It is thought that Paleo-Inuit were the first people that settled the American Arctic about 5000 BP (before the present) from a migration that crossed Beringia from Northeast Asia. It is theorized that this group initially migrated to the North Slopes of Alaska and subsequently expanded eastward, eventually reaching Greenland. A second circumpolar dispersal of Neo-Inuit from the North Slopes associated with the Thule-Inuk culture has been postulated to have extended eastward around 800 BP, totally replacing the original Paleo-Inuit without admixing. Although generally accepted, this migration scenario is incompatible with previously reported indications of east to west gene flow across the American Arctic. Here we report on the Y-chromosome haplogroup and Y-STR diversity of the four circumpolar populations of the Tuva Republic (N = 24), Northeast Siberia (N = 9), Bethel, Alaska (N = 40), and Barrow, Alaska (N = 31). Four haplogroup lineages  were detected, Q-NWT01 and Q-M3 being the most abundant at 11.11 and 66.67% in Northeast Siberia, 32.50 and 65.00% in Bethel, and 67.74 and 32.26% in Barrow, respectively. The same samples genotyped for Y-chromosome SNPs were typed for 17 Y-STYR loci using the AmpFlSTR Yfiler system. Age estimates and diversity values for the Q-NWT01 and Q-M3 mutations suggest extensive movement of male individuals along the entire longitudinal stretch of the American circumpolar region. Throughout the entire region, Q-M3 exhibits a west to east decreasing gradient in age and diversity while Q-NWT01 indicates the opposite with older TMRCA and higher diversity values running from east to west with the most recent estimates in Canada and Alaska. The high age and diversity values in Greenland are congruent with an origin of the Q-NWT01 mutation in the east of the circumpolar range about 2000–3000 ya. This scenario is incompatible with a complete biological replacement starting about 700 BP of Paleo-Inuit like the Dorset by the Thule-Inuit (Neo-Inuit), as is currently thought, and more parsimonious with gene flow carrying the NWT01 mutation from a pre-Thule population to the ancestors of the present-day Inuit. A window of opportunity for terrestrial migration from Northeast Asia towards the New World opened between ~ 30,000 ya (years ago) to ~ 16,000 ya when a drop in global temperatures caused glacial build-up and a drop in sea level. These changes led to creation of a land bridge known as Beringia3. Yet, glaciers prevented the Beringian migrant population from moving eastward into America or westward into Asia, and the migrants became trapped on the land bridge4. At approximately 18,000 ya a period of global warming commenced, and the sea levels began to rise as glaciers melted, allowing the movement of people into North America6. It is generally thought that this population eventually gave rise to the Amerind natives of north and south America8. More recently, about 9000 ya, a different group of people who probably had ancestry with groups that lived in Lake Baikal area in Northeast Asia migrated into North America, the ancestors of the Na-Dene people9. This dispersal expanded eastward into Alberta, Canada and southward along the British Columbia coast, eventually populating part of Southwest United States, today represented by groups such as the Apache and Navajo10. A third major dispersal from the Chukotka area occurred about 5000 before the present (BP)11. It is postulated that this group initially migrated to the North Slopes of Alaska ~ 5000 BP from Northeast Asia, developed the Arctic Small Tool tradition and subsequently its descendants expanded eastward12. A second circumpolar dispersal associated with the Thule culture dispersed eastward from the North Slopes around 800 BP11.Anthropological and genetic data indicates that the New World was populated from northeastern Asia in a series of waves11. Although the tradition evolved in the New World, its origins are thought to reside in Northeast Siberia rooted in Neolithic cultures13. Early Paleo-Inuit were the first group of people to live in the North America Arctic exposed to severe cold, very limited seasonal food resources and scarcity of fuel and raw materials14. Members of the Norton culture, an Early Paleo-Inuit group, hunted for large sea mammals on the open sea ice2. They practiced a flexible economy and were able to colonize rapidly a vast territory from northern Alaska across Arctic Canada into northern Greenland, 5000 km east to west and 3000 km north to south15.The earliest identified Inuk group, the Early Paleo-Inuit, date back to about 5000 BP in the North Slope of Alaska16. The Choris (3600–2500 BP), Norton (3000–1200 BP) and Ipiutak (2400–1200 BP) cultures appear in northern Alaska17. About the same time in the eastern Arctic region (eastern Canadian Arctic and Greenland), the Dorset tradition emerged and persisted from ~ 2800 to 700 BP18. The Dorset used triangular end-blades, soapstone oil lamps and burins. It has been theorized that the Dorset developed from one or several Early Paleo-Inuk traditions, such as the Pre-Dorset, Saqqaq or Independence I, although the earlier cultures had bow and arrow as well as drilling technology, which the Dorset lacked. The Dorset vanished about 700 BP coinciding with the arrival from the Bering Strait area of a culturally different group of people, the Thule19.Since then, several cultures have developed under two major traditions, the Paleo-Inuit and Neo-Inuit. During the period spanning 5000–2800 BP cultures such as the Denbigh Flint Complex, Pre-Dorset, Early/Middle/Lare Dorset, Independence I, Saqqaq, Choris and Norton under the Early Paleo-Inuit tradition are recognized20. It is theorized that the Thule people were the descendents of the Birnirk culture of Chukotka19. With the Thule culture came more effective means of transportation, including dog sleds and large skin boats, complex tool kits like sinew-backed bows and harpoons, float gear for hunting large whales and sinew-backed bows19. The most recent estimates indicate that the Thule-Inuk culture migration started about 800 BP and spread quickly throughout the eastern Arctic, rapidly replacing the Dorsets in most regions20.The Thule culture differs from the Dorset in their dependence on whale hunting for their subsistence21 or are the result of admixture events involving Dorset populations and Thule groups22. Most of the reports based on uniparental23 and genome-wide genetic markers support a total replacement scenario without admixture involving Paleo-Inuit and Neo-Inuit as the latter migrated eastward from North Alaska to Greenland about 800 BP25. Instances of technology transfer, such as the Thule adoption of Dorset harpoon-head styles, have been attributed to the salvage of remains from abandoned Dorset sites19.One of the central issues that have captured the interest of investigators in the study of the settlement of the American Arctic is whether genetic continuity occurred among the various Inuk cultures, specifically the Paleo-Inuk and Neo-Inuk cultures. Controversy still exists regarding whether contemporary Inuk groups entirely derive from Thule populations that migrated across the Arctic about 800 BP11. In one report, mtDNA haplogroup D2a1, abundant in the Saqqaq and Dorset in Paleo-Inuit of Greenland, but totally absent in Neo-Inuit, supports the idea that the ancestors of the two cultures entered the North American Arctic from Northeast Siberia as a single migration around 4000 BP and remained isolated until their replacement by the Thule about 700 BP19. Simulation analyses performed in the same study using genomic sequences from a high-coverage of a 4000-year-old Saqqaq individual also demonstrate that admixture likely occurred from the Neo-Inuk lineage into the Saqqaq, likely in Northeast Siberia. More recent studies based on genomic data using rare allele and haplotype sharing indicate that genetically Paleo-Inuit and Thule Neo-Inuit are closely related, but not identical21. Two years later Flegontov and collaborators demonstrated that Neo-Inuit from the American Arctic and Northeast Siberia inherited many of their genes from Paleo-Inuit, and the authors suggested that the Neo-Inuk migrants in the American Arctic region are genetically linked to a single Paleo-Inuit Siberian source12.Some reports involving mtDNA haplogroups and genome-wide sequences suggest genetic discontinuity between Paleo-Inuk and Thule populations dating to the replacement period (~ 700 BP) but provide evidence of ancient admixture events in Northeast Siberia around 4000 BP between the ancestors of Paleo-Inuit and Neo-Inuit22. Also, mtDNA data based on A2, A2a, A2b, D4b1a, and D2a haplogroups indicate the presence of basal lineages in the Alaskan North Slope, which is consistent with northern Alaska as the starting point for both Paleo- and Neo-Inuk migrations eastward into Canada and Greenland. The study also detected haplogroup D2a, previously seen only in Aleuts and Paleo-Inuit, in the Alaskan North Slope, suggesting genetic continuity between Paleo-Inuit and contemporary Inuit in this region26. Also, Y-chromosome results from contemporary Greenland Inuit under the Q-NWT01 sub-haplogroup suggest that these individuals may be descendents of the Dorset Paleo-Inuk culture and thus indicating the possibility that the contemporary population of Greenland represents admixture between Dorset Paleo-Inuit and Thule Neo-Inuit27. In addition, radio-carbon dates from the Canadian Arctic indicate 50–200 years of temporal and regional coexistence involving Dorset and Thule groups that may have provided opportunity for gene flow20. It is likely that the genetic data presented above arguing for and against admixture between Thule and Dorset populations represents differences in the marker systems used, regional differences, sex bias gene flow and/or errors introduced by limited number of subjects examined. At this stage, it is difficult to access the true underlining reasons.Not all available data suggest a total replacement of Dorset by Thule populations. Several reports provide data in favor of recent gene flow between Paleo-Inuit and Neo-Inuit. For example, studies that explored the mtDNA make-up of contemporary Inuk populations of Canada and Greenland detected diverse complex distribution of A2b founder haplotypes difficult to explain by just an advancing Thule expansion. The observed complex A2b haplotypic distribution is more parsimoniously explained by a matrilineal contribution to contemporary Inuit populations by the Dorset peoples who inhabited Greenland and the Canadian Arctic prior to the Thule expansion28. However, only populations from the North West Territories of Canada29 and Greenland28 have been previously phylogenetically examined. Q-M3 decreases in frequency in groups from Greenland compared to the North West Territories of Canada while Q-NWT01 exhibits the opposite distributions27. It has been suggested that the Paleo- and Neo-Inuk cultures originated in the Bering Strait region20. Thus, despite the fact that knowledge of the Alaskan distributions of the Q-M3 and Q-NWT01 haplogroups is paramount for a complete understanding of the genetic origins of the Yupik and Inuit, this geographic region has not been studied. To address this gap in our understanding of these populations, we analyzed Y-chromosome haplogroup and Y-STR data to address the informational lacuna that exists regarding the peopling of the Alaskan circumpolar region. Together with the previously published Y-chromosome data, these analyses indicate that a west to east decreasing gradient exists in the frequency and diversity of Q-M3 values. We also sought to determine if the decreasing trend in frequency and diversity values of Q-NWT01 in the North West Territories of Canada compared to Greenland that has been previously reported also extends into Alaska.Two Y-chromosome haplogroups, Q-M3 and Q-NWT01, predominate in the American circumpolar populations that have been studiedA total of 104 male individuals belonging to seven populations from Central Asia, Northeast Asia and Alaska were found to belong to Y-chromosome haplogroup Q-M242. The populations from Asia include three from the Tuva Republic in Central Asia  and two from the northeast tip of Siberia . The Alaskan populations include one from Bethel in West Alaska  and one from Barrow in the North Slope (Inuit = 31). The geographic locations of the above-mentioned groups in addition to the reference populations are illustrated in Fig. Following the ethical guidelines stipulated by the research institutions involved in this project, all samples were procured voluntarily and with informed consent while closely adhering to the ethical guidelines stipulated by Colorado College, Colorado Springs, Colorado USA. The study adhered to the tenets of the Declaration of Helsinki for the protection of human subjects. The IRB of Colorado College approved this study. All experimental protocols were approved by the IRB of Colorado College.30. A NanoDrop 1000 Spectrophotometer (Thermo Scientific) was used for DNA quantitation. Samples were stored as stock solutions in 10 mM Tris–EDTA at − 80 °C.DNA extraction from blood was performed using the standard phenol–chloroform method and ethanol-precipitated as described previously31 and Q-NWT0129, were hierarchically genotyped by standard methods. These methods included: PCR/RFLP, sequencing and allele specific PCR32. The phylogenetic relationship of these markers and the haplogroups they represent are included in Fig. http://www.isogg.org/tree/ .A total of six Y-chromosome bi-allelic markers, Q-M242, Q-MEH2, Q-M120, Q-M346, Q-M3www.appliedbiosystem.com) specifications. The PCR products generated were separated in an ABI Prism 3130xl Genetic Analyzer and the Amplicon sizes and alleles designations were assess using the GeneMapper v 6.0TM program.DNA samples with a Q-M242 background were also typed for 17 Y-STR loci  using the AmpFlSTR Yfiler kit . PCR was performed as per manufacturer’s . DYS385 was excluded from the haplotype diversity calculations because it is not possible to discriminate between the DYS385a and DYS385b loci with the Y STR kit. The number of repeats at DYS389II was calculated by subtracting the number of repeats at DYS389I. Discrimination capacity was calculated by dividing the number of different haplotypes by the total number of individuals in the population. The fraction of unique haplotypes was determined as the percent proportion of unique haplotypes.Y-chromosome STR haplotypes of haplogroup Q males from a total of 8 previously published, geographically targeted populations , in which the Y-STR markers were weighted inversely to their repeat variance. Average gene diversity (GD) based on Y-STR loci were computed according to Nei35. Intra-haplogroup diversity (mean microsatellite variance: Vp)36 was calculated across 15 loci for each Q haplogroup and relevant population. Y-STR haplotypes were used to estimate the time to the most recent common ancestor (TMRCA). With this aim, rho statistic (ρ)22 and weighted rho (ρW)37 were estimated with an R script available in GitHub . Mutation rates were obtained from the Y-Chromosome STR Haplotype Database  in March 2020. The statistical significance of the time estimate differences was assessed using the Past 4.02 software .Median-joining (MJ) networksie, Q-M346 in Tuva, Q-M3 in Northeast Siberia, Q-NWT01 in Barrow and Q-M3 in Bethel), the four populations are not monomorphic in their sub-haplogroup constitution , the groups partition diagonally with the two dimensions contributing to the separation of the populations. Quadrant III of the graph exhibits a cluster made up of all but one  of the Greenland Inuit communities, which plots near in quadrant II. The Barrow Inuit of the Bering Strait region and the North West Territories Inuit from Canada partition in close proximity to this Greenland cluster. The Northeast Siberia groups of Chukchi/New Chaplino and the Yupik of Bethel partition close by in the diagonal line. More distant diagonally in quadrant I, the North Altai samples separate. The Tuva and South Altai populations separate distantly from the rest of the populations along the first dimension in quadrant I and IV, respectively. It is evident from the plot that an affinity exists among the populations from Greenland, North West Territories and Barrow, which is considerably less relative to Northeast Siberia and Bethel, which plot close to each other.Rst pairwise genetic distances among genotyped and reference populations were calculated based on 15-loci Y-STRs and are provided in Supplementary Table The network analysis based on 15-loci Y STR haplotypes within Haplogroup Q including sub-haplogroups Q1b-M346, Q1b1a1a-M3 and Q1a1-NWT01 is illustrated in Fig. To examine in more detail, the relationships among individuals within the two predominant American circumpolar sub-haplogroups, Q-M3- and Q-NWT01-specific network analyses were performed. Supplementary Fig. The Q-NWT01 network exhibits a star-like topology with a central node made up of all the American circumpolar populations  using the weighted age method of Calafell and 899 years with the Kilin-Klyosov method  in the Tuva Republic  is considerably older than the Inuit of Barrow (3940–4727 ya) and the Yupik of Bethel (4998–5999 ya) in Alaska Table . Except 28 and Northwestern Canada29, no comparable Y-chromosome phylogenetic analyses exist on populations from the western regions of the Americas, specifically Alaska. The Y-chromosome Y-SNP and Y-STR profiles of the circumpolar populations examined in the present study illustrate a region recently populated (~ 6000 BP) by migrants from Northeast Asia. The Y-chromosome haplogroups and haplotypes observed throughout the American circumpolar region from Alaska to Greenland exhibit strong genetic affinities with the ones in Northeast Siberian populations just across the Bering Strait in the Chukchi Peninsula. Yet, the Y-chromosome similarities with groups further west in the Altai and Tuva regions are limited.Although previous investigations have been conducted on communities from the eastern portion of circumpolar America including Greenland29 while in Greenland Q-NWT01 is 57.0% and Q-M3 is 43.0% of the Inuk population27.The Y-chromosome differences between the populations in the Altai/Tuva region and the Inuk populations of the Americas is reflected at the haplogroup level in the almost complete presence of the Q-M346 haplogroup in the former and the dominance of Q-M3 and Q-NWT01 in the latter. In the extreme Northeast Asia in the Chukchi Peninsula, although Q-M3 predominates, Q-M346 and Q-NWT01 were detected as well. Across the American circumpolar region Q-M3 and Q-NWT01 dominate or represent the entire Native Y-chromosome component of the expanse. In coastal west Alaska, the Yupik population of Bethel is 65.0% Q-M3 and 32.5% Q-NWT01 while the Inuit of Barrow in the Northern Slopes are 67.7% QNMT01 and 32.3% Q-M3. In the North West Territories of Canada 81.0% of the Inuit are Q-NWT01 and 19.0% are Q-M3The Y-STR profiles within the Q haplogroup indicate marked partitioning of haplogroup-specific Y-STRs into separate clusters Fig. . ClusterThe Y-chromosome landscape provided by the network analyses is mirrored by the MDS plot based on haplogroup Q Y-STR data Fig. . The MDSThe age estimates and diversity values provide additional insight into the history of the circumpolar populations. Due to the dominance of the Native Q-M3 and Q-NWT01 lineages in the circumpolar populations, we base our analyses and discussion on these two sub-haplogroups. The communities of Northeast Siberia exhibit the oldest age estimates and highest diversity values of the Q-M3 lineage in the populations examined. Compared to the geographically closest groups of Alaska, some of the age estimates from Northeast Siberia are about twice as old and some of the diversities (Vp) are twice as high Table . AlthougThe Q-NWT01 lineage circumpolar landscape is the opposite of the Q-M3 lineage Fig. . The hig27 are considerable different from the ones reported in the current study (2072/3391 ya). Even within the same study of Olofsson et al.27, an age difference of over 7000 ya was estimated by two different methods. This phenomenon has been previously described and attributed to variation in the calibrations of Y-STR mutation rates as well as differences in methodology among studies41. Therefore, the age and diversity values reported in this study may be useful only for comparisons among the circumpolar populations studied in this report.It is noteworthy that the age estimates generated by Olofsson and colleages 27 in their study of Greenland Inuk communities. The map in Fig. 11 adds support to this alternative explanation of stochastic fluctuations. The QNWT01 mutation could have originated in the population ancestral to both the Paleo-Inuit and the Thule groups. More recent, or contemporary admixture events that could have transferred the Q-NWT01 mutation from some other present-day group that also carried the mutation to comtempory Inuit are incongruent with the age/diversity estimations and the observed graient. Analyses of archaeological samples of Thule/Punuk/Birnirk individuals would confirm that the Q-NWT01 mutation is not the result of genetic continuity within the Bering Strait and is due to admixture with later Dorset groups. It should be emphasized that the proposed model does not contradict the possibility of ancient admiture events involving the ancestors of Paleo-Inuit and Neo-Inuit in North East Asia as suggested by genome-wide studies19. Uniparental mutations such as Q-M3 and Q-NWT01 could have been transfer from Paleo-Inuit to Neo-Inuit subsequently and independently. Yet, despite the large contribution to the male line of descent from Paleo-Inuit to Neo-Inuit, so far genome-wide studies show no traces of admixture between the two in the North American Artic. Thus, confirmation of admixture from genome-wide studies should be explore further.Congruently, the topology of the Q network with all the Q-NWT01 samples grouped into a single compact cluster and the extensive inter-population haplotype sharing, many in stem locations, also suggest that all the Q-NWT01 haplotypes had a common origin and rapidly dispersed among populations connected by gene flow Fig. . This prThe Y-chromosome analyses performed in this investigation support the notion that the American circumpolar region was settled by Asian migrants that crossed Beringia and rapidly advanced eastward, reaching Greenland at least 4000 BP. The Q-M3 and NWT01 Y-chromosome lineages predominate in an extensive territory that extend from Northeast Siberia to Greenland. Inter-population haplotype sharing suggests extensive movement of people along the entire longitudinal stretch of the circumpolar region. Throughout the entire American circumpolar range, the Q-M3 lineage exhibits a west to east gradient in age and diversity while the Q-NWT01 lineage indicates the opposite with older TMRCA and diversity values running from east to west with the most recent estimates in Alaska and Canada. High age estimates and diversity values in Greenland suggest an origin of the Q-NWT01 mutation in the eastern portion of the circumpolar range about 2000–3000 ya, a scenario incongruent with a complete biological replacement starting about 800 BP of Paleo-Inuit like Dorset by the Thule (Neo-Inuit) and more parsimonious with gene flow carrying the Q-NWT01 mutation from a pre-Thule population to the ancestors of present-day Inuit. Also, more ancient admixture events in the Western American Artic and subsequent migration carrying the Q-NWT01 mutation eastward are difficult to reconcile with the age/diversity estimations and the observed gradient. Altogether, the evidence presented in this article points to bidirectional gene flow and interbreeding involving Paleo-Inuk and Neo-Inuk populations shaping the present circumpolar populations.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.Supplementary Information 12.Supplementary Information 13.


FAW is native to the Americas, and its invasion was first reported in West Africa in 2016. Then it quickly spread through Africa, Asia, and Oceania, becoming one of the main threats to corn production. We analyzed whole genome sequences of 177 FAW individuals from 12 locations on four continents to infer evolutionary processes of invasion. Principal component analysis from the TPI gene and whole genome sequences shows that invasive FAW populations originated from the corn strain. Ancestry coefficient and phylogenetic analyses from the nuclear genome indicate that invasive populations are derived from a single ancestry, distinct from native populations, while the mitochondrial phylogenetic tree supports the hypothesis of multiple introductions. Adaptive evolution specific to invasive populations was observed in detoxification, chemosensory, and digestion genes. We concluded that extant invasive FAW populations originated from the corn strain with potential contributions of adaptive evolution.The fall armyworm (FAW; S As human trade is constantly increasing, the number of reported cases of invasive species has consequently surged up6, particularly in insect species7. Indeed, insects comprise 88.24% of reported invertebrate invasive cases in the Global Invasive Species Database8 (accessed on 27th July 2021). Consequently, invasive pest insects cause serious losses in major crop production9, especially in sub-Saharan African countries, where the national economy is heavily dependent on agriculture, as up to 35% of national gross domestic product (GDP) is lost due to invasive pests10.Biological invasion by pest species is one of the main causes of economic losses in agriculture11. The first step is the introduction of an alien species outside its native range. Invasive species are often introduced through anthropogenic transport, such as trade, urbanization, irrigation, and roads and railways12. The second step is the establishment of stable populations, thanks to a higher intrinsic growth rate13 or a higher competitive ability14 than native species. The third step is range expansion from the established population. Adaptive evolution may contribute to all of these steps by (i) fixation of beneficial de novo mutations or standing genetic variations, (ii) hybridization between introduced and native species, or (iii) genome doubling (reviewed in11). A time gap between introduction and range expansion has been often observed, and this gap period was coined the ‘lag phase’15. Since the eradication of an introduced pest species is only realistic during its lag phase, it is important to identify alien species before they become invasive for pest management.The process of an invasion typically involves three key stepsSpodoptera frugiperda (J.E. Smith) , is one of the most infamous insect pests due to its severe impact on major crops including corn, cotton, rice, sorghum, and soybean17. The FAW exhibits high dispersal ability combined with a marked migratory behavior18, the ability to rapidly develop resistance against synthetic insecticides19 and Bacillus thuringiensis proteins22, alongside the potential for occasional outbreaks. The FAW consists of two host plant strains with differentiated ranges of host plants, the corn strain (sfC) and the rice strain (sfR) (named after their preferred host plants). These two strains are observed in sympatry throughout their native range25. Since sfC and sfR are morphologically indistinguishable, the two strains can only be identified with molecular markers27. The nuclear triosephosphate isomerase (TPI) gene on Z chromosome28 and the mitochondrial cytochrome c oxidase subunit 1 (COX1) gene29 are commonly used for this purpose.The fall armyworm, 30. In the years following its first report, FAWs were detected throughout sub-Saharan Africa, followed by their widespread detection in India, South East Asia, East Asia, Egypt, and Oceania . Invasive FAW larvae cause significant economic losses, especially on corn with a production yield loss of 21–53% in Africa31, where corn is one of the most important staple crops . Chemical insecticides are commonly used to control invasive FAW populations, but field-evolved insecticide resistance against chemical insecticides has been reported from Chinese populations34.The FAW is native to the Americas, and its invasion of the Old World was first reported in West Africa in 201635 reported that the Chinese populations most likely originated from African populations using population genomics analyses. Schlum et al.36 also performed population genomics analyses of one invasive (Kenya) and four native  populations, but they observed no clear pattern of population structure. Since these studies are based on the FAW samples collected from corn or sorghum, which are presumably the preferred plants of sfC25, the genetic relatedness among native sfC, native sfR and invasive FAW populations has yet to be determined in these studies.Since the global invasion of FAWs is a pressing issue due to the severe economic impacts, there is an urgent need to obtain information on the population structure of invasive and native FAW populations in the context of geography and host plants. Gui et al.39, such as corns and sorghums, several genetics studies argued that invasive FAWs are hybrids between sfC and sfR. Marker-based studies showed that most invasive FAWs are sfC according to the mitochondrial COX1 genes and sfR according to the TPI genes, arguing that invasive FAWs were generated through interstrain hybridization41. But it is still unclear whether the same pattern can be supported if whole genome sequences are analyzed. Zhang et al. reported through genomics analysis that Chinese FAW populations are hybrids between sfC and sfR33. However, since they included only one sfC sample and one sfR sample from native populations, it is still unclear whether the same pattern will be observed when population data from native population are included. In short, the argument that invasive FAWs are hybrids between sfC and sfR needs to be revisited using population genomics data. If invasive FAWs are indeed hybrids, it is not easy to explain why they are found almost exclusively in sfC-preferred host plants.Even though invasive FAW populations are found almost exclusively in sfC-preferred plantsIn this study, we aim at inferring the population structure of FAW populations, including native sfC, sfR, and invasive populations using whole genome sequences from samples collected globally from North and South America (native range), East and West Africa, and South and East Asia (invasive ranges). Native FAW samples were collected from both corn (sfC preferred plants), as well as grass or rice (sfR preferred plants). We also identified loci with signatures of adaptive evolution that may contribute to the FAW invasive success.The total number of individuals used in this study was 177 (99 from native populations and 78 from invasive populations) from 12 geographic populations  Fig. A. The exA principal component analysis was performed using 27,117,672 nuclear SNVs to infer FAW population structure. The first principal component explained 15.31% of the variance in allele frequency Fig. A, of whiST statistics show significant genetic differentiation between the native and invasive populations in the Corn group . This result shows that genetic variations can be explained primarily by three groupings  and that the invasive populations were derived from the native population(s) in the Corn group.The first principal component in Fig. A whole nuclear genome phylogenetic tree reconstructed from native FAWs showed that ancestral FAWs split between the Rice group and (the Corn.Mex + the Corn groups) in the area of origin, and a unidirectional gene flow from Rice group to Corn group was observed when the number of migration edges was six Fig. C. This p51 to infer the ancestry of each individual. Invasive populations appeared to share a single common ancestor, distinct from all native populations, in a range of K values  and in the native populations   Fig. A. Invasimes Fig. B, sugges54. The median value of the composite likelihood was 0.4350. A locus was considered to be targeted by selective sweep if the composite likelihood was higher than 100, a threshold that was arbitrarily chosen. We were able to pinpoint the apparent outlier of the composite likelihood through eyeballing as well. In total, 26 grids had a composite likelihood higher than 100. These grids represented 0.090% of the total 29,000, meaning that a very stringent criterion was applied here to infer loci under selective sweeps. Neighboring grids constituted seven loci on three chromosomes. We considered that these loci are potential targets of selective sweeps  , the patterns observed in this study could be affected by temporal or geographical sampling bias, even though the analyzed FAWs were collected from a wide range of geographic locations in four continents. Since geography does not have a major effect on the population structure within the Corn group Fig. A, we do In this study, we showed that invasive FAWs originated from sfC in the native area. The invasive FAWs appear to be derived from a single origin despite multiple introductions, suggesting the possibility of genetic admixture during a lag phase. We also observed footprints of adaptive evolution specific to invasive populations. Adaptive evolution specific to the identified invasive populations may have caused insecticide resistance or host plant adaptation, possibly contributing to invasive success. We argue that comparative genomics and functional studies on host plant adaptation and insecticide resistance are urgently needed to identify the main evolutionary selective forces responsible for invasive success, as this information may be useful to better control FAW in the Old World.43. Genomic DNA was extracted using the Promega Wizard Genomic DNA Kit or the Qiagen DNeasy®lood and Tissue Kit. Libraries for whole genome resequencing were constructed from 1.0 μg of DNA per sample using the NEBNext DNA Library Prep Kit. We performed whole genome sequencing from FAW samples collected in Benin , India (14), Mexico (26), Florida (24), French Guiana (3), and Guadeloupe (4) using the NovaSeq 6000 System with 20X coverage on average with 150 bp paired-end and 300 bp insert length. Then, we combined resequencing data from Santa Isabel in Puerto Rico (15) (NCBI SRA: PRJNA577869), Stoneville in Mississippi (17) (PRJNA494340), and from Citra and Jacksonville in Florida (USA) (24) (PRJNA639296), which were generated for our previous studies48. The resequencing data from Kunming in China (2)44 was also included in the analysis. Finally, we took advantage of using resequencing data from the State of Goiás in Brazil (10), from nine districts including Blantyre, Chiradzulu, Machinga, Mulanje, Thyolo, Zomba, Salima, and Karonga, Mzimba, and Nkhata Bay in Malawi (16), and from eight districts including Amolatar, Katakwi, Kumi, Ngora, Pallisa, Soroti, Mbarara and Wakiso in Uganda (7), which were generated by Commonwealth Scientific and Industrial Research Organisation for other studies50. The total number of geographic locations is 12, which covers a large proportion of the FAW distribution area. The total number of individuals used in this study is 177 (99 from native populations and 78 from invasive populations) , from Texcoco in Mexico (2009), from French Guiana (1992), and from Petit-Bourg and Port-Louis in Guadeloupe (2013). We obtained gDNA from India, which was used by Sharanabasappa et al.7, from T72. Reads were mapped against the ver7 reference genome (https://bipaa.genouest.org/sp/spodoptera_frugiperda_pub/download)48, which had chromosome-sized scaffolds, using bowtie2 v2.3.4.1 with the -very-sensitive-local preset73. The read depth of mapping of resequencing data was 17.72X in the median. Then, we performed local re-assembly of haplotypes using GATK v4.1.2.074. The resulting gvcf files were merged into one gvcf file with CombineGVCFs command and variant calling was performed with GenotypeGVCF command in the same software. Filtering was performed from the resulting VCF  file if QD is lower than 2.0, FS is higher than 60.0, MQ is lower than 40.0, or MQRankSum is lower than − 12.5, or ReadPosRankSum is lower than − 8.0. The total number of genetic variance is 158,277,530, which includes 89,387,997 SNVs (single nucleotide variations). After filtering, 27,117,672 SNVs remained.Nucleotides with a Phred score less than 20 and adapter sequences were removed from the reads using AdapterRemoval v2.1.775. We first mapped the Illumina reads against mitochondrial genomes (NCBI: KM362176) using bowtie2 v2.3.4.1 with -very-sensitive-local preset73, followed by extracting mitochondrial reads using samtools v1.976. Mitochondrial genomes were assembled from these reads, and mitochondrial genes were annotated using MitoZ using the default options77. Mitochondrial COX1 sequences were aligned together with a COX1 sequence from a specimen of another Spodoptera species, S. exigua (Hübner) (NCBI: JX316220), using MUSCLE v3.8.3178, and a maximum likelihood phylogenetic tree was reconstructed using MEGA X79. We used the Kimura 2-parameter model, and a uniform substitution rate across the alignment was assumed to avoid potential statistical artifacts caused by parameter richness. Non-parametric bootstrapping was performed with 1,000 replications to calculate statistical support at each node. The phylogenetic tree was visualized using iTOL v652. We determined the mitochondrial strains from sfC and sfR clades with the individuals from Mississippi and Puerto Rico, in which strains were already determined for each individual in our previous studies47.FAW strains were identified using a phylogenetic tree reconstructed from the mitochondrial COX1 gene, which is the universal barcode gene and is also commonly used for FAW strain identification80. Principal component analysis was performed from the TPI locus using plink v1.981. Then, the strain of each sample was assigned according to the grouping.FAW strains were also identified using the TPI marker. First, we extracted the TPI locus from the vcf file using tabix v1.10.2-381. Ten principal components were extracted, and the main groups in FAW were identified from two major principal components. Significant genetic differentiation between groups was tested using FST82 calculated from VCFtools v0.1.1583. FST was calculated from 100 kb windows. The genomic average FST was calculated from Weir and Cockerham’s weighted mean FST. Significant genetic differentiation between two groups was tested with a permutation test84. More specifically, (i) FST was calculated from two groups, (ii) FST was calculated from randomly generated groups with 100 replications, and (iii) the proportion of random groups that have higher FST than real groups was calculated. Since this proportion represents type I statistical error, the proportion was interpreted as a p-value. Ancestry coefficient analysis was performed using admixture v1.3.0 in a wide range of K values51.We identified a native population that was genetically most closely related to the invasive populations from the population structure. The principal component analysis was performed to identify the main groups in FAW using plink v1.985. First, a new VCF file with individuals from native populations was generated by subsetting the original VCF file with 177 individuals. Second, S. litura (Fabricius), which is one of the most closely related species to FAW in the Spodoptera genus86, was used as an outgroup, and a VCF file was generated by mapping S. litura Illumina reads  against the FAW reference genome using bowtie2 with -very-sensitive-local preset73 and by variant calling using GATK v4.1.2.074. Third, these two VCF files were merged using VCFtools v0.1.1583, while missing data of S. litura was not allowed. The number of used SNVs is 697,498. Fourth, the newly merged VCF file was converted to TreeMIX format using vcf2treemix.sh (https://github.com/speciationgenomics/scripts/blob/master/vcf2treemix.sh). Fifth, we ran a TreeMIX binary with the number of migration edges ranging from three to six while the resulting tree was rooted by S. litura. The number of SNVs per block was 500 to estimate covariance matrices. Finally, the resulting file was visualized using the internal R library (plotting_funcs.R) in the TreeMix software.Phylogenetic relationships with gene flow among the main groups of native populations were estimated to infer evolutionary history using TreeMIX v1.1347 to infer phylogenetic relationships at the level of individuals out of whole genome sequences. First, the euclidean distance of allele frequency between each pair of individuals was calculated from the biallelic variant positions in which genotypes are determined from all individuals using VCFphylo47. Transversional variants were weighted to two. Then, bootstrapping distance matrices were generated with 1,000 replications, and we generated BIO-NJ trees for each matrix using FastME v2.1.587. Then, a consensus tree was made using consense in the Phylip package v3.69788. The tree was visualized using iTOL v652.We also used the VCFphylo approach54 from the largest 29 scaffolds, which are believed to be chromosome-sized. The proportion of the assembly covered by these 29 scaffolds is 93.52%. The number of grids in which composite likelihood was calculated was 1,000 per scaffold. Based on the assumption that most genomic sequences were not targeted by selective sweeps, outliers of composite likelihood were identified as loci targeted by selective sweeps. The annotation of Cytochrome P450 genes was manually curated, as described in Gouin et al.46. The name of the CYP gene was validated by the P450 nomenclature committee89.Potential targets of selective sweeps were identified from the composite likelihood of being targeted by selected sweeps from the site frequency spectrum using SweeD v3.2.1Supplementary Information.


Moschus berezovskii) has been subject to illegal hunting for hundreds of years due to the medical and commercial values of musk, resulting in a significant decline in population size. However, it is still unclear to what extent the genetic exchange and inbreeding levels are between geographically isolated populations. By using whole-genome data, we reconstructed the demographic history, evaluated genetic diversity, and characterized the population genetic structure of Forest Musk Deer from one wild population in Sichuan Province and two captive populations from two ex-situ centers in Shaanxi Province.Many endangered species exist in small, genetically depauperate, or inbred populations, hence promoting genetic differentiation and reducing long-term population viability. Forest Musk Deer  for Forest Musk Deer at the genomic scale, and varied significantly among three populations , with the Sichuan population having the lowest (0.11%). The nucleotide diversity of three populations varied significantly , with the Sichuan population having the lowest genetic θπ (1.69 × 10–3).SNP calling by GATK resulted in a total of 44,008,662 SNPs. Principal component analysis (PCA), phylogenetic tree (NJ tree), ancestral component analysis (ADMIXTURE) and the ABBA-BABA test separated Sichuan and Shaanxi Forest Musk Deer as two genetic clusters, but no obvious genetic differentiation was observed between the two captive populations. The average pairwise FGenetic diversity of Forest Musk Deer was moderate at the genomic scale compared with other endangered species. Genetic differentiation between populations in Sichuan and Shaanxi may not only result from historical biogeographical factors but also be associated with contemporary human disturbances. Our findings provide scientific aid for the conservation and management of Forest Musk Deer. They can extend the proposed measures at the genomic level to apply to other musk deer species worldwide.The online version contains supplementary material available at 10.1186/s12864-022-08896-9. Many endangered species exist in small, genetically depauperate, or inbred populations , hence pGenetic diversity available for a species is determined by the genetic background of the ancestor populations, which in turn is heavily impacted by the demographic history and contemporary human disturbance . Small eMoschus spp.) are assessed to be globally threatened due to poaching and habitat loss [M. moschiferus) according to The IUCN Red List of Threatened Species v. 2015 [M. berezovskii) was once widely distributed in China [https://china.huanqiu.com/article/9CaKrnJWMYT). During the breeding, Forest Musk Deer is challenged with disease severity and immunity reduction, which may be exacerbated by inbreeding and genetic diversity declines in Forest Musk Deer [All musk deer species  data were used to reconstruct the demographic history, evaluate genetic diversity, characterize genetic structure, and reveal inbreeding pattern of Forest Musk Deer in one wild population in Sichuan Province and two captive populations in Shaanxi Province, China. The current captive individuals are descendants of wild individuals captured from the natural habitats in the Qinling Mountains in Shaanxi. Therefore, we assume that the two captive populations can represent the wild population in the Qinling Mountains in Shaanxi, or at least the imprints of demographic history on the genetic background remain. We tested the hypothesis that genetic differentiation exists between populations in Sichuan and Shaanxi, and inferred the pattern of genetic composition.Error rate of sequencing data was lower than 0.04% for all 15 samples of Forest Musk Deer , and two captive populations in Shaanxi, east Qinling (EQL) and west Qinling (WQL) Fig. a, the prp < 0.05), with the wild WSC population having the lowest (0.11%). The nucleotide diversity of three populations varied significantly (p < 0.05), in contrast, WSC had the lowest genetic θπ (1.69 × 10–3) and θw (1.59 × 10–3), while WQL had the highest θπ (2.01 × 10–3) and θw (2.00 × 10–3) following by EQL (θπ: 1.98 × 10–3 and θw: 1.94 × 10–3). In terms of both heterozygous SNP rate and nucleotide diversity, it could be seen that the wild population (WSC) in Sichuan had lower genetic diversity than the two captive populations in Shaanxi (EQL and WQL).By estimating the proportion of heterozygous SNPs per base pair, the mean heterozygous SNP rate was 0.14% (0.11%—0.15%) for Forest Musk Deer at the genomic scale, and varied significantly among three populations  for all Forest Musk Deer populations using the MSMC . All popr2 < 0.2 within 10 kb, but declined more slowly .Based on the linkage disequilibrium (LD) analysis, the extent of LD in both populations of WQL and EQL appeared consistently, but was quite different from the pattern in WSC. LD decayed to wly Fig. a. Consiswly Fig. b, but noWe found that the genetic diversity of the wild population in west Sichuan (WSC) was lower than that of both captive populations in Shaanxi (WQL and EQL), which was consistent with previous studies revealed by microsatellites , mitochoAiluropoda melanoleuca) [The phylogeographic reconstruction, PCA and ADMIXTURE analyses provided evidence of moderate population genetic differentiation between populations of Forest Musk Deer located in Shaanxi and Sichuan, but weak genetic differentiation between two captive populations within Shaanxi. The distribution of wild Forest Musk Deer is divided into small populations by geographical barriers, such as Qinling Mountains and Minjiang River , 58, andnoleuca) , 60. CooIt is important to note that most mammals contract during the most recent glacial period approximately 12.1 – 34.8 kya . Forest 2 [The demographic history based on genomic data further provided evidence that human disturbance was closely associated with the population declines of Forest Musk Deer. It could be inferred that the effective population size of Forest Musk Deer was impacted by human activities because the Chinese Han population continued to increase since 30 kya, whereas the effective population size experienced a steep decline in Forest Musk Deer. Forest Musk Deer, a species that adapts to high mountain forests and is characterized with shy and timid personality, is more vulnerable to the natural landscape changes and the destruction of hidden conditions . Dramati2 ).Musk has been used for perfume and medicinal purposes for thousands of years Unfortunately for the endangered but economically important species, the species conservation and musk utilization are unbalanced. Current conservation efforts remain insufficient to offset the negative effects of population declines. The conservation priority is to enhance the patrol time to eliminate poaching , 64, whiThe effects of recent severe demographic and genetic declines can be inferred by comparing the contemporary samples with the historical samples, or by running simulations under alternative historical scenarios based on the coalescent approach. This will be the research priority to evaluate the genetic consequences of wildlife commercial and medical exploitation within a comparative context. For captive populations, breeding management is an important factor in determining the genetic composition and population genetic structure, which means that pedigree records are strongly suggested to be done in order to avoid inbreeding in captivity. The proposed measures at the genomic level could be extended to apply to other musk deer species worldwide, since the knowledge from population genomics will contribute to the conservation of those endangered species.Tissue or blood samples of 15 Forest Musk Deer individuals were collected from one wild population and two ex-situ populations. Specifically, five skin tissue samples were collected from the wild individuals captured in the west of Sichuan (WSC), five blood samples from the ex-situ center in Fengxian located in the west of Qinling Mountains (WQL) and five blood samples from the ex-situ center in Meixian located in the east of Qinling Mountains (EQL) . The libraries were prepared as described in the commercial TruSeq kit . Whole-genome paired-end sequencing (PE150) was performed within the Illumina Novaseq 6000 platform using standard procedures . The raw FASTQ data was de-multiplexed, and the adapters were trimmed using Trimmomatic v0.36.0 . Clean sVariant calling was conducted using HaplotypeCaller and GenotypeGVCFs modules in GATK4 . To ensuπ was calculated using VCFtools v0.1.13 [, and the Watterson’s estimator θw was calculated using VariScan v2.0.3 [In order to assess genetic diversity within and among Forest Musk Deer populations, the heterozygosity of each individual was calculated by dividing the total number of heterozygous SNPs by genome size as previously described and averaged over each population . Populat v0.1.13 , and then v2.0.3 .http://treesoft.sourceforge.net/) with the aim to visualize the genetic distance relationship between individuals, with a bootstrap value of 1,000. The standard VCF file was filtered and thinned according to user-specified command line options  by VCFtools v0.1.13 [The population structure analysis was performed using the commonly used methods , includi v0.1.13 . Additio v0.1.13 , and the v0.1.13 . The mos v0.1.13  to calcuμ)　of 2.2 × 10–9 mutations per site per year for Forest Musk Deer used in this study was according to the reference [Multiple sequential Markovian coalescence (MSMC) model can be used to reconstruct each population’s demographic history based on genomic information . MSMC iseference . The geneference .The Patterson's D-statistic, also called ABBA-BABA test, is the most widely used statistical approach that is applied to detect introgression across genomes between.M. moschiferus) from NCBI (Project ID: PRJNA574937), and used it as the outgroup. We performed the test using angsd v0.935 [p-values were computed based on a two-tailed binomial test. Meanwhile, inference of gene flow was conducted based on genome-wide allele frequency data using the software TreeMix v1.12 [populations within a species or among species , and revd v0.935 , under td v0.935 , and z six v1.12 .2) between any two loci from each population was calculated using VCFtools v0.1.13 [r2 values were identified for pairwise SNPs by keeping the same distance.Detection of linkage disequilibrium (LD) patterns is necessary to infer historical changes of population and inbreeding patterns in the species demographic history , 82. To  v0.1.13 , with thTo detect the genomic signatures of inbreeding in Forest Musk Deer, genome-wide runs of homozygosity (RoHs) was identified for each individual using the tool in PLINK v.1.9 , with thAdditional file 1: Table S1. The sample information of 15 Forest Musk Deer individuals and the BioProject accession number to retrive raw data from the NCBI website is PRJNA765065. Table S2. Raw reads, sequencing error rate and GC content of 15 Forest Musk Deer samples. Table S3. Mapping and coverage statistics for 15 samples of Forest Musk Deer. Table S4. The kinship coefficient between individuals. The values below the diagonal refer to the kinship coefficient for all individual pairs. Table S5. The result of ABBA-BABA test results based on the Observe. Table S6. The result of ABBA-BABA test results based on the TransRem.Additional file 2: Figure S1. Cross validation error (CV) plot from ADMIXTURE. Figure S2. Comparison of the genome-wide heterozygosity among species under different conservation status. (a) Crossoptilon mantchuricum exhibits lowest level of genetic diversity among bird species based on available estimates from genome-wide sequencing data. Genome-wide heterozygosity was a useful indicator of genetic diversity, which was measured as proportion of heterozygous SNPs per base pair and plotted in rank order for 91 mammal species including Forest musk deer. Colored bars indicate the IUCN Red List Categories and Criteria (https://www.iucnredlist.org/). References for each species were listed and provided in the supplementary dataset. Data of 89 mammal species except Forest musk deer and Père David's Deer were from the supplementary file in Hu JY, Hao ZQ, Frantz L, Wu SF, Chen W, Jiang YF, Wu H, Kuang WM, Li H, Zhang YP et al: Genomic consequences of population decline in critically endangered pangolins and their demographic histories. Natl Sci Rev 2020, 7(4):798-814.


Agricultural weeds may originate from wild populations, but the origination patterns and genetics underlying this transition remain largely unknown. Analysis of weedy‐wild paired populations from independent locations may provide evidence to identify key genetic variation contributing to this adaptive shift. We performed genetic variation and expression analyses on transcriptome data from 67 giant ragweed samples collected from different locations in Ohio, Iowa, and Minnesota and found geographically separated weedy populations likely originated independently from their adjacent wild populations, but subsequent spreading of weedy populations also occurred locally. By using eight closely related weedy‐wild paired populations, we identified thousands of unique transcripts in weedy populations that reflect shared or specific functions corresponding, respectively, to both convergently evolved and population‐specific weediness processes. In addition, differential expression of specific groups of genes was detected between weedy and wild giant ragweed populations using gene expression diversity and gene co‐expression network analyses. Our study suggests an integrated route of weedy giant ragweed origination, consisting of independent origination combined with the subsequent spreading of certain weedy populations, and provides several lines of evidence to support the hypothesis that gene expression variability plays a key role in the evolution of weedy species. We investigated the population genetic structure and expression patterns of giant ragweed plants sourced from crop and noncrop habitats across the Eastern Corn Belt of the USA, where this native species appears to have undergone one or more evolutionary transitions to succeed in crop fields as a major agricultural weed. The results of our study suggest an integrated route of weedy giant ragweed origination, consisting of independent origination combined with the subsequent spreading of certain weedy populations, and provide several lines of evidence to support the hypothesis that gene expression variability plays a key role in the evolution of weedy species. Its large population sizes and outcrossing mating system may also increase genetic variance and adaptation in response to natural selection. Beyond sharing these characteristics with other North American native weeds, giant ragweed is well‐suited to investigate the drivers of repeated invasion of crop fields by wild species because it is widely distributed among diverse early‐successional habitats, yet varies geographically in its presence and severity as an agricultural weed  and the genetics distinguishing weedy from wild populations, we conducted RNA‐sequencing for 67 samples collected from 20 giant ragweed populations growing across the east‐central U.S. Corn Belt, either in nonagricultural wild habitats or in crop fields (weedy populations). We studied population structure, identified the most closely related wild populations to each of our weedy populations, and uncovered gene expression variation that distinguished weedy from wild populations. Our study suggests weedy giant ragweed populations arose via multiple independent origination events from local wild populations, followed by the spread of weedy populations regionally across crop fields. Differentially expressed genes, especially those involved in seed germination, vegetative stage change, and abiotic stress responses, may contribute to weediness in giant ragweed. Our results support the hypothesis that gene expression variability plays a key role in the convergent evolution of weedy plants.22.1n = 20 populations) in fall 2011 and stored at 4°C until planting. Seeds from a total of 20 populations  were collected from two regions, Ohio and Iowa‐Minnesota  following manufacturer's instructions. RNA‐seq libraries were prepared using the Illumina TruSeq Stranded mRNA Library Prep Kit , with library quality subsequently analyzed by qPCR using KAPA Library Quantification kits , Qubit 2.0 Fluorometric , and a 2100 Bioanalyzer. Sample libraries were pooled according to the sequencing instrument's requirements and sequenced using Illumina HiSeq2500.2.3https://www.uniprot.org/) and to cDNA data from closely related, Helianthus annuus (https://plants.ensembl.org), since a reference genome and gene annotation for Ambrosia trifida is not available. We removed redundant transcripts by applying CD‐HIT to cluster transcripts with >98% similarity , we combined GATK RNA‐seq best practices with Joint genotyping  among populations were calculated using VCFtools  were performed to correlate pairwise FST versus geographical distances and test for isolation by distance within region; these tests did not encompass all populations in a single analysis because doing so violates model assumptions. We calculated observed and expected heterozygosity (Ho and He) and nucleotide diversity (π) per population using VCFtools.Geographic distances between each pair of populations were calculated using latitudes and longitudes. Fixation indices .RNA‐seq data were mapped onto our reference transcriptome by HiSAT2  term and function description to aligned giant ragweed proteins to create a giant ragweed GO annotation file. Differentially expressed genes were subjected to functional enrichment analysis using ClusterProfiler.We conducted differential gene expression analysis by estimating raw read counts for each gene by HTSeq based on mapping results ; thus, transcripts unique to  other samples, like weedy populations, would be absent from the reference sequence. To recover these unique transcripts, we collected RNA‐seq reads that were unmapped after alignment to the reference transcriptome. For each population, unmapped reads from individuals were collected for de novo transcript assembly. The transcripts from each weedy population were then used as references for mapping RNA‐seq reads from wild samples to filter out the weedy‐wild shared transcripts and vice versa. The unmapped transcripts were collected as unique transcripts for each weedy or wild population. These unique transcripts were then used to predict open reading frames (ORFs), removing transcripts without predicted protein‐coding ability from further analysis. Unique transcripts showing sequence similarity from different populations were identified using OrthoVenn2 with cutoff as 1e2.9p‐values transformed into −log10(p). Functional annotations were conducted on the most conserved and variable modules with ClusterProfiler.Two independent co‐expression gene networks were established for weedy and wild populations. We filtered out transcripts with low expression . Then, gene networks were constructed using WGCNA , glutamine synthetase (GS), acetyl‐coenzyme A carboxylase (ACCase), acetolactate synthase (ALS), 4‐hydroxyphenylpyruvate dioxygenase (HPPD), phytoene desaturase (PDS), and protoporphyrinogen oxidase (PPO)) were retrieved from the International Survey of Herbicide Resistant Weeds (http://www.weedscience.org). BLASTn was used to search the full giant ragweed transcriptome with these cDNA sequences as queries , averaged across all giant ragweed populations, to be 0.0024. Nucleotide diversity did not differ between weedy and wild populations  was lower than expected heterozygosity (He) for all but one population  had the smallest cross‐validation error  among all population pairs was low (FST range = 0.002–0.109), suggesting that our giant ragweed populations were not greatly differentiated overall  and OH versus MN samples (0.0518 ± 0.0032), relative to IA versus MN (0.0277 ± 0.0035), indicating some degree of differentiation between OH versus IA‐MN populations. Mantel tests performed separately by region indicated that genetic distance was significantly correlated with geographical distance within OH  among populations. The fixation index  for expression patterns among all samples, with the highest similarity among samples from the same population  reflects the population‐level expression variability of every gene; genes with high GED have variable expression across samples, and genes with low GED have similar expression patterns across samples. We first compared GED between weedy and wild samples by region, finding significantly higher GED in wild populations than in weedy populations Figure . We notiTo better capture which genes contributed to the differences in GED, we performed weedy‐wild pairwise comparisons of expression diversity on eight genetically determined population pairs, based on our previous phylogenetic results Figure ; note thWe identified between 17 and 458 differentially expressed genes (DEGs) across our eight weedy‐wild population pairs, with 21.7% to 71.3% upregulated in weedy populations Figure . However3.4presence of unique transcripts) may contribute to the independent evolution of weediness across sites.To fully capture the identities of differentially expressed genes, we developed a novel pipeline to recover unique transcripts for each weedy‐wild population pair that could not be mapped to our reference transcriptome  ≥ 30) and 29 moderately overlapping (5 < −log(p) ≤ 30)) and eight variable modules  ≤ 5; Langfelder & Horvath, We established independent co‐expression gene networks for weedy and wild samples by grouping together transcripts with similar expression profiles into modules, resulting in networks of 45 and 27 gene modules, respectively Section . We then3.6p = .052); thus, changes to the regulation or copy number of this gene in weedy individuals may have occurred.We tested for constitutive differential expression of transcripts for eight major herbicide‐target genes Figure  between Besides gene overexpression, changes in herbicide‐target gene coding sequences could also affect herbicide resistance. Several key mutations in genes coding for proteins targeted by herbicides have been identified that confer herbicide resistance . Genetic similarity between populations was therefore relatively high, especially in the IA‐MN region. Thus, even if most weedy populations arose independently from nearby wild populations, some likely originated from more distant wild and/or weedy populations. Rapid spread of weedy giant ragweed populations in crop fields has been attributed in part to mechanical seed dispersal via harvesting equipment, particularly in regions with large farm sizes and where farmers outsource grain harvesting to third‐party operators who harvest from multiple farms . The annotated pathways of these two groups of genes showed considerable overlap, suggesting that the evolution of weediness may involve increases 4.3.2Using the conventional DESeq2 approach, we identified relatively few DEGs and thus relatively little differentiation in gene expression among weedy and wild populations. The differentiation we did identify was mostly population‐specific, supporting the hypothesis of multiple, independent origins of weedy populations through different genetic means. These results are consistent with genomic and gene expression studies of weedy and wild sunflower populations in the U.S. , suggesting constitutive overexpression of ALS may provide a survival advantage to weedy individuals in agriculture fields. Future genomic analyses of weedy and wild individuals will allow for the identification of genetic variation in regulatory regions contributing to this differential expression.Herbicide resistance is often implicated as a major facilitator of the rapid expansion of weedy biotypes in agricultural fields across species Baucom, . We hypoWe also investigated SNP variation within herbicide resistance gene transcript sequences between weedy and wild giant ragweed individuals, which could contribute to resistance phenotypes. However, our investigation of ALS transcript sequences suggested that point mutations previously confirmed to confer resistance to ALS‐inhibiting herbicides were not the only factors driving the evolution of weediness in our study populations, since they occurred in both weedy and wild populations. Of course, the presence of herbicide‐resistant genotypes in wild populations could reflect the transmission of point mutations from resistant weedy individuals through pollen flow, but standing variation in wild giant ragweed populations that remains in the genome neutrally or is selected for directly via herbicide application to field margins and rights‐of‐way could also contribute ; data curation (lead); formal analysis (lead); investigation ; methodology ; validation ; visualization (lead); writing – original draft (lead). Andrea R. Gschwend: Conceptualization ; funding acquisition (supporting); investigation ; methodology (supporting); project administration ; supervision ; writing – review and editing . Stephen M. Hovick: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration ; supervision ; writing – review and editing . Amanda Gutek: Data curation ; formal analysis (supporting); investigation (supporting). Leah McHale: Conceptualization (supporting); supervision (supporting); writing – review and editing (supporting). S. Kent Harrison: Conceptualization (supporting); funding acquisition (supporting); writing – review and editing (supporting). Emilie E. Regnier: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration ; supervision ; writing – review and editing .Figures S1‐S11Click here for additional data file.Table S1Click here for additional data file.Table S7Click here for additional data file.Supplementary MaterialClick here for additional data file.


By studying genome-wide data from 54 individuals from eighth- to fifth-century Sicily, we gain insights into the composition of Classical Greek armies (ca. fifth c. BCE) and the populace of a Greek colony. The presence of mercenaries in Greek armies fighting in the Mediterranean, as early as 480 BCE and with origins as far away as northern Europe and the Caucasus, is absent from historical texts and thus so far underappreciated in ancient classical scholarship. Our interdisciplinary study both underlines the value of integrating genetic studies to complement archaeological and historical research and highlights the importance of warfare in facilitating continental-scale human mobility, cultural contact, and cooperation in the Mediterranean of the Classical period. Trade and colonization caused an unprecedented increase in Mediterranean human mobility in the first millennium BCE. Often seen as a dividing force, warfare is in fact another catalyst of culture contact. We provide insight into the demographic dynamics of ancient warfare by reporting genome-wide data from fifth-century soldiers who fought for the army of the Greek Sicilian colony of Himera, along with representatives of the civilian population, nearby indigenous settlements, and 96 present-day individuals from Italy and Greece. Unlike the rest of the sample, many soldiers had ancestral origins in northern Europe, the Steppe, and the Caucasus. Integrating genetic, archaeological, isotopic, and historical data, these results illustrate the significant role mercenaries played in ancient Greek armies and highlight how participation in war contributed to continental-scale human mobility in the Classical world. The classical Mediterranean world was characterized by long-distance interactions mediated not only by trade and colonization –4 but al6There is extensive archaeological evidence for links between prehistoric Sicily and the Eastern Mediterranean, as documented by material culture exchange beginning at least in the Early Bronze Age , 9–11. T9131621SI Appendix, Figs. S1–S7 and Table S1). According to written sources, Himera won the battle of 480 BCE, owing to relief forces sent by its Greek allies in Syracuse and Agrigento (Diod. 11.20–25)  , but los1.20–25) . Himera Archaic and Classical Greek armies are often understood as being primarily composed of hoplites, self-funded Greek citizen-soldiers who wore heavy armor to defend their poleis and allied settlements –38. The Recently, strontium and oxygen isotope evidence from the mass graves corroborated historical accounts by revealing a large number of nonlocal individuals in mass graves associated with 480 BCE and mostly local individuals in mass graves associated with 409 BCE . SignifiDataset S1). Filtering for standard quality criteria and using a cutoff of at least 10,000 covered SNPs, we retain a dataset of 16 individuals buried in mass graves associated with the 480 BCE battle , 5 associated with the 409 BCE battle , 11 attributed to the civilian population buried in Himera’s West necropolis, and 1 attributed to the civilian population buried at the East necropolis (Dataset S2). All of the soldiers are genetically sexed as males, while seven civilian individuals are genetically sexed as male and the other five as female. A total of 19 individuals were analyzed from the Iron Age site of Polizzello (ninth and eighth centuries BCE) and 2 from the site of Monte Falcone at Baucina (sixth to fifth centuries BCE), representing both males and females. One pair of second-degree relatives was detected at Polizzello . Overall, the average coverage on samples from the new dataset ranges from 0.011- to 4.298-fold on targeted SNPs on the autosomes with between 11,393 and 905,741 SNPs covered. Nuclear contamination point estimates on the X chromosome, which can be estimated for males, ranged from 0 to 1.6%. To contextualize the newly reported ancient individuals, we also generated new genotyping data for 96 modern-day Italian, Greek, and Cretan individuals on the Affymetrix Human Origins (HO) SNP set .We generated double- and single-stranded next-generation sequencing libraries , 46 both SNP set  (SI AppeDatasets S3 and S4) onto the first two PCs , early European farmers (EEFs), and Caucasus hunter-gatherers (CHGs) . We observe a separation of most of the individuals into distinct clusters in PC space that remain stable considering the 95% CI ellipses of low coverage samples and when constructing PCs on a worldwide and Eurasian set of modern-day individuals  and that correlate with the proportion of different components in ADMIXTURE analysis. We confirm these genetic clusters with pairwise qpWave analyses , group the individuals according to their genetic cluster and chronological and archaeological contexts, and perform subsequent ancestry modeling on a per-group and per-individual basis .We computed principal components (PCs) on the newly generated HO data, together with 985 previously published present-day individuals from 64 diverse West Eurasian and North African populations , and pro two PCs A. As pre two PCs , modern  two PCs  on a worqpAdm, this group can be modeled as an admixture of four sources that distantly contributed to the genetic composition of Europeans (P = 0.179): Northwestern Anatolian Neolithic farmers , WHGs (6.4 ± 1.0%), early farmers from Iran , and Early Bronze Age (EBA) Steppe herders associated with the Yamnaya cultural complex , which indicates an increase of Iranian-related admixture compared with the preceding LBA Sicily group, which can be best modeled without that component . Models using more proximal Bronze Age (BA) to Iron Age (IA) ancestry sources are not rejected for two-way mixtures between a local Sicilian source  on the one hand (57.1–98.7%) and Italy_IA_Republic.SG, Spain_BA, Spain_IA, Armenia_LBA.SG, or Balkan_IA (1.3–42.9%) on the other hand . This indicates that the population might not have been completely continuous to the BA inhabitants of Sicily sampled to date and instead received some gene flow from other populations that are most plausibly from outside of Sicily. We caution that there are only published data from 15 individuals from BA Sicily, leaving the possibility that some unsampled Sicilian populations were genetically more continuous with later groups. The potential connection to Spain_IA is intriguing, as structural similarities in EBA fortifications have been documented at Castelluccio near Syracuse and artifacts, such as Bell Beakers, have been found at BA sites near Palermo (Dataset S6), a lineage already found among Sicilian inhabitants in the Middle Bronze Age (MBA) and LBA , providing no evidence for endogamous practices and pointing toward large effective population sizes (We find that the IA Sicilians (Sicily_IA), associated with the Sicani culture, form a homogenous cluster distinct from most of the individuals excavated at Himera. They fall in the PC space occupied by earlier Middle and Late Bronze Age Sicilians . Of the three higher-coverage individuals, both I20166/W3182 and I20168/W3702 are cladal with Greek_LBA . I20163/W1838 appears distinct, with models of ancestry with the highest P values (P > 0.1) involving a contribution of 38–55% of his ancestry from a local Sicilian MBA, LBA, or IA source and around 45–62% from a group closely related to Punic individuals from Sardinia that harbor North African ancestry or alternatively around 87.6 ± 3.1% Sicily_IA ancestry with 12.4± 3.1% ancestry deriving from a group represented by genetic outlier from Chalcolithic Sardinia carrying fully North African ancestry . It is notable that four of the eight individuals that could be analyzed with ADMIXTURE carry a small proportion (2.2–3.8%) of a genetic component maximized in Africans that is not found to the same extent in our other Sicilian individuals but appears in Punic and Levantine individuals . Considering the low coverage of the civilian individuals, comparisons of within-group variance and centroid distance on the first two PCs nevertheless suggest that the civilian sample overlapped with the soldiers of both battles in the groups Sicily_Himera_480BCE_1 and Sicily_Himera_409BCE, described below  than the soldiers, who derived most of their ancestry from Aegean-related populations and plausibly represented Greek settlers or their descendants. This greater genetic diversity is likely not simply explained by the lower coverage of the civilian individuals . A greater sample size ideally from a wider diversity of burial contexts at Himera would be necessary to test whether they could represent a genetically stratified subset of a more-diverse civilian population. Greek colonies were the meeting grounds of culturally and genetically diverse people  included people with ancestors from the Greek mainland and Greek isles but also people from the colony’s hinterlands and members of other cultural groups, such as Phoenicians and Etruscans, which could explain the signal we observe  to perform formal statistics, but all individuals are consistent with a broadly Central or Eastern Mediterranean genetic profile in PC analysis (PCA) and ADMIXTURE . Potential social stratification has been suggested associated with the burial sites, as individuals interred in the East necropolis show lower δ13C and δ15N values and higher prevalence of skeletal pathology than those at the West necropolis, indicating a different access to resources , suggesting that many soldiers  were plausibly the descendants of the Greek colonizers of Sicily and that intermarriage between Greeks and Sicilian locals was practiced  and all 5 of the soldiers of the 409 BCE battle (Sicily_Himera_409BCE) are part of this main genetic cluster. Using the racticed . This geracticed , 64, 65.racticed , 66.Beyond the intermarriage between locals and Himera’s Greek settlers, it is important to consider the possibility that the composition of the population might also have been influenced by a large-scale influx of Dorians from Agrigento after a political takeover by the Agrigentine tyrant, Theron, in 476 BCE, during which thousands of Himera’s inhabitants were killed (Diod. 11.49); however, the limited sample size available for analysis at this point and the lack of comparative data from Dorian contexts does not allow us to test this hypothesis. In any case, both groups of soldiers are heterogeneous in their admixture with local Sicilians, indicating a different genetic history for each individual; while we can compellingly document that significant genetic variation existed, a larger sample size than we have available would be necessary to adequately characterize the distribution of genetic variation encompassed by Sicilian Greek ethnicity in this period.SI Appendix, Figs. S10–S13). We tested ancestry models with qpAdm for the higher coverage individuals.Among the soldiers of the 480 BCE battle, we find nine individuals that carry genetic ancestry not consistent with the first group  and one source related to central or western European groups . A genetic origin in the Balkans is suggested by their Y chromosomes, belonging to the E-V13 lineage, which has its highest modern-day frequency in that region  that fall on the PCA intermediate between the main cluster and central European individuals, a differentiation also indicated by the relatively higher proportion of the WHG genetic cluster in ADMIXTURE. Testing a wide range of possible BA and IA sources with t region .P = 0.129).Two individuals  fall with modern northeastern European groups and eastern Baltic populations of the first millennium BCE and can be modeled using exclusively BA individuals from Lithuania as a proxy source . In qpAdm, their ancestry is consistent, with around 85–89% deriving from IA Central steppe nomads and 11–15% from an Aegean-like source, an admixture that plausibly could have taken place among the genetically diverse populations of the Steppe  fall with individuals from IA nomadic contexts in the Eurasian Steppe and carry in the ADMIXTURE analysis genetic components maximized in Han and Karitiana (Native Americans) that characterize most IA Steppe nomads . Similarly, the second low-coverage individual, I17872/W0428, falls closest to populations from the Caucasus on the PCA  falls with modern Caucasus populations and intermediate to ancient Steppe and Caucasus individuals on the PCA and carries the highest proportion of the CHG component in ADMIXTURE . A singl the PCA A.3f statistics with ancient groups reflect the same general patterns of genetic affinities , as do proportions of distal ancestry estimated with qpAdm . The groups can all be modeled as deriving differing proportions of ancestry from groups related to Turkey_N_Barcin, WHG, Iran_GanjDareh_N/CHG, and Russia_Samara_EBA_Yamnaya, except for group 4, which requires the addition of Siberian ancestry.Outgroup-While the genetic makeup of a person gives insight into the origin of his or her ancestors, whether recently or many generations back in time, it does not provide information about the geographic region where the person lived or grew up. Strontium and oxygen isotope ratios reflect the local geology and hydrology of a person’s place of residence during the period of tooth or bone formation and provide information about individual mobility in a single lifespan . Thus, gWe correlated the diversity in genetic histories with strontium and oxygen isotope data gathered from the teeth of Himeran individuals in previous research . For allpoleis on Sicily were governed by tyrants capable of extracting the kind of wealth needed to fund armies of mercenaries and usually in need of bodyguards to maintain their control , whereas all individuals from mass graves Nos. 5–7  fall within this cluster, a statistically significant difference  . The correlation of burial location of individuals with their genetic ancestries suggests deliberate placement of soldiers with similar ancestral backgrounds in the same mass grave, reflecting the importance of identity in determining burial in this society  . Mass gr society , 59, 81.These results suggest that individuals in mass graves Nos. 5–7 may have had greater prestige among survivors who buried them, consistent with previous arguments based on mortuary context . IndividDataset S2), were interred in larger mass graves Nos. 1–4, and individuals with genetic affinities to other Greek populations were interred in the smaller mass graves Nos. 5–7. Nevertheless, it is not clear that nonlocals were considered as a homogeneous group of “others”. It is possible that the more-particular origins of the deceased were acknowledged and acted upon; for example, mass graves Nos. 1/2 contain both of the individuals with northeastern European ancestry. Identification of the fallen might have been done by surviving fellow soldiers who knew them, on the basis of their armor and weapons, or on the basis of phenotypic characteristics . Many other soldiers may have been buried in single interments, rendering them archaeologically invisible as soldiers. At Himera, some single interments do include individuals with perimortem trauma suggestive of being subjects of violence at the time of their deaths. These individuals could have died in battle and been interred in single burials by those who knew them. It is possible that some bodies were repatriated, as is documented in classical antiquity , large-Historically contextualized genetic and isotopic evidence of foreign soldiers at Himera is another indicator of the heterogeneity and evolving complexity characterizing IA and post-IA societies of Europe, the Middle East, and other world regions . IdentifDataset S1). We enriched the amplified libraries using two consecutive rounds of hybridization capture for sequences overlapping about 1.2 million nuclear targets  or a custom script (https://github.com/DReichLab/ADNA-Tools) and merging paired-end reads that overlap in at least 15 bases , taking the highest quality base in the merged segment to represent the allele. Using the samse command of BWA (version 0.6.1)  , we mappome RSRS . We filtcontamMix to test for contamination based on polymorphism in the mtDNA (haplogrep2 (https://www.yfull.com/chart/tree/) using all reads mapping to chromosome Y after trimming the read ends according to the UDG treatment. We determined the genetic sex of individuals by computing the ratio of the numbers of sequences mapping to the Y chromosome to the combined number of sequences mapping to the X and Y chromosomes . We used the smartpca function of EIGENSOFT  and applied quality control analyses as previously described (Dataset S3), biological samples were collected from healthy and unrelated subjects with all four grandparents born in the same region. DNAs were extracted from mouthwash with classical phenol/chloroform protocols, after a cell precipitation step. All participants gave informed consent to study protocols, which were reviewed and approved by the Ethics Committee of the University of Perugia  and by the Ethics Committee for Clinical Experimentation of the University of Pavia . See ref. (We genotyped 96 present-day individuals from Greece and Italy on the HO SNP array  (Datasetescribed . All indSee ref.  for addiSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File