup (34.6%) in Iraqi Arabs was J1 as detected earlier40. Furthermore, Whit Athey’s Haplogroup Predictor and the median-joining tree complement each other.A comparison of the accuracy of three haplogroup prediction software packages found that the precision was 98.80% in Whit Athey’s Haplogroup Predictor, 98.19% in Y Predictor by Vadim Urasin 1.5.0, and 97.59% in Jim Cullen’s Haplogroup Predictor27. Increasing the number of the Kidd Ancestry Informative SNP markers might improve its HapMap discriminatory power between the overlapping populations.The global Y-STR HapMap generated in this study not only showed a stronger geographical proximity of the population samples, but also a stronger sub-grouping of the corresponding populations than the Kidd Ancestry Informative SNPs HapMap, which shows overlapping genotypes of some regions of the world. This can be explained by STRUCTURE handling autosomal markers differently from the haploid markers, since in autosomal analysis STRUCTURE will define clusters by finding Mendelian populations of individuals. Another factor could be the number of individuals in each input population, with more in the Y-STR than the SNPs analysis30.Out-of-Africa migration and peopling of the Middle East has been studied extensively and various routes of migration have been suggestedThe Bayesian inference and the coalescence theory in Migrate-n indicated that most of the gene flow of the Y-STR from Africa to Arabia occurred following coastal pathways and crossing the Sinai Peninsula to Arabia. All the migration routes favoured divergence from ancestral populations without an ongoing migration model (model 2) and showed a probability of 1.0.43.Two dispersal routes might explain the out-of-Africa model: a northern route through the Sinai Peninsula and the Levant, and a southern route followed the coast around Arabian Peninsula45. It is also thought that modern humans preferred the southern route because the Bab al Mandab Strait was narrow and shallow at that time; there is no geographical evidence of the existence of an intercontinental bridge 80,000 years ago, when such human intercontinental migrations occurred45. This study shows that this migration route is the less probable one.The southern coastal route crossing the Bab al Mandab Strait (the narrowest point between Africa and Yemen) to Arabia was proposed as an alternative to the northern route in Ice Age because aridity in the Levant was a strong barrier to human expansion46.This study supports the theory that the Levantine corridor served as a migratory route from East Africa through ancient Egypt into IraqBlood samples were collected with informed consent from 254 Iraqi males in the Paternity Department of the Medico-Legal Institute in Baghdad using FTA cards. A small disc of 1.2 mm diameter was manually punched out of the card, using a Harris Punch, and used for direct amplification of DNA. Ethical permission for recruitment and analysis was provided by the University of Central Lancashire STEMH Ethics Committee (STEMH 246/June 2014). All methods were performed in accordance with the relevant guidelines and regulations.The PowerPlex Y23 System contains 23 loci: DYS576, DYS389I, DYS448, DYS389II, DYS19, DYS391, DYS481, DYS549, DYS533, DYS438, DYS437, DYS570, DYS635, DYS390, DYS439, DYS392, DYS643, DYS393, DYS458, DYS385a/b, DYS456 and Y-GATA-H4. PCRs were conducted using one third of the recommended quantities and a total reaction volume of 8 μl. Amplification was performed using the manufacturer’s recommended cycling conditions. Fragments were detected using an ABI3500 Genetic Analyzer (Thermo Fisher Scientific) using the manufacturer’s recommended protocols. GeneMapper IDX software V1.4 was used for allele calling and interpretation.47, HD = (1 − Σ pi2) *n/(n − 1) where n is the sample size and pi is the ith’s haplotype frequency. Genetic diversity (GD) was calculated as 1 − Σ pi2, where pi is the allele frequency. The match probability (MP) was calculated as Σ pi2, where pi is the frequency of the ith haplotype. Discriminatory capacity (DC) was calculated by dividing the number of different haplotypes by the total number of samples in a given population; in the formula DC = h/n, h is the number of different haplotypes in the observed population and n is the total number of the population48. The haplotype match probability (HMP) was calculated as HMP = 1 − HD49.The haplotype frequencies were calculated by the counting method. Haplotype diversity was estimated by Nei’s formula23 was used to calculate the average pairwise differences between (PiXY) and within populations (PiX), in addition to the corrected average pairwise difference between populations (PiXY − (PiX + PiY)/2).Molecular data were obtained for the Iraqi population using Y-STRs based on the PowerPlex Y 23 System, and subjected to comparative analyses with available data on other close and distant populations. Comparison with other datasets required reduction of the number of STRs to a shared set of 15, so that more Middle Eastern populations could be included in this analysis. Arlequin 3.5.2.2 softwareAiming to assess genetic affinity and structuring of the Iraqi sample, AMOVA computations were performed, considering other populations according to their geographical location; Middle Eastern populations were represented by Yemen, Turkey, Kuwait, Saudi Arabia, Iraq (Kurd), UAE, Qatar and Lebanon; African populations by Morocco, Egypt, Eretria, Ethiopia, Djibouti and Kenya, European populations by Germany, Belgium, Finland and Sweden; and East Asian populations by India, China, Japan and South Korea.12. DYS549, DYS543 and DYS533 were excluded from the data because the first was not included in the program and the last two because no allele frequency data was available12.The full Y23 haplotypes were used to allocate haplotypes to their most likely haplogroup using Athey’s Haplogroup PredictorThe microvariant alleles were truncated to the next lowest integer value since values in the database were treated similarly. Null alleles were simply treated the same as untested markers .At GATA-H4, one unit was subtracted from each H4 value to put it on the same basis in the program. There were a number of samples for which the program did not make a prediction (no haplogroup met the criteria), and in those cases the haplotypes were manually examined, with results for some of them .26 and NETWORK Publisher v2.1.1.2 (Fluxus Technology Ltd)25. Following the recommendations of the Network’s authors, the intermediate alleles were rounded to the nearest integer; the locus DYS385a/b was removed for network construction. Missing alleles were coded ‘99′ in input files.Median-joining networks were constructed using the software NETWORK v5.0.1.0.50 with an admixture model. The HapMap was generated for two panels, the 55 Kidd Ancestry Informative SNPs (AISNPs) genotypes of 140 populations 27 and Y-STR data for 19 markers of 134 populations 17. Four markers were excluded from the PowerPlex Y23 System, the two rapidly mutating STR , and the markers , so that more Middle Eastern populations could be included in this analysis.Population structure was investigated using the program STRUCTURE version 2.3.7For each run, the number of clusters, K, was specified in advance and values in the range 6–11 was used for both Y-STR data and the Kidd AISNPs data. For both tests the program was run with 10,000 burn-ins and 10,000 Markov Chain Monte Carlo (MCMC) iterations.51. Then the multiple replicate analyses of each data set were aligned using CLUMPP52 and the output files were used to draw the two HapMaps using Distruct53.To assess and visualise likelihood values across multiple values of K and to detect the number of genetic groups that best fit the data, STRUCTURE output was processed with STRUCTURE HARVESTER54 using coalescence theory.Migration rates between other populations and Iraqi were inferred with the MIGRATE program version 4.2.14The Bayesian inference procedure was chosen for the estimation of population genetic parameters. One long chain was run, with a long sampling increment of 1,000. The sampling increment allows a wider search of genealogy space since not every genealogy will be sampled. The number of discarded trees per chain (burn-in) was set to 5,000. According to the increment value and the number of discarded trees, each sample was visited 5,000,000 times .57. The search was run with four chains at different temperatures  with an adaptive heating scheme that manipulated the temperatures according to their swapping success . The hotter chains move more freely and explore more genealogy space than the cold chains.Metropolis-Coupled MCMC (“MCMCMC”) or “heating” was applied for auxiliary searches with more permissive acceptance criteria58. Gene flow was investigated at three levels: level one is the out-of-Africa migration to the Arabian Peninsula; level two investigated the movement of Arabs inside the Arabian Peninsula; and level three investigated the migration rate between the three neighbouring countries Iraq, Saudi Arabia and Kuwait.Input data files were prepared using the PGD Spider data converting toolFour gene flow models were designed. The first model represents direct migration from one population to the other, the second divergence from an ancestral population and the third divergence from the ancestral population with ongoing immigration. The fourth model assumes that two populations belong to the same panmictic population, and is only used in level three. The log marginal likelihood of the different runs was used to generate the Bayes factors. The Bayes factors were used for model comparison, where their magnitudes give evidence of how different the models are. Supplementary Figure Supplementary Legends.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Figure S8.Supplementary Figure S9.Supplementary Figure S10.Supplementary Tables.


Ovis canadensis) translocations and population isolation to enhance understanding of evolutionary processes that affect population genetics and inform future restoration strategies. We conducted a population genomic analysis of 511 bighorn sheep from 17 areas, including native and reintroduced populations that received 0–10 translocations. Using the Illumina High Density Ovine array, we generated datasets of 6,155 to 33,289 single nucleotide polymorphisms and completed clustering, population tree, and kinship analyses. Our analyses determined that natural gene flow did not occur between most populations, including two pairs of native herds that had past connectivity. We synthesized genomic evidence across analyses to evaluate 24 different translocation events and detected eight successful reintroductions  and five successful augmentations  based on genetic similarity with the source populations. A single native population founded six of the reintroduced herds, suggesting that environmental conditions did not need to match for populations to persist following reintroduction. Augmentations consisting of 18–57 animals including males and females succeeded, whereas augmentations of two males did not result in a detectable genetic signature. Our results provide insight on genomic distinctiveness of native and reintroduced herds, information on the relative success of reintroduction and augmentation efforts and their associated attributes, and guidance to enhance genetic contribution of augmentations and reintroductions to aid in bighorn sheep restoration.Wildlife restoration often involves translocation efforts to reintroduce species and supplement small, fragmented populations. We examined the genomic consequences of bighorn sheep ( Ovis canadensis) translocations and population isolation to enhance understanding of evolutionary processes that affect population genetics and inform future restoration strategies. We conducted a population genomic analysis of 511 bighorn sheep from 17 areas, including native and reintroduced populations that received 0–10 translocations. Our results provide insight on genomic distinctiveness of native and reintroduced herds, information on the relative success of reintroduction and augmentation efforts and their associated attributes, and guidance to enhance genetic contribution of augmentations and reintroductions to aid in bighorn sheep restoration.We examined the genomic consequences of bighorn sheep ( We sougWe also evaluated nine reintroduced herds. For eight of those herds, one or all of the initial founding sources were included in this study Figure . We consOvis canadensis sierrae; Buchalski et al., As a true outgroup, we evaluated a different subspecies, Sierra Nevada bighorn sheep (2.2Bighorn sheep were live‐captured for genetic sampling between 2002 and 2018. Each population was sampled over a time span of 1 day to 4 years (less than one generation), except for the Glacier population that was sampled over a period of 9 years (about 1.5 generations). Animal capture and handling protocols were approved by Institutional Care and Use Committees at Montana State University , Montana Department of Fish, Wildlife, and Parks (Permit # 2016‐005), Parks Canada (Permit # WL‐2005‐638), Wyoming Game and Fish Department (Permit # 854), U.S. Geological Survey , and University of Montana (Permit # 024‐07MHWB‐071807 and 012‐16MMMCWRU‐022916). Genetic samples included FTA Classic gene cards, whole blood samples, biopsy punches from ear cartilage, and muscle or lung tissue from hunter‐harvested or road killed animals. We extracted DNA with Maxwell 16 LEV Blood DNA and Tissue Kits using the Promega Maxwell 16 Magnetic Particle Automated Extractor per kit instructions. For gene cards, we used a modification of the Promega LEV Blood DNA kit. Specifically, we incubated two to three 5 mm gene card punches with proteinase K and lysis buffer in a DNA IQ spin basket (Promega), spun at 3000 XG for 5 min, and loaded the flow‐through into the Maxwell 16 LEV Blood DNA cartridge.2.3Ovis dalli; Kijas et al., We genotyped bighorn sheep samples using the Illumina High Density (HD) Ovine array, also referred to as a SNP (single nucleotide polymorphism) chip. For genotyping, we used samples with a minimum of 300 ng of DNA, a minimum DNA concentration of 20 ng/µl, and a 260 nm/280 nm ratio of 1.0 to 1.5. The Ovine array is composed of 606,006 SNPs and was originally developed for domestic sheep with a density of one SNP per 4.279 kb, but its development included five bighorn sheep and four Dall's sheep  in our dataset and estimate global ancestry , we used fastStructure software  analysis using KING v2.1.4 . After filtering, 33,289 SNPs were used for kinship estimates; 6,155 SNPs were used for the remaining analyses  populations, where little genetic differentiation existed due to translocations to repopulate previously occupied areas  for reintroduction, geneticists recommend using a large number of founders to replicate the genetic profile of the founding source, but recommendations can range from 20 to 50 animals and depend on the species  identify which populations need augmentation based on demographic attributes and levels of population fragmentation, genetic diversity, and inbreeding, (b) identify source populations based on mean kinship, other direct measures of diversity, and other management concerns like disease history, (c) determine how many and which individuals should be moved, and (d) monitor recipient populations for realized genetic contribution, connectivity with nearby populations, genetic diversity, and demographic response, to determine if additional augmentations are needed ; Data curation ; Formal analysis (lead); Funding acquisition ; Investigation ; Methodology (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Tabitha A. Graves: Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Resources ; Supervision ; Writing‐review & editing . Jennifer Thomson: Conceptualization ; Data curation ; Formal analysis ; Funding acquisition ; Investigation ; Methodology ; Project administration ; Resources ; Supervision ; Writing‐review & editing . Kelly M. Proffitt: Data curation ; Funding acquisition (supporting); Investigation ; Writing‐review & editing . PJ White: Funding acquisition ; Project administration ; Resources ; Writing‐review & editing . Thomas R. Stephenson: Data curation ; Funding acquisition ; Resources ; Writing‐review & editing . Robert A Garrott: Conceptualization ; Data curation ; Formal analysis (supporting); Funding acquisition ; Investigation ; Methodology ; Project administration (lead); Resources ; Supervision ; Writing‐original draft (supporting); Writing‐review & editing .Supplementary MaterialClick here for additional data file.


Cornus kousa (Asian dogwood), an East Asia native tree, is the most economically important species of the dogwood genus, owing to its desirable horticultural traits and ability to hybridize with North America‐native dogwoods. To assess the species genetic diversity and to better inform the ongoing and future breeding efforts, we assembled an herbarium and arboretum collection of 131 noncultivated C. kousa specimens. Genotyping and capillary electrophoresis analyses of our C. kousa collection with the newly developed genic and published nuclear genomic microsatellites permitted assessment of genetic diversity and evolutionary history of the species. Regardless of the microsatellite type used, the study yielded generally similar insights into the C. kousa diversity with subtle differences deriving from and underlining the marker used. The accrued evidence pointed to the species distinct genetic pools related to the plant country of origin. This can be helpful in the development of the commercial cultivars for this important ornamental crop with increased pyramided utility traits. Analyses of the C. kousa evolutionary history using the accrued genotyping datasets pointed to an unsampled ancestor population, possibly now extinct, as per the phylogeography of the region. To our knowledge, there are few studies utilizing the same gDNA collection to compare performance of genomic and genic microsatellites. This is the first detailed report on C. kousa species diversity and evolutionary history inference. Cornus kousa (Asian dogwood) is the most economically important species of the dogwood genus, owing to its desirable ornamental traits and ability to hybridize with North America‐native flowering dogwoods. To assess the species genetic diversity and to better inform the ongoing and future breeding efforts, we genotyped a collection of noncultivated C. kousa from US herbaria and arboreta samples using genomic an genic microsatellites. The data pointed to the species distinct genetic pools related to the tree country of origin, and informed the species phylogeographic history, which can help future breeding efforts. Buerger ex Hance), a small‐ to medium‐sized deciduous tree, is a counterpart to the North America‐native C. florida L.  are considered a valuable tool for inferring species genetic diversity and population structure  and utilized the available genomic SSRs  and Ray J. Davis Herbarium, Idaho State University . The specimens were collected from 1953 to 1992 and were dated as per their collection year. Specimens were grouped into meta‐populations per their country of origin, with the land contiguity in mind.Samples of noncultivated Total DNA (gDNA) was extracted from the samples of the living specimens using the DNeasy Plant Mini Kit  with minor protocol modifications  used in this research project were developed in previous studies  were developed based on the available 2.3C. kousa. The reaction of 10 µl contained 4 µl of 2 × AccuStart II PCR SuperMix (QuantaBio), 0.5 µM of each primer , and 4 ng of gDNA. The touch‐down thermal profile for amplification of both types of SSRs was as follows: initial denaturation at 94°C for 3 min; 10 touch‐down cycles of 94°C for 40 s, 63°C of 40 s (−0.5°C/cycle), and 72°C for 30 s; followed by 35 cycles of 94°C for 40 s, 58°C for 40 s, and 72°C for 30 s; and final extension at 72°C for 4 min. The PCR products were visualized using the QIAxcel Advanced electrophoresis capillary electrophoresis (Qiagen) and analyzed using a 25‐ to 500‐bp DNA size marker and an internal 15/600‐bp alignment marker. We screened the results of genotyping with 20 gSSRs and 31 eSSRs for specificity , amplification of more than 70% of C. kousa gDNA collection, and productivity. Based on these criteria, the results of amplification with 18 gSSRs and 17 eSSRs . Clonal correction of those datasets revealed that no identical multilocus genotypes (MLGs) were present in any of the datasets. Datasets were then normalized per each given SSR motif length, to reflect number of repeats rather than PCR allele size  with 250,000 generations of burn‐in followed by 750,000 of the actual run for each K value (1–5). structure results were analyzed with PopHelper version 1.0.10 utilizing the Evanno method  was performed in R using the package adegenet version 2.1.1  and a uniform prior distribution. Similarities between the real and simulated datasets were assessed per each regarded scenario as instructed in the DIYABC manual using the summary statistics both within and between populations. Summary statistics for single populations included the mean number of alleles, mean genetic diversity, and mean size variance. Summary statistics for pairs of populations included pairwise FST, classification index, and (δμ)2 distance for pairs of populations. Principal component analysis (PCA) pre‐evaluation step was also performed to ensure that at least one combination of scenarios and priors produced simulated datasets close enough to the observed dataset. The relative posterior probabilities of the scenarios undergoing comparison were inferred by logistic regression on the 1% of simulated datasets closest to the observed dataset  SSRs were absent in the assembled transcriptome. Of the possible 252 unique tetranucleotide‐motif repeats, only 99 were identified in the C. kousa transcriptome. The eSSRs found in the transcriptome showed frequencies similar to the eSSRs with primers, in the repeat‐length manner and repeat‐motif manner  dinucleotide‐motif repeat SSRs were found with varying frequencies in the Appendix .3.2C. kousa accessions . Finally, the low amplitudes (low variability) of the pairwise linkage disequilibrium index (r¯d) implied that the SSRs used in the study were dispersed throughout the C. kousa genome, albeit with varying distances between one another , likely stemming from the range of sampling time and wide geographic origins of the s Figure . In our Appendix . Index oAppendix .3.3r = .219; p < .001). Standardizing the geographic distance matrix by the year of sample collection  did not significantly change the results  further underscored that the spatial impact on the population structure of our C. kousa collection was not strong  index, Simpson's index, high genetic diversity, and the observed heterozygosity indices  were observed  and RST  indicated that mutation was not the major driving force in the C. kousa differentiation  and ORANGE inferred clusters (mostly present in the “Japan” subpopulation) contributed comparably to structure within the data  of the egSSR dataset placed the three analyzed subpopulations of r Figure . Countrye Figure . The sub3.5Implementation of the BOTTLENECK program using the applicable mutation models T.P.M. and S.M.M. provided no evidence of a population size variation, and the minor heterozygosity excess/deficiency in few loci lacked significance (data not shown). A normal L‐shaped distribution of each meta‐population was detected with strong statistical support, thus documenting lack of population bottleneck (data not shown).C. kousa . The effective population sizes were estimated as several thousands of individuals , whereas the originating “ghost population” size was estimated at about 5,000 individuals   Table .C. kousa genotyped egSSR dataset were carried out using the program SPAGeDi, by permuting the allele sizes among alleles within locus. The mean RST value over all loci  did not differ from the observed RST value of 0.10. It was also not larger than the observed FST value of 0.09 . This observation implied a phylogeographic signal among populations, that is, alleles were more related between nearby populations of C. kousa than between distant populations. Finally, this analysis also estimated the value of (δμ)2 parameter as 1.831. After implementing the DIYABC mutation rate of ~0.0003 per locus per generation, it resulted with the C. kousa populations split time estimated at about 305 generations into the coalescent.Further analyses of the phylogenetic patterns in our 4C. kousa, a popular ornamental tree species of economic importance, in its native range. In order to reach that goal, we applied genotyping using genomic  are encouraging information for the species diversity preservation  has been successful. Yet, clear species‐level differences were observed in the chloroplast DNA  have been released, following the market demand, and broadening the available selections  QY Wadlford and C. hongkongensis (Hemsl.) Hutch.; Shearer & Ranney, C. kousa for potential pharmaceuticals  population possibly following the typical invasion and expansion mode. The DIYABC analyses congruently with the SPAGeDi (δμ)2 estimated the divergence time following the last glacial maximum geological models for the species native region  implied that the C. kousa mutation rate is negligible compared with the species migration rate. By contrast, permuting the linear or ln slope of population geographic distances showed the phylogeographic signal extant among the C. kousa populations. This implied that differentiation between distant populations is greater than between nearby ones. Such conclusion was also supported by small the yet significant score of the isolation‐by‐distance test. As such, the differentiation of C. kousa may be attributed to the recent divergence driven by the species geographic expansion. The divergence was not mitigated by the intensive gene flow which sustained the modest within‐population genetic diversity.Comparison of the permuted 5C. kousa, a tree species of economic importance in the nursery industry. Genotyping analyses of our collection with the newly developed eSSRs and the available gSSRs yielded insights into the species diversity. Analyses of the species evolutionary history using the accrued genotyping datasets pointed to an unsampled ancestor population, possibly now extinct, as per the phylogeography. Recognition of the species‐distinct genetic pools related to the plant country of origin can be helpful in the ongoing and future breeding projects for the commercial cultivars, and for the possible germplasm conservation efforts. On the practical side, this information may allow for breeding/pyramiding utility traits, including hybridization with the North America‐native dogwoods.Available herbarium and arboretum resources enabled the assessment of the species diversity and evolutionary history of the Asian dogwood, No conflict of interest has been declared by the author(s).Marcin Nowicki: Conceptualization ; Data curation ; Formal analysis ; Investigation ; Methodology ; Project administration ; Validation ; Visualization ; Writing‐original draft ; Writing‐review & editing . Logan C. Houston: Data curation ; Formal analysis ; Investigation ; Writing‐review & editing . Sarah L. Boggess: Data curation ; Formal analysis ; Project administration ; Resources ; Visualization ; Writing‐review & editing . Anthony S. Aiello: Resources ; Writing‐review & editing . Miriam Payá‐Milans: Software ; Validation ; Writing‐review & editing . Margaret E. Staton: Resources ; Software ; Validation . Mitsuhiro Hayashida: Resources ; Writing‐review & editing . Masahiro Yamanaka: Resources ; Writing‐review & editing . Shigetoshi Eda: Resources ; Writing‐review & editing . Robert N. Trigiano: Funding acquisition (lead); Investigation ; Project administration ; Resources ; Supervision ; Writing‐original draft ; Writing‐review & editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Table S1‐S3Click here for additional data file.


Euterpe edulis (Arecaceae) Mart has high ecological and economic importance providing food resources for more than 58 species of birds and 20 species of mammals, including humans. E. edulis is the second most exploited nontimber product from Brazilian Atlantic Forest. Due to overexploitation and destruction of habitats, E. edulis is threatened by extinction. Euterpe edulis populations have large morphological variations, with individuals having green, red, or yellow leaf sheath. However, no study has related phenotypic distinctions between populations and their levels of genetic structure. Thus, this study aimed to evaluate the diversity and genetic structure of different E. edulis morphotypes. We sampled 250 adult individuals in eight populations with the different morphotypes. Using 14 microsatellite markers, we access genetic diversity through population genetic parameters calculated in the GenAlex program and the diveRsity package in R. We used the Wilcoxon test to verify population bottlenecks and the genetic distance of Nei and Bayesian analysis for genetic clusters. The eight populations showed low allele richness, low observed heterozygosity, and high inbreeding values (f). In addition, six of the eight populations experienced genetic bottlenecks, which would partly explain the low genetic diversity in populations. Cluster analysis identified two clusters (K = 2), with green morphotype genetically distinguishing from yellow and red morphotypes. Thus, we show, for the first time, a strong genetic structure among E. edulis morphotypes even for geographically close populations. Euterpe edulis populations, which also have unusual genetic structure for populations of this species. In addition, the evaluated populations formed two genetic clusters, separating typical populations (green morphotype) from populations with yellow or red morphotypes that have restricted occurrence within the Brazilian Atlantic Forest. Thus, we showed, for the first time, that morphologically distinct populations of E. edulis have a strong genetic structure, as a possible response to local environmental factors.We recorded low genetic variability and genetic bottleneck effect in  To consider the 95% confidence interval of f and the standard deviation for HE and HO, we calculated 10,000 bootstraps which the divBasic function.The genetic diversity of To identify population bottenecks, the Wilcoxon test in the software BOTTLENECK version 1.2.02 02 . Subsequently, to assess whether geographical distance (km) influenced in FST values obtained between populations, we performed a Mantel test .To estimate the level of genetic structure among the populations, we calculated the observed  Mantel,  using th Mantel,  in the sE. edulis in heatmaply package  was performed for values ranging from 1 to 10 with ten independent chains, each chain with a burn‐in length of 50,000 iterations, followed by 100,000 repetitions of the MCMC (Markov and Monte Carlo chain). The ΔK  was determined on the basis of the average values of L (K) as produced for ten repetitions for each K, according to the method of Evanno et al. . However, only 36% in average of the 14 loci used present expected genotypic frequencies at Hardy–Weinberg equilibrium (Table S1).E. edulis showed low to moderate allelic richness (Mean = 5.31 ± 0.50), and all populations had private alleles (Table HO) was low to moderate (Mean = 0.53 ± 0.21), except for BN and RE with high values (Table HE) was moderate to high (Mean = 0.69 ± 0 0.14), with emphasis for AE, BN, and RE (Table f) were high , except for BN and RE, although no values are nonzero  used in the Wilcoxon test for population bottlenecks showed divergent results. The IAM model detected significant excess of heterozygosity for the BS, AE, EU, ST, BN, and RE populations, while the SMM and TPM models did not detect genetic bottlenecks in any of the eight ed Table .3.2FST revealed that the populations have moderate to high genetic differentiation, though this is lessor for BN‐RE populations , six populations have low or moderate values and lower than those reported in other populations of the species  values are moderate to high and always higher than HO values in all populations, indicating deviation from that expected under drift–mutation equilibrium (see Table f), indicating nonrandom mating occurring, which may cause reduction of genetic diversity  values and the lowest inbreeding measures, indicating the best genetic conservation status among the studied populations. These values are probably influenced by the occurrence of gene flow between RE and BN, which may favor the maintenance of genetic variability over time , revealed that six of the eight populations studied have recently experienced genetic bottlenecks. However, the two‐phase model (TPM) and stepwise mutation model (SMM) had divergent results, not detecting genetic bottleneck in any of the evaluated populations. This result was expected to some extent because, although microsatellite markers may adhere to different mutational models , genetic bottlenecks have been reported mainly for the AIM model  is not explained by geographical distance as theoretically expected, in which geographically closer populations are genetically more similar  from the others. However, complementary genetic studies  should be performed to test this taxonomic separation.The grouping into two clusters Figures  and 4 inE. edulis morphotypes are a consequence of populations being shaped in different environmental or ecological conditions. This hypothesis is based on recent studies using SNPs (single‐nucleotide polymorphism) markers, demonstrating that E. edulis populations inserted in different environmental and ecological conditions are genetically different ; Investigation (lead); Methodology (lead); Validation (lead); Visualization (supporting); Writing‐original draft (supporting). Alesandro Souza Santos: Data curation (lead); Formal analysis (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Ivandilson Pessoa Pinto de Menezes: Conceptualization (supporting); Writing‐original draft (supporting). Roberto Tarazi: Conceptualization ; Writing‐original draft . Fernanda Maria Oliveira Souza: Conceptualization (supporting); Writing‐original draft (supporting). Maria das Graças Conceição Parada Costa Silva: Conceptualization (supporting); Writing‐original draft (supporting). Fernanda Amato Gaiotto: Conceptualization (lead); Funding acquisition (lead); Investigation (lead); Methodology (lead); Project administration (lead); Resources (lead); Supervision (lead); Writing‐original draft .Supplementary MaterialClick here for additional data file.


Cryphonectria parasitica first decimated the North American chestnut, and a more recent outbreak threatens European chestnut stands. To unravel the chestnut blight invasion of southeastern Europe, we sequenced 230 genomes of predominantly European strains. Genotypes outside of the invasion zone showed high levels of diversity with evidence for frequent and ongoing recombination. The invasive lineage emerged from the highly diverse European genotype pool rather than a secondary introduction from Asia or North America. The expansion across southeastern Europe was mostly clonal and is dominated by a single mating type, suggesting a fitness advantage of asexual reproduction. Our findings show how an intermediary, highly diverse bridgehead population gave rise to an invasive, largely clonally expanding pathogen.Invasive microbial species constitute a major threat to biodiversity, agricultural production and human health. Invasions are often dominated by one or a small number of genotypes, yet the underlying factors driving invasions are poorly understood. The chestnut blight fungus  Over the past century, a multitude of invasive species have emerged as threats to forest and agricultural ecosystems worldwide . IncreasA major model explaining the successful expansion of invasive populations despite low initial genetic diversity is the so-called bridgehead effect . In thisOphiostoma novo-ulmi causing Dutch elm disease . Sexual reproduction can potentially promote the emergence of more virulent strains through the reshuffling of genetic variants, enhancing invasive success . In cont disease , or the  ramorum , are domoduction . Introgr O. ulmi . Althoug O. ulmi , mechani O. ulmi . Beyond  O. ulmi . Even ra O. ulmi . In cont O. ulmi .Ophiostoma species causing Dutch elm disease show signals of frequent hybridization and introgression events, giving rise to lineages with increased virulence or growth at higher temperatures  has created highly successful outbreak lineages  Barr. is the causal agent of chestnut blight, a lethal bark disease of Castanea species (Castanea dentata). In the following years, the disease rapidly spread throughout the distribution range of C. dentata, causing the vast decimation of this native tree species (Castanea sativa) near Genoa  and is now established in all major chestnut-growing countries in Europe (Cryphonectria hypovirus 1 (CHV1), which acts as a biological control agent of chestnut blight  locus (C. parasitica populations based on sequence characterized amplified region (SCAR) markers suggested that the region was largely colonized by a single and likely asexual lineage also identified as S12  locus , and natT) locus . Previoud as S12 . The lind as S12 . Based od as S12 . HoweverC. parasitica strains with a fine-scaled sampling throughout the S12 invasion zone. Based on high-confidence genome-wide single nucleotide polymorphisms (SNPs), we identified the most likely origin and recapitulated the invasion process across southeastern Europe. We show that the invasive C. parasitica lineage arose through an intermediary, highly diverse bridgehead population. During the expansion, the lineage became dominated by a single mating type but retained the ability to reproduce sexually.In this study, we sequenced complete genomes of a comprehensive collection of European C. parasitica isolates covering the global chestnut blight distribution range, as well as the European outbreak region of the invasive S12 lineage . Consistent with analyses by C. parasitica across southeastern Europe. Additionally, the phylogenetic network revealed closely related but not identical S12 genotypes of mating type MAT-2  and Georgia (MAK23 with MAT-2)  . Consisth MAT-2) , we alsoh MAT-2) . Additioh MAT-2) . Surprish MAT-2) .C. parasitica to the European environment.We investigated potential selective sweeps in the focal European/North American cluster. To avoid a bias by the deep sampling of the S12 lineage, we excluded all but one of the S12 MAT-1 isolates . We used RAiSD  to produn = 197). Within this cluster, K = 43 genetic subclusters were identified with fineSTRUCTURE  loci are located in regions devoid of TEs. In fungal pathogens, effector genes and TEs are often co-localized in fast-evolving compartments of the so-called ‘two-speed genome’  and π = 1.80×10−7 for the Turkish/Georgian subgroup (n = 8) excluding the diverse group of Greek isolates. The spread of the Greek isolates suggests that these might have recently undergone sexual reproduction and admixture events. As we identified non-S12 genotypes in Greece, either within-lineage recombination or exchange with other lineages may underlie the reticulation and increased diversity.To infer potential invasion routes of the S12 outbreak lineage n = 105; , we inveWe tested experimentally whether S12 mating type MAT-1 isolates were still able to reproduce sexually. We selected S12 isolates from populations in Bosnia, Kosovo, and Italy matching locations where the opposite mating type was also detected . After 5C. parasitica across southeastern Europe after a first establishment on the continent. The European population is characterized by the presence of a small number of highly distinct genotypes. Deep sampling of recent invasive genotypes showed that the secondary invasion in southeastern Europe was caused by a single, highly homogeneous lineage consisting nearly exclusively of a single mating type. Given the likely origin from the European gene pool with clear evidence for sexual reproduction, the invasive lineage likely transitioned from biparental mating populations to a single mating type. The spread across southeastern Europe left no clear geographic imprint. This lack of genetic differentiation along the invasion route may be due to high levels of gene flow after the initial colonization or a very rapid spread from the center of origin to multiple locations across southeastern Europe. We show that ongoing TE activity created unexpected levels of insertion polymorphism within the invasive lineage. In addition, the lineage carries a distinct gene deletion and duplication profile compared to the European and North American pool of C. parasitica.We analyzed the invasion of the chestnut blight pathogen C. parasitica populations likely originated from North American lineages. Clusters of European and North American genotypes were largely overlapping. Shared genotype clusters between the continents are consistent with historic gene flow from North America to Europe. These genome-scale analyses are consistent with previous findings documenting multiple North American introductions into Central Europe, but also directly from Asia to Western Europe , and Turkey, the S12 lineage coexists with other genotypes  of both mating types, potentially enabling sexual recombination and diversification in situ. The invasive S12 lineage was potentially pre-adapted to the southeastern European niche as we traced the origins to a likely bridgehead population located in southern Switzerland, northern Italy, or the northern Balkans. Niche availability and benefits associated with asexual reproduction to colonize new areas may have pre-disposed the European C. parasitica bridgehead population to produce a highly invasive lineage. Such potential pre-adaptation to the southeastern European environmental conditions, as well as reduced niche availability, may also explain why genotypes belonging to the S12 lineage have rarely been found elsewhere in Europe.Despite the loss of a mating type in the S12 lineage, we found limited evidence for reticulation indicating at least low levels of recombination. If mating in C. parasitica often rely less on effector-based gene-for-gene interactions and tend to deploy toxins or cell-wall degrading enzymes , South Korea (n = 8), Japan (n = 8), North America (n = 8), Azerbaijan (n = 3), Georgia (n = 4), Portugal (n = 3), Spain (n = 3), and France (n = 5)  . A totalAll isolates were first inoculated onto cellophane-covered potato dextrose agar plates   and incuC. parasitica reference genome (43.9 Mb) EP155 v2.0 (http://jgi.doe.gov/). Variant calling, selection, and filtration were conducted with the Genome Analysis Toolkit GATK v3.8 and v4.0.2.0 . The libraries were sequenced at the Functional Genomics Centre Zurich (ETH Zurich and University of Zurich) on Illumina platforms (Illumina). The obtained sequences were trimmed with Trimmomatic v0.36  and alig155 v2.0  availablv4.0.2.0 . We retav4.0.2.0 . Furtherv4.0.2.0  and the v4.0.2.0  for querv4.0.2.0  for downv4.0.2.0  and retr4,312 bp .The filtered whole-genome SNP dataset was used to build unrooted phylogenetic networks using SplitsTree v4.14.6 . SplitsTWe generated an averaged coancestry matrix as inferred by fineSTRUCTURE v2.1.3 . The sofWe computed allele frequencies and estimated the allele spectrum using VCFtools. We used RStudio  and ggpldadi (sub-module dadi.Demographics1D). For all models, we ran the optimization 50 times with initial parameters drawn randomly from the bounded distributions (specified for each model below) and the optimization method optimiselog with the maximum number of iterations set to 100  model (snm function) without population size change.Sudden expansion model (two_epoch function) with nuF bounded between 1 and 1000 and T between 0 and 2000.Sudden contraction model (two_epoch function) with nuF bounded between 1 and 1000 and T between 0 and 2000.Model implementing a single instantaneous change in population size at a specific point in time followed by exponential growth (bottlegrowth function) with nuF bounded between 0.01 and 1000, nuB between 0.01 and 100, and T between 0 and 500.Model implementing a sudden population contraction followed by a sudden expansion  with nuF bounded between 1 and 2000, nuB between 0.01 and 1, TB (length of the bottleneck) between 0 and 100, and TF (time since the bottleneck) between 0 and 500.https://github.com/crolllab/datasets (swh:1:rev:fa908efdb71d5ba90823c70bf2467e10ce6f12a7).Datasets and scripts for all demographic analyses are accessible here: µ statistic of positive selection. To investigate potential false positive selection signals due to the demographic history of the populations, we performed 10,000 ms simulations to model three demographic scenarios in the European population: constant, expansion, and bottleneck. We used the average theta value obtained from the demographic simulations with dadi on the two populations for which we could estimate the best fitting demographic history model. The recombination rate was estimated with LDhat v2.2 (µ statistic.Identification of putative selective sweeps was performed using RAiSD v2.5 software that is based on three diﬀerent genetic signatures of positive selection (with -y 1 -M 0 -w 50 -c 1 parameters) . The Manhat v2.2 , with -mhat v2.2 . The avehat v2.2 . The botC. parasitica reference genome EP155 v2.0 using RepeatModeler v2.0.1  for all isolates using the CNVcaller pipeline , Kosovo (Molliq), and Bosnia  (C. sativa) stems (15–20 mm in diameter) were split longitudinally in half. The wood pieces were autoclaved twice and placed onto sterile Petri dishes (90 mm diameter), which were filled with 1.5% water agar to enclose the pieces. Mating pairs were inoculated on opposite sides of each halved stem with three replicates per pairing. The inoculated plates were then incubated at 25°C under a 16 hr photoperiod (2500 lux) for 14 days. After 2 weeks, mating was stimulated by adding 5 mL sterile water to the plates to suspend and distribute the conidia produced by both isolates over the stem segment. Any excess water was subsequently removed and the plates were incubated at 18°C under an 8 hr photoperiod (2500 lux). After 5 months of incubation, perithecia formation was assessed under a dissecting microscope. To confirm successful outcrossing, single perithecia were carefully extracted from the stromata and crushed in a drop of sterile distilled water. The resulting ascospore suspensions were plated on PDA and incubated at 25°C for 24–36 hr. Afterwards, single germinating ascospores were transferred to PDA and incubated in the dark for 3–5 days at 25°C. DNA was then extracted from 10 mg of lyophilized mycelium using the kit and instructions by KingFisher (Thermo Fisher Scientific). All single ascospore cultures were screened for mating types by performing a multiplex PCR following the protocol described in The ability of  Projsa) . All iso In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This study combines whole genome resequencing of a comprehensive set of natural accessions to provide novel insight into the spread of an important tree disease, chestnut blight. The results provide support for the so-called “bridgehead” effect, a process by which successful invasive lineages arise by recombination among genotypes that are already established in an area of first introduction, and which is thought to be a major driver for alien species invasions.Decision letter after peer review:eLife. Your article has been reviewed by four peer reviewers, including Vincent Castric as the Reviewing Editor and Reviewer #4, and the evaluation has been overseen by Detlef Weigel as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Anna-Liisa Laine (Reviewer #2).Thank you for submitting your article "Emergence and diversification of a highly invasive chestnut pathogen lineage across south-eastern Europe" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional analyses are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Summary:Cryphonectria parasitica across Europe. The results confirm previous accounts that the recent invasion in south-eastern Europe involved a single clonal lineage. The invasive lineage was closely related to nearby European lineages, where outcrossing was predominant. This is in line with the “bridgehead population” hypothesis suggesting that the invasive lineage arose in situ rather than through secondary introduction. In spite of very low nucleotide diversity, the invasive lineage shows some variation in TE content and indels overlapping genic sequences. The manuscript is generally well written and easy to follow.Stauber and colleagues have analysed complete genomes of 188 natural isolates of the chesnut pathogen Essential revisions:1) Provide an explicit demographic model– The current study follows a previous analysis  that had largely clarified the invasion scenario in this species. As the manuscript currently stands, the additional insight gained from having sequenced complete genomes is not clearly explained. In particular, beyond the better "marker resolution" as compared to the SCARS markers, polymorphism data gained from complete genomes should enable a comprehensive demographic reconstruction of the invasion scenario. This demographic scenario should then be used as a baseline for all diversity and divergence patterns reported. More generally, a more formal hypotheses testing framework is necessary throughout the paper, as several assertions are made based on the simple observation of patterns, without associated statistical tests.– This demographic model is especially important for the analysis of natural selection signatures, since these signals are strongly dependent upon specifics of the demographic history.– Finally, the sweep analysis is based on the non-S12 samples only, so it is unclear how sweeps in the recombinant strains relates to the broader issue of adaptation in the invasive lineage.2) Modify general framing of the study– The study presents the scenario under the "bridgehead population effect", but Maynard-Smith et al., 1993, developed a model of epidemic clones, where a successful clone emerges and expands from a recombinant background. While the model was formulated for bacteria, it is similar to the bridgehead effect mentioned here, and should at least be cited and briefly discussed.– The Abstract claims that the study is aimed to "unravel the mechanisms underpinning the invasion" and to identify the "underlying factors driving invasion", but the results actually provide little insight on these mechanistic aspects, beside clarifying the invasion scenario. At least the underlying hypotheses for these claims should be made more explicit (see below).– The Introduction covers nicely aspects of adaptation and the importance of genetic diversity. There are indeed examples of invasive events that are triggered by limited genetic diversity, but on the other hand, the analysis by Philibert and colleagues (2011) identified sexual reproduction as one of the three key traits predicting the invasion success of forest pathogens. While it is important to understand the specific event leading to the emergence of an invasive strain, the Introduction could expand a bit on what are the properties of populations that are likely to lead to such events in the first place.– The Introduction would benefit from some ecological insight. Sexual vs. asexual strategies have ecological consequences relevant for invasive success beyond levels of genetic diversity or mating costs. Sexual vs. asexual life-history stages often vary for example in survival ability during unfavorable environmental periods, in their dispersal abilities, and in environmental cues that trigger germination.3) Better integrate the genomic analyses– The structural variation analysis is poorly connected to the main aims of the paper. For instance, the “two-speed genome” hypothesis is interesting, but it is unclear how it fits in the questions addressed in the study. More generally, a precise formulation of hypotheses for the genomic aspects would be needed, as these aspects are presently absent from the Introduction section.– This part of the manuscript also critically lacks statistical support. For instance, the following assertions “The total TE count variation among the […] non-S12 isolates was larger than the clonal S12 isolates”, “Genes tend to overlap duplications rather than deletions and TEs tend to overall deletions rather than duplications” and “Scaffold 4 and 6 were overall rich in duplications and scaffolds 6 and 7 were rich in deletions” need to be supported by statistical tests.– Finally, the manuscript repeats at several places that there is a strong contrast between the much lower nucleotide diversity in S12 vs non-S12, but a comparable amount of diversity in TE presence/absence. While this is a potentially interesting observation (raising the question of how TE polymorphisms may behave differently from SNPs), again without formal testing it is currently impossible to evaluate the reality of this contrast.eLife. Your article has been evaluated by Vincent Castric as Reviewing Editor and Detlef Weigel as Senior Editor.Thank you for submitting your article "Emergence and diversification of a highly invasive chestnut pathogen lineage across south-eastern Europe" for consideration by eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in eLife, either of which would be linked to the original paper.Our expectation is that the authors will eventually carry out the additional experiments and report on how they affect the relevant conclusions either in a preprint on bioRxiv or medRxiv, or if appropriate, as a Research Advance in eLife.This is a substantial revision, and several samples and new analyses have been added to the study, resulting in clearer patterns. However, the manuscript still suffers from two main shortcomings that need to be addressed, and that are listed below. Please note that we want to see this work published in – The main concern was the lack of a demographic scenario, possibly confounding the selection analysis. The authors now use the dadi package to calculate the likelihood of three demographic scenarios  in sets of samples clustered by PCA. However, beside the fact that the Materials and methods and Results are not presented in sufficient details , the demographic reconstruction performs very poorly in the cluster containing the focal European accessions. The reasons for this should at least be discussed . This limitation should also be much more clearly acknowledged throughout the manuscript, especially with regard to the selection analysis .– The results are still difficult to follow because each analysis is presented in turn, such that the text goes back and forth between the different geographical scales considered. I strongly recommend to 1) start with “global” analyses to identify the four main clusters, 2) then go for the specific analysis of the European cluster, including evidence for recombination, the challenges in attempting to reconstruct its demographic history, and ensuing selection analysis, and finally 3) provide the detailed focus on the invasive S12 lineage, including the co-ancestry analysis, the evidence for recombination within S12, the specific derived variants, TEs and SV they carry, the attempt to reconstruct its possible invasion route and the retention of mating competence. I think this would improve readability by a lot.eLife. Your article has been evaluated by Vincent Castric as Reviewing Editor and Detlef Weigel as the Senior Editor.Thank you for resubmitting your article "Emergence and diversification of a highly invasive chestnut pathogen lineage across south-eastern Europe" for consideration by eLife but that some conclusions require a modest amount of additional new data, as they do with your paper, we are asking that the manuscript be revised to either limit claims to those supported by data in hand, or to explicitly state that the relevant conclusions require additional supporting data.We would like to draw your attention to changes in our policy on revisions we have made in response to COVID-19 (https://elifesciences.org/articles/57162). Specifically, when editors judge that a submitted work as a whole belongs in This revision is a lot clearer, and most of our previous comments have been convincingly dealt with. One important issue remains though with the demographic analysis:– As pointed in the previous decision letter, obtaining strictly identical maximum likelihood values for all three demographic models implemented is quite unexpected. Unfortunately, the manuscript currently lacks essential details to understand why this happened. For instance, how many iterations of the likelihood optimization process were used , how many independent replicates were run ? starting from which (different) initial values ? what parameter bounds were specified ? All these important details are essential but they are lacking from the Materials and methods section. Providing a complete table of all parameters used, along with the dadi input file is essential so that readers can reproduce the analysis.– I could also not find anywhere in the manuscript how the likelihoods were compared. By a likelihood ratio test ? If so, how were differences in parameter numbers among models taken into account ?– It is possible that after performing these additional optimization steps the model still fails to converge simply because the unusual shape of the SFS cannot be explained by demography alone, especially in the first cluster. But even in that case it would be essential to report how this conclusion was reached, and to explicitly mention this fact by stating "lack of convergence" in Figure 2 .Finally, the Discussion contains an assertion that is not supported by the results: "…we found evidence of a demographic expansion consistent with the spread to North America…". In fact, the North-American samples all belong to cluster 1, where the demographic reconstruction is problematic, so this sentence should be rephrased. Essential revisions:1) Provide an explicit demographic model– The current study follows a previous analysis  that had largely clarified the invasion scenario in this species. As the manuscript currently stands, the additional insight gained from having sequenced complete genomes is not clearly explained. In particular, beyond the better "marker resolution" as compared to the SCARS markers, polymorphism data gained from complete genomes should enable a comprehensive demographic reconstruction of the invasion scenario. This demographic scenario should then be used as a baseline for all diversity and divergence patterns reported. More generally, a more formal hypotheses testing framework is necessary throughout the paper, as several assertions are made based on the simple observation of patterns, without associated statistical tests.– This demographic model is especially important for the analysis of natural selection signatures, since these signals are strongly dependent upon specifics of the demographic history.We have now revised our manuscript in multiple ways including new datasets and new analyses to address the issues highlighted above.C. parasitica first colonized Europe to form a bridgehead for a secondary invasion by the S12 lineage across southeastern Europe. To more accurately assess the impact on the pathogen diversity after the arrival in Europe, we added samples across the global distribution range including from China, South Korea, Japan and the USA . We believe that the US source population is still under-sampled, however the additional samples provide now a more robust framework to estimate the demography of this species and the history of the European colonization. The new samples are now integrated in the first part of the analyses, before focusing specifically on the S12 lineage. Figures and tables were updated accordingly.Additional samples: Our manuscript documents how Demographic modelling: We performed demographic modelling as requested to provide a more formal framework to investigate the history of the European invasion. Analyzing the global set of isolates . Our analyses on the global collection showed signatures of recent population expansions consistent with the spread to the United States and more recently Europe. We could not find a good-enough fit in the narrower group of European/North American isolates, as the observed folded site frequency spectrum (SFS) of the European/North American group is W-shaped, instead of a steadily decline curve (as found in the simulated data). This unusual frequency spectrum likely stems from many quasi-clones from several groups and inbreeding. We show now all demographic model fits in Figure 2.C. parasitica data set.The selection analyses in the non-S12 European population has now also been revised to use demography as a baseline thus increasing the confidence that the genomic signatures observed in this analysis are indeed due to selection. Notably, we used simulated populations under constant size, bottleneck, expansion scenarios, to determine a threshold above which signals can confidently be assigned to selection in the – Finally, the sweep analysis is based on the non-S12 samples only, so it is unclear how sweeps in the recombinant strains relates to the broader issue of adaptation in the invasive lineage.We have indeed performed the selection analyses on the European population. Our rationale was to identify recent signatures of selection important for the colonization of the European habitats. Performing the selection scans on the clonal set of genotypes of the S12 would not be possible due the very high degree of linkage disequilibrium and extremely low diversity. We have previously performed an analysis on the frequency spectrum of the accumulated mutations and their putative impact. We found low-frequency deleterious mutations segregating in the S12 lineage.2) Modify general framing of the study– The study presents the scenario under the "bridgehead population effect", but Maynard-Smith et al., 1993, developed a model of epidemic clones, where a successful clone emerges and expands from a recombinant background. While the model was formulated for bacteria, it is similar to the bridgehead effect mentioned here, and should at least be cited and briefly discussed.We are grateful for this request and have now modified the Introduction when discussing the conceptually related model of the "bridgehead effect".– The Abstract claims that the study is aimed to "unravel the mechanisms underpinning the invasion" and to identify the "underlying factors driving invasion", but the results actually provide little insight on these mechanistic aspects, beside clarifying the invasion scenario. At least the underlying hypotheses for these claims should be made more explicit (see below).We have now clarified our aims to primarily unravel the invasion history of chestnut blight in south-eastern Europe. Moreover, we have better integrated the genomic results that highlight the genetic architecture of chestnut blight.– The Introduction covers nicely aspects of adaptation and the importance of genetic diversity. There are indeed examples of invasive events that are triggered by limited genetic diversity, but on the other hand, the analysis by Philibert and colleagues (2011) identified sexual reproduction as one of the three key traits predicting the invasion success of forest pathogens. While it is important to understand the specific event leading to the emergence of an invasive strain, the Introduction could expand a bit on what are the properties of populations that are likely to lead to such events in the first place.We now broadly introduce critical factors linked to successful invasions include mode of reproduction .– The Introduction would benefit from some ecological insight. Sexual vs. asexual strategies have ecological consequences relevant for invasive success beyond levels of genetic diversity or mating costs. Sexual vs. asexual life-history stages often vary for example in survival ability during unfavorable environmental periods, in their dispersal abilities, and in environmental cues that trigger germination.We have now added a more ecological perspective to the Introduction in particular related to environmental constraints, spore production and dispersal.3) Better integrate the genomic analyses– The structural variation analysis is poorly connected to the main aims of the paper. For instance, the “two-speed genome” hypothesis is interesting, but it is unclear how it fits in the questions addressed in the study. More generally, a precise formulation of hypotheses for the genomic aspects would be needed, as these aspects are presently absent from the Introduction section.We have now expanded the Introduction to cover genomic insights into pathogen emergence and set up hypotheses to be tested.– This part of the manuscript also critically lacks statistical support. For instance, the following assertions “The total TE count variation among the […] non-S12 isolates was larger than the clonal S12 isolates”, “Genes tend to overlap duplications rather than deletions and TEs tend to overall deletions rather than duplications” and “Scaffold 4 and 6 were overall rich in duplications and scaffolds 6 and 7 were rich in deletions” need to be supported by statistical tests.We have now added a series of statistical tests to the genomic analyses to provide a clearer statistical framework and enable hypothesis testing.– Finally, the manuscript repeats at several places that there is a strong contrast between the much lower nucleotide diversity in S12 vs non-S12, but a comparable amount of diversity in TE presence/absence. While this is a potentially interesting observation (raising the question of how TE polymorphisms may behave differently from SNPs), again without formal testing it is currently impossible to evaluate the reality of this contrast.We address now these differences in a quantitative way and provide statistical support where this is feasible. We now also briefly discuss these observations.This is a substantial revision, and several samples and new analyses have been added to the study, resulting in clearer patterns. However, the manuscript still suffers from two main shortcomings that need to be addressed, and that are listed below. Please note that we want to see this work published in eLife.– The main concern was the lack of a demographic scenario, possibly confounding the selection analysis. The authors now use the dadi package to calculate the likelihood of three demographic scenarios  in sets of samples clustered by PCA. However, beside the fact that the Materials and methods and Results are not presented in sufficient details , the demographic reconstruction performs very poorly in the cluster containing the focal European accessions. The reasons for this should at least be discussed .We added the requested details about the modelling to the Materials and methods section. We also added a supplementary table (Supplementary file 2) including more details about the results from the analyses including the best-fit parameters and likelihoods.We now more explicitly state in the Materials and methods and Results that we have excluded all S12 isolates (except one) for demographic analyses. This has also always been the case for the selection scans. We regret that our wording was not clear enough in this regard. Hence, the large number of S12 clone genotypes in our overall dataset never had an impact on either demographic or selection analyses.Additionally, we now briefly discuss possible reasons for the poor demographic reconstruction within the focal European cluster. Even after excluding S12 genotypes, the pairwise genetic distances between genotypes are somewhat bimodal with some fairly close genotypes of possible clonal origin.This limitation should also be much more clearly acknowledged throughout the manuscript, especially with regard to the selection analysis .We now state more explicitly the limitation of the demographic reconstruction with regard to the selection analysis.– The results are still difficult to follow because each analysis is presented in turn, such that the text goes back and forth between the different geographical scales considered. I strongly recommend to 1) start with “global” analyses to identify the four main clusters, 2) then go for the specific analysis of the European cluster, including evidence for recombination, the challenges in attempting to reconstruct its demographic history, and ensuing selection analysis, and finally 3) provide the detailed focus on the invasive S12 lineage, including the co-ancestry analysis, the evidence for recombination within S12, the specific derived variants, TEs and SV they carry, the attempt to reconstruct its possible invasion route and the retention of mating competence. I think this would improve readability by a lot.We are grateful for this suggestion. We have now changed the order of the Results to:– Global analyses: Identification of clusters– Demographic analyses: for global and European cluster – European analyses: phylogenetic network, recombination and selection analyses– S12 analyses: Coancestry, TE and SV, variants in S12 and retention of mating competenceThis revision is a lot clearer, and most of our previous comments have been convincingly dealt with. One important issue remains though with the demographic analysis:– As pointed in the previous decision letter, obtaining strictly identical maximum likelihood values for all three demographic models implemented is quite unexpected. Unfortunately, the manuscript currently lacks essential details to understand why this happened. For instance, how many iterations of the likelihood optimization process were used , how many independent replicates were run ? starting from which (different) initial values ? what parameter bounds were specified ? All these important details are essential but they are lacking from the Materials and methods section. Providing a complete table of all parameters used, along with the dadi input file is essential so that readers can reproduce the analysis.We are now stating more clearly in the Materials and methods section what parameters were used for demographic inference, including the number of iterations, the number of independent replicates and parameter bounds. We have also updated the Supplementary file 2, which now also includes the initial parameters and AIC values for model comparisons. Moreover, data sets and scripts for demographic analyses (including the genetic dataset) can now also be found at: https://github.com/crolllab/datasets .– I could also not find anywhere in the manuscript how the likelihoods were compared. By a likelihood ratio test ? If so, how were differences in parameter numbers among models taken into account ?We are thankful for this comment. We have now implemented the Akaike Information Criterion (AIC) for model comparisons. This is now clearly stated in the Materials and methods sections. Moreover, we provide a full list of AIC values for all models in Supplementary file 2.– It is possible that after performing these additional optimization steps the model still fails to converge simply because the unusual shape of the SFS cannot be explained by demography alone, especially in the first cluster. But even in that case it would be essential to report how this conclusion was reached, and to explicitly mention this fact by stating "lack of convergence" in Figure 2 .We now state more explicitly that no parameter optimization was reached for the CL1 cluster.Finally, the Discussion contains an assertion that is not supported by the results: "…we found evidence of a demographic expansion consistent with the spread to North America…". In fact, the North-American samples all belong to cluster 1, where the demographic reconstruction is problematic, so this sentence should be rephrased.We fully agree and have decided to delete the sentence entirely.