Quercus is an important model clade, having extraordinary diverse and abundant members in the Northern hemisphere, that are used to studying the introgression of species boundaries and adaptive processes. China is the second-largest distribution center of Quercus, but there are limited studies on introgressive hybridization.Hybridization and introgression are vital sources of novel genetic variation driving diversification during reticulated evolution. Quercus acutissima, Quercus variabilis, Quercus fabri, and Quercus serrata) in 10 populations. We identified 361 alleles in the four-oak species across 17 loci, and all loci were characterized by high genetic variability (HE = 0.844–0.944) and moderate differentiation (FST = 0.037–0.156) levels. A population differentiation analysis revealed the following: allopatric homologous (FST = 0.064) < sympatric heterogeneous (FST = 0.071) < allopatric heterogeneous (FST = 0.084). A Bayesian admixture analysis determined four types of hybrids  and their asymmetric introgression. Our results revealed that interspecific hybridization is commonly observed within the section Quercus, with members having tendency to hybridize.Here, we screened 17 co-dominant nuclear microsatellite markers to investigate the hybridization and introgression of four oaks (Our study determined the basic hybridization and introgression states among the studied four oak species and extended our understanding of the evolutionary role of hybridization. The results provide useful theoretical data for formulating conservation strategies.The online version contains supplementary material available at 10.1186/s12870-021-03007-4. Introgressive hybridization is a common natural phenomenon among biological organisms, especially oaks . InterspQuercus L. (oaks) contains more than 400 species that are widespread in the Northern hemisphere [Quercus, naturally occurring interspecific hybrids are common [Quercus, the American oaks and European white oaks have been well-studied [Quercus petraea and Quercus robur) [Quercus series Virentes) for phylogenetic inference and determining introgressions between lineages [The genus misphere . Oak is misphere . In Quere common , 15. Wit-studied . For exas robur) . Eaton elineages . In Chinlineages . For conQuercus, with 62 species described in the Flora of China that are divided into five morphology-based sections: Quercus, Aegilops, Heterobalanus, Engleriana, and Echinolepides [Quercus and Aegilops) are composed of deciduous oak species which correspond to the Quercus and Cerris proposed by Denk [Quercus acutissima Carruthers and Quercus variabilis Blume belong to Group Cerris, while Quercus fabri Hance and Quercus serrata Murray belong to Group Quercus. A new classification of Quercus L. was proposed by Denk harbouring eight sections: Cyclobalanopsis, Cerris, Ilex, Lobatae, Quercus, Ponticae, Protobalanus, and Virentes [Cerris and Quercus Groups.China is the second-largest distribution center of olepides –26. The  by Denk . For ourVirentes . The phyQ. acutissima, Q. variabilis, Q. fabri, and Q. serrata), having an overlapping distribution in subtropical areas, are important species in subtropical mountain and warm temperate deciduous broad-leaved forests, which occupy important positions in China’s forest ecosystem [Quercus, as well as the biological processes behind their biodiversity, and has implications for forest management.The studied four oak species , 10.7 (range:7.1 to 14.2), and 0.550 (range: 0.254 to 0.980), respectively . Two populations, LT-F and ZW-B, did not departure from Hardy–Weinberg equilibrium, and seven populations, BY-A, BY-V, LT-B, ZW-V, ZJ-A, ZJ-V, ZJ-F, and ZJ-B, did not departure from Hardy–Weinberg equilibrium after excluding the null alleles bias.Comparison of DIC values across the 10 populations showed that models with null alleles had higher support than models without null alleles, and the average estimated frequency of across loci was slightly high (ranging from 0.102 to 0.188) , 10.7 (range: 8.7 to 11.8), and 0.829 (range: 0.782 to 0.862), respectively (Table Cerris and Quercus (groups AV and FB) based on the STRUCTURE analysis. The Ho of group FB (0.607) was greater than that of group AV (0.497) as determined using FSTAT (P < 0.05). The highest intraspecific genetic diversity  was observed in the Q. fabri population compared with the three other investigated species and was significantly different from Q. variabilis (P < 0.05). Among the four different sites, Liangting had highest Ho, which was significantly different from that of Zhongwu. Significant differences in AR were observed between Zijin and Zhongwu while HS and FST showed no significant differences among sites.At the DNA level, the four oak species had high degree of genetic diversity where mean FST across all loci with  and without  using the “exclusion null alleles” correction (Table FST (range: 0.033 to 0.168) and standardized differentiation G′ST (range: 0.033 to 0.190) were relatively low at the nominal level (5%). Thus, a weak genetic differentiation occurred among the four-oak species. We also compared the genetic differentiation between pairs of populations  < sympatric heterogeneous (FST = 0.071) < allopatric heterogeneous (FST = 0.084). The interspecific genetic differentiation levels were more significant than genetic differentiation among regions as observed using two-sided P-values  of the genetic variation occurred among regions and 9.78% (P < 0.0001) among populations within regions, with the greatest genetic differentiation of 89.17% (P < 0.0001) being harbored within populations , with 7.2% (P < 0.0001) occurring among populations within species and the greatest genetic differentiation of 88.74% (P < 0.0001) being harbored within populations. The intraspecific gene flow of Q. acutissima, Q. variabilis, Q. fabri, and Q. serrata were estimated to be 3.31, 2.56, 4.72, and 3.8, respectively  and Q. fabri and Q. serrata (group FB) individuals were clustered into another large branch above the tree. The same species, along with other species, showed intraspecific differentiations and interspecies variations. Additionally, NJ graph showed considerable variation and somehow weak clustering of Q. fabri and Q. serrata. The clustering results of leaf variation were basically consistent with the molecular results. We clustered the four species and found that 14 individuals of Q. fabri and all of Q. serrata samples clustered into a single branch. These results indicated that the leaf traits were quite different from those of other Q. fabri individuals. There was a small deviation at the population-level clustering, which mainly occurred for the Q. variabilis population in Bayan, as well as for Q. fabri and Q. serrata populations in Liangting and Zijinshan , with an average proportion of membership (QI = 0.98), and most Q. fabri and Q. serrata individuals (100%) were assigned to cluster II (QII = 0.99). Only three remaining individuals (two Q. acutissima and one Q. variabilis) showed different degree of admixture with assignment probabilities of < 0.90. The 10 populations were divided into two large groups (Groups AV and FB). Using K = 4, the hybridization scenario among the four oak species was more complicated. Q. fabri individuals were assigned to cluster I (QI = 0.80), Q. acutissima individuals were assigned to cluster II (QII = 0.94), Q. serrata individuals were assigned to cluster III (QIII = 0.54), and Q. variabilis individuals were assigned to cluster IV (QIV = 0.66) , including 67 F1 hybrids and 121 backcross individuals. Among the four sampled plots, the highest hybridization rate was found in Bayan plots (100%). Among the four-oak species, the hybridization rate was as follows: Q. acutissima (Hr = 18.0%) < Q. fabri (Hr = 50.7%) < Q. variabilis (Hr = 74.2%) < Q. serrata (Hr = 94.9%). Most hybridizations occurred in the same groups (AV or FB); however, there were also introgression individuals produced by cross breeding among different groups. Among them, individuals of Q. variabilis hybrids in Bayan were unique, having three types of ancestry (Q. acutissima—Q. variabilis—Q. fabri).We observed four types of hybridization, ri Table . A totalQuercus) are notorious for hybridization with species extinction, and this may also play a critical role in adaptive evolution [Q. acutissima × Q. variabilis, Q. fabri × Q. serrata, Q. acutissima × Q. fabri, and Q. acutissima × Q. variabilis × Q. fabri. Introgression occurred more readily in the Quercus group than in the Cerris group, and cross-group hybridization also existed; however, it was rare. This suggested that a low frequency of interspecific hybridization occurred with incomplete reproductive isolation. Reproductive isolation mechanisms contributed to the simultaneous coexistence of the four oaks.Introgression studies usually focus on large-scale pairs of individual species  and sympvolution , 32. Hybvolution , 32–34. Q. fabri in the Liangting and Zijing sites having the highest value. Long-lived trees span broad geographical ranges through a combination of adaptive plasticity and adaptive differentiation, as well as the standing genetic variation that occurs in response to environmental changes [FST values of the 10 populations ranged from 0.05 to 0.15, which indicates moderate differentiation. Additionally, the order of of genetic differentiation among the 10 populations was as follows: allopatric homologous < sympatric heterogeneous < allopatric heterogeneous. Q. acutissima and Q. variabilis diverged 13.2 Ma, and the divergence time of Q. fabri and Q. acutissima was 12.33 Ma [Q. acutissima, Q. variabilis, and Q. fabri) showed different migration routes in response to climatic changes. In addition, the strong uplift of the Tibetan Plateau in China led to the intensification of the Asian monsoon climate, which may promote species differentiation [Introgression is a vital source of novel genetic variation and drives diversification during reticulate evolution . Owing t changes . A high 12.33 Ma , 36. Durntiation –39. The ntiation .FST values, we speculated that gene flow occurred among these oaks. The limited gene flow prevented hybridization-induced genetic swamping, instead of providing more sources of genetic variation for heterozygotes. Additionally, we found that genetic variation among the four oaks mainly occurred within populations (88.74%), with only a small percentage occurring among populations (7.2%). Ramırez-Valiente et al. (2018) found that drought drove the evolution of genetic differences in functional traits among oaks [By studying 17 pairs of polymorphic loci with different ong oaks . The cluong oaks . AlthougNm > 1), intermediate (0.25 < Nm < 0.99), and low (Nm < 0.25) by Govindaraju [Q. acutissima, Q. variabilis, Q. fabri, and Q. serrata were estimated to be 3.31, 2.56, 4.72, and 3.8, respectively. Compared with species in other families, such as Picea asperata (0.75) and Liriodendron chinense (1.028), the gene flow was stronger, being similar to those of other oak species, such as Quercus aquifolioides (3.749). Gene flow is an important attribute affecting species ability to spread its genes [Quercus is also related to many factors, such as tree height, vegetation density, and leaf area [Q.fabri and Q. serrata groups while the STRUCTURE clustering revealed similar genetic separation. However, it may also be caused by variation within population, as stated above, the proportion of variation within population is very high (88.74%). We found recurrent interspecific gene flow among the four oaks. The STRUCTURE clustering revealed that most populations contained hybrids between two species. For example, the ZJ-A population showed F1 hybrids between Q. acutissima and Q. variabilis, while BY-A population showed hybridization between Q. acutissima and Q. fabri. Interestingly, a large number of individuals in the BY-V population appeared to be trihybrids. Additionally, STRUCTURE clustering showed that ZJ-F and ZJ-B populations did not form two strong groups and their hybrid individuals are relatively uniform, probably representing a mixed group. However, the phenotypic data indicated that these are two distinct species. Additionally, these two populations, BY-V and ZW-B, have more uniform hybrids, and we are not sure whether this is due to ancestral lineage or recent gene flow, and furthermore we did not rule out the existing possibility of shared variation. Because no suitable outgroups were utilized in the present study, the observed complex admixture patterns maybe are attributable to their ancestral contributions from other Quercus spp. Additional samples of related species are needed to elucidate this case. At the same time, the Bayan site appeared to be an especially active zone of introgression among these three oaks.Compared with intraspecific gene flow dynamics, interspecific introgression is more complex owing to the interactions of multiple species. Gene flow levels were distinguished as high  are all widely distributed in China. A palaeo distributive simulation showed that Q. acutissima, Q. variabilis, and Q. fabri tended toward expansion and contraction in glacial and interglacial periods, respectively [Quercus. To increase the efficacy of conservation strategies, conservation management should consider evolutionary theory. Thus, managers should consider whether hybrids co-exist with the parental species when formulating conservation strategies because natural hybrids have conservation value. Natural hybridization zones are good sites for studying species adaptive evolution using newly developed genomics techniques.Natural hybridization is a common phenomenon in plants, occurring in 25% of existing species , 52. Ferectively –39. Adapectively , 54. In Q. acutissima × Q. variabilis, Q. fabri × Q. serrata, Q. acutissima × Q. fabri, and Q. acutissima × Q. variabilis × Q. fabri) were determined, accompanied by asymmetric introgression. We concluded that interspecific hybridization is commonly observed within the section and that section Quercus has a high tendency to hybridize. In future work, we hope to use genomic data to study the dynamic nature of gene flow and adaptive introgression in these hybrid populations.The long-term evolutionary divergence of the studied four oaks, and the resulting interspecific introgression, led to their high levels of genetic diversity and moderate differentiation. The genetic variation among the four oaks mainly occurred within populations, with only a small percentage occurring among populations. Four types of hybrids  in which Quercus is divided into eight sections: Cyclobalanopsis, Cerris, Ilex, Lobatae, Quercus, Ponticae, Protobalanus, and Virentes [Quercus, two wild populations of Q. acutissima, three of Q. variabilis, two of Q. fabri, and three of Q. serrata, for genetic analyses, and they were sampled randomly within four plots distributed in Jiangsu Province (Plot Zijing) and Anhui Province   Fig. . Within wu) Fig. . From eaNine leaf morphological attributes were measured using 10 mature leaves from each of the 300 trees Table  S. The oTotal genomic DNA was extracted from 30–40 mg dried leaves per individual using a Plant Genomic DNA Kit . The quality and concentration of the genomic DNA were evaluated using a One-drop spectrophotometer  and electrophoresis was conducted in 1% agarose gels. DNA samples were diluted to 20 ng/μL and stored at -20 °C.2O. PCR products were separated on an ABI3730xl automated Genetic Analyzer using ROX-500 as an internal standard . Allele sizes were determined manually using Genemarker version 2.2.0 (Applied Biosystems).All DNA samples were genotyped at 17 nuclear microsatellite loci (nSSRs) . Using the Euclidean distances, nine phenotypic characteristics were clustered at the individual and population level. The obtained population clustering graphs and individual clustering diagrams were used along with the molecular results in a comparative analysis.n), inbreeding coefficients (f), and genotyping failures (b). The number of Markov Chain Monte Carlo iterations and burn-in were set at 500,000 and 50,000 cycles, respectively. Thinning maintained every 50th update. To conduct a Bayesian model comparison, we performed the analysis using the model with null alleles (nfb) and without null alleles (fb) to evaluate the significance of null alleles within each population. We set nfb significance using low deviance information criterion (DIC). For this method, the thresholds were set to a maximum of less than 0.2 and a mean of less than 0.1, which resulted in the final selection of all 17 loci for subsequent analyses. Linkage disequilibrium for all the locus pairs in each population and significant deviations from Hardy–Weinberg equilibrium were determined using Genepop version 4.6.9 [For the SSR data, we used INEST, version 2.2  to detecon 4.6.9 .N), observed (HO) and expected (HE) heterozygosity and effective number of alleles (NE). Polymorphic information content (PIC) was estimated in Cervus 2.0 [AR), genetic diversity within populations (HS), and inbreeding coefficient (FIS) were estimated using FSTAT version 2.9.3.2 [FIS′) for each population was also evaluated using INEST version 2.2 [nfb). The significance of FIS′ was assessed by comparing DIC values of inbreeding and without inbreeding models.Genetic diversity statistics of each population and each locus were estimated using POPGENE, version 1.32 . The starvus 2.0 , while a 2.9.3.2 . The corsion 2.2  with theFST and cFST [FST and cFST across loci were obtained through bootstrap resampling in the same program. The FST of each pair of populations was estimated in GENALEX 6.5 [Nm = 1—FST/4 FST. Additionally, we performed a principal coordinate analysis based on pairwise population FST values using GENALEX 6.5 [AR, HO, HS, and FST among geographic regions and among species using FSTAT, version 2.9.3.2 [P-values were obtained after 10,000 permutations. To examine the genetic variances of allopatric and heterogeneous types we conducted analysis of molecular variance (AMOVA) using F-statistics using ARLEQUIN, version 3.5 [FCT, FSC, and FST) were tested with 10,000 permutations.We estimated global and cFST  both witand cFST . The 95%ALEX 6.5  and visuALEX 6.5 . Gene flALEX 6.5 . We test 2.9.3.2 . The twosion 3.5 . SignifiK) capturing the data major structure, a burn-in period of 500,000 Markov Chain Monte Carlo iterations was used, with a 500,000-run length. In total, 20 independent runs were performed for each simulated K value, ranging from 1 to 9. All the iterations were run with the admixture model, which assumes that individuals may have mixed ancestry, because of the likelihood of inter-population and interspecific crossing among the four oaks. The optimal K value was identified from the maximum value of ΔK [STRUCTURE software  was usedue of ΔK  as impleue of ΔK . Clusterue of ΔK , and DISue of ΔK  was emplue of ΔK , and wasue of ΔK  with theue of ΔK .1 generation and backcrosses [1 hybrids have q-values = 0.5, but the coefficients of backcrosses are biased toward one of the parental species and produce q-values between 0.5 and 0.9 [The admixture coefficient  generated from STRUCTURE was used to classify individuals into purebreds and hybrids, with a threshold q-value of = 0.1, where samples with q-values < 0.1 or > 0.9 were classified as purebreds, and those with 0.1 < q-values < 0.9 as hybrids, including the Fkcrosses , 82. F1  and 0.9 . Taking Table S1. Deviance information criterion values for nfb, nb, and fb models in 10 populations obtained by INEST. Table S2. Probability of membership to each genetic cluster in each population of four oaks when K = 2 and 4. Table S3. Locations of 10 populations of four oak species. Table 4S.. Leaf macromorphological features examined. Table S5. SSR primer sets, number and size of alleles amplified in four oak species study.Additional file 1:


Identification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data.We have extended two principal component analysis based selection statistics to genotype likelihood data and applied them to low-coverage sequencing data from the 1000 Genomes Project for populations with European and East Asian ancestry to detect signals of selection in samples with continuous population structure.Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes.We show that selection scans of low-coverage sequencing data of populations with similar ancestry perform on par with that obtained from high quality genotype data. Moreover, we demonstrate that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data without the need for ad-hoc filtering.The online version contains supplementary material available at 10.1186/s12859-021-04375-2. Natural selection is the main driver of local adaptation. Instead of tracing the adaptive phenotypic trait, a “reverse ecology” approach is commonly applied , where tentclass1pt{minimaThe reduced expenses for whole genome DNA sequencing, thanks to advanced High-throughput DNA sequencing technologies, has facilitated larger sample sizes in population genetics studies in the recent years, including samples with similar genetic ancestry , 29, 30.K principal components (PCs) and variant weights through a truncated singular value decomposition (SVD)  and pcadapt [3] applied to the four East Asian populations obtained. Red horizontal line is the Bonferroni adjusted significance level. pcadapt has been corrected for genomic inflation. CG standard: Called genotypes from low-coverage data with a genotype quality threshold on 20. Figure S5: QQ-plots and Manhattan plots of the selection statistics from FastPCA and pcadapt applied to the four European populations obtained. Red horizontal line is the Bonferroni adjusted significance level. pcadapt has been corrected for genomic inflation. CG standard: Called genotypes from low-coverage data with a genotype quality threshold on 20. Figure S6: PCA plot, QQ-plots and Manhattan plots of the selection statistics obtained from PCAngsd, FastPCA and pcadapt applied to a European (CEU), Asian (CHB), and African (AFR) population. Red horizontal line is the Bonferroni adjusted significance level. Only one PCA plot is shown as they were all identical. pcadapt has been corrected for genomic inflation. HQG: High quality genotype data. Figure S7: Read length bias in the low-coverage sequencing data of the East Asian populations. (AB) PCA plots of the data only filtered using a callability filter, where in (A) individuals are colored by population, and (B) displays the individuals colored by sequencing read length. (C-D) PCA plots of the data filtered by a callability filter and corrected for read length bias. Figure S8: No read length bias in the low-coverage sequencing data of the European populations. (A-B) PCA plots of the data filtered using a callability filter, where in (A) individuals are colored by population, and (B) displays the individuals colored by sequencing read length. Figure S9: PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four East Asian populations for SNPs called from the low-coverage sequencing data using ANGSD [2]. The called SNPs have additionally been filtered using a callability filter and corrected for readlength bias. Red horizontal line is the Bonferroni adjusted significance level. Figure S10: PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four European populations for SNPs called from the low-coverage sequencing data using ANGSD [2]. The called SNPs have additionally been filtered using a callability filter. Red horizontal line is the Bonferroni adjusted significance level. Figure S11: Downsampling to 0.5 fraction of the reads of the low-coverage sequencing data. PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four East Asian populations for SNPs called from the downsampled low-coverage sequencing data using ANGSD [2]. Red horizontal line is the Bonferroni adjusted significance level. Figure S12: Downsampling to 0.5 fraction of the reads of the low-coverage sequencing data. PCA plot, QQ plots and Manhattan plots of the selection statistics obtained from PCAngsd applied to the four European populations for SNPs called from the downsampled low-coverage sequencing data using ANGSD [2]. Red horizontal line is the Bonferroni adjusted significance level. Figure S13: PCA plots from PCAngsd based on the low-coverage sequencing datasets with individuals colored by their estimated individual allele frequencies in the top hits for the East Asian and European populations, respectively. The individual allele frequencies reveal the direction of the PC-based selection signals in regards to the reference allele. (A) shows the top significant hit on PC1 for the East Asian populations for the LILRA3 region (rs434124), and (B) shows the top significant hit on PC1 for the European for the LCT/MCM6 region (rs6754311).


Iris loczyi, using five chloroplast DNA (cpDNA) fragments and seven microsatellite markers. A total of 15 populations, and 149 individuals were sampled throughout the QTP. High genetic diversity was detected both in cpDNA (Hd = 0.820) and SSR . Ten cpDNA haplotypes and 163 alleles were identified. AMOVA and clustering analyses revealed obvious differentiation between regions. The Nst, Gst and Mantel test showed significant phylogeographic structure of I. loczyi. The neutrality test and mismatch distribution analyses indicated that I. loczyi could not have undergone a historical population expansion, but population XS from the Qilian Mountain area could have experienced a local expansion. Bottleneck analyses indicated that I. loczyi had not experienced bottleneck recently. Based on cpDNA and SSR results, the Qilian Mountain area was inferred as a potential glacial refuge, and the southern Tibet valley was considered as a ‘microrefugia’ for I. loczyi. These findings provided new insights into the location of glacial refuges for the species distributed in QTP, and supplemented more plant species data for the response of QTP species to the Quaternary climate.Quaternary climate oscillations and complex topography have tremendous effects on current distribution and genetic structure of species, and hence the Qinghai–Tibet Plateau (QTP), the largest plateau in the world, has become a hotspot for many phylogeographic studies. However, little is known about the phylogeographic pattern of herbaceous plants in QTP. Here, we investigate the genetic diversity, population structure and historical dynamics of  Iris loczyi distributed in QTP based on chloroplast DNA and microsatellite markers. The geographic isolation caused by multiple mountain ranges was contributed to the current distribution pattern of this species. Our findings provided new insights into the location of glacial refuges for the species distributed in QTP, and supplemented more species data for the response of QTP species to the Quaternary climate.As the largest plateau in the world, the Qinghai–Tibet Plateau (QTP) is a hotspot for manyphylogeographic studies, but little is known about herbaceous plants. Here, we revealed the population genetic structure and two possible refuges of  There is growing evidence that the Quaternary climate oscillations have had tremendous effects on current distribution and genetic structure of species, which may vary with latitude and topography , with thThe QTP is the youngest and largest plateau in the world covering most of Qinghai Province and Tibet of China, with a mean elevation of 4500 m , surrounPrevious studies have revealed two major patterns of plants response to glacial climate change on the QTP: one is that species survived in a refuge on the QTP during the Last Glacial Maximum (LGM), then expanded during the postglacial periods (Iris loczyi (Iridaceae) is a dense perennial herb with knobby rhizomes, linear leaves, tough texture. The flower stem is short, and the base is often surrounded by lanceolate membranous sheath-like leaves. Flowers pale purple, outer segments oblanceolate or narrowly obovate, and inner segments oblanceolate. The capsule is obovate to cylindrical, reddish brown. Flowering from May to June, fruiting from July to September  estimate the genetic diversity and phylogeographic structure of I. loczyi, (ii) infer the potential LGM refugia of I. loczyi and determine whether it occupied a single refugium or multiple refugia.Here, we conducted a phylogeographic study for I. loczyi leaf samples were collected from 15 populations distributed in eastern Tibet and Qinghai Province, covering most of the geographic range of I. loczyi across QTP .A total of 149 ross QTP . One hunross QTP . The dis[see], namely trnS-trnG, trnS-trnfM, rpl20-5′rps12, psaI-accD and trnH-pbsA (2+), 10 mM dNTP mixture, 0.1 μM forward and reverse primers and 2.5 U rTaq DNA polymerase . PCR amplification was performed under the following conditions: 5 min predenaturation at 95 °C; followed by 35 cycles of 30 s denaturation at 94 °C, 30 s annealing at 48 °C (47 °C for trnH-pbsA), 60 s elongation at 72 °C and final elongation step of 10 min at 72 °C. All PCR products were detected by electrophoresis on 1.5 % agarose gels and sequenced with the ABI3730 sequencer .We amplified and sequenced five chloroplast fragments selected from 56 primer pairs collected from previous research [see], three  of which were selected from previously published SSR markers in Iris laevigata  were selected from primers independently developed by our team. PCR were performed in volume of 20 μL containing 20–50 ng DNA, 10× buffer (Mg2+), 10 mM dNTP mixture, Bovine Serum Albumin, 0.1 μM forward and reverse primer and 2.5 U rTaq DNA polymerase. PCR amplification was performed under the same conditions as chloroplast DNA (cpDNA), except for the annealing temperature [see]. The amplified fragments were separated on an ABI 3730 DNA sequence (Applied Biosystems) capillary electrophoresis instrument, and the sizes were assessed using GENEMAPPER 3.7 (Applied Biosystems). To reduce score error, allele size determination was performed twice.A total of seven microsatellite markers were used in this study S), number of haplotypes (h), haplotype diversity (Hd) and nucleotide diversity (π), were calculated using DnaSP 5.10 (NST) and unordered alleles (GST). To determine the best grouping for all populations, Spatial Analysis of Molecular Variance (SAMOVA) analyses  were performed in Arlequin 3.11  and geographical distances was detected using Mantel test in Arlequin, with 1000 randomizations.For cpDNA data, the sequences of each primer pairs alignment were performed in MAFFT 7.222  and editThe haplotype network was constructed using TCS 1.21 (Na), inbreeding coefficient (Fis), observed heterozygosity (Ho) and expected heterozygosity (He) were calculated using Genepop (http://www.genepop.curtin.edu.au) (Nm = (1 − FST)/4FST (K (K = 2–8) with 1 × 105 burn-in, followed by 1 × 106 MCMC steps. STRUCTURE HARVESTER . Principal coordinate analysis (PCoA) was carried out based on population genetic distance in GenAIEx 6.5 (http://bioinformatics.org/project/?group_id=84) based on Nei’s standard genetic distance between populations.For SSR data, genetic diversity parameters consisting of mean number of alleles (.edu.au)  and Popg.edu.au) . AMOVA aST)/4FST . STRUCTUARVESTER  was usedAIEx 6.5 . The UPGD, Fu and Li’s D and Fu and Li’s F (HRag)  . In addiF (HRag)  based onHd) and nucleotide diversity (π) were 0.820 (ranging from 0 to 0.688) and 0.00981 (ranging from 0 to 0.00934), respectively. Population JM from the Qilian Mountain area had the highest genetic diversity , and contained the most haplotypes , followed by populations TJ , JC  and XS , all of which contained two haplotypes . The remaining 11 populations had only one haplotype. There are different haplotypes distributed in the Qilian Mountain area (H1–H7) and the southern Tibet valley (H8–H10), and no shared haplotypes .The total alignment length of cpDNA sequence was 3445 bp, containing 108 polymorphic sites . Ten happlotypes . The happlotypes , which wplotypes . All hapK = 4, which means all 15 populations were divided into four groups. Population XS from the Qilian Mountain area clustered separately into a group and the remaining 10 populations  in this region composed a group. Population JC from the southern Tibet valley composed a group and the remaining three populations  were assigned to a group. AMOVA analyses revealed that the majority of genetic variation (92.11 %) existed among populations, while, with grouping based on SAMOVA analyses, the majority of genetic variation (91.14 %) existed among groups. The FST value (0.921) also indicated significant genetic differentiation between populations. PermutCpSSR analysis showed that Nst  was significantly higher than Gst , which indicated a significant phylogeographic structure. The Mantel test  also showed a significant correlation between genetic distance and geographic distance.SAMOVA analyses indicated that the optimal number of groups was I. loczyi populations  to 39 (IK6 locus) with a mean of 23.29. The observed heterozygosity (Ho) ranged from 0.4898 (IK2 locus) to 0.9664 (IK13 locus) with a mean of 0.6817 while expected heterozygosity (He) ranged from 0.7135 (IEST12 locus) to 0.9259 (IK6 locus) with a mean of 0.8602. Diversity parameters for each population were listed in Na) ranged from 3.714 (SY) to 7.429 (JM) with a mean number of 5.873. The Ho ranged from 0.554 (YH) to 0.893 (RY) with an average value of 0.689, the He ranged from 0.520 (JC) to 0.808 (JM) with an average value of 0.699. The inbreeding coefficient (Fis) ranged from −0.155 (RY) to 0.228 (XS) with an average value of 0.014. Population JM had the highest genetic diversity , which was consistent with the result of cpDNA. The genetic diversity of the Qilian Mountain area is higher, compared with the southern Tibet valley.A total of 163 alleles at seven SSR loci were revealed across 149 individuals from 15 Nm > 1) in the Qilian Mountain area, and moderate in the southern Tibet valley, but there was low (Nm < 1) gene flow between populations in the two regions [see], which were consistent with AMOVA analyses.AMOVA analyses showed that the majority of genetic variation (80.82 %) existed within populations . A similK value was 3  was non-significant positive value while Fu and Li’s D  and Fu and Li’s F  were both significant positive values , and the mismatch distribution curve was unimodal with the non-significant SSD and HRag , and MDA analyses showed multimodal  (Rhodiola sect. Trifida (Hd = 0.923) (Hd = 0.670) of 170 angiosperm species  , which was generally higher than other studied herbaceous plants in the QTP, e.g. Rheum tanguticum   (Hordeum vulgare (Ho = 0.126)   . Iris loI. loczyi populations  between genetic and geographic distances. The geographical distribution of haplotypes indicated that there was no shared haplotype between the southern Tibet valley  was identified at the level of all populations. Estimates of gene flow between population pairs suggested that there was moderate/frequent gene flow between populations within regions, but low gene flow between the two regions [see]. These findings indicated the existence of geographical isolation between the two regions. Population structure and the level of gene flow can be affected by life history traits and environmental effects , and Group I had a low diversity . The Qilian Mountain area is composed of fault-block mountains and valleys, with numerous peaks, parallel ridges and valleys closely intertwined. The complex topography has contributed to the diversity of populations in the region. For the population XS from Group I, it is located in the Huangshui River Valley Basin, with the Daban Mountain in the north, the Laji Mountain in the south and the Riyue Mountain in the west, surrounded by hills. The geographical barriers limited the dispersal of pollen and seeds, which impeded its communication with other populations; this might have contributed to the low diversity. STRUCTURE analyses indicated that population JM had gene flow from population XS . So, the Qilian Mountain area might have been a refuge of I. loczyi during the LGM. On the other hand, the Bayesian phylogenetic tree of haplotypes showed that haplotypes (H8–H10) from the southern Tibet valley were resolved as a sister group to the haplotypes H1–H5 from the Qilian Mountain area. STRUCTURE analyses showed that the populations from the southern Tibet valley were clustered into a separate group. In addition, H8 was an endemic haplotype confine to the southern Tibet valley with high frequency (17.4 %). The distribution of populations with high genetic diversity and frequency of private haplotypes indicated the existence of ‘microrefugia’ throughout the distributional range (I. loczyi in LGM.In the past decade, the response of species on the QTP and its adjacent areas to Quaternary climate oscillations has been extensively studied (xpansion ; Fig. 4,I. loczyi in QTP occupies two different geographical regions, the Qilian Mountain area and the southern Tibet valley, that both were considered as the refugia of I. loczyi in the glacial period. These findings provide new insights into the location of glacial refuges for the species distributed in QTP, and the study of historical population dynamics supplements more plant species data for the response of QTP species to the Quaternary climate oscillations.As the world’s largest and highest plateau, the southern and the south-eastern edges of the QTP, especially the Himalaya and Hengduan Mountains, were considered as the biodiversity hotspots  and impoThe following additional information is available in the online version of this article—I. loczyi.Nm values between population pairs of I. loczyi based on SSR loci.I. loczyi for mutation drift equilibrium under different models.plab070_suppl_Supplementary_Tables_S1-S6Click here for additional data file.


Schinus terebinthifolia is a species native to different ecoregions in the Brazilian Atlantic Forest. The plant is listed on the National Relation of Medicinal Plants and recommended as phytomedicine, however while extractive exploitation prevails as the main route of raw material a significant variation of compounds will be detected. To assure the expansion of productive chain it is important to start by studying population diversity and chemical variations. We used SSR markers for studies of genetic structure among populations from dense ombrophilous forest (ES); the deciduous seasonal forest (SM); the savanna (DOU) and the sandbanks (ITA and MSP), and compared the results to their chemical profiles of essential oil. Genetic structure revealed differences among populations and significant fixation rates. Pairwise studies and Bayesian analysis showed similarities between ITA and SM and between DOU and MSP, proving that the patterns of distribution for the species do not follow the isolation by distance or similarity by environmental conditions. The comparison between PCA of genotypes and chemodiversity reinforces the unique profile for each population despite the environmental similarity observed and genetic analysis. The most divergent genotype and chemical group was found at the ombrophilous forest, strong evidence that we should undertake conservation efforts to prevent losses of biodiversity in that area. Schinus terebinthifolia Raddi (Anacardiaceae) is an aromatic spice commonly known as pink pepper, Brazilian peppertree, faux poivrier, baie rose. The species is native to the Brazilian Atlantic Forest and has considerable ecoplasticity. In Brazil, it can be seen throughout several ecosystems from the sandbanks in south Pernambuco (08° 03′ 14″ S and 34° 52′ 52″ W) to the deciduous seasonal forests in Rio Grande do Sul (30° 01′ 59″ S and 51° 13′ 48″ W). As a small evergreen tree, it grows up to 3–13 m and behaves as a pioneer in degraded areas showing rapid and aggressive development by preventing regeneration of other native species. There’s a historical link attributing the interest in the aromatic and spicy features of its fruits to the spread of the species during Brazilian colonization, followed by recent re-introductions for ornamental purposes. Hence, it’s now dispersed worldwide and colonizing abandoned agricultural areas, degraded forests, coastal ecosystems, wetlands, and riparian zones, which has influenced its ranking among the 100 worst invasive species on the planet5.6. In recent years, a number of pharmacological studies have confirmed its antimicrobial, antioxidant, vasodilatory, and antitumor activities. These studies have encouraged the Brazilian Ministry of Health to include the Brazilian peppertree among the species of therapeutic potential to be distributed by the National Health System (SUS)16.On the other hand, the Brazilian peppertree is well known for its medicinal properties. The leaves and the fruits are widely used in traditional medicine as anti-inflammatory, antipyretic, analgesic and purifying agents18. However, the Brazilian peppertree has a short generation time, is a highly prolific seed producer, and the seeds are readily consumed and spread by birds20, it is imperative to take into account the global warning about the difficult control of Brazilian peppertree dispersion and the risks it implies to the environment.Therefore, a series of studies encouraging the propagation of Brazilian peppertree by family farming and its use for reforesting degraded areas increased substantially. Some authors, considering the idea of the occurrence of chemotypes, defend the necessity of preventing extractive exploitation and the management of units for conservation of the species21. Nevertheless, there are still many gaps related to the distribution and dispersion of the Brazilian peppertree, mainly because the sample groups in previous works have always been restricted to a few native ecogeographic regions. Essential oil composition may be affected by a variety of factors including soil properties, solar radiation, temperature, altitude, humidity and ontogenetic stage, and physiological stages of fruit maturation24. The sum of all these factors can potentially affect the bioactivity of a final product. Studies comparing the genetic diversity and chemodiversity of the species across different environmental conditions for growth is crucial for the development of elite lines for traditional medicine and the pharmaceutical industry.Studies of genetic diversity and spatial structure were carried out with distinct populations of Brazilian peppertree28, with a great variation among their concentration.The essential oil of the Brazilian peppertree is already used in the formula of many cosmetics and pharmaceutical products. Although considering the heterogeneity of its composition observed on pharmacological studies, we could not find in the literature studies in comparison of chemodiversity of the species considering environmental conditions of growth. Nevertheless, major constituents often observed are α-pinene, limonene, sabinene, β-pinene, α-copaene, α-phellandrene, germacrene and cis-salveneIn this study, we compare genetic diversity and chemodiversity among five populations representing distinct Brazilian ecosystems: decidual seasonal forest from Santa Maria—Rio Grande do Sul (Cfa); savanna from Dourados—Mato Grosso do Sul (Cwa); dense ombrophilous forest from São Mateus—Espírito Santo (Aw); sandbanks from Itaparica—Bahia (Af) and from Morro de São Paulo—Bahia (Af). We ask if differences in chemodiversity may be related to ecoregion or genetic differences among these populations.To access the plant material in this study, the authors followed all Brazilian legal frameworks  to Genetic Heritage for purpose of scientific research. A former Certificate was emitted by the Genetic Heritage Management Council in the name of Universidade Federal da Grande Dourados and can be accessed in the National System of Genetic Resource Management and Associated Traditional Knowledge (SISGEN nº A9CDAAE). The material collected never left Brazilian territory and all the analysis were made in our laboratory under our legislation and security.Fresh young leaves of native accessions were randomly collected to represent ecogeographical groups in Brazil Table , Fig. 1.29.After collection, the plant material was sterilized in a 3% NaClO solution for 10 min, then rinsed, dried and stored in liquid nitrogen until DNA extraction. Total genomic DNA was extracted from leaf tissue using the CTAB method30  for polymorphisms. PCR were performed using a multiplex system as described in Table We used a panel of seven microsatellite loci adapted from Williams et al.PCR final volume (25 μl) contained: 4.5 µl of ultrapure water, 12.5 µl of PCR master mix, 1.0 µl of each primers, 2U of Taq DNA Polymerase and 2.3 µl of DNA (25–30 ng). The thermocycling program consisted of an initial denaturation at 94 °C for 2 min, followed by 30 denaturation cycles at 94 °C for 30 s, annealing at 50 °C for 30 s and extension at 72 °C for 30 s. A final extension step at 72 °C for 10 min was performed at the end of the 30 cycles.Amplified products were separated in 6% polyacrylamide gel, prepared on a 34-well sandwich type glass plate. A pre-run of 50 min at 70 W was carried out before applying the PCR samples. The 5 μL aliquot from PCR product was applied and the electrophoresis run lasted for approximately 2 h, with constant 70 W. A 10 bp ladder molecular weight marker was loaded on the side edge of each gel to assign a fragment size to each band. The gels were stained with silver nitrate.31. The parameters were as follows: allele frequency was estimated by direct counting, expected (He) and observed heterozygosity (Ho), polymorphic information content (PIC) and Hardy–Weinberg equilibrium (HWE). The diveRsity R package (R Development Core Team 2020)32 was used to calculate allelic richness (AR) and estimates of Wright's F statistics . Confidence intervals were obtained with 10,000 bootstrap replicates. The influence of null alleles was determined in FREENA33 by computing the genetic divergence parameter (FST) values using an excluding null alleles (ENA) correction. After accounting for null allele frequencies, loci with frequencies of ≥ 0.2 were considered potentially problematic for the calculations.Parameters of loci diversity were estimated for all microsatellites, in all populations, using the CERVUS 3.0 program34 to reveal the diversity within localities and among localities. The dendrogram was constructed by cluster analysis using the UPGMA method, based on calculations of Nei's genetic distances and 1000 bootstrap resamplings using poppr R package35.The P-value was adjusted using the Bonferroni procedure with the same statistical package. Population structure was evaluated by analysis of molecular variance (AMOVA) using the ARLEQUIN program version 3.5.2.236. The tests were performed using an admixture model where the allelic frequencies were correlated. To select the appropriate number of inferred populations, several analyzes were conducted with k (number of populations inferred) ranging from 2 to 6, with 30,000 interactions (burn-in period of 300), with three independent replications for each analysis. The real values of K were inferred from the magnitude of ΔK and given as a function of K, with the aid of the Structure Harvester program37 following the model proposed by Evanno et al.38 To corroborate the outputs from the Bayesian analysis, we also analyzed the datasets via a discriminant analysis of principal components (DAPC)39 using the ADEGENET package40. The number of clusters was assessed using the function find.clusters, which runs successive rounds of K-means clustering with an increasing number of clusters (K). To select the optimal number of clusters, we chose the lowest associated Bayesian information criterion (BIC) value.Based on the allelic frequency of the seven microsatellite loci, the individuals were grouped in a given number of populations and probabilistically marked into groups inferred by Bayesian analysis using the Structure programWe used a Clevenger-type apparatus for the extraction of the essential oil from ripe berries of Brazilian peppertree. A pool of fruits from several individuals representative from each ecogeographic region was weight (300 g) and submitted to 3 h of hydrodistillation. The Fig. −1. The gas chromatograph used was a GC-2010 Plus  coupled to a mass spectrometer (GC–MS 2010 Ultra) using a DB-5 column  coated with 5% phenyl dimethylpolysiloxane on capillary fused silica . The conditions of analysis were as follows: injection volume 1 µL in split 1:20 mode; heating ramp with initial temperature of 50 °C, reaching 280 °C at a rate of 3 °C min−1 and remaining at the final temperature for 10 min; and injector temperature of 280 °C. The temperatures of detector and transfer line were 290 °C. The parameters of mass spectrometry included scanning MS voltage electron impact ionization of 70 eV with m/z 40–600 and scanning range of 0.3 s. Compound identifications were performed using the calculated retention index (RI) and the linear alkane  standard, along with comparisons of the RI with indexes found in the literature41 and also used our interpretation of mass spectra obtained from the samples and compared with the databases (NIST21 and WILEY229). The peak area of each compound was determined by manual integration of each total ion chromatogram (TIC). Then all areas were transformed into relative percentage areas .The essential oil was stored in sterile microtubules at − 20 °C until further analysis. The samples were prepared in hexane at the concentration 100 µg mLThe access to genetic resources were assured by SISGEN license nº A9CDAAE.ST (P < 0.001).Population structure analysis revealed differentiation among them of 37.72% ; SM ; ITA ; MSP  and DOU . After null allele correction (ENA), overall FST changed only slightly (from 0.377 to 0.369). Pairwise FST values were highest for DOU, followed by MSP while FST was the lowest between ITA and SM , carotol (only in ITA and SM) and eremoligenol (only in ES) , β-ocimene, evadone (only in ITA and MSP), aromadendrene, S) Table .Table 6CPrincipal Components Analysis (PCA) plots were performed using genotypes and chemical compound data. Clusters found for genotypes reinforced the patterns of previous genetic structure analysis. ES individuals group in a separate cluster, while ITA and SM individuals cluster together. MSP and DOU individuals form two clusters that are close to each other and separate from the other populations. However, when the populations were grouped by their chemodiversity, the PCA revealed that each population has a unique chemical profile Fig. .Figure 4IS) were low and genetic diversity was high in most of the sampled populations with the exception of DOU (Cerrado—Brazilian Savanna). The excess of heterozygotes and low allelic richness suggest this population has experienced a recent bottleneck46. This population is located in an area recently classified as an ‘Ecological Transition Zone’ due to anthropic pressures  which may be responsible for the bottleneck. Similar bottlenecks in Brazilian peppertree populations have been detected in other fragmented areas in Brazil and highlight the negative effect that anthropogenic factors can have on this species in its native range. The ES population was distinct in all clustering analyses and had the highest genetic diversity compared to the other sites. The ES population is located in an important conservation area (dense ombrophilous forest) which may have protected this population from genetic erosion. The presence of important species for phytomedicine such as Brazilian peppertree in these areas further supports the conservation of those forests.As expected for an outcrossing species, the inbreeding coefficients . When a population experiences a reduction of its effective size, it generally develops a heterozygosity excess which may persist only for a certain number of generations until a new equilibrium is established42. Moreover, DOU showed the lowest rates for allelic richness (2.43). Reinforcing the hypothesis of bottleneck since the loss of the average number of alleles per locus is also related to genetic drift typical in populations that have their effective size dramatically reduced43.This assumption can also be supported by the values of an observed heterozygosity higher than the expected  and it proves that neither the time nor human pressure were enough to disrupt their distribution patterns—typical in invasive species. Possibly due to the high spatial density and efficient mechanisms for seed dispersal which allowed an intense gene exchange and consequent genotypic recombination, responsible to increase its evolutionary potential and adaptability.To understand the genetic diversity of Brazilian peppertree it’s important to consider the high ecoplasticity and invasive behavior of the species. The populations analyzed in this work are originally from five different ecogeographic regions with particular characteristics Table . Neverth44 had inferred about the species distribution patterns do not fit within the isolation by distance patterns. Pinto et al.5 have analyzed haplotypes distribution of Brazilian peppertree considering different biomes, and the authors suggested a cluster of 3 haplotypes based on the intravarietal polymorphisms. Nevertheless, in our study, population from ES showed the highest distance instead of DOU, while SM and ITA corroborate by clustering together. ES also presented the higher allelic richness (4.98), coincidentally this population occurs in the ecogeographic region of greater preservation compared to the others (dense ombrophilous forest).Structure analysis Fig.  showed u45. We believe the similarities found between populations from SM and ITA may be correlated to the same colonization event and a reasonable explanation for divergence of the other three genotypes may be due to environmental barriers—DOU occurs in a fragmental zone; MSP in an isolated island and ES surrounded by the dense forest. Thus, an isolation by environment (IBE) could be expected under the action of natural selection and/or environmentally influenced mating or migration47. Furthermore, Williams et al.21 described the existence of well-structured populations of Brazilian peppertree in their native habitat. This phenomenon becomes evident in pairwise study when greater differentiation rates are obtained by DOU and MSP, respectively, two populations established in a narrow area and environmentally isolated49.The patterns of genetic variation across a species range can provide information about the processes of distribution and help us to understand how ecology, evolution and geography intersectThe dendrogram based on Nei distance Fig.  confirms23, it would be unexpected some clusters formation. Consistent with this expectation, our sampled populations, with the exception of ITA and MSP, were in different ecosystems and had unique chemical profiles. ITA and MSP are located geographically close together in the same pedo-climatic conditions and yet they had the greatest divergences for chemical composition between the populations. This suggests that ecosystem designations are not capturing all of the relevant variables that could result in chemical profile differences. For instance, differences in predators, disease, or pollinators could also influence secondary metabolite synthesis. Future studies will need to take into consideration a wider variety of factors to determine which are important for differences in chemodiversity of populations.The analysis of GC–MS of the essential oil of the fruits detected 38 different compounds (major and minor), some of which were not present in all five populations. The PCA revealed that each population had a unique chemical profile. Secondary metabolites are mostly related to environmental factors including soil properties, solar radiation, temperature, altitude, humidity and harvest timeFrom a pharmacological perspective, it is important to consider every factor that might influence the bioactivity of a phytomedicine. Understanding how environmental factors affect the chemodiversity of Brazilian peppertree will make it possible to develop elite lines for industry. As more studies are conducted across a wide variety of systems, we may be able to predict how different factors will influence patterns of chemodiversity.


The Middle East region is important to understand human evolution and migrations but is underrepresented in genomic studies. Here, we generated 137 high-coverage physically phased genome sequences from eight Middle Eastern populations using linked-read sequencing. We found no genetic traces of early expansions out-of-Africa in present-day populations but found Arabians have elevated Basal Eurasian ancestry that dilutes their Neanderthal ancestry. Population sizes within the region started diverging 15–20 kya, when Levantines expanded while Arabians maintained smaller populations that derived ancestry from local hunter-gatherers. Arabians suffered a population bottleneck around the aridification of Arabia 6 kya, while Levantines had a distinct bottleneck overlapping the 4.2 kya aridification event. We found an association between movement and admixture of populations in the region and the spread of Semitic languages. Finally, we identify variants that show evidence of selection, including polygenic selection. Our results provide detailed insights into the genomic and selective histories of the Middle East.  •Middle Easterners do not have ancestry from an early out-of-Africa expansion•Basal Eurasian and African ancestry in Arabians deplete their Neanderthal ancestry•Populations experienced bottlenecks overlapping aridification events•Identification of recent single and polygenic signals of selection in Arabia A high-coverage resource of physically phased genomes from eight Middle Eastern populations generated via linked-read sequencing provides insights into a genetically understudied region and enables more comprehensive study of population history and the detection of millions of variants common to the Middle East but outside short-read accessibility masks and not previously cataloged. It enhances our understanding of regional ancestry, the spread of languages, the effects of climate change on populations, and the evolutionary history of genetic variants. Global whole-genome sequencing projects have provided insights into human diversity, dispersals, and past admixture events . HoweverWe sequenced 137 whole genomes from eight Middle Eastern populations A to an aUncovering population structure and past admixture events is important for understanding population history and for designing and interpreting medical studies. We explored the structure and diversity of our dataset using both single-variant and haplotype-based methods. After merging our dataset with global populations, fineSTRUCTURE  identifiWe next analyzed our samples in the context of ancient regional and global populations. Principal component analysis D and S1 In addition to differences in EHG and African ancestries, we observed an excess of Natufian ancestry in Arabia compared with the Levant B and 1E.In addition to the local ancestry from Epipaleolithic/Neolithic people, we found an ancestry related to ancient Iranians that is ubiquitous today in all Middle Easterners  could be a source of ancestry for some modern Levantine and Arabian populations  and S4. Historical effective population sizes can be inferred through the distribution of coalescence times between chromosomes sampled from a population . HoweverWe next studied the population separation history of Middle Eastern populations among themselves and from global populations. The importance of accurate phasing in this analysis is illustrated by an earlier finding that suggested, based on statistically phased data, that modern-day Papuans harbor ancestry of an early expansion of modern humans out of Africa . HoweverWe then compared the separation times of populations within the Middle East and found the oldest divergence times were between Arabia and the Levant/Iraq C and S4.Within the Levant and Iraq, all splits occurred in the past 3–4 ky. Within Arabia, Yemenis split from Emiratis ∼4 kya and Saudis appear as the least divergent population to both the Emiratis and Yemenis, with recent splits within the last 2 ky. We note that the separation curves within the region appear gradual, suggesting ongoing gene flow after separation rather than clean splits. We also note that the separation curves will reflect the admixture histories of these populations.The similar amount of Neanderthal ancestry in most non-African populations and the low diversity of introgressed haplotypes suggest that modern humans likely experienced a single pulse of Neanderthal admixture as they expanded out of Africa . Middle −6) between the increase in deep ancestry and the amount of Neanderthal ancestry in the modern Middle Easterners. When testing all ancient populations, we found two clines . Previous studies identified two correlated variants (rs41380347 and rs55660827), distinct from the known European variant (rs4988235), that are associated with lactase persistence in Arabia  with it.We also identified additional variants that show an increase in frequency recently. A variant within Plasmodium vivax infection .An advantage of using genome-wide genealogies is its power to detect relatively weak selection. We subsequently searched for evidence of polygenic adaptation in Arabian populations across 20 polygenic traits specifically over the past 2,000 years . For mosIn this study, we have generated a high-coverage resource from the genetically understudied Middle East region. All samples studied are experimentally phased using linked-read sequencing, allowing the reconstruction of large and accurate haplotypes. We found millions of variants that are not cataloged in previous global sequencing projects, with a significant proportion being common in the Middle East. A majority of these common variants reside outside of short-read accessibility masks, highlighting the limitation of standard short-read sequencing based studies.The large number of physically phased haplotypes allowed us to study population history from relatively old periods (>100 kya) to very recent times (1 kya). We found no evidence that an early expansion of humans out of Africa has contributed genetically to present-day populations in the region. This finding adds to the growing consensus that all contemporary non-African modern humans descend from a single expansion out-of-Africa, quickly followed by admixture with Neanderthals, before populating the rest of the world . We founBy modeling contemporary populations using ancient genomes, we identified differences between the Levant and Arabia. The Levant today has higher European/Anatolian-related ancestry while Arabia has higher African and Natufian-like ancestries. The contrast between the regions is also illustrated by their population-size histories that diverged before the Neolithic (15–20 kya) and suggest that the transition to a sedentary agricultural lifestyle allowed the growth of populations in the Levant but was not paralleled in Arabia. It has been suggested that population discontinuity occurred between the late Pleistocene and Early Holocene in Arabia and that the peninsula was repopulated by Neolithic farmers from the Fertile Crescent . Our resAn additional source of ancestry needed to model modern Middle Easterners is related to ancient Iranians. Our admixture tests show that this ancestry first reached the Levant and subsequently reached Arabia and East Africa. The timings of these events interestingly overlap with the origin and spread of the Semitic languages , suggestThe application of ancestral recombination graphs to reconstruct the evolutionary history of variants offers a powerful method to study natural selection. We refined and identified signals of selection in Arabian populations. The example of the lactase persistence associated variant, which during the past few thousand years increased to a frequency reaching 50% and is almost absent outside the region, demonstrates the importance of studying underrepresented populations to understand human history and adaptation. Our results indicate that polygenic selection might have played a role in increasing the frequency of variants that were potentially beneficial in the past but today are associated with diseases such as T2D. We found few signals of polygenic selection in Arabian populations relative to other populations , which mFuture ancient DNA studies from Arabia are needed to refine the formation of Arabian populations and to further clarify prehistorical connections between the Levant and Arabia. As Middle Eastern populations are among the most underrepresented in GWAS , this lima17@sanger.ac.uk).Further information and requests should be directed to Mohamed A. Almarri (This study did not generate new reagents.This study was approved by Dubai Scientific Research Ethics Committee (DSREC-SR-02/2018_01) and by the Wellcome Sanger Institute Human Materials and Data Management Committee (HMDMC 18/026). All individuals who donated samples for this project were interviewed and provided consent for participation and were recruited in the UAE. We use the term ‘Arabian’ in this study to refer to samples from the Arabian Peninsula , Levantine for Syrians and Jordanians, and Iraqi-Arabs and Iraqi-Kurds for samples from Iraq. Saliva samples were collected using the Oragene DNA kits (OG-600) and DNA was subsequently extracted using the QIAGEN MagAttract HMW kit (Cat No. 67563). Fragment sizes and quality were determined through a pulsed-field capillary electrophoresis (Femto Pulse system). Libraries were prepared using 10X Genomics Chromium kits and each sample was sequenced in a separate lane on a HiSeq X instrument.https://github.com/brentp/goleft/) on the phased BAMs. For each VCF we assessed the number of phased variants through summary.csv file output by Long Ranger, we found on average ∼98% of variants to be physically-phased in each sample. For each VCF we assessed the quality of variant calls output by Long Ranger using the QUALITY filter for each variant. The pipeline uses the haplotype structure informed by the physical phasing to tag variants that are likely to be false positives, as each haplotype can only have one allele. We find that variants that do not have a PASS quality label tend to have low Ts/Tv values, suggesting they contain false positives. We used a stringent filter by setting all non-PASS variants to missing and merged all samples using bcftools v1.9 merge option −0. We removed variants that show excessive heterozygosity as calculated by bcftools ExcHet tag (< 1e-6) and then set any variant with genotype quality (GQ) < 20 and variants located in regions more than twice the average sample depth to missing. The final dataset composed of 23.1 million single nucleotide variants (SNVs). The Ts/Tv after quality control was 1.97, and remained consistent throughout different allele frequency bins suggesting that the variants are of high quality. The TS/TV for all newly identified variants outside the accessibility mask is 1.72, while the Het/Hom for the newly identified variants (> 1% AF) outside the strict mask is 2.08 across all samples. We then examined possible relatedness in our dataset using the genome option in plink-v1.9  to process raw fastq files into phased BAM files and phased VCF files. The average sequencing depth for all samples was 32x, median 31x, calculated using covstats . Samples used in our analysis were published in We merged our new data with published ancient data extracted from the Allen Ancient DNA Resource curated dataset v44.3; Y chromosome data of 79 males from the current study were complemented by 46 samples from −9  single nucleotide mutations per bp per year (http://tree.bio.ed.ac.uk/software/figtree/).All 79 males from the current study, 35 males from per year  was usedWe used the Phewas search option in the GWAS atlas  and Genehttps://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data). Samples from the curated dataset analyzed in this study were published in We combined our dataset with published modern global populations extracted from the Allen Ancient DNA Resource curated dataset v44.3  then used the output in the EstimatePopulationSize.sh script with options -m 1.25e-8 –years_per_gen 29.We leveraged the physical-phasing in our dataset using RELATE v1.1  to examiWe were concerned that the decrease in population size we find around 4-5kya could be a result on recent consanguinity, which is common in the Middle East. To test this, we repeated the population size analysis, first including samples with high total sum of runs of homozygosity , second including samples with relatively low sROH (< 50 Mb) and third by choosing one haplotype per individual, instead of two haplotypes. This will remove the effect of recent consanguinity, as we also removed any related samples as discussed in the ‘Samples Processing and Quality Control’ section. We show the results of these tests in As we find a divergence in population size between Arabia and the Levant before 10kya, i.e., before the Neolithic era, we reran the analysis using another method , removed variants that are CpG, restricted analysis to the previously described HGDP accessibility mask, in addition to the archaic genome accessibility masks downloaded from https://bioinf.eva.mpg.de/altai_minimal_filters/. We removed singletons, variants that deviate from Hardy-Weinberg equilibrium (< 1e-10), show excessive heterozygosity (< 1e-8), and only included biallelic SNVs. As performed in We used IBDMix  to call We also ran Sprime  on a simWe applied MSMC2 using 4 haplotypes (2 diploid samples) to examine the separation history between our populations and the high coverage Vindija Neanderthal  as we di. We first ran RELATE v1.1 on 272 haplotypes using the same parameters described in the demographic history section. We extracted the genealogies of the Arabian samples and used the output of the previous step as input to the DetectSelection.sh script accounting for the population history of the populations using the option -m 1.25e-8 –years_per_gen 29. From the resulting .sele file, we extracted p values from the column “when_mutation_has_freq2” which tests for evidence of selection over the lifetime of a particular variant. We were conservative and only included variants that show significance at a “genome-wide threshold” of p < 5e-8. This test for significance has been shown to be well calibrated (https://github.com/35ajstern/clues). From the output of Relate above, we ran the SampleBranchLengths.sh script to sample branch lengths from the posterior in order to account for uncertainty. We ran 100 samples (–num_samples 100) using a mutation rate of 1.25e-8 and accounted for the population size history by supplied the .coal files from the previous step. We then ran CLUES (inference.py script) with the option –coal {.coal file} to again account for population size changes. We fine-mapped variants using the likelihood ratio statistic produced by CLUES as suggested by We used the Relate Selection Test in RELATE v1.1  to look librated , but to https://github.com/35ajstern/palm). We avoided the use of GWAS summary statistics that were calculated from meta-analysis, due to the potential effect of uncorrected population stratification. We extracted GWAS summary statistics performed on the UK BioBank . These statistics were nominally corrected for population structure using either a family history-based approach, fixed PCs, or a linear mixed model. For each trait investigated, we split the genome into 1,700 approximately independent blocks (To test for polygenic selection, we used PALM (k (UKBB;  download_409K/ - ; https:/stats/ -  ; and frt blocks  and selet blocks . VariantWe used plinkv1.9  to firstWe used RFMix v2.03  to ident


Carinascincus ocellatus. While a comparatively high elevation population has genotypic sex determination, in a lower elevation population there is an additional temperature component to sex determination. Based on 11,107 SNP markers, these populations appear genetically isolated. “Isolation with Migration” analysis also suggests these populations diverged in the absence of gene flow, across a period encompassing multiple Pleistocene glaciations and likely greater geographic proximity of populations. However, further experiments are required to establish whether genetic isolation may be a cause or consequence of differences in sex determination. Given the influence of temperature on sex in one lineage, we also discuss the implications for the persistence of this polymorphism under climate change.Polymorphisms can lead to genetic isolation if there is differential mating success among conspecifics divergent for a trait. Polymorphism for sex‐determining system may fall into this category, given strong selection for the production of viable males and females and the low success of heterogametic hybrids when sex chromosomes differ . Here we investigated whether populations exhibiting polymorphism for sex determination are genetically isolated, using the viviparous snow skink  Carinascincus ocellatus.Polymorphism for sex determining system might be expected to isolate gene pools, given strong selection for the production of viable males and females. We investigated this question using a rare example of a species exhibiting polymorphism for sex determination: the viviparous snow skink  If mating success is lower among individuals that differ for a given trait, lineages will diverge in the frequency of that trait and experience further reductions in gene flow, potentially initiating speciation. Trait polymorphisms that may particularly promote speciation include those related to mate choice  with a male genotype (ZZ) , is an extraordinary example of a species exhibiting incipient divergence in sex determination  and a cooler high elevation population  and those not in Hardy–Weinberg equilibrium  in either population were then filtered from the data using Genepop  using the “genetics” package  were obtained from Cliff et al., . NeutralRousset, . From th2.3−06, approximating one individual per generation). Upper limits on uniform priors were initially set broadly, and then based on inspection of posterior distributions, were narrowed in a subsequent run to encompass the range of this posterior plus a margin of error; overly large priors reduce the precision of estimates given the use of a finite number of bins to represent the posterior distribution.The level of gene flow accompanying divergence of GSD and GSD + EE populations, along with their duration of divergence, was assessed under the “Isolation with Migration” Bayesian framework of Hey and Nielsen , employiIsolation with Migration analysis was performed using Markov Chain Monte Carlo sampling with 112 chains distributed across 14 processors, and a geometric chain heating scheme with first and second heating parameters of 0.95 and 0.50, respectively. To reduce overall run‐time, an initial analysis was run and traces inspected to ensure stationarity of the sampling distribution was achieved, and this was then used to seed four simultaneous analyses, each run for 24 hr following a 10‐min burnin and using unique random number seeds, to ensure independence among runs. All runs were assessed for convergence using Tracer 1.7.1 . Mitochondrial mutation rates were employed in the analysis, against which mutation rates at the nuclear loci would be scaled. We followed the mean rate estimate of 1.52% divergence per million years based on calibrations from other squamates  and GSD (high elevation) populations of K = 2; Figure Pairwise Fst between the populations is 0.24, consistent with negligible gene flow compared with both inter and intraspecific values reported for squamates  is small relative to those still shared between populations ; data curation (lead); formal analysis ; funding acquisition ; methodology ; project administration ; writing–original draft (lead). Erik Wapstra: Conceptualization ; funding acquisition (lead); project administration (lead); resources ; supervision ; writing–review and editing . Tariq Ezaz: Conceptualization ; formal analysis ; funding acquisition ; project administration ; resources ; supervision ; writing–review and editing . Christopher Burridge: Conceptualization ; formal analysis ; funding acquisition ; methodology (lead); project administration ; software (lead); supervision (lead); writing–review and editing .


Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al Pairo-Castineira et al., 2021), two SNPs, namely, rs10735079 and rs2109069 are also present in the Indian Genome Variation Consortium (IGVC). The IGVC was a large-scale comprehensive study of the Indian sub-populations that was conducted to shed light on the genetic diversity among geographically and ethnically diverse Indian sub-populations. This study had identified a high degree of genetic distinctness, with respect to SNPs, in different Indian sub-populations . PRS provides an estimate of the genetic propensity of an individual to develop a disease and/or a trait . TransetHere, with prior information from the data of stratified Indian sub-populations, we calculated the PRSs with an aim to explore and identify Indian sub-populations that could be at a higher risk for COVID-19-mediated mortality. Considering the challenges associated with the transferability of the effect sizes, we also analyzed the differences in the patterns of LD, and used the SNPs with similar LD patterns in the discovery population and Indian population to ensure good prediction accuracy of the PRS. Based on these PRSs, we evaluated the population-wise susceptibility that can be of potential utility in more effective vaccine distribution schemes among Indian sub-populations.Pairo-Castineira et al., 2021). The study had identified genetic signals related to host antiviral defense pathways and those mediating inflammatory organ damage in critical COVID-19 patients using Mendelian randomization, GWAS and transcriptome-wide association studies. DNA samples were genotyped using Illumina Global Screening Array v.3.0 + multi-disease bead chips (GSAMD-24v3-0-EA) and Infinium chemistry . Further, genotype data of 390 samples across 25 populations from the IGVC (Phase 3) were used for analyzing the Indian sub-populations. Briefly, they represent diverse ethno-linguistic and geographical regions of India, and house information about genome-wide SNPs across Indian populations. The data span four major linguistic lineages - Indo-European (IE), Dravidian (DR), Austro-Asiatic (AA), and Tibeto Burman (TB) from different geographic locations  from contrasting ethnic backgrounds and ethnicity sub-categorized as caste groups (LP), religious groups (SP), and tribal isolated populations (IP) , ∼11% of South Asian, 8% African and 7% of East Asian ancestries (ons (IP) . Clusterdia.org/ .-values (below the genome-wide threshold p < 10−06). The top 100 such SNPs from the study represented in the IGVC data were analysed for and ascertained to have similar LD patterns (for applicability of effect sizes from non-SAS ancestry) across the Indian sub-populations and the GWAS discovery population and were used for polygenic score analysis. The effect sizes of 30 SNPs were derived from Europeans, 40 from South Asian, 21 from East-Asian and seven from African ancestries. PRS of each individual was calculated using PLINKv1.9 .While applying effect sizes derived from one ancestral population to another to calculate PRS, the accuracy is better when SNPs with similar LD patterns or allele frequencies between the discovery (majority of Europeans) and target (Indian) populations are used. Directly comparing LD patterns between Europeans (CEU) and IGVC populations was not possible because of the smaller number of SNPs represented in the OMNI array of the IGVC. So we first overlapped the GWAS results with the IGVC SNP data, from which the top 100 significant SNPs  were selected and verified for LD conservation. The genetic closeness of individuals of the IGVC to the SAS populations of the 1,000 Genomes has been shown in a recent study . AnotherThe LD pattern 5 MB around the top 100 most significantly associated SNPs were compared between each of these non-Indian ancestral populations with ITU using the varLD (v1.0) tool–a tool to compare the extent of LD differentiation at loci between pairwise populations, to assess LD structure across the discovery GWAS population and the target Indian population. . The minhttps://censusindia.gov.in). A similar model was constructed for the data with the IENLP1 population removed as this was seen as an outlier due to a very high value for deaths/million. The pseudo R2 values were calculated as (model$null.deviance-model$deviance)/model$null.deviance  was fit for deaths per million population of each district due to COVID-19 and PRS of the corresponding district. To account for potential confounders, we added percentage of population above 45 years of age, and sex ratio  to the GLM. Poisson distribution was used with the respective population of each district as an offset to control for overdispersion. The data were collected from the census records of India , and applied on 25 geographically and ethnically diverse sub-populations of the Indian sub-continent (p = 2.95 ✕ 10−06). Based on the PRS for each district, the susceptible population, i.e., the number of individuals in a population at risk for developing severe illness when infected with SARS-CoV-2 was also evaluated  on mortality while the potential confounders considered had significant (p < 2 x 10−16) but negligible effects. The same trend was observed on removing an outlier population, IENLP1 and the coefficient estimate for PRS had improved two-fold and the fit improved (pseudo R2 = 0.46)  . Based o = 0.46) ; Table 12) and the other closely located districts within Maharashtra have also been severely affected that could have promoted higher viral spread, and given the high PRS, they may present higher susceptibility to COVID-19 infection and associated inflammatory responses.No significant association of PRS derived from non-risk SNPs with the COVID-19 mortality was observed in the GLMs as shown in Previous observation of genetic differences in the individuals from different regions of India, and the strong association between genetic variants and COVID-19 illness led us to evaluate the genetics risk for SARS-CoV-2 mediated illness in Indian sub-populations. In this report, we have calculated the PRSs of SNPs in different Indian sub-populations, followed by a regression analysis with COVID-19 related mortality in different districts of India. Our results indicate a significant association between the number of COVID-19-related deaths with the PRS, and thereby can provide support for population specific prioritization in COVID-19 vaccination program.Pairo-Castineira et al., 2021). It has also been noted that the individuals belonging to different linguistic lineages in different geographic locations of India exhibit genetic distinctness (In a recent GWAS, these SNPs have been robustly associated with critical illness in patients with COVID-19 (inctness . Here, oinctness . Our resinctness . In the There are certain limitations of the present study. The GWAS was not directly conducted in the individuals of the Indian sub-populations, and the PRSs were based on effect sizes from different ancestral groups with COVID-19 infection. The mortality may also be affected by comorbidities like diabetes, hypertension, cardiovascular diseases , environIn this study, we provide a methodological framework for predicting Indian sub-populations that could be at a higher risk for developing COVID-19 mediated critical illness but not any clinical evidence. These scores in conjunction with the commonly noted comorbidities could provide a good prediction in ascertaining high COVID-19 risk groups. Such accurate identification of vulnerable populations is crucial for the development of effective prevention and vaccination strategies. Such strategies applied to populations with defined genetic histories such as in the Indian subcontinent can be easily extended to model population level susceptibility to several other important diseases that strain the public health system in India, and provide a necessary use case justifying national scale projects such as GenomeIndia.