Arabidopsis thaliana that were sampled twice over a decade. Annual weather records were compared to their historical records to extract patterns of climatic shifts over time. We evaluated the differences between samplings in flowering time, a key life-history trait with adaptive significance, with a field experiment. We also estimated genetic diversity and differentiation based on neutral nuclear markers and nucleotide diversity in candidate flowering time (FRI and FLC) and seed dormancy (DOG1) genes. The role of genetic drift was estimated by computing effective population sizes with the temporal method. Overall, two climatic scenarios were detected: intense warming with increased precipitation and moderate warming with decreased precipitation. The average flowering time varied little between samplings. Instead, within-population variation in flowering time exhibited a decreasing trend over time. Substantial temporal changes in genetic diversity and differentiation were observed with both nuclear microsatellites and candidate genes in all populations, which were interpreted as the result of natural demographic fluctuations. We conclude that drought stress caused by moderate warming with decreased precipitation may have the potential to reduce within-population variation in key life-cycle traits, perhaps as a result of stabilizing selection on them, and to constrain the genetic differentiation over time. Besides, the demographic behaviour of populations probably accounts for the substantial temporal patterns of genetic variation, while keeping rather constant those of phenotypic variation.Rapid evolution in annual plants can be quantified by comparing phenotypic and genetic changes between past and contemporary individuals from the same populations over several generations. Such knowledge will help understand the response of plants to rapid environmental shifts, such as the ones imposed by global climate change. To that end, we undertook a resurrection approach in Spanish populations of the annual plant  Arabidopsis thaliana exhibited substantial genetic changes over just a decade. Interestingly, such changes seemed to be mediated by the combination of the extent of warming and precipitation. Nevertheless, populations remained viable over time suggesting that plants may possess the means to cope with global climate change.Plant populations vary over time as a result of the effects of environmental variation on life-cycle traits and genetic diversity. It is important to quantify how much populations actually change over time to better understand the response of plants to rapid environmental shifts. Our resurrection approach indicated that populations of the annual plant  All plant populations exhibit to some extent spatio-temporal phenotypic and genetic changes as a result of environmental variation. The quantification of the pace and intensity of such phenotypic and genetic changes, as well as their impact on fitness and population persistence, is a particularly pressing issue to understand the evolutionary potential of plants in rapidly changing environments, such as the ones currently posed by global climate change . The domThe resurrection approach represents a powerful, albeit time-consuming, means to quantify the extent of phenotypic and genetic variation in plant populations over time. In particular, the resurrection approach compares past and contemporary performance under common conditions of individuals, i.e. the ancestors and their descendants, whose seeds were collected from the same populations at different points in time encompassing several generations . Third, we quantified the spatio-temporal patterns of nucleotide diversity in well-known genes affecting life-history traits in populations from the two samplings. In particular, we sequenced FRI and FLC, two genes involved in the vernalization pathway for flowering  and sandy open sites and edges of Pinus pinaster forests (CDC). In all populations, A. thaliana occurred in patches of different size and density in the two samplings. Populations were also rather similar in terms of general weather conditions  and in 2013 (N = 41), but for the sake of simplicity we did not make any distinction between them. Important for this study, the two samplings were carried out around a GPS location recorded in the first sampling characterizing a representative abundant patch within each population. To the extent possible, we took the precaution to sample A. thaliana individuals separated from each other by at least 1 m within the same area around the GPS location (~50 × 50 m2) in the two samplings.All populations were sampled twice: AGU in 2004 and 2012 (spanning 8 years), CDC in 2003 and 2012 (9 years), MAR in 2003 and 2012 (9 years) and SAN in 2003 and 2013 (10 years). In the first and second samplings, we collected seeds from 20–32 and 60–70 individuals per population, respectively. In MAR, the individuals sampled in the second sampling were collected in 2012  and second (2012–13) sampling from each population.Field-collected seeds from the first sampling were multiplied by the single seed descent method in a glasshouse from the Centro Nacional de Biotecnología (CNB-CSIC) of Madrid a few months after collection. Multiplied seeds were stored in dry conditions in cellophane bags at room temperature in darkness, storing conditions that can preserve A. thaliana population, weather records were obtained from the Agencia Estatal de Meteorología of Spain (AEMET), including daily records of minimum and maximum temperatures and precipitation from the nearest automatic meteorological stations. We used data from 1–3 meteorological stations per population depending on their geographical distance to populations, as well as on the degree of data completeness for the study period. On average, meteorological stations were located at 28.9 km (range = 2.4–48.8 km) far from A. thaliana populations. When more than one meteorological station was used per population, i.e. MAR and SAN, we computed daily mean values across meteorological stations for minimum temperature, maximum temperature and precipitation. For each population, we also obtained historical climate data on average annual minimum temperature, average annual maximum temperature and total annual precipitation. Historical data were obtained from the Digital Climatic Atlas from the Iberian Peninsula, based on a spatial interpolation using records from a total of 2285 meteorological stations across Portugal and Spain during the period 1951–99.For each A. thaliana populations over the study period [see]. AGU, MAR and SAN did not show any significant major natural disturbance, e.g. fires or landslides, between samplings. In contrast, CDC exhibited some partial changes in tree density [see]. Unlike the other populations, CDC occupies sandy open sites and edges of P. pinaster forests, which have been exploited for centuries for resin extraction. Nonetheless, A. thaliana distribution seemed not to be affected by this partial modification, probably because A. thaliana only occurred along P. pinaster forest edges. However, we ignore whether other biotic factors, e.g. herbivores and pathogens, have affected these populations over the time period of study. We can at least ensure that we did not detect any specific damage during the two samplings or in visits conducted between samplings to some of the populations, such as CDC and MAR .Aerial orthophotographs were retrieved from different public administrations in Spain  and used to detect major landscape changes in A. thaliana accessions at the same facility (3) filled with standard soil mixture . Previously, batches of 60 seeds were prepared 1 month before each sowing, and stored in 1.5-mL plastic tubes at room temperature in darkness until the sowing day.We conducted a total of three field experiments using 15 haphazardly chosen individuals per population  and sampling . Due to technical problems, individuals from AGU from the 2012 sampling could not be multiplied and this population was excluded from the experiments. In addition, CDC and MAR from the first sampling were eventually represented by 14 individuals. Experiments were conducted at El Castillejo Botanical Garden of Sierra de Grazalema Natural Park in south-west Spain  followinA. thaliana individuals in three different scenarios determined by sowing dates. Hence, all individuals from each population and sampling were allowed to germinate in mid autumn (sowing date: 7 October 2014), late autumn (sowing date: 5 December 2014) and mid winter (sowing date: 27 January 2015). Such sowing schedule reproduced quite well the germination behaviour observed in natural populations from different Iberian environments . Flowering time in experimental field conditions was estimated as the number of days between 15 days after sowing, when most seeds had already germinated in all experiments, and flowering date. Flowering date was given at the pot level when the majority of plants in the pot, which were sisters and showed homogeneous flowering behaviour, had the first flower open. In these experiments, we only focused on flowering time because this trait showed the largest quantitative genetic differentiation among populations when compared with other life-cycle traits, e.g. recruitment and fecundity , where VG is the estimated among-individual variance component and VE is the residual variance variances method using restricted maximum likelihood (REML) variance components , population  and individuals nested within population  on flowering time. We inspected the distribution of the residuals to check that the assumptions of the analyses were met. For each population, sampling and experiment, we estimated the broad sense heritability of flowering time as variance . The 95 mponents . AnalyseA. thaliana individuals were collected in the second sampling (2012–13). They were genotyped for 12 nuclear microsatellites  and mean gene diversity (HS). We also computed outcrossing rates (OR) as (1 − FIS)/(1 + FIS), where FIS is the inbreeding coefficient. For each population, genetic differentiation was also estimated by partitioning the genetic variance among samplings, among individuals within samplings and within individuals, calculating F-statistics via the analysis of molecular variance (AMOVA) with GenAlEx. Significance of F-statistics was estimated by performing 1000 permutations. Final values for genetic diversity and genetic differentiation parameters were obtained by calculating mean values across the 100 random subsamples of equal size. Finally, we computed the number and frequency of non-redundant multilocus genotypes (NG) per population and sampling, which provided a direct measure of unique combinations of alleles per population and sampling. We used NG to perform principal coordinate analysis (PCoA) with GenAlEx for visualizing the relationship among non-redundant multilocus genotypes, revealing the genetic structure of our study system in space and time.The number of individuals genotyped per population in the second sampling more than doubled those from the first one. In order to avoid sample size effects and subsequent bias in genetic parameters, we created 100 random subsamples of 20 individuals each, which was the minimum sample size used in this study, per population and sampling. Genetic diversity was estimated with GenAlEx v.6.5 , includiNe) using the temporal method of Ne parameter is computed by relating the observed amount of temporal change in allele frequency to that expected under pure genetic drift. The comparison of Ne values among populations allows the inference of the extent of genetic drift in them, assuming that the effect of systematic forces, i.e. mutation, selection and migration, was low and that sampling effects were similar in all populations  and one seed dormancy gene (DOG1) were sequenced in 21–23 haphazardly chosen individuals per population of the second sampling. We used existing sequence data on these genes from 9–10 individuals of the same populations from previous works corresponding to the first sampling  per gene, population and sampling. We did not use random subsamples of equal size to estimate gene parameters because variation in sample sizes was much lower in genes than in microsatellites. Besides, the ratio between the number of individuals from the first and second samplings used for gene sequencing was practically the same for all populations. GenBank accession numbers of DNA sequences generated in this study from the second sampling are MF142982–MF143073 for FRI, MF142894–MF142981 for FLC and MF142804–MF142893 for DOG1. Accession numbers of gene sequences from the first sampling are available elsewhere , but not in CDC  values for flowering time for each population and experiment varied between 0.25 and 0.79 for individuals from the first sampling and between 0.00 and 0.65 for individuals from the second sampling , our results showed contrasting and pronounced patterns of temporal changes in genetic diversity parameters in A. thaliana populations. For example, the genetic diversity (HS) in AGU and MAR increased by 103.2 and 13.1 % between the two samplings, respectively (HO) and outcrossing rates (OR) substantially increased in all populations over the study period, except those in AGU that showed the opposite pattern .The AMOVA indicated that the extent of genetic differentiation between the two samplings was substantial in all populations, with values as high as 34.0 % for AGU, 23.6 % for CDC, 25.1 % for MAR and 19.9 % for SAN . These rA. thaliana individuals (230 of 329) were non-redundant multilocus genotypes  with the temporal method. AGU exhibited a Ne value of 10.8 (95 % CI = 7.3–15.3). The rest of populations had higher Ne estimates with values of 18.4 (12.8–25.4) for CDC, 20.5 (12.5–31.1) for MAR and 21.5 (16.0–28.3) for SAN. Hence, the effects of genetic drift in CDC, MAR and SAN were rather similar, whereas those in AGU were more intense. This result can be related to the fact that the lowest genetic diversity (HS) was in AGU in the 2004 sampling  and one seed dormancy gene (DOG1) in individuals from the two samplings from each population. FRI was the gene with more polymorphisms in all populations (range = 2–20 single nucleotide polymorphisms [SNPs]) generating more multilocus haplotypes  values for flowering time did show a more meaningful pattern of variation between individuals from the two samplings. In particular, there was a trend for reduction in h2 values for flowering time in populations from the second sampling  estimates indicating that the effects of genetic drift on these populations were rather similar, except for AGU that showed the lowest Ne value.From a genetic viewpoint, me value . It is wThis demographic scenario would also be applicable to the temporal patterns of variation in silent nucleotide diversity of flowering time and seed dormancy genes, which were rather erratic and gene dependent, probably as a result of the lower number of polymorphisms found in these genes . DespiteA. thaliana populations exhibited a quite dynamic demographic behaviour, which is known to be characterized by dramatic year-to-year variation in plant abundance . A.M. acknowledges the Agency for Management of University and Research Grants of the Generalitat de Catalunya (2014-SGR-913).F.X.P. and C.A.-B. planned and designed the research. R.G., B.M.-V., A.M., C.A.-B. and F.X.P. conducted field work, performed experiments and/or analysed data. F.X.P. wrote the first draft of the manuscript and all authors contributed to the final version of it.None declared.Supplementary Figure S1Click here for additional data file.


Helicoverpa armigera was first detected in Brazil with subsequent reports from Paraguay, Argentina, Bolivia, and Uruguay. This pattern suggests that the H. armigera spread across the South American continent following incursions into northern/central Brazil, however, this hypothesis has not been tested. Here we compare northern and central Brazilian H. armigera mtDNA COI haplotypes with those from southern Brazil, Uruguay, Argentina, and Paraguay. We infer spatial genetic and gene flow patterns of this dispersive pest in the agricultural landscape of South America. We show that the spatial distribution of H. armigera mtDNA haplotypes and its inferred gene flow patterns in the southwestern region of South America exhibited signatures inconsistent with a single incursion hypothesis. Simulations on spatial distribution patterns show that the detection of rare and/or the absence of dominant mtDNA haplotypes in southern H. armigera populations are inconsistent with genetic signatures observed in northern and central Brazil. Incursions of H. armigera into the New World are therefore likely to have involved independent events in northern/central Brazil, and southern Brazil/Uruguay-Argentina-Paraguay. This study demonstrates the significant biosecurity challenges facing the South American continent, and highlights alternate pathways for introductions of alien species into the New World.The Old World cotton bollworm  In Brazil alone, 35 new pests have been detected6 or confirmed8 in the last 10 years.Biological invasions are major ecological phenomena that influence the worldwide distribution of species. They can be a major driver of ecological change, affecting conservation  high polyphagy, where larvae of this insect pest are known to feed on over 180 plant hosts from at least 68 plant families13; (ii) its long distance migratory ability with migrations of up to 2,000 km17; (iii) the ability to enter a facultative diapause as pupae under unfavourable environmental conditions such as extreme high or low temperatures12; and (iv) high fecundity and a short generation time, capable of completing up to 10 to 11 generations per year19. Such fast generation times could aid in building population size, and thus contribute to a successful invasion20.Invasive insects cost a minimum of US$70 billion per year globally for goods and servicesH. armigera was confirmed in January-February 201322, and the incursion has resulted in over USD$800 million in losses and control costs since 201224. The presence of H. armigera was also reported in rapid succession in neighbouring western and south-western countries, including Paraguay in October 201326, Argentina in August-October 201327, and in 2014/2015 in Uruguay28. H. armigera was also confirmed in the Caribbean countries of Puerto Rico29 and Dominican Republic31 and on the mainland of United States of America29. The detection of H. armigera from southern Florida down to Argentina over just 3 years demonstrates the speed at which this species has established in the New World. It clearly has the ability to spread through a connected landscape and via an island hopping, stepping-stone dispersal model. The pathway of H. armigera into the South American continent may be linked with commodity movements involving importations of agricultural and horticultural products from multiple Old World destinations31. Whilst multiple origins of H. armigera in Brazil are likely, it remained unclear whether these founder individuals arrived as a single or as multiple event(s). By examining the genetic signature of the populations found in the South American continent using mtDNA markers, it may be possible to utilise spatial genetic signatures of the insect to infer frequencies of introductions.In Brazil, H. armigera had been present in South America for a period of time [e.g34.] prior to its first identification in Brazil21, remaining undetected due to its close morphology with the New World sister species H. zea, and the difficulty of detecting invasive pests at the early stages of incursions35. It is unknown whether the H. armigera populations detected across South America arose from a single, or multiple, original introductions, and where these were. A detailed population genetic study is needed to test the hypothesis that there were multiple unrelated introduction events into South America, which would have significant implications for biosecurity preparedness for the South American continent and the potential reintroduction of novel adaptive ecotypes36 into the Old World34. To test this hypothesis, we undertook the present study, in which we show that gene flow and spatial distribution patterns of H. armigera mtDNA haplotypes support multiple introductions of the H. armigera into South America, with the incursion(s) in the southern regions likely independent from the northern/central Brazilian incursions.It is almost certain that 7) using the Noc-COI-F/R primer pairs. Sequence identity searches against the NCBI GenBank database confirmed that all suspected moths matched  published H. armigera sequences, and did not contain premature stop codons.All specimens from the southern/south-western regions of South America were successfully sequenced for the mtDNA COI fragment . For Harm_BC47, this involved an L/V change, and in Harm_BC42 a I/M change. Both substitutions involved amino acids with hydrophobic side chain. In Harm_BC43 the unique amino acid involved an A to G change where both amino acids belonged to the ‘small’ category. All remaining unique haplotypes  had nucleotide transition substitutions at 3H. armigera within Asia  and within Australia were both 0.00–0.04%, while within Europe (Germany and unknown sites) and within South America  were both 0.00–0.02%. Estimates of evolutionary divergence between all H. armigera sequences from Australia, Asia, Europe and South America were therefore likewise low and ranged between 0.00–0.04%. Observed nucleotide diversity between countries/continents in H. armigera ranged from 0.0024 ± 0.0004 (s.e.) to 0.0040 ± 0.0006 (s.e.)  of .) Table .Table 1C38 and from this study. A total of 47 haplotypes were identified from 314 individuals that consisted of 44 individuals from Asia, 18 from Australia, 28 from Europe and 226 from South America  and also 5 haplotypes with Europe . Haplotypes Harm_BC01 and Harm_BC02 were present in all Brazilian states, and the Harm_BC01 haplotype was shared with all other locations, except Argentina, Paraguay and Uruguay, which had unique haplotypes . The haplotype Harm_BC02 in Brazil was shared with Asia, Europe and Uruguay. Brazil also had 12 unique haplotypes , and 80% of Brazilian individuals belonged to haplotypes Harm_BC01, Harm_BC02, Harm_BC03, Harm_BC04 and Harm_BC05.Results from validation of the null model indicated no tendency for the test to generate either Type I or Type II errors . At the 5% confidence level, almost exactly 5% of tests yielded statistically significant results with randomly generated data for the full dataset  strongly supported some mtDNA haplotypes were differentially distributed across sampling sites, and the entclass1pt{minimaentclass1pt{minimaIn the ‘Brazil vs. non-Brazil’ treatment of haplotype distribution data , and Old World samples, showed that a significant FST value (0.2879) was detected between Brazilian and non-Brazilian samples, indicating significant population sub-structure  between these populations  suggested consistent gene flow between Brazilian and Old World populations. Between the Non-Brazilian and the Old World populations, the significant pairwise FST estimate (0.2022) obtained suggested limited gene flow between these populations as inferred from the maternally inherited mtDNA COI gene.Overall FST  of variation could be explained by the heterogeneous populations present across different geographic regions in the South American continent  among populations of H. armigera across much of the South American continent has generated a very large population with significant impacts on agricultural production. In contrast to usual invasions such as the African incursion of the New World fall armyworm S. frugiperda42, the invasive H. armigera population in the New World appears to be very diverse. Furthermore, the spatial distribution of this diversity strongly suggests that the population has spread from two different regions of introduction. Statistical analyses of the haplotype distribution patterns show that some of the H. armigera haplotypes most commonly found in Brazil appeared to be uncommon in Argentina  and Paraguay (Harm_BC02), and conversely, that some of the less common and/or unique haplotypes found in the non-Brazilian countries  appeared disproportionately uncommon in Brazil. Furthermore, FST analyses suggest reduced gene flow between populations from the Cone Sul region  and populations from either northern/central Brazil or from the Old World. Within individual countries, disproportionally over-represented haplotypes were identified , and this finding is supported by studies based on limited nuclear markers (e.g.49). Anderson et al.36 demonstrated differences between sub-species of H. armigera present in Australia/New Zealand  and the Old World sub-species , but not between global H. armigera armigera populations using genome-wide SNP markers. In northern/central Brazil, gene flow patterns of H. armigera showed non-significant levels of population substructure37 but exhibited reduced gene flow with southern South American populations of H. armigera. These southern South American populations, and particularly that of Paraguay and Argentina, also exhibited significant population substructure with Old World populations, suggesting that their origins differed from the origins of the founding populations present in northern/central Brazil.Population structure studies in H. armigera populations38, and that H. armigera in Argentina, Paraguay and Uruguay27 was confirmed shortly after the Brazilian detections, it would be a logical assumption that this likely represented natural migration, and/or movements of contaminated agricultural commodities from Brazil to the southern regions of South America. It was therefore unexpected to find unique mtDNA haplotypes in the southern South American populations that were as yet unreported in Brazil. Equally as unexpected was the lack of the most common haplotypes  in these countries given that they represented the major Brazil, and in fact, global, haplotypes.Given that sufficient gene flow to prevent population structure had previously been detected in Brazilian H. armigera into South America.To further explain the observed heterogeneous haplotype patterns across Paraguay, Uruguay, Argentina, and the southern Brazilian states of Rio Grande do Sul, Santa Catarina and Paraná , two hypotheses may be put forward: (I) intrinsic factors associated with new biological incursions  in a new environment, and (II) independent incursion pathways of H. armigera was first reported, stochastic lineage sorting of founding populations  could lead to the observed heterogeneous haplotype distribution patterns. This could involve factors such as lower population density and variability at population introduction phase and/or the lag-phase, high variability of reproductive success rates , variable adaptation success rates , susceptibility to viral/bacterial/fungal pathogen attacks, climatic stress, etc. to the novel New World environments at the early incursion stages.In Brazil where the incursion of H. armigera has been detected37 as represented by multiple maternal lineages . These suggested scenarios involving the highly volatile and variable periods of an exotic organism’s biology contrast the scenarios offered by Leite et al.37, where repeated bottleneck effects such as potentially associated with differential pest control/management strategies were deemed likely factors that underpinned the rapid population expansion signatures in both H. armigera and the New World endemic and closely related H. zea (but see44). In fact, H. zea in the New World was hypothesised as the outcome of an earlier incursion and the subsequent divergence (ca. 1.5 million years ago) from its common ancestor with H. armigera55, and involved a founder population with limited genetic diversity56. The H. zea genome, sequenced prior to the recent arrival of H. armigera in the New World, showed no evidence for subsequent introgression with H. armigera, and no evidence for the gain of additional genes affecting host use, but rather for the loss of genes already present in H. armigera57.In Brazil, across a number of studies, unexpectedly high genetic diversity of 54. With repeated introduction events, the likelihood of diverse maternal lineages that ultimately contribute to propagule pressure is high. Together with lineage sorting and stochastic processes  experienced by the invasive species in the new environment, sampling of the mtDNA COI gene and the construction of a haplotype network will likely appear similar to one of a rapid population expansion . This scenario of a ‘star-shaped’ haplotype network, as detected in H. armigera populations in the South Americas , differed fundamentally to that reported for H. zea . Multiple introductions of an invasive pest insect that resulted in a mtDNA genetic signature similar to a rapid population expansion signature, has also been previously reported in Brazil .Repeated introductions and high propagule pressure are increasingly being recognised as important factors that underpin the establishment of an alien speciesH. armigera in mind, the high frequency  of the two most common Harm_BC01 and Harm_BC02 haplotypes in Brazil populations, and a lack of population structures in northern/central Brazil (e.g.37) and the rest of the world, it was perhaps unexpected to observe statistically significant spatial mtDNA COI haplotype patterns and FST estimates in the Cone Sul region. The significant over- and underrepresented haplotypes in the Cone Sul region suggest that this population likely originated from somewhere outside the extensively sampled areas of central and northern Brazilian populations . For example, haplotypes Harm_BC13 and Harm_BC17 for Paraguay and Harm_BC44 and Harm_BC47 for Uruguay and Argentina, respectively, were over-represented in these countries and to a lesser extent, also the over-representation of haplotypes Harm_BC43, Harm_BC44, Harm_BC46 and Harm_BC47,although the P-values (0.100–0.114) lie outside of the range usually considered significant, thereby adding support that these maternal lineages likely originated from non-Brazil source populations .With the migration and dispersal ability of H. armigera genetic spatial signatures identified in this work provide the first insights into potential transnational patterns of the incursion of this species into the New World. This reflects opportunities to improve biosecurity protocols relating to phytosanitary practices of agricultural and horticultural commodity movements in the Cone Sul region. Although the haplotype distribution pattern from Rio Grande do Sul was excluded by our spatial analysis, pairwise FST estimates indicated a limited gene flow of the Rio Grande do Sul population with populations from three Brazilian states (see Suppl. Table H. armigera into South America.The detection of the H. armigera from non-Brazilian and Brazilian populations. This observation should be further explored and should consider utilising genome-wide SNP markers to increase detection efficiency of population admixture59. Although the low sample size (n = 2) in Uruguay has prevented meaningful interpretations of gene flow patterns based on the mtDNA COI marker, future genome-wide SNP markers studies on these individuals may enable migration patterns and the introduction history to be interpreted with more confidence. Taken as a whole, these FST results suggest that non-Brazilian H. armigera populations, and particularly those from Paraguay, followed by Argentina, and to a small extend that of the Rio Grande do Sul population, likely represent populations that were wholly or partially derived from alternative incursions event(s) from that detected in northern/central Brazil.Similarly, the presence of limited gene flow between Rio Grande do Sul and Argentina populations and the various Brazilian populations might suggest different degrees of admixture between 31 and from genome-wide SNP markers59 have also demonstrated multiple introductions from globally diverse H. armigera populations in the invasive Brazilian populations. Taken as a whole, these findings provided evidence to support multiple independent introductions of H. armigera into the South American continent over the effect of stochastic lineage sorting. Our finding is not without precedent, with the globally invasive hemipteran whitefly Bemisia tabaci MED species  also being shown to have been independently introduced into this southern region of South America61 in addition to an earlier introduction elsewhere in Brazil62.Single locus markers have clear limitations, however recent studies from multiple mtDNA markersH. armigera in the South American continent likely also involved multiple introduction pathways was fortuitous, because the pattern identified in this study is unlikely to be maintained over time as mixing with other populations in South America and further incursions are likely. That incursions of H. armigera from the Old World potentially involved multiple pathways will have significant implications to pest and resistance management strategies in the New World. For example, populations of H. armigera around the world have developed resistance to conventional pesticides  randomly installed in soybean fields in the Brazilian states Rio Grande do Sul, Santa Catarina and Paraná, and also in Uruguay, Argentina and Paraguay, in the 2014/15 cropping season  and Noc-COI-R (5′- CCAAAAAATCAAAATAAATGTTG-3′).Suspected 38, with the published data from Arnemann et al.7 also here included as part of the southern populations of H. armigera, while the studies of Leite et al.37 and Mastrangelo et al.38 represented the most comprehensive population diversity surveys at the mtDNA COI region in Brazil and fulfilled all four criteria.For our analysis, the following criteria underpinned our selection of mtDNA COI from publicly available sources: (A) only include published sequences from studies that had undergone critical review processes to avoid inclusion of untested haplotypes; (B) the sequences must originate from samples collected at a similar sampling time period as our material, (C) the published sequences must match our characterised partial mtDNA COI gene region, and (D) the populations must include northern/central Brazil to enable spatial comparisons to our Southern populations. Based on these criteria, sequences from three studies were chosen46 were used for editing DNA trace files and to assemble sequence contigs . Assembled mtDNA COI haplotypes were checked for premature stop codons that may be indicative of pseudogenes. Categorisation of global mtDNA COI haplotypes at the 5′ gene region,  involved 548 bp of the mtDNA COI partial gene in the final dataset. The evolutionary divergence between haplotypes was estimated using the maximum composite likelihood model72, and included all  codon positions using MEGA673. Estimates of haplotype diversity (h ± SE) and nucleotide diversity (π ± SE) were carried out using the molecular evolution software package DNA sequence polymorphism (DnaSP) v. 5.10.0174. Assessments for presence of PCR errors, or nuclear mitochondrial sequences (NUMTs) involved (1) examination for presence of premature stop codons in the sequenced partial mtDNA COI gene region, (2) ascertaining for conservation of amino acid substitution patterns where presence of unique amino acid changes were further assessed for conservation of biochemical properties and/or their molecule sizes, and (3) sequence characterisation of rare haplotypes as confirmed by multiple independent PCR and sequencing efforts.The programs Pregap and Gap4 within the Staden sequence analysis packageH. armigera haplotypes in the South American continent, a matrix table was prepared for the frequencies of the 25 H. armigera mtDNA COI haplotypes identified to-date from 11 South American locations , Maranhão (MA), Mato Grosso (MT), Piauí (PI), Roraima (RR), Paraná (PR), Santa Catarina (SC) and Rio Grande do Sul (RS); and from Argentina, Uruguay, and Paraguay; Suppl. Table 2 statistic to detect departures, as detailed below. The statistical test was based on the randomisation of haplotypes x locations generated according to an appropriate null model (see ‘null model’ below), similar to Gotelli’s75 analysis of species co-occurrence data.To better investigate the spatial distribution patterns of Due to the perceived unevenness of mtDNA COI haplotype spatial patterns, a statistical test was applied to ascertain whether the diversity and frequencies of individual haplotypes had occurred independently and at random across South American sampling sites, or whether there was evidence for spatial segregation. For such a test the null hypothesis is therefore that mtDNA COI haplotypes are distributed randomly across the locations. The alternative hypothesis therefore considers at least one haplotype as being either more or less common, in at least one location, than expected due to chance alone .2 statistic to detect departures from randomness:i and column j within the matrix , and also the number of times NgreaterEqual), based on 10,000 randomly generated tables. The two-tailed P-value79 is given by:P-values for P-values for n pseudo-observed data sets under the null model, followed by application of the test to each of the created data sets to confirm appropriate Type I and Type II error rates. At the 5% confidence level only 5% of pseudo random data sets should be statistically significant, and the expected P-value distribution across the n tests should be rectangular. If it is found that more than 5% of test results are significant at the 5% confidence level, then this indicates an elevated Type I error rate . Conversely, when less than the nominal number of pseudo random data sets yield a significant result it can lead to incorrect acceptance of the null hypothesis ).The statistical test was validated by creating The null model was validated through repeated analyses  using random pseudo-data reconstructed to match the n = 266 observations  samples. Note that this analysis was carried out as a guide to assist interpretation for the observed within-table patterns of deviations, and have not impacted our main findings which was the result from the full dataset  and neighbouring countries .The population pairwise Supplementary tables and figuresHaplotype distribution


Native rabbits in smallholder system are considered as important genetic resources, and the present study was aimed to study the genetic and phenotypic characterization and detection of the maternal origin of the native rabbit populations located at the Middle of Egypt.b) and the microsatellite markers.A survey of native rabbit populations was conducted in three governorates . The phenotypic characterization of rabbits included the profile body of the head, ears, eyes, neck, and legs and the coat colors. The blood samples were collected for genetic characterization based on mitochondrial (cytochrome b) analysis revealed that the rabbits from the three governments belonged to lineage A except one animal was recorded as lineage G from MN’s rabbit population. The results of the microsatellite markers revealed that the genetic diversity between the three populations showed genetic interferences; however, a closer genetic relationship was observed between BN and MN than FY. The majority of the genetic diversity was the individual variability.The phenotypic characterization of the body parts in the three populations was almost similar. The body weight of the mature rabbits in MN Government was significantly heaviest, and the measurements for the main body parts  were the highest compared to the two populations. The results of mitochondrial (cytochrome The mitochondrial lineage A is the major lineage in rabbit populations in the area of the Middle Egypt understudy. The genetic populations’ structure is the interferences among the three populations. A large-scale survey should be done on native rabbit populations for the sustainable management and conservation of the local breeds’ genetic resources. Native rabbit breeds by smallholder under low-input systems consider as important genetic resources because of their adaptation to harsh environmental conditions and their tolerance to a wide range of diseases. Accordingly, local breeds should be protected against any threat factors as a priority of sustainable management .In Egypt, native rabbits were used early to develop many breeds such as Giza White, Baladi Red, Baladi Black, and Baladi White by selection and/or different crosses between native and exotic rabbit breeds, although the importance of the native breeds, few studies have been done concerning these rabbits .Phenotypic and genetic characterizations of the native rabbits are essential studies for the identification and improvement of the breeding programs and to help for their conservation . The popb), and (3) to evaluate the genetic diversity between the populations using microsatellite markers.The purposes of this study were (1) to measure the phenotypic characterization of native rabbit populations in three governorates  at the Middle Egypt region through evaluation of external body measurements, (2) to identify the maternal origin using the mitochondrial DNA , BN (n=31), and MN (n=21) were used for genetic characterization. Blood samples were collected into 5 mL Vacutainer tubes containing EDTA as an anticoagulant and stored at 4°C until molecular analyses were performed. DNA was extracted from rabbit blood samples using Chelex 100Clelland .b gene were used according to Branco et al. [Taq polymerase (H. T. Biotechnology), and 1 μL of DNA extract. The PCR program consisted of 4 min of denaturation at 94°C, followed by 40 cycles of 1 min at 94°C, 1 min at 55°C, and 1 min 30 s at 72°C, plus a final extension of 10 min at 72°C. The PCR products were digested with AluI restriction enzymes , following the manufacture instruction. The PCR products and 100-bp DNA ladder  were electrophoresed on 2% agarose gel . The amplified products were visualized in the gel documentation apparatus, photographed and analyzed according to the number and the size of fragments resulted from restriction enzyme digestion.Two primers of the cytochrome o et al.   [Taq polymerase (H. T. Biotechnology), and 0.5 μL of DNA extract. PCR programs involved 5 min of initial denaturation at 95°C, followed by 30 cycles of 30 s at 95°C, 30 s between 55°C and 60°C, and 30 s at 72°C, followed by a final extension step of 10 min at 72°C. The PCR products were separated on denaturing electrophoresis in 3% polyacrylamide gels containing urea, and bands were visualized by rapid silver staining [Eight microsatellite markers were used for genotyping analysis d Sat16) . PCR rea . PCR reth populations (P), µ=overall mean, Pi=the effect of the populations, i=1, 2, and 3, and eij=the random error. Significant differences between the populations were defined by Duncan test.Least square means for body weight and body dimension measurements were estimated using the GLM procedure of SAS version 9.1.3 . The model used was Yij=µ+Pi+eij, where Yij=any observation of rabbit within ioN and eN), heterozygosity observed and expected (oH and eH) using GENALEX software version 6.0 [PIC) was assessed using CERVUS software version 3 [STF [ISF, STF, and ITF) across the three studied populations were calculated using the GENEPOP software version 3.4 [Genetic diversity was evaluated by calculating the observed and effective number of alleles  ranged between 3.64 and 10 with an average 6.125. The majority of the markers were characterized by high allelic values per locus except for Sat2 and Sat7. The polymorphism information content per locus (PIC) index which is revealed the degree of microsatellite loci polymorphism recorded higher values exceeding 0.5 in all markers except for Sat2 (0.326) and Sat7 (0.474). The total population heterozygosity (eH) of microsatellite loci ranged between 0.340 and 0.878 with an average 0.705. These results are relatively higher than the study of Xin-Sheng et al. [oN, 0.680 for eH, and 0.642 for PIC. However, our results were lower than reported by El-Aksher et al. [oH (0.650), eH (0.800), and PIC (0.760) across four Egyptian rabbit populations different than the rabbit populations understudy. The studied microsatellite markers deviated significantly from Hardy–Weinberg equilibrium (p<0.001) except Sat2 and Sat7. The results suggested that the rabbits under study might be a non-random mating or undergo certain kind of selection for some favorable traits.The genetic diversity of the NMER populations shows that the alleles detected across eight microsatellite loci in the three studied populations were 46 . The numg et al.  in Wan lr et al.  whose reoN and effective eN numbers of alleles, observed (oH) and expected (eH) heterozygosities, and the fixation coefficient of an individual within a subpopulation (ISF) are shown in oN (6.625) and eN (4.327) was recorded for FY population, while the lowest values (oN = 5.625 and eN = 4.008) were recorded for MN rabbit population. BN and MN populations showed higher expected heterozygosity  than FY population (eH = 0.693). The higher observed heterozygosity in the two populations could be attributed to several crosses due to human activities such as exchanging and marketing between different villages. The result could be supported by the higher performance traits of the body weight and dimensions observed in MN population  [ISF among the three rabbit populations was 0.071 which is relatively lower (0.172) than that reported in the Spanish rabbit populations by Grimal et al. [et al. [oH ranging from 0. 365 to 0.482) [oH ranging from 0.36 to 0.48) [et al. [The results of the mean number of the observed alleles were  = 6.75) . The vall et al. . On the  [et al. , while io 0.482)  and Spanto 0.48) . The varThe NJ phylogenetic tree of the investigated rabbit populations showed the close relationship between MN and BN than FY . This coSTF values among the three populations were ranged from 0.024 to 0.028. The STF values between populations indicated a generally low level of genetic differentiation. The highest STF value (0.028) was recorded between MN and FY, while the lowest value (0.024) was between BN and MN government  = 2 where the FY population incompletely separated in the first cluster, while BN and MN clustered together showing admixed mosaic cluster with high interferences between them (K=3, all populations were the interferences and not detected in separate clusters. This result was confirmed by individual phylogenetic tree of the investigated 83 rabbits (The investigated population structure using the Bayesian approach revealed that the most structural clustering pattern was shown at the number of clusters (een them . In the  rabbits . This fiThe phenotypic characterization of the NMER populations showed that the body weights, body length, chest circumference, and abdominal girth of MN rabbit population recorded the highest values among the three populations under the study. The major lineage in the three populations was lineage A. The genetic diversity between the three populations showed a closer genetic relationship between BN and MN rabbit populations than FY, while the result of the genetic distance between the three populations was small which suggested that the three populations were closely related and had a common ancestor. The majority of the genetic diversity was explained by individual variability. A large-scale survey should be done on native rabbit populations for developing sustainable genetic improvement and conservation programs of the local breeds’ genetic resources.EMA and SSA planned and designed the study. AE, NIA, SR, and AF collected the blood samples and provided the help for statistical analysis. NIA, AE, and SSEA carried out the laboratory work. SR and SS provided the help for the data analysis and interpretation of the laboratory results. EMA drafted the manuscript. All authors participated in the revision of the manuscript. All authors read and approved the final manuscript.


Tugarinovia (Family Asteraceae) is a monotypic genus. It’s sole species, Tugarinovia mongolica Iljin, is distributed in the northern part of Inner Mongolia, with one additional variety, Tugarinovia mongolica var ovatifolia, which is distributed in the southern part of Inner Mongolia. The species has a limited geographical range and declining populations. To understand the phylogeographic structure of T. mongolica, we sequenced two chloroplast DNA regions (psbA-trnH and psbK-psbI) from 219 individuals of 16 populations, and investigated the genetic variation and phylogeographic patterns of T. mongolica. The results identified a total of 17 (H1-H17) chloroplast haplotypes. There were no haplotypes shared between the northern (T. mongolica) and southern groups (T. mongolica var. ovatifolia), and they formed two distinct lineages. The regional split was also supported by AMOVA and BEAST analyses. AMOVA showed the main variation that occurred between the two geographic groups. The time of divergence of the two groups can be dated to the early Pleistocene epoch, when climate fluctuations most likely resulted in the allopatric divergence of T. mongolica. The formation of the desert blocked genetic flow and enhanced the divergence of the northern and southern groups. Our results indicate that the genetic differences between T. mongolica and T. mongolica var. ovatifolia are consistent with previously proposed morphological differences. We speculate that the dry, cold climate and the expansion of the desert during the Quaternary resulted in the currently observed distribution of extant populations of T. mongolica. In the northern group, the populations Chuanjinsumu, Wuliji and Yingen displayed the highest genetic diversity and should be given priority protection. The southern group showed a higher genetic drift , and the inbreeding load (HS = 0) required protection for each population. Our results propose that the protection of T. mongolica should be implemented through in situ and ex situ conservation practices to increase the effective population size and genetic diversity. In recent years, phylogeographic studies of the arid region of Northwest China have increased and mainly focus on the impact of the Quaternary climate fluctuations on species’ phylogeographic patterns –3. An inTugarinovia is a monotypic genus (Tugarinovia mongolica Iljin) with one additional variety (T. mongolica var. ovatifolia). T. mongolica, which is a member of the China Species Red List [T. mongolica var. ovatifolia shows differences in morphology and habitat [T. mongolica is mostly distributed in the northern regions of the Alxa Desert, whereas its variety T. mongolica var. ovatifolia only occurs in narrow swaths southeast of the Alxa Desert. Currently, the combination of narrow distribution and overgrazing has resulted in a rapid decline of the species. Previous studies of T. mongolica have concentrated on embryology, taxonomy, origin, migratory route, and distribution patterns [The arid regions of western Inner Mongolia contain many endemic species, several of which are considered endangered –21. SpecRed List , is endeRed List . It is aRed List . T. mong habitat –28. The  habitat . Based opatterns –32 but, psbA-trnH and psbK-psbI) to investigate the phylogeographic pattern of 16 populations of the genus Tugarinovia throughout its distributional range. Our study had the following aims. First, determine whether intraspecific phenotype variations of the genus are consistent with genetic differentiation. Second, identify whether Quaternary climate fluctuations (such as aridification and desert formation) affect the differentiation of Tugarinovia. Third, based on the genetic diversity and genetic structure of T. mongolica and T. mongolica var. ovatifolia populations, propose effective protection measures for the species.In this study, we sequenced two chloroplast DNA sequences  were sampled from northern Alxa, and six populations belonging to T. mongolica var. ovatifolia (11–16) from southeastern Alxa. We collected between 11 and 22 individuals from each population. Fresh leaves were sampled and dried immediately using silica gel in the field. Then, one sample of each population was deposited as a voucher specimen at the Herbarium of Xinjiang Institute of Ecology and Geography, Chinese Academy of Science (XJBI). We selected Brachylaena huillensis and Atractylodes lancea as outgroups in the phylogenetic analysis [The study area was not a nature reserve and no specific permissions were required by the authoritative organization. Only one leaf was used as experimental material, so there was no serious damage to the target plant during field sampling. We collected leaf samples of 16 natural populations, which covered nearly the entire region occupied by Mongolia , Fig 1. analysis .trnS-trnG, psbA-trnH, psbK-psbI, rps16-trnK, rpl32-trnL, ycf6-psbM, trnC-rpoB, trnV, trnD-trnT and trnL-trnF); however, only two plastid intergenic spacers (psbA-trnH and psbK-psbI) were found to be highly variable among individuals in the populations. Polymerase chain reaction (PCR) amplifications were performed in a total volume of 25 UL reactions with the amplification of two cpDNA regions, which were conducted by the following procedure: 95°C for 4 min, 36 cycles at 94°C for 30 s, 52°C for 30 s, 72°C for 1 min and, finally, 72°C for 10 min. PCR products were detected on 1.0% agarose gel and were purified using the QIAquick Gel Extraction Kit (Qiagen). These were then sequenced using an ABI Prism 3730 automated sequencer from Sangon Biotech Co., Ltd., Shanghai, China. Sequencing alignments were carried out in CLUSTAL W [The total genomic DNA was extracted using a modified cetyltrimethy ammonium bromide (CTAB) protocol . InitialLUSTAL W  and wereT), within-population genetic diversity (HS), and genetic differentiation  were estimated using the program Permut 1.0 with 1,000 permutation tests [ST and GST to check whether a phylogeographic structure existed for all populations and the two geographic groups. Analysis of molecular variance (AMOVA) [CT. However, this configuration was excluded when a single population appeared in the geographic group [To understand the levels of genetic diversity of the species, haplotype diversity (Hd) and nucleotide diversity (π) for each population, the two geographic groups, and across all populations were calculated in ARLEQUIN 3.5 . The toton tests . We used (AMOVA)  was also (AMOVA) . The phy (AMOVA) , followe (AMOVA) . Finallyic group , 41.S were estimated [S value indicated that the population had experienced demographic expansion. The sum of squared deviations (SSD) and raggedness index of Harpending (HRag) were also calculated [P-value, for which a nonsignificant P-value and unimodal distribution of pairwise differences indicated that the population experienced recent expansion, whereas a significant P-value and multimodal mismatched distribution of pairwise differences indicated that the population did not experience recent expansion. Significant tests of the above analyses were estimated using Arlequin 3.5, with 1,000 permutation tests [To test whether the species experienced demographic expansions, the parameters of Tajima’s D and Fu’s Fstimated , 43. A slculated . At the lculated . The SSDon tests .−9 s/s/y) based on the cpDNA substitution rates of most angiosperm species [−9 and an SD of 6.080×10−10 of the distribution to estimate the divergence times in this study [We estimated divergence times among the cpDNA haplotypes using BEAST v. 1.6.1 . Since t species . Based ois study , 48, 49.psbA-trnH and psbK-psbI were both successfully amplified and sequenced in the 219 individuals from the 16 natural populations. The total length of the combined alignments was 897 bp. We were able to detect 21 variable sites, which included 16 substitutions and 5 indels , and (2) populations 11–16 belonged to the southern group (T. mongolica var. ovatifolia).The results of the SAMOVA also showed that as the number of groups increased to three, a single population emerged. We found that the optimal population grouping pattern of K = 2 was optimal: (1) populations 1–10 belonged to the northern group  was greater than GST (0.805), there was no significant difference between these two values (P>0.05). At the regional level, HT and HS were 0.858 and 0.296, and NST (0.623) was less than GST (0.655) for the northern group; for the southern group, the genetic diversity, HT, was 1; the average within-population diversity, HS, was 0; and NST (1) was equal to GST (1)  was high, and the haplotype diversity of the southern group (Hd = 0.8408±0.0085) was slightly higher than that of the northern group (Hd = 0.8250±0.0148) . The tot GST (1) . We founST = 0.88853, P<0.0001). In the northern group, variations among populations and within populations were 66.15% and 33.85%, respectively, whereas in the southern group, all variations were mainly presented among populations, with no variation within populations   .S and Tajiam’s D were positive and not significant  and southern group (T. mongolica var. ovatifolia), which was determined from the BEAST analysis, occurred at 2.4976 (95%HPD: 1.2094–4.2318) Mya (We found that the divergence time between the northern group (318) Mya , during T. mongolica, there were two distinct clades that were clearly distributed in the northern and southern regions of Inner Mongolia  and low nucleotide diversity (π = 0.0092). Low nucleotide diversity is usually associated with a low seed amount and a small effective population size in some endangered species [T. mongolica may be attributed to the small effective population sizes that are associated with the reproductive barrier . In general, species that have narrow distributions and small effective population sizes show high genetic differentiation among populations [T. mongolica showed strong genetic differentiation (GST = 0.805) and low genetic diversity within populations (HS = 0.185). The above results indicated a high level of genetic differentiation among populations in T. mongolica that was due to restricted gene flow.All populations of  species . Howeverulations , 18, whiT. mongolica. In addition, the divergence time of two geographic groups  of T. mongolica could have resulted in the distribution of extant populations. The fragmentation distribution of T. mongolica var. ovatifolia may also be related to desert expansion, but evidence of male sterility and pistil abortion in this group requires further research.In this study, each population of the southern group contained one specific haplotype, and most populations in the northern group shared one haplotype . The difThe results of genetic diversity and population structure are important to consider for the implementation of effective conservation strategies, particularly for endemic and endangered species , 59. TheST = 0.88853, GST = 0.805) and inbreeding load (HS = 0.185) showed a significant extinction risk in the genus Tugarinovia , and high inbreeding load (HS = 0)  Tables  and 3. Apriority , it is rpriority , 63.T. mongolica, we propose establishing the following conservation measures. First, enact additional in situ conservation measures for the species, such as the creation of additional nature reserves in the northern and southeast Alxa Desert, especially the CJSM, WLJ, and YG populations of the northern areas. ’. In paS1 Table(DOC)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.


Europeans and American Indians were major genetic ancestry of Hispanics in the U.S. These ancestral groups have markedly different incidence rates and outcomes in many types of cancers. Therefore, the genetic admixture may cause biased genetic association study with cancer susceptibility variants specifically in Hispanics. For example, the incidence rate of liver cancer has been shown with substantial disparity between Hispanic, Asian and non-Hispanic white populations. Currently, ancestry informative marker (AIM) panels have been widely utilized with up to a few hundred ancestry-informative single nucleotide polymorphisms (SNPs) to infer ancestry admixture. Notably, current available AIMs are predominantly located in intron and intergenic regions, while the whole exome sequencing (WES) protocols commonly used in translational research and clinical practice do not cover these markers. Thus, it remains challenging to accurately determine a patient’s admixture proportion without additional DNA testing.In this study we designed an unique AIM panel that infers 3-way genetic admixture from three distinct and selective continental populations (African (AFR), European (EUR), and East Asian (EAS)) within evolutionarily conserved exonic regions. Initially, about 1 million exonic SNPs from selective three populations in the 1000 Genomes Project were trimmed by their linkage disequilibrium (LD), restricted to biallelic variants, and finally we optimized to an AIM panel with 250 SNP markers, or the UT-AIM250 panel, using their ancestral informativeness statistics. Comparing to published AIM panels, UT-AIM250 performed better accuracy when we tested with three ancestral populations . We further demonstrated the performance of the UT-AIM250 panel to admixed American (AMR) samples of the 1000 Genomes Project and obtained similar results  to previously published AIM panels . Subsequently, we applied the UT-AIM250 panel to a clinical dataset of 26 self-reported Hispanic patients in South Texas with hepatocellular carcinoma (HCC). We estimated the admixture proportions using WES data of adjacent non-cancer liver tissues . Similar admixture proportions were identified from corresponding tumor tissues. In addition, we estimated admixture proportions of The Cancer Genome Atlas (TCGA) collection of hepatocellular carcinoma (TCGA-LIHC) samples (376 patients) using the UT-AIM250 panel. The panel obtained consistent admixture proportions from tumor and matched normal tissues, identified 3 possible incorrectly reported race/ethnicity, and/or provided race/ethnicity determination if necessary.https://github.com/chenlabgccri/UT-AIM250.Here we demonstrated the feasibility of using evolutionarily conserved exonic regions to infer admixture proportions and provided a robust and reliable control for sample collection or patient stratification for genetic analysis. R implementation of UT-AIM250 is available at  Due to ulations . In otheHispanics in the U.S. have genetic ancestry from European, African and Native American. The admixture population presents opportunity for the study of health disparity due to disease susceptibility , 4 or drRecently, whole exome sequencing (WES) has become a standard protocol in translational research and clinical diagnostics to identify the underlying genetic cause of diseases due to the fact that most pathogenic variants are located in exonic regions and the drastically reduced cost of WES –22. WES In-statistics. We validated our optimal panel with 250 AIMs using the admixed American (AMR) of the 1000 Genomes Project, and compared our results to several published AIM panels with SNPs designed mostly in intronic/intergenic regions. Finally, we applied our AIM panel to TCGA-LIHC data and an in-house hepatocellular carcinoma (HCC) study with self-reported Hispanic patients enrolled in South Texas.In this study, we aimed to re-tune an AIM design pipeline to precisely determine ancestry admixture of Hispanic populations using WES data. Using the 1000 Genomes Project data, we selected SNPs that have different MAF of African (AFR), European (EUR), and East Asian (EAS) populations and quantified by ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/). The 1000 Genomes Phase III data were aligned with hg19 human reference genome. The SNPs were then extracted by ancestral populations (Table We use the 1000 Genomes Phase III Whole Genome Sequencing (WGS) data as the resource to identify AIMs . Data war2 < 0.2, plink option: --r2), allele frequency (AF) calculation, and minor allele frequency  elimination by PLINK . The output files from PLINK were processed by the AIM generator  [et. al, performs LD pruning and select AIMs based on Rosenberg’s In Statistic [The genome-wide data from the 1000 Genomes Project were first constrained to exonic region. Obtained SNPs were further subject to linkage disequilibrium filtering (ator.py) . This pytatistic  which depA and pa are the frequencies of 2 alleles across all individuals for a given marker, and pi,A and pi,a are the corresponding allele frequencies in the ith population. If a marker is unique in the ith population only, the second term in Eq. (1) will be 0, or In will be the largest, while In = 0 if the marker is equally distributed among all populations. To design our AIM panel, we first obtained nested subsets of AIMs up to 5000 candidate SNPs . We expected 5000 SNP candidates would allow us to select robust AIM panel considering SNPs with balanced In from overall population, as well as least bias between pair-wise In. The ancestry distribution of AIMs was provided in Table where m in Eq.  will be Ancestral proportions were inferenced by STRUCTURE  and ADMIfk,i is the admixture proportion of ith person’s identified kth population , and k = {EUR, EAS, and AFR}. A person will be classified into kth population if he/she has a maximum kth population proportion estimated by STRUCTURE and ADMIXTURE, thus we can estimate the error according to Eq. (2).where we assume ek), of classified known population did not improve by adding more candidate SNPs within the 5000-SNP pool. We selected AIMs with an optimal balance in three populations  and its In Statistics were provided in Additional file The optimal number of AIMs were determined when the observed accuracy, (1− ns Table  from paihttp://broadinstitute.github.io/picard) and realigned with GATK [WES was performed with Illumina HiSeq 3000 system at the GCCRI Genome Sequencing Facility, using Illumina’s TruSeq Rapid Exome Library Prep kit  which covers ~ 45 Mb with 99.45% of NCBI RefSeq regions. All exomeCapture sequencing was performed with 100 bp paired-end (PE) module, and pooled 6 samples per lane with targeted ~100x fold coverage. Paired reads were aligned to human reference genome hg19 (the same genome build used by the 1000 Genomes Project) with Burrows-Wheeler Aligner (BWA) . Duplicaith GATK  considerith GATK . To repoPCA was performed on dataset of multi-locus genotypes to identify population distribution of each individual. The genotype matrix was obtained by applying the “read.vcfR” function of the R package . Then, wTo assess the robustness of AIM panel that separates 3 continental populations, we first projected three populations into 3D space using PCA as described previously. We assume each population follows multi-variate normal distribution,μk is 1xd mean vector (here d = 3) of the kth population, and Σk is a d-by-d co-variance matrix. After estimation of the multivariate distributions of all 3 continental populations, we estimated the probability of mis-classified samples from one population to the other two when the probability of a given sample with known population origin was lower than those assigned to the other two groups, or the misclassification probability of samples in ith population into jth population is PAIM = ∑all i ≠ jPm as a measure of the capacity separating populations using a specific AIM panel. A smaller PAIM indicates less chance of a sample to be misclassified using a given AIM panel, or in other words, farther separation between 3 populations.where We started by pruning in-house WES data from 26 HCC patients with matched adjacent non-tumor (Adj. NT) and tumor. Initial pruning was performed by sequencing depth of each SNP, and only biallelic SNPs were considered . A SNP was eliminated if it had more than 10% missing genotype across all samples by VCFtools (vcftools options: --max-missing 0.9 --recode).https://docs.gdc.cancer.gov/API/Users_Guide/BAM_Slicing/). The tool enables to download specific regions of BAM files instead of the whole BAM file for a given TCGA sample. These BAM slices were then processed with VarScan to determine variant fraction as described in previous sub-sections. The TCGA-LIHC whole exome data were derived from 4 sample types . According to race and ethnicity in clinical data of TCGA-LIHC, we re-classified 7 population groups  . The SNPs were selected if it has more than 90% genotype throughout all sample by VCFtools, and further required biallelic SNPs.We extracted specific SNP positions of UT-AIM250 from 788 TCGA-LIHC samples (376 patients) by using GDC BAM slicing tool (n = 1305) in the 1000 Genomes Project  minor allele frequency (MAF) < 0.01 to avoid sequencing artifact, and (3) evaluation of ancestral informativeness by using Eq. (1) In-statistic for all pair-wise comparisons of 3 continental populations as described in the Methods section. A total of 100,295 SNPs met the first 2 criteria, and among them, we generated AIMs panels with 10, 50, 100, 250, 500, and up to 5000 AIMs  , EUR: 0.988 ± 0.021 vs 0.969 ± 0.034 (P = 1.70 × 10− 20), and EAS: 0.996 ± 0.009 vs 0.989 ± 0.017 (P = 2.92 × 10− 13). With 250 AIMs, we observed the best grouping accuracy and lowest SD in three ancestral populations with the STRUCTURE algorithm , while ADMIXTURE required more than 250 AIMs to gain desirable accuracy , AFR vs EAS: (1.185 × 10− 5 to 0.623); and EAS vs EUR: (0 to 0.645), and overall population (0.134 to 0.569)  in the 1000 Genomes Project on UT-AIM250 panel and confirmed that the UT-AIM250 panel had sufficient discriminating capacity to separate three ancestral populations  to estimate population structure. The efficiency of ADMIXTURE is known to be higher with multi-thread capability compared to STRUCTURE without much compromise in accuracy. As expected, the accuracy of STRUCTURE in population estimation was better than ADMIXTURE (both set at  3) Fig. a, b. Forn = 347) and 3 continental populations (n = 1305). The admixture of populations was estimated by STRUCTURE and plotted by both bar charts and principal component plots . The population structure showed a very similar trend among the three panels . The ancestral proportions of our HCC patients are AFR = 0.065 ± 0.044, EUR = 0.595 ± 0.151, and EAS = 0.340 ± 0.163, similar to those of MXL. In triangle plots , HCC patients were mostly aligned along the axis of EAS and EUR, similar to the PCA plot. One patient (HCC-3) was predicted as Asian . Taken together, our UT-AIM250 panel is accurate and robust to determine the ancestral proportion from normal or even tumor samples.The key to design UT-AIM250 is to validate self-reported race/ethnicity of Hispanic patients for translational study without adding specific ancestral markers to standard exome capture kits for sequencing library preparation. We applied the UT-AIM250 panel to estimate the ancestral proportion of a collection of 26 HCC patients  with matched tumor tissues and Adj. NT tissues. We extracted genotypes of 250 SNPs from Adj. NT and tumors using VarScan (see Methods), merged with 1305 continental populations from the 1000 Genomes Project, and visualized using the first 2 principal components . Based on race and ethnicity of each patient reported, we divided all 376 patients to 7 populations . The PCA plots showed similar patterns in both normal and tumor  to calculate ancestral components . Although WES could be applied to analyze population structure using all variants , it may The AIMs were selected from three continental populations . These populations were the major groups which contributed to the ancestral genetic variety of people in the U.S. through various migration routes . There aFuture studies may use the Native American as one of the continental populations. However, as shown in Fig. Along with the development of precision medicine, the population determination plays an important role . Both inThere are many different types of variants besides SNPs, such as insertions, deletions, and haplotypes. In this study, we focused on biallelic SNPs only. Extending to insertions and deletions may complicate the analysis due to the precise definition of these variants in each patient. Recognized that, in the population genetic field, these potential factors are typically considered and analyzed on the distribution of population proportions , future https://github.com/chenlabgccri/UT-AIM250.Here we constructed a unique AIM panel, UT-AIM250, designed within the evolutionarily conserved exonic regions, to determine the admixture proportions of three continental populations  for Hispanic in South Texas. We demonstrated the accuracy using AMR subpopulations from the 1000 Genomes Project and compared to the published Phillips-AIM34 and Wei-AIM278 panels. We further applied our panel to 26 Hispanic HCC patients and 375 TCGA-LIHC patients with matched tumor and adjacent non-tumor tissues. The estimated ancestral proportions showed no significant difference between non-tumor and tumor tissues, enabling us to evaluate patients’ tumor specimens to verify self-reported Hispanic patients and/or their specific genetic analysis groups. Since WES is one of the dominant genome-wide variant analysis platforms, the UT-AIM250 panel offers a cost-effective yet accurate method for the determination of patients’ ancestral composition. R implementation of UT-AIM250 is available at Additional file 1: Table S1. Informativeness of AIMs across different panels.Additional file 2: Table S2. Informativeness of AIMs of the UT-AIM250 panel.Additional file 3: Table S3. The ancestral proportions and clinical data of TCGA-LIHC samples .


Pinus tabuliformis is the dominant coniferous tree in northern China, with a large distribution across wide environmental gradients. We examined genetic variation in this species using genotyping‐by‐sequencing and mitochondrial (mt) DNA markers. We found population structure on both nuclear and mt genomes with a geographic pattern that corresponds well with the landscape of northern China. To understand the contributions of environment, geography, and colonization history to the observed population structure, we performed ecological niche modeling and partitioned the among‐population genomic variance into isolation by environment (IBE), isolation by distance (IBD), and isolation by colonization (IBC). We used mtDNA, which is transmitted by seeds in pine, to reflect colonization. We found little impact of IBE, IBD, and IBC on variation in neutral SNPs, but significant impact of IBE on a group of outlier loci. The lack of IBC illustrates that the maternal history can be quickly eroded from the nuclear genome by high rates of gene flow. Our results suggest that genomic variation in P. tabuliformis is largely affected by neutral and stochastic processes, and the signature of local adaptation is visible only at robust outlier loci. This study enriches our understanding on the complex evolutionary forces that shape the distribution of genetic variation in plant taxa in northern China, and guides breeding, conservation, and reforestation programs for P. tabuliformis.The phylogeographic histories of plants in East Asia are complex and shaped by both past large‐scale climatic oscillations and dramatic tectonic events. The impact of these historic events, as well as ecological adaptation, on the distribution of biodiversity remains to be elucidated.  Neutral demographic changes, like range shifts caused by large‐scale topographic movement and climatic oscillations, can result in increased population divergence via genetic drift. On the other hand, in situations where populations spread over heterogeneous landscapes, adaptation to distinct niches can also drive genetic divergence among local populations due to low fitness of immigrants or hybrids in their new environments, which limits the potential for gene flow  to the Pleistocene  and chloroplast (cp) DNA markers revealed the structure of mt‐ and cpDNA variation across the species distribution and suggested that the species survived in multiple glacial refugia during the Last Glacial Maximum (LGM) and then colonized its current range during the postglacial period  to reconstruct the recent demographic history of this species; and (c) to evaluate the impact of environment, geography, and colonization history on intraspecific differentiation. To do this, we sampled populations across the range of P. tabuliformis and examined genetic variation in both nuclear and mt genomes. We used GBS to identify genomewide polymorphisms and simulate the demographic history of the species. We incorporated mtDNA information as a proxy for colonization history. MtDNA is maternally inherited and transmitted by seeds in pine  to gain a better view of genomewide patterns of diversity and population structure in 22.1P. tabuliformis. The distribution of these populations is illustrated in Figure nad1 intron2, nad4 intron3, and nad5 intron1) as in Wang et al. . For genotyping of mt genomes, we used DNA extracted from needles. For GBS, we extracted DNA from seedlings for 12 populations. One seed from each tree was germinated on Petri dishes, and genomic DNA was extracted from seedlings one week after germination. For the other five populations , GBS was based on DNA from needles.2.2PstI high fidelity restriction enzyme  following the protocol of Pan et al.  from each read using fastx_trimmer command in FASTX‐Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/index.html). Then, all paired and orphan reads were mapped to the Pinus taeda draft genome v1.01 , close (<5 bp) to indels and with mapping quality (MQ) <30, were discarded. Genotypes with genotype quality (GQ) <20 and sequencing depth <5 were masked as missing. SNPs with minor allele frequency (MAF) <0.05, proportion of missing genotypes (missing rate) ≥0.3, heterozygosity >50%, mean depth ≥400 , and genotyped in fewer than four individuals in any population were further filtered out. This final set of SNPs (4077) was used for all analyses in this study except for the demographic simulations, which was based on all SNPs including low‐frequency ones (see section Inference of demographic history for details).Adapter sequences and low‐quality bases  from the tail of each read were removed using Trimmomatic  and pruned SNPs (1679). Distribution of genetic variance was also assessed by principal component analysis (PCA) in EIGENSOFT v6.0  of the population parameters of the best model was calculated using a non‐parametric block bootstrap method, in which each GBS fragment is treated as an independent genomic block, resampled with replacement to generate 100 datasets. Generation time of 50 years and a mutation rate per year of 7 × 10−10 for the genus Pinus  to identify SNPs involved in local adaptation. Outliers were selected by performing the q‐value procedure at false discovery rate (FDR) 0.05 using the R package qvalue . This set of SNPs was then used to estimate the covariance matrix with 100,000 iterations. Covariance matrices were compared after three independent runs with different seed numbers to ensure convergence. Environmental correlations were assessed by averaging five independent runs of Bayenv2 with 100,000 Markov chain Monte Carlo (MCMC) iterations with different random seeds. We only considered the SNPs that were in the top 1% of the Bayes factor (BF) values, with BF >3, and in the top 5% of the absolute values of Spearman rank correlation coefficients (ρ) as robust candidate adaptive loci. Fourteen environmental variables with correlation coefficients <0.80 across the range of P. tabuliformis were used in Bayenv2 analyses .Bayenv2 uses a set of putatively neutral loci to estimate the empirical pattern of covariance in allele frequencies between populations. It then uses this as a null model for testing correlation between individual SNPs and environmental variables  using ade4  to ensure that linear gradients in the data, as well as more complex patterns, were extracted. We further performed a forward selection with a stringent α value of 0.05 on geography to prevent overfitting of dependent and independent matrices. This resulted in the retention of one spatial variable (y). Results were similar if we included both geographic factors x and y (data not shown). The geographic factor matrix was centered, and the other three matrices were left untransformed. For the mtDNA matrix, after removing two singleton mitotypes, we first estimated the frequencies of each mitotype in each population and then performed PCA on the mitotype frequency matrix. PCs 1–4, which cumulatively explained 75.3% of the variations in mtDNA, were retained for RDA. Among‐population variation was partitioned into pure effects of environment, geography, and colonization history, as well as their combined effects. The significance of the partitioning was tested using the anova.cca function in VEGAN with 999 permutations. We performed RDA on datasets including neutral SNPs and outlier SNPs identified by the two outlier detection methods.A series of redundancy analyses (RDA) were conducted to estimate the degree to which genomic variation among 2.7http://www.cvh.org.cn/englishindex.asp). A total of 164 unique occurrence records .Population genetic analysis identified three major groups over the distribution: south, north, and west see Results.P. tabuliformis group. Model accuracy was evaluated by assessing the area under the curve (AUC) of the receiver‐operating characteristic (ROC) plot. According to Swets’ scale, predictions are considered poor when AUC values are in the range 0.5–0.7, useful in the range 0.7–0.9, and good when greater than 0.9 (1 is perfect). Overlaying of grids, grid cell counting, and visualization of the model were conducted in ArcGIS 9.2 . A SDM‐based background test was performed to examine whether each pair of the ecotypic groups was more or less divergent than would be expected from the differences in the local environmental backgrounds of the regions where they occur. This method compares the niche of a focal population with a set of pseudoniches modeled from random sampling of the geographic range of the sister population  via maximum entropy in Maxent 3.3.1 with the default settings  with observed niche divergence (dn) on the PCA‐reduced axes, with the null hypothesis db = dn  is significant (according to a t test), whereas niche conservatism is supported if db > dn. For each distribution region, the 14 environmental variables, longitude, latitude, and altitude were extracted from the occurrence points and from 1,000 random background points within the background region of each ecotype using the dismo and raster packages in R. The 14 variables were reduced by PCA of the correlation matrix using the ade4 package. Correlations between the reduced PCA axes and the geographic variables  were examined with a non‐parametric correlation test implemented in perm  and of the 1,000 background points of the two compared background habitats (db), on the reduced PCA axes. The distributions of db and dn were generated with 1,000 resamplings, and the mean of dn was compared to the 95% confidence interval of db to determine its significance. The significance of the observed divergence between two compared niches (dn) was determined by a permutation t test in perm.We applied a niche space‐based multivariate test to assess the possibility that the allopatrically distributed ecotypic groups occupy similar niches. This test compares background divergence  were each detected under K = 3 and K = 4, respectively , a large northern group , and a western group , and two small groups each represented by a single population (ZW and HL)  and recovered the same grouping patterns (data not shown).Partitioning the genetic variance along the first four PC axes clearly separated all five groups detected by Admixture  among the five groups was 0.029, and the differentiation between the south group and the north and west groups . The two single populations, ZW and HL, were differentiated from the other groups at levels of FST = 0.024–0.046 (Supporting Information Table Genetic differentiation (HM467715–HM467734). Among the 10 mitotypes, two were singletons and were removed from RDA. The spatial distribution of the mitotypes is summarized in Figure FST = 0.808) as expected for seed‐dispersed genome.A total of 216 individuals were sequenced for three mtDNA segments Table  , and north and west (11–18 migrants per generation). It should be noted that with an effective population size in the order of 105 and a long generation time of 50 years, the divergence among the three groups is still rather recent on a coalescent timescale, 0.12–0.73 Ne generations. The recent divergence  and high rate of gene flow among populations could explain the observed low genetic differentiation among them  models, the one that assumed that the south group diverged first and each group had constant population size after splitting was assigned the highest likelihoods , water availability (13 SNPs), soil type (10 SNPs), and UVB (3 SNPs) variables ‐repeat superfamily  outlier SNPs, and Bayenv2 identified 228 (5.59%) SNPs significantly associated with at least one of the 14 examined environmental variables. Because outliers detected by only one method may contain a high proportion of false positives, we chose the 46 SNPs detected by both approaches as more robust adaptive outliers . To further disentangle the contributions of climate and geography to among‐population variation, we performed a series of partial RDA .We used RDA to partition the population genetic variation into three causal components: environment, geography, and colonization history from multiple refugia. First, we conducted RDA using 3,780 neutral SNPs . In the uncorrected RDA, environment and geography explained small but significant proportions of genetic differentiation in p < 0.05) of the differentiation in the 46 SNPs, but geography alone was insignificant .We then performed RDA using outlier SNPs and found again insignificant impact of mtDNA but much greater contribution of environment and geography, which jointly explained 21.9%–35.8% of the variation. The contribution of environment and geography was even higher on the 46 outlier SNPs identified jointly by both Pcadapt and Bayenve2. In the uncorrected RDA, both environment and geography were significant and explained 35.4% and 15.0% of the variation, respectively. In the corrected RDA, environment exclusively explained 20.7% . The predicted distributions of the three groups were generally consistent with the actual geographic ranges of their occurrences. The niche similarity between the north and west groups (Schoener's D = 0.11 and Warren's I = 0.30) was the highest of the three niche pair comparisons , strongly suggesting the involvement of ecological factors in population divergence. The north group occupies a niche with an arid climate of strong seasonality. The south group is less seasonal, more mild, and moist than north, while the west group is characterized as having strong UVB and low heat energy  that explained more than 80% of the total variation in each of the three pairwise comparisons Table . In all gy Table . Taken t44.1P. tabuliformis initiated in the late Neogene era. The first splitting event between the south group and the ancestral population of the north and west groups occurred 3.67 MYA (95% CI: 8.04–2.65 MYA). This event coincided with the uplift of the Qinling Mountains from the late Miocene and early Pliocene (7.28–3.60 MYA) to the Pleistocene , coinciding with the Pleistocene glaciations in northern and western China including regions surrounding the eastern Tibetan Plateau , the estimated time of the two splitting events in P. tabuliformis is 0.78 MYA (between south group and the ancestor of north and west groups) and 0.12 MYA (between north and west groups), which were still much earlier than the LGM  revealed that only 3.5% of the among‐population variation could be explained by climate and geography, leaving 96.5% of the genetic variation unexplained. In contrast, a significant 20.7% of the variation in a group of 46 outliers was attributed to climate and only 0.4% to geography. These results improve our confidence in the outlier SNPs we identified in this study. The absence of IBD in P. strobus and P. monticola in North America  rather than loci involved in local adaptation (exogenous barrier). Bierne, Welch, Loire, Bonhomme, and David (P. tabuliformis, with each group occupying a well‐defined geoclimatic zone. The northern niche is characterized by a continental and arid climate with strong seasonal variation, while the southern niche is characterized by a more mild and moist climate with less seasonality. The western niche is characterized as having high UV exposure and low temperatures. Taken together, these results lead us to the conclusion that each of the three groups of P. tabuliformis occupies a different environmental niche, and such niche diversity could have important consequences for local adaptation in P. tabuliformis. However, given the polygenic nature of climate adaptation in trees, the genomic signature of adaptation is expected to be weak and difficult to detect without comprehensive genome‐scan.In the face of gene flow, 46 SNPs stands out as significantly associated with local environments. Annotation of the 46 outliers identified 27 genes involved in plant stress responses, which may have mediated environmental adaptation in nd David  suggest nd David  does not5P. tabuliformis. Our attempt to incorporate GBS, mtDNA, geography, and climate data in RDA offers a novel approach to understanding the complex interactions of IBE, IBD, and IBC and their impacts on population genomic structure. Our results show that most of the observed genomic structure in P. tabuliformis is affected by neutral and stochastic processes, and the impact of maternal history has been eroded by gene flow. The signature of IBE is not clearly visible, which is in line with the expectation for quantitative traits in a large complex genome background. Nonetheless, we recovered support for environment‐driven genetic differentiation in a group of 46 outlier loci, which represent 1% of the survey SNPs or 2% of the SNPs from genic regions of 886 genes, in the face of strong gene flow. These findings from limited sampling of the genome represent an important first step in understanding the origin and maintenance of genetic variation in P. tabuliformis. Based on these results, further genome scans can be optimized to better characterize adaptive genetic diversity in this species.This study provides the first genomewide investigation into the diversity and evolutionary history of Deep sampling of the genome is necessary to uncover the fraction of the genome involved in adaptation due to low linkage disequilibrium in conifers (Tiffin & Ross‐Ibarra, P. tabuliformis and the association of these groups to distinct niches will inform forest management decisions and seed transfer guidelines following ecological zones. Tree breeding programs are established for different climate conditions, and our ecological niche characterization over the species range provides a good reference for deciding breeding zones. The unique refugium populations identified by this study call for additional investigations into their breeding and conservation values.The discovery of major genetic groups in SRP153314.Raw Illumina reads are available in the NCBI SRA database under project accession ID:  Click here for additional data file. Click here for additional data file.