Podarcis muralis) in central Italy. This species is highly abundant throughout this region and populations belong to a single genetic lineage, yet there is extensive phenotypic variation across climatic regimes. We used redundancy analysis to, first, quantify the effect of climate and geography on population genomic variation in this region and, second, to test if climate consistently sorts specific alleles across the landscape. Climate explained 5% of the population genomic variation across the landscape, about half of which was collinear with geography. Linear models and redundancy analyses identified loci that were significantly differentiated across climatic regimes. These loci were distributed across the genome and physically associated with genes putatively involved in thermal tolerance, regulation of temperature-dependent metabolism and reproductive activity, and body colouration. Together, these findings suggest that climate can exercise sufficient selection in lizards to promote genetic differentiation across the landscape in spite of high gene flow.Species distributed across climatic gradients will typically experience spatial variation in selection, but gene flow can prevent such selection from causing population genetic differentiation and local adaptation. Here, we studied genomic variation of 415 individuals across 34 populations of the common wall lizard ( Understanding how the spatial distribution of environments shapes population differentiation is a shared goal of biogeography, ecology, and evolutionary biology. The balance between selection and gene flow determines the extent to which populations will diverge genetically along environmental gradients and exhibit adaptation to local environmental conditions  in central Italy are well suited to quantify the degree of population genetic differentiation across climatic regimes in the absence of physical barriers to gene flow, and to investigate if local adaptation is accompanied by differentiation at particular loci. The climatic gradients in central Italy can be steep, with a hot, dry Mediterranean climate on the western coast and Tuscan hills, transitioning into a cooler and wetter climate in the Apennine Mountains  variation in body size, shape, and colour. This variation likely results from climatic effects on the strength of sexual selection  analyses to investigate the evidence for, and distribution of, putative signatures of climatic selection in the genome.Podarcis muralis) is a small lizard occurring throughout southern Europe  resolution for the 34 sampling locations analysed. The first three axes explained 90%  of the variation , and distance-based Moran Eigenvector Maps (dbMEM). The methods are explained by Legendre and Legendre , and allWe created a matrix of geographic distance between locations using the coordinates of each sampling location. Locations were linked within a minimum geographic distance using the longest edge in a minimum spanning tree  aims to detect polygenic responses to selection by investigating changes in allele frequencies  and adjusted the false discovery rate using a Benjamini-Hochberg algorithm . We identified outlier loci on the constrained axes based on the “locus score”, which represents the loading of each locus in the ordination space. We calculated the mean locus score across all loci for each significant  RDA axis, and individual loci with a score greater than 3.5 standard deviations from the mean were considered candidates for selection  annotation using clusterProfiler package with default parameters . The global Fst for the 34 populations was 0.0615 (CI: 0.0606 – 0.0642 from 100 iterations of bootstrap).After stringent filtering for sequencing depth and rate of missing data, and excluding all but the first SNP per RAD locus, 35,227 SNPs were retained. Expected heterozygosity, observed heterozygosity, and inbreeding coefficients were similar across populations , and climate variables . The total amount of variation in the Hellinger transformed population allele frequencies explained by the linear trend, dbMEMs and climate was 17.6%. An RDA on the non-detrended  Hellinger transformed allele frequencies, using forward selection of climatic principal components, resulted in retention of climatic PC1 and PC2. These two predictors uniquely explained about 2% of the variation in allele frequencies , but a very high overlap in terms of significant outlier SNPs following LFMM and RDA (see below). This suggests that the results are robust at varying cut-offs, and we therefore report here only the results for the data set filtered at r2 = 0.8.After filtering on LD at rK = 6 in the LFMM analyses. The LFMM identified 221 candidate loci, of which 75 were associated with climatic PC1, 57 with PC2, and 89 with PC3. Manual adjustment of the genomic inflation factors (GIF) had only minor effect on these numbers.A principal component analysis and the broken stick method did not support a significant genetic clustering Fig. , but sNMThe RDA returned three significant axes, on which we identified 820 unique candidate loci. The majority of these SNPs were most strongly associated with climatic PC1 (361), followed by PC3 (247) and PC2 (212). GO enrichment for these RDA outliers can be found in Table 2 = 0.5 was 43, of which 38 were shared with results for r2 = 0.8). These 48 outliers were dispersed throughout the genome, found on 16 of the 19 chromosomes; 23 outliers were most strongly associated with climatic PC1, 11 with PC2, and 14 with PC3. These SNPs were associated with 37 annotated genes  in central Italy. This region is characterised by very high lizard densities, no physical barriers to gene flow, consistently high estimates of population genetic diversity (even at climatic extremes), and a genetic structure characterized by isolation by distance  and its accessory protein (DUOXA2), both located on chromosome 14, are putative candidates since they are involved in thyroid hormone biosynthesis (Carvalho and Dupuy P. muralis nigriventris”; Böhme P. muralis from western central Italy (Ruiz Miñano et al. It is not surprising to find candidate genes that are consistent with the expectation that climatic selection will lead to population divergence in genes that regulate thermal physiology and annual life cycles. In common wall lizards, climate is also associated with a marked shift in several morphological characters that are under sexual selection, including head size, shape, and colour ornamentation (While et al. It is encouraging to find candidate genes that have previously been shown to be involved in both thermal physiology and animal colouration. However, genotype-environment associations are limited in the extent to which they can inform us about causality. Thus, these candidates remain putative loci of adaptation until quantitative experiments confirm the results. Furthermore, RAD data only covers a small part of the genome and most major effect candidates will therefore go undetected. Nevertheless, the results of this study indicates that climatic selection may affect a broad range of genes dispersed throughout the genome. Whole-genome sequences across steep climatic gradients, accompanied by transitions in phenotype (Ruiz Miñano et al. In summary, this study demonstrates that climate exercises an effect on population genetic differentiation in an abundant lizard with extensive gene flow. Further, the analyses suggest that particular alleles are consistently favoured in certain climatic regimes. More targeted approaches, combining functional studies with whole-genome sequencing, will be necessary to identify additional genes underlying population differentiation and local adaptation.Supplementary Tables and Figures


Triticum aestivum L.) populations established from 371 landraces genotyped at 200,062 SNPs. We used linkage disequilibrium (LD) patterns to estimate in each population the past distribution of recombination along the genome and characterize its fine-scale heterogeneity. At the megabase scale, recombination rates derived from LD patterns were consistent with family-based estimates obtained from a population of 406 recombinant inbred lines. Among the four populations, recombination landscapes were positively correlated between each other and shared a statistically significant proportion of highly recombinant intervals. However, this comparison also highlighted that the similarity in recombination landscapes between populations was significantly decreasing with their genetic differentiation in most regions of the genome. This observation was found to be robust to SNPs ascertainment and demography and suggests a relatively rapid evolution of factors determining the fine-scale localization of recombination in bread wheat.Reciprocal exchanges of DNA (crossovers) that occur during meiosis are mandatory to ensure the production of fertile gametes in sexually reproducing species. They also contribute to shuffle parental alleles into new combinations thereby fueling genetic variation and evolution. However, due to biological constraints, the recombination landscape is highly heterogeneous along the genome which limits the range of allelic combinations and the adaptability of populations. An approach to better understand the constraints on the recombination process is to study how it evolved in the past. In this work, we tackled this question by constructing recombination profiles in four diverging bread wheat ( SignificanceRecombination is the fundamental biological process that shuffles chromosomes during meiosis to create new allelic combinations in gametes. It has been shown to be controlled by genetic factors in some species but those remain unknown in many. Understanding the genetic determinism of recombination can help to better describe its underlying biological make up, its constraints and understand its evolvability. One approach to study this question is to evaluate if the recombination process differs between groups of individuals that are genetically distinct (populations). Here we propose methods to implement this approach and investigate the recombination process in bread wheat, one of the most widespread crops. We show that recombination patterns between two populations are increasingly correlated when their genetic differentiation decreases. This suggests that recombination in bread wheat can evolve rapidly possibly associated to an underlying modification of its genetic determinism.Meiotic recombination  is the obligate genetic exchange between homologous chromosomes that occurs during the production of gametes in sexually reproducing species. Besides its role in ensuring proper segregation of chromosomes in gametes, it also impacts evolution by breaking linkage between advantageous and deleterious alleles and by creating novel combinations of alleles , for example, more than 80% of the recombination events occur in the terminal regions of the chromosomes representing less than 20% of the genome , recombination events are more evenly distributed along the chromosomes with the exception of the centromeres  called hotspots . In someOryza sativa ssp. japonica and O. s. ssp. indica (Theobroma cacao), only little overlap of recombination hotspots was observed across ten diverging populations, with less divergent populations showing higher level of overlap (H. vulgare) compared with wild barley (Hordeum vulgare ssp. spontaneum)  or in differentiated populations of the same species . For exa. indica  although overlap . Note thntaneum)  while dontaneum) . A finerntaneum) .High-density genotyping SNP arrays as well as new generation sequencing (NGS) approaches now allow to analyze large collections of wild/domesticated, ancient/modern populations of both animals and plants. Such a large amount of accurate data permits to better decipher the recombination landscape from patterns of linkage disequilibrium (LD) . The advDespite these limitations, the LD-based approach was successfully applied at the whole-genome level in many species including birds , yeast , Arabidon = 6x = 42) derived from two successive interspecific crosses involving three diploid species , a yet-unknown species related to the Sitopsis section (SS genome related to the wheat BB genome) and Aegilops tauschii (DD genome). However, international efforts combined with appropriate and original strategies using chromosome sorting, chromosome-specific BAC libraries, paired-end short-read sequencing and relevant assembly approaches, led to the publication of a high-standard, annotated, oriented and anchored sequence of the wheat genome (The complexity and huge size (16 gigabases) of the wheat genome have long hampered the development of high throughput genomic tools as well as the establishment of a whole-genome sequence. Bread wheat is an allo-hexaploid species , whereas WE and EA are the more divergent ones (FST = 0.085) and also the most geographically distant. The WA population is the closest population to the tree root possibly because it includes accessions that were collected not far from the center of domestication of bread Wheat . The EA population appears as a very differentiated and homogenous population. WE and EE are less differentiated because they separated more recently from each other . This population is derived from a cross between two bread wheat varieties: Chinese Spring and Renan belonging, respectively, to the EA and WE gene pools. The CsRe population was previously genotyped for the same set of SNPs as the landraces . RecombiLD-based recombination maps were inferred from patterns of LD between polymorphic SNPs for each landrace population independently using PHASE . As LD iBefore estimating LD-based recombination rates, SNPs were filtered out on Minor Allele Frequency with a minimum value of 3% within each population, yielding to 170,509 SNPs for WE, 161,137 for EE, 171,901 for WA, and 131,585 for EA. The average marker density was 11 SNPs/Mb with most of the SNPs located at telomeres (25 SNPs/Mb) whereas centromeres were depleted in SNPs   . This ins; DDNN) . Interesicularly  online.hotspot as functional hotspots typically span much smaller genomic regions  than our defined HRIs (median size = 20 kb). Therefore, we cannot be sure that an HRI harbors a single recombination hotspot. The repartition of HRIs along the genome was heterogeneous. Most HRIs (73%) were located in telomeric R1 or R3 regions, and the other HRIs (27%) in pericentromeric R2a or R2b regions. As HRIs corresponded to, respectively, 2% and 1% of intervals in those regions, telomeres were significantly enriched in HRIs compared with pericentromeres . These HRIs represented 15% of LD-based genetic distance (from 12% in EA to 18% in WA) and around 9% of the physical distance (from 6% in EA to 10% in WE). On average, in all genomic regions, the 8,713 HRIs tend to highly co-localize with open-chromatin features compared with non-HRIs intervals. For example, the proportion of HRIs overlapping genes was 80%, but this proportion dramatically decreased to 53% when considering non-HRIs intervals . The proportion of CsRe crossovers overlapping HRIs ranged from 20% in EA to 37% in WE. Most HRIs (82%) overlapped at least one CsRe crossover.Both CsRe and LD-based maps show a high heterogeneity in the distribution of recombination rates along chromosomes: On average 36% (± 1%) of physical distance represents 80% of genetic distance in all our populations. To further study the distribution of chromosome sites cumulating historical crossovers, we defined highly recombining intervals (HRIs) in the four landrace populations as intervals with an LD-based recombination rate exceeding four-times the background recombination rate . Combining all four populations, this resulted in 8,713 HRIs , with a median deviation to background recombination rate λ = 6.5 (range λ = 4 to λ = 511). Note that we avoid here the term LD-based recombination ntervals  online. Brt), tenacious glume (Tg), homoeologous pairing (Ph), or nonfree-threshing character (Q)] or recent crop improvement  were found in regions outliers for the ρ/CsRe ratio. The results showed no evidence of reduced recombination around these genes . This ranking between populations could be explained by genetic diversity levels  as well The similarity of λ profiles along the genome was evaluated by fitting a linear mixed model on the variations ofThe average correlation of local variations of recombination rates across genomic regions was twice higher for the highest correlated pair WE–EE (0.47 ± 0.11) than for the lowest one EE–EA (0.20 ± 0.11), with an average value of 0.32 . The uneP-value = 0.06). To check if such an overlap across populations can be explained by chance alone, we compared the observed repartition of hot windows with a simulated distribution obtained by a random assignment of HRIs corresponding to the null hypothesis of the absence of HRI population sharing . The proportion of common hot windows under this random assignment is represented by gray boxplots in P-value < 2e−16). In addition, the probability for a hot window to be associated with scaffold boundaries decreased with the level of sharing . However, these enrichments are not sufficient to explain the patterns of sharing described above. Hence, a significant amount of sharing of HRIs could be due to an underlying partial conservation of recombination hotspots.In light of these significant differences in the local repartition of recombination events, we investigated whether this could be explained by difference in the localization of crossover hotspots by comparing that of the HRIs (see above). We first defined “hot windows” as genomic regions that harbor an HRI in at least one population. FST) on the similarity of recombination profiles (measured by their correlation) for all genomic regions  on all chromosomes (1A to 7D) (FST effects (slopes) were negative, revealing a striking pattern where the similarity in recombination intensity decreases proportionally with genetic divergence: Almost all genomic regions (67 among 84) had a negative slope estimate significantly different from 0 and others genomic regions (15 among 84) had negative but nonsignificant slope estimates different from 0. Note that the similarity in recombination profiles is based on the relative local recombination intensity (parameter λ) that should not be affected by the evolutionary history of populations. FST were calculated from haplotypes rather than single SNPs to avoid an ascertainment effect. But results based on FST calculated from SNPs showed the same pattern (Further examination of the increase in recombination intensity in A to 7D) . We foun pattern  online.  pattern  online,  pattern  online.  pattern  online. FST) was particularly associated to the pairwise correlation of recombination profiles between populations. We computed FST around 54 genes known to be involved in the meiosis process . Only asy4, located in the 4AR3 genomic region, showed a significantly more negative slope than control genes in its genomic region    online.In our study, we estimated LD-based recombination rates for the first time at the whole-genome scale in bread wheat. Previous studies were done at local scale only  but suggAt a fine scale, LD-based maps revealed that 1–2% of intervals of telomeric and pericentromeric regions (depending on the population) exhibited especially high recombination rate (HRIs), suggesting that these intervals overlapped recombination hotspots. The accumulation of crossovers in recombination hotspots was already observed in bread wheat  and seemIn principle, LD-based recombination maps should be suited to study the similarity of recombination profiles of diverging populations. In our study, they allowed to compare recombination rates of four populations with about twice more SNPs than the densest genetic maps currently available  affect LD patterns they can also affect recombination rate estimates . To measThe overall similarity between the meiotic map and LD-based maps shows that LD-based recombination patterns offer a robust representation of the distribution of recombination along the bread wheat genome.P-value < 2.2e−16). Although this varying number of HRIs per population could result from a variation in recombination patterns, it is likely also due to differences in the power to detect HRIs in each population which would be consistent with results from Despite good concordance with the meiotic map, LD-based recombination maps can still be locally affected by demographic effects, and thus result in bias when interpreting differences or similarities between populations. For example, FST index. The decreasing similarity of recombination rates with genetic differentiation still hold when estimating LD-based recombination rates on a population specific SNPs data set or a common SNPs data set. The co-localization of HRIs was also not influenced by the SNPs data set  and cocoa-tree , poplar  on chromosome 2A (2A/2N translocation) and to eye-spot (Pch1) on chromosome 7D , this study further shows that this divergence can be quite fast. Reasons for this divergence remain to be found but our results can hint at some possibilities. Further analyses are needed to settle this question, which should greatly help developing original approaches useful for wheat improvement and breeding.This study demonstrates the evolution of the recombination profile at a genome-wide scale in closely related wheat populations with increasing genetic divergence. Based on recombination landscapes robust to demographic events, the comparison of the four landrace populations revealed a clear signal of a decreasing similarity between fine-scale recombination landscapes with increasing genetic divergence. Specifically, we found 1) that HRIs were more shared between closely related populations, 2) recombination intensities at HRIs detected in one population decreased in the other populations with their genetic divergence, and 3) the correlation of recombination landscapes between pairs of population decreases with their local genetic differentiation as measured by A collection of 632 bread wheat landraces  was genoThe physical positions of SNPs on the 21 bread wheat chromosomes were determined using Basic Local Alignment Search Tool Blast;  of conteDue to the relatively low number of meiosis sampled in the CsRe data, a Bayesian model inspired from .The likelihood .r/βr gives the mean of the Gamma distribution and Ci is also a Gamma distribution:To specify a prior distribution of .Ci (in M/Mb) is then:The posterior mean of .The parameters  regions  online.  regions  over the.Note that null recombination rates were replaced by the lowest non-null estimate of recombination rates of the region to allow fitting the Gamma distribution. We derived the meiotic recombination rates from the RILs recombination rates using the Haldane and Waddington formula  and the yi). Missing data on genomic segments with no parental allele switch at segment extremities were imputed. A crossover was counted at each parental allele switch, yielding 26,239 crossovers. Due to the presence of missing data in RILs genotypes, a number of switches did not occur between pairs of immediately adjacent markers. In such cases, the crossover cannot be assigned with certainty to a single interval of two successive SNPs. For example, an RIL genotype AA/–/BB identifies a switch between the first and third marker but cannot discriminate a recombination in the first versus the second interval. In such cases, we accounted for the uncertainty in crossover location following the sampling procedure of For estimation of recombination rates, it was necessary to count the number of recombinants in CsRe intervals The 534 landraces were gathered into 4 groups using a hierarchical clustering on the pairwise distance matrix estimated in We discarded closely related individuals within each population to avoid over representing family specific recombination events. Pairs of individuals exhibiting a very low genetic difference were discarded, keeping a total of 371 landraces.FST) of the four populations were computed within each genomic region  using alleles of 8,741 haplotypic blocks  identifies the window which interval i belongs to. The parameter The ratio PHASE samples jointly ρi (/kb)  online. ρi (/kb)  online.i and per population p. The similarity between LD-based recombination profiles was measured by correlating theTo compare LD-based recombination profiles, it was necessary to obtain a common set of intervals across the four populations , as polymorphic SNPs sets were different. We defined smaller intervals formed of successive markers that were polymorphic in at least one population  online. ommended  and usinThe model was applied independently to each genomic region  figuring as outliers in λ distribution  online, To test for the hypothesis that the observed proportion of HRIs shared by populations is due to chance, an empirical range of plausible values of co-localization due to chance was estimated by simulation. In 1,000 simulations, each HRI of each population was assigned to a random interval within the genomic region it belongs (1AR1 to 7DR3) and the proportion of shared hot windows was computed  online.i the interval and j one posterior distribution value among 1,000 (The comparison between meiotic (CsRe) and LD-based recombination rates were done on windows of 4 Mb  along the genome. Meiotic recombination rates were estimated using the Bayesian model described above, the attribution of crossover to windows being done using the  approachng 1,000  online.Genome Biology and Evolution online.evab152_Supplementary_DataClick here for additional data file.


Chamaesium species distributed across the HHM and QTP regions. In total, 525 individuals from 56 populations of the nine species were analyzed based on three maternally inherited chloroplast fragments  and one nuclear DNA region . Fifty-two chloroplast DNA (cpDNA) and 47 ITS haplotypes were identified in nine species. All of the cpDNA and ITS haplotypes were species-specific. Phylogenetic analysis suggested that all nine species form a monophyletic clade with high support. Dating analysis and ancestral area reconstruction revealed that the ancestral group of Chamaesium originated in the southern Himalayan region at the beginning of the Paleogene (60.85 Ma). The nine species of Chamaesium then separated well during the last 25 million years started in Miocene. Our maxent modeling indicated the broad-scale distributions of all nine species remained fairly stable from LIG to the present and predicted that it will remain stable into the future. The initial split of Chamaesium was triggered by climate changes following the collision of the Indian plate with the Eurasia plate during the Eocene. Subsequently, divergences within Chamaesium may have been induced by the intense uplift of the QTP, the onset of the monsoon system, and Central Asian aridification. Long evolutionary history, sexual reproduction, and habitat fragmentation could contribute to the high level of genetic diversity of Chamaesium. The higher genetic differentiation among Chamaesium populations may be related to the drastic changes of the external environment in this region and limited seed/pollen dispersal capacity.Exploring the effects of orographic events and climatic shifts on the geographic distribution of organisms in the Himalayas-Hengduan Mountains (HHM) region and Qinghai-Tibetan Plateau (QTP) is crucial to understand the impact of environmental changes on organism evolution. To gain further insight into these processes, we reconstructed the evolutionary history of nine  The Himalayan-Hengduan Mountains (HHM) are key features of the biodiversity hotspots of South and East Asia , with alThe uplift of the QTP caused a series of topographical and climatic changes, which possibly served as a major force in species diversification . For exaChamaesium H. Wolff.With such an intricate geological and ecological diversity, the HHM region is attractive for studying the drivers of species diversification and evolution. Previous phylogenetic and biogeographical studies have focused on species-level diversification resulting from the uplift of the QTP . PhylogeChamaesium is the only genus of the Chamaesium clade in Apiaceae and is endemic to the HHM region, where it grows in alpine meadows, shrublands and forests above 3,000 m  reconstruct the phylogenetic relationships within the genus Chamaesium based on materials from 56 populations; (2) explore the origin and species diversification of Chamaesium throughout the HHM region and its adjacent areas (QTP); (3) explore the geological and climatic changes that influenced distribution shifts and genetic diversity of Chamaesium.In this study, we reconstructed the phylogenetic tree of Chamaesium between 2015 and 2019. These individuals were from 56 populations at elevations above 3,000 m in Tibet (Xizang), Sichuan, Yunnan, Qinghai and Gansu provinces   protocol. We amplified and sequenced ITS (ITS1–5.8sRNA–ITS2)  and thre, rpl16) . Polymer1. Finally, ML bootstrap support (ML BS) and BI posterior probabilities (BI PP) were presented at the nodes.All the DNA sequences were edited by SeqMan  to obtain consensus sequences. We used program MAFFT v.7.369b  to alignChamaesium fossil records, yet there are relatively consistent results of the crown age of Chamaesium based on cpDNA and ITS  using the Tracer v.1.7.1 (see text footnote 1). Maximum clade credibility (MCC) trees were produced with TreeAnnotator 1.8.4 in BEAST and were visualized in FigTree v1.4.42.There are no  and ITS . Therefo and ITS  based on and ITS . BEAUti  and ITS  and a Yu3  (HT), within-population diversity (HS) and population differentiation indices (GST and NST)  with 1,000 permutations using ARLEQUIN version 3.5  (Chamaesium. The smoothness of observed mismatch distribution was detected by the sum of squared deviations (SSD) between observed and expected mismatch distributions and Harpending’s raggedness index (Hrag) (Chamaesium.Haplotype diversity (Hd)  and nuclHd)  for eachHd)  to verifHd)  was usedand NST) . We alsosion 3.5  to detecima’s D)  and mismima’s D)  were conx (Hrag) . Additiox (Hrag)  was usedChamaesium during four time periods: Last Interglacial (LIG), Last Glacial Maximum (LGM), and the present and future. A total of 208 distribution sites acquired from field investigations and online herbarium records  were used in analyses. Nineteen bioclimatic environment variables  curve” (AUC) (We used MAXENT 3.3.3K  to prediariables  from foue” (AUC)  to obsere” (AUC) .trnT-trnL, trnQ-rps16, rpl16) and ITS were used to analyze 525 individuals from 56 populations of the nine Chamaesium species. The cpDNA and ITS haplotype frequencies of each population are listed in C. delavayi, only one haplotype (C1) was shared by two populations. A population with three haplotypes only occurred in C. spatuliferum  and C. wolffianum . Besides, the sequenced ITS’s total length was 635 bp, and 106 polymorphic sites were recovered with 47 haplotypes (N1–N47). Like cpDNA haplotypes, all ITS haplotypes were species-specific in Chamaesium  of cpDNA ranged from 0.000 to 0.733, and nucleotide diversity (π) ranged from 0.000 to 0.002 across all species (HT) value was higher than average gene diversity within populations (HS) at the level of genus. Additionally, the number of substitution types (NST) was higher than interpopulation differentiation (GST), which indicated that a significant phylogeographic structure existed in Chamaesium (Hd) of ITS ranged from 0.000 to 0.800, and nucleotide diversity (π) ranged from 0.000 to 0.005 across all species (HT) value was higher than the average gene diversity within populations (HS), whether at the level of species or genus. Total NST was significantly higher than GST, indicating significant phylogeographical structure in Chamaesium  occurred among populations . In addiChamaesium mallaeanum, C. novem-jugum, C. wolffianum, and C. viridiflorum. Chamaesium mallaeanum had a close affinity to C. novem-jugum, and C. wolffianum was sister to C. viridiflorum in the ITS tree. However, these four species formed their own individual branch in the cpDNA tree. Overall, no haplotypes were shared between the nine Chamaesium species, and each species formed its own individual branch in the ITS and cpDNA trees.The ML and BI topologies of cpDNA and ITS haplotype phylogenetic trees were partly inconsistent . InconsiChamaesium was dated to be 60.85 Ma (95% HPD: 35.69–101.38 Ma). The divergence times of C. mallaeanum and C. novem-jugum were estimated at 48.90 Ma (95%HPD: 32.30–64.53 Ma) and 42.04 Ma (95%HPD: 26.40–57.99 Ma). C. wolffianum, C. viridiflorum, and C. delavayi branched off around 36.78 Ma (95% HPD: 22.37–52.28 Ma), 30.75 Ma (95% HPD: 17.95–45.41 Ma) and 24.41 Ma (95% HPD: 12.67–36.69 Ma), respectively. The divergence of C. thalictrifolium, C. spatuliferum, and C. paradoxum occurred at 20.25 Ma (95% HPD: 10.42–31.54 Ma), 13.87 Ma (95% HPD: 6.82–23.00 Ma), and 8.37 Ma (95% HPD: 3.50–15.18 Ma), respectively. The divergence times estimated from ITS (Chamaesium was dated to be 52.05 Ma (95% HPD: 46.51–57.61 Ma). The ancestor of C. mallaeanum and C. novem-jugum separated about 37.49 Ma (95% HPD: 25.42–49.22). The ancestor of C. wolffianum and C. viridiflorum separated around 32.79 Ma (95% HPD: 22.05–44.98). Furthermore, the divergence times of C. delavayi, C. thalictrifolium, C. spatuliferum, and C. paradoxum were estimated at 27.60 Ma (95% HPD: 16.96–39.50 Ma), 21.75 Ma (95% HPD: 12.69–31.96 Ma), 17.15 Ma (95% HPD: 9.33–26.00 Ma), and 11.90 Ma (95% HPD: 4.72–20.32 Ma), respectively. The results of divergence time based on cpDNA and ITS were similar. Since the molecular markers of chloroplast are closer to neutral and have matrilineal genetic stability, which is widely used in the process of divergence time estimation and biogeographic deduction, the results obtained cpDNA were selected for the subsequent elaboration of the differentiation history of Chamaesium.According to the beast-derived age estimate based on cpDNA , the crofrom ITS  indicateChamaesium in the southern Himalayas based on cpDNA data (node I). Several ancestral populations moved eastwards to the eastern Himalayas and continued eastward, diversifying in the HHM region during the early Oligocene. One vicariance and two dispersal events were identified within populations from the eastern Himalayas to the HHM region (node II). Furthermore, the BBM analyses identified several dispersal events within C. novem-jugum, C. thalictrifolium, and C. spatuliferum. For example, C. spatuliferum populations separated and one group colonized the Tangut region or eastern Himalayas from HHM region, while the other continued diversifying in the HHM region.The BBM analysis of ancestral distribution  areas waD and Fu’s Fs were non-significant for all species in the neutrality test , the area under the AUC value for the potential climatically suitable areas of Chamaesium was high (>0.95), indicating the highest predictive capacity. The distribution ranges predicted for Chamaesium were consistent with the actual geographic distributions. This modeling  of Chamaesium. The climate decreased sharply from LIG to LGM, and the optimal distribution range of Chamaesium changed from the northern to the southern part of Hengduan Mountain. From LGM to the present, the main optimum distribution range changed from the southern to the northern part of the Hengduan Mountains. The optimal distribution area in the future is obviously reduced compared with that in the present.Considering modeling  showed tChamaesium showed a high level of total genetic diversity based on the two datasets (Chamaesium could be attributed to the following reasons: (1) Chamaesium originated around the beginning of Paleogene. During its long evolutionary history, Chamaesium has experienced many geological events, climate changes and accumulated a lot of genetic variation. (2) Chamaesium is commonly distributed at an altitude of more than 3,000 m in the HHM, where the topography is very complex. Habitat fragmentation caused by the unique “island sky” prompts the differentiation among populations and increases the probability of genetic drift within the population, which will accumulate abundant genetic variation. (3) The reproductive mode of Chamaesium is sexual reproduction, which makes the species not only to maintain genetic stability but also enrich genetic variability. Generally speaking, a high level of genetic diversity plays a crucial role in maintaining both the biodiversity of plant groups and the dynamic balance of population (Pedicularis longensis (cpDNA: HT = 0.77), Eriophyton wallichii , Thalictrum squamiferum (cpDNA: HT = 0.973), Paraquilegia microphylla , Chionocharis hookeri , Notopterygium incisum   .Chamaesium was taken as a whole, the genetic variation among 56 populations was large  and the genetic variation within the population was relatively small , which may be related to the following reasons: (1) Field studies have shown that the seed/pollen dispersal capacity of Chamaesium is very limited, and the gene flow levels of populations based on two types of datasets are very low. Most of the inter-population differentiation may be due to limited gene flow. Similar results were found in Quercus sect. Heterobalanus, Allium section Sikkimensia, Angelica nitida, Bupleurum longiradiatum, and Thalictrum squamiferum , while the AMOVA results of C. wolffianum based on the two datasets indicated that the probability of genetic variation between populations  and within populations  was very similar. Except for C. mallaeanum and C. delavayi, the genetic variation of other species occurred mainly between populations. The reasons for this genetic structure inconsistency are speculated as follows: (1) Three haplotypes  were detected in the eight populations of C. paradoxum based on ITS data, of which N12 was the haplotype shared by the eight populations, thus reducing the genetic variation among the populations. (2) Six haplotypes (C47–C52) were detected in three populations of C. wolffianum based on cpDNA. These haplotypes were all unique and existed within and between populations of C. wolffianum, so the proportion of genetic variation was very close between and within populations.In addition, AMOVA was performed based on two datasets. The results showed  that if amiferum . (2) It Chamaesium using PopART and drew geographic distribution maps of haplotypes  haplotypes were restricted to single sites or neighboring populations. Therefore, we speculate that due to a combination of extrinsic and species-specific factors, these high-altitude populations remained isolated and confined within a mountainous area, not only during the glacial period but also during the interglacial period. This is mainly because: (1) The Himalaya-Hengduan Mountain region has complex topographic features, especially in the Hengduan Mountain region, where many high mountains above 4,000 m are separated by valleys. (2) Limited pollen/seed dispersal capacity of Chamaesium. (3) These alpine plants are not adapted to lowland conditions. Nevertheless, different haplotypes were found in a large number of populations, both based on ITS and cpDNA datasets, which still requires further investigation.Both ITS and cpDNA datasets showed a high level of haplotype diversity. We also constructed haplotype TCS networks of plotypes . The resChamaesium populations we collected were mainly distributed in four regions: A: southern Himalayas, B: Tangut region, C: eastern Himalayas, D: Hengduan Mountains region. We estimate the time of the origin of Chamaesium and the differentiation of nine species and reconstruct the ancestral distribution of Chamaesium . And the most widely accepted theory is that the uplift of the QTP resulting from the collision of the Indian plate with Eurasia began at about 55–50 Ma, which also led to dramatic changes in the topography of this region. Great habitat changes have also contributed to the formation and differentiation of species, such as Gentiana  and the dispersal event at node II  and late Oligocene . Therefore, we infer that the colonization of the newly available climate and terrain facilitated the differentiation of several species in Chamaesium. Similar results have been found in other species, such as Primulina, Cardiocrinum, and Saxifraga (During the Oligocene (36.5–23 Ma), the uplift progressed particularly northward and southward, causing the extension of the QTP. Due to its considerable size and elevation, the QTP and its adjacent mountains progressively acted as an orographic barrier to the Asian atmospheric circulation, which became a direct cause of the formation of the monsoon climate. BEAST analysis showed that  node II  tested iaxifraga .Cyclocarya paliurus, Cardiocrinum, and Rabdosia  and middle  Miocene. Therefore, we speculate that the division of Chamaesium at this time is closely related to the drought event in Central Asia and the change of habitat topography.Before the late Miocene (23–10 Ma), both the Himalayas and the Tianshan Mountains experienced considerable elevation. Combined with the global temperature drop since the Miocene, drought events occurred in Central Asia. Some studies have shown that other factors, including changes in the size of global glaciers and the loss of the Tethys Sea, contributed to the Central Asian Aridification. The dramatic reduction in rainfall has led to great changes in many biological communities, such as Rabdosia . Our molAllium section Sikkimensia, Cardiocrinum, Dolomiaea, Rheum, and Myricaria . In addition, BBM analysis showed that there were some dispersal events in C. thalictrifolium and C. spatuliferum. We infer that complex geological activity and climate change play important roles in the differentiation and dispersal of Chamaesium.In the late Miocene and later (10 Ma–present), QTP experienced further uplift and expansion, especially in the eastern margin region, which contains many mountain ranges, most notably the Hengduan biodiversity hotspot. Warm and moist air from the Indian Ocean is blocked by the Himalayas and the KailasRange, so it enters China from the Hengduan Mountains . This hayricaria . BEAST aQuaternary glaciation has always been the background event discussed in the phylogeography study. Drastic fluctuation in the climate is generated by the recurrence of glaciation, which caused large-scale migration of organisms and natural disaster avoidance for survival. At the same time, the genetic structure and distribution of the surviving organisms can change dramatically . QTP nevChamaesium have separated well during the Miocene. Our maxent modeling predicted that the simulated distribution range of Chamaesium did not change much during LIG, LGM and present  and endemic haplotypes in populations from spa2, spa13, and spa21  can be found below: H-YZ, X-LG, X-JH, and S-DZ conceived the idea. H-YZ and X-LG contributed to the collecting data, designed, and performed the experiment, and prepared the manuscript. H-YZ, X-LG, MP, X-JH, and S-DZ authored the drafts of the article. All authors contributed, made multiple revisions, and approved the final draft.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Salmo trutta L.), the most important factor in population fragmentation is disrupted river-segment connectivity. The main goal of the study was to use genetic analysis to estimate the level of gene flow among resident and migratory brown trout in potential hybridization areas located downstream of impassable barriers in one river basin in the southern Baltic Sea region. First, spawning redds were counted in the upper river basin downstream of impassable barriers. Next, samples were collected from juveniles in spawning areas located downstream of barriers and from adults downstream and upstream of barriers. Subsequently, genetic analysis was performed using a panel of 13 microsatellite loci and the Salmo trutta 5 K SNP microarray. The genetic differentiation estimated between the resident form sampled upstream of the barriers and the anadromous specimens downstream of the barriers was high and significant. Analysis revealed that gene flow occurred between the two forms in the hybridization zone investigated and that isolated resident specimens shared spawning grounds with sea trout downstream of the barriers. The brown trout population from the river system investigated was slightly, internally diversified in the area accessible to migration. Simultaneously, the isolated part of the population was very different from that in the rest of the basin. The spawning areas of the anadromous form located downstream of the barriers were in a hybridization zone and gene flow was confirmed to be unidirectional. Although they constituted a small percentage, the genotypes typical upstream of the barriers were admixed downstream of them. The lack of genotypes noted upstream of the barriers among adult anadromous individuals might indicate that migrants of upstream origin and hybrids preferred residency.Selection, genetic drift, and gene flow affect genetic variation within populations and genetic differences among populations. Both drift and selection tend to decrease variation within populations and increase differences among populations, whereas gene flow increases variation within populations but leads to populations being related. In brown trout ( Salmo trutta L.) is a Palearctic salmonid species naturally distributed in Europe from the White Sea to North Africa and from the UK to western Asia1. It is a highly polymorphic teleost fish with several life strategies. Anadromous trout, referred to as sea trout, migrate from natal rivers or streams to the sea, where they feed until reaching sexual maturity, and subsequently return to their natal rivers to spawn. In contrast, resident trout spend their entire lives in rivers or streams and often spawn in the same area in smaller tributaries upstream2. Brown trout populations are of great economic importance as they are a significant component of fishery resources, and they play an important role in angling tourism in many European countries.Brown trout . The majority of the Parsęta River and its main tributaries remain largely natural21. The fish fauna is represented by approximately 20 species with Salmo trutta L. and Cottus gobio L. being dominant22.The Parsęta River is the largest Pomeranian river with a length of 143 km and a catchment area of 3084 km3. Sea trout spawning is monitored by counting redds in the tributaries and the upper part of the main river. Female trout excavate nests in gravel substrates, deposit eggs that are fertilized externally by one or more males, then quickly cover these with gravel and begin to dig other nests. A contiguous series of these nests is called a redd23. In many salmonid rivers, long time-series of redd count data is sometimes available for salmonid populations24. In 2017 and 2018, several potential hybridization zones located downstream of barriers were selected based on redd counts at sites where hybridization between isolated and potentially differentiated residents upstream of barriers (or their descendants) and anadromous forms was anticipated  were collected from 579 individuals. Genomic DNA was extracted from fin tissue preserved in 96% ethanol with Genomic Mini Kits (A&A Biotechnology) and diluted to a concentration of 30–100 ng/µl. DNA solutions were measured in a Qubit fluorimeter (Thermo Fisher) and normalized to a concentration of 50 ng/µl to perform genotyping via an SNP microarray. Sampling details are presented in Table Sampling began in summer 2017 when 260 juveniles aged 0 + were collected at 7 locations and 65 anadromous adults were collected in the middle part of the main river . The 7 µl multiplex PCR reaction was performed with about 100 ng of template DNA, multiplex PCR master mix, and 0.2–0.6 µM of each primer. Amplifications were carried out in a TProfessional Basic Gradient thermal cycler (Biometra) with an initial heat of 95 °C for 5 min followed by 38 cycles of denaturation at 94 °C for 30 s, annealing at 55 °C for 90 s, and extension at 72 °C for 60 s. The PCR was terminated after 30 min and the final extension was at 60 °C. PCR products were genotyped in single capillary electrophoresis on an ABI Prism 3130xl genetic analyzer (Applied Biosystems) along with GeneScan 600LIZ size standard (Applied Biosystems). DNA fragments were estimated using a Peak Scanner v1.2 (Applied Biosystems).A set of 13 fluorescently labelled polymorphic microsatellite loci—S. trutta) Illumina iSelect SNP microarray that was custom developed at the Centre for Integrative Genetics (CIGENE), Norway34. In total, 144 trout were genotyped. The array was based on assays for 5509 SNP loci. The results were manually inspected using GenomeStudio® , and 1538 SNPs were excluded for one or more of the following reasons: (1) cluster patterns suggested multi-site or paralogous site variants; (2) unknown nearby variant ; (3) a SNP was monomorphic or mitochondrial. The results were transferred to a spreadsheet and the acceptable threshold of missing data across samples was determined at 5%, which led to the removal of 14 more SNPs. Another 75 loci with minor allele frequencies (MAF) of less than 0.01 were also discarded. After filtering, the data from the remaining a set of 3882 polymorphic SNPs were analyzed.Part of the samples collected in 2017 and 2018 were genotyped with a brown trout  were detected with Chi-square tests in GenAlex 6.536. HPRARE was used to calculate allelic richness  and the richness of private alleles 37. Overall, the F-statistic  was estimated by analyzing molecular variance (AMOVA) implemented in Arlequin 3.5.2.2. STRUCTURE 2.3.4 was applied to detect genetic structure and gene flow38. The Evanno method (∆K) was chosen39 to infer the best number of clusters (K) based on the rate of change in log probability among consecutive K values. Five iterations of each K were performed with 100,000 burn-ins and 200,000 Markov Chain Monte Carlo (MCMC) repetitions. Then the Clumpak program identified the optimal alignment of inferred clusters across different values of K40. Next, STRUCTURE results were used to detect hybrids. The membership coefficients (q) obtained for optimal K were averaged from five independent runs. When the value of q was higher than 0.8 for any cluster, the fish were categorized as clade members. When q values were estimated between 0.8 and 0.2, which are conservative thresholds, the fish were categorized as hybrids. Additionally, genetic heterogeneity and hybrid detection was tested with the pairwise assignment tests in GenAlex 6.536. A second hybrid detection method was used to validate and authenticate the results from the STRUCTURE algorithm. Individuals for which the values of the difference of the assessment logarithms of likelihood were closest to zero and were up to 20% of this value were assumed to be potential hybrids. Individuals with higher values of belonging to genotypes upstream of a barrier (MD) were treated as F0 migrants.The following estimates were made for the calculations based on microsatellite DNA. Observed and expected heterozygosity and the mean number of alleles  were calculated using Arlequin 3.5.2.241 was also applied to detect deviations from HWE.The same scheme was performed for calculations based on SNPs except for allelic richness and private allele estimations. Instead, the number of polymorphic loci and the mean number of alleles are shown, both of which were calculated in Arlequin 3.5.2.2. The sequential Bonferroni correctionAll methods were carried out in accordance with relevant guidelines and regulation. The study complies with the current laws of the Republic of Poland. All applicable international, national, and institutional guidelines for the care and use of animals were followed . Field protocols for the capture, handling, and release of fish were approved by the Department of Environmental Protection, Marshal’s Office of the West Pomeranian Voivodeship (Certificate no. WRiR-I.7143.29.2017.TM) and the Water Management Department in the Union of Towns and Communes of the Parsęta River Basin (Certificate no. ZDPII.04.611.15.2017.AM).2 p < 0.05) was found in specimens that originated from the upper part of the Pokrzywnica River (17PU and 18PU). Population-specific FIS values were insignificant (p < 0.05) in all stocks.The mean number of alleles in individuals from the stocks investigated ranged between 3.23 and 11.46  and all other populations across catchment . Samples collected upstream of the first barrier on this river (17MU and 18MU) were relatively closely related to samples collected at the uppermost site (18MUU) upstream of the second barrier (0.040 < FST < 0.053). Values between all other populations were largely lower (0.005 < FST < 0.067). The lowest pairwise difference was detected for samples collected in the Pokrzywnica River pair 17PD vs. 18PDL (FST = 0.05) and between adult anadromous spawners (17PAM) and samples from the Pokrzywnica River (17PD) .The highest FTo understand the spatial distribution in the brown trout population from the Parsęta River, the genetic structure of all samples collected in 2017 and 2018 was analyzed. The Bayesian estimation of genetic structure and individual membership indicated that the maximum value of ΔK was K = 2 in both 2017 (ΔK = 125.4)  and in 2018 ΔK = 493.65) . Brown trout from a tributary blocked by barriers clustered separately . All other locations were in one cluster that corresponded to the part of the river basin that was open to migration  or there were no trout upstream of the barriers (Wogra River). Juvenile trout from the hybridization zone in the Mogilica River were compared to trout upstream of the barrier and anadromous spawners from the same years. Hybrid detection based on the STRUCTURE membership coefficient showed the presence of potential hybrids in both 2017 and 2018 Figs. , 5. The The results obtained from the pairwise assignment tests in GenAlex 6.5 were generally congruent with Bayesian estimations, and, in most cases, the same fish were detected as hybrids or F0 upstream migrants. In the computations, the trout upstream of the barrier (17MU in 2017 and 18MU in 2018) were compared to juveniles from the potential hybridization zone (17MDH in 2017 and 18MDH in 2018). Among the juveniles from the 17MDH samples collected in 2017, 7 fish were marked as putative hybrids (14%) and 4 as F0 migrants (8%) upstream of the barrier  in all stocks demonstrating general panmixia across these sites.Basic statistics were calculated on the SNP genotype matrix limited to samples from five sites totalizing 144 trout . The highest percentage of variation was detected within individuals at 91.75%. Overall FIS and FIT were 0.013 and 0.082, respectively, and were significant (P < 0.05). The value of general FST was caused by the distinctness of the samples collected upstream of the barriers (18MUU and 17MU). The dissimilarity of these samples was well evidenced by FST pairwise comparisons  . Brown trout from the tributary blocked by barriers comprised one cluster (17MU and 18MUU) and anadromous spawners (17PAM) with juveniles from the hybridization zone (17MDH and 18MDH) comprised the second cluster . After computing the genetic structure, hybrid detection analysis was performed. Hybrid detection based on the STRUCTURE membership coefficient (0.8–0.2 threshold) indicated the presence of potential hybrids in both 2017 and 2018 . On the other hand, the population from the rest of the basin was rather homogeneous probably because free migration was possible. The level of gene flow was illustrated well by the results of genetic structure analysis, which showed the extent of anadromous alleles. The overall FST was much lower (0.024) when only the free migration area was considered without juveniles from hybridization zones. This level of internal population differentiation was similar to that of neighboring Pomeranian rivers or the Vistula River43 and to that in anadromous populations from larger Baltic Sea rivers such as the Umealven, Laisalven, or Luga45. Evidence of genetic differentiation among brown trout populations that are fragmented by barriers are reported by many authors48; however, such high differences among neighboring locations in one river are reported less frequently49.The overall level of genetic diversity of the brown trout from the Parsęta River basin was moderate. The results for both molecular markers were similar. The genetic differentiation between populations situated upstream of barriers and those downstream of them in the rest of the basin was high or even very high , the lowest number of alleles, the lowest level of allelic richness and, almost no private alleles. In turn, the highest values of these parameters were noted in adult anadromous individuals. This tendency was also visible in the analysis with the SNP microarray, indicating that an isolated population had, typically, about 30% fewer polymorphic loci. Most likely, the loss of genetic diversity in isolated upstream zones resulted from genetic drift and probably the small population sizes in a small area of the basin of about 65 km50. It would be interesting to know whether these individuals smoltify with the offspring of anadromous fish. The analysis of the genotypes of large, adult anadromous individuals in no way revealed the presence of a complete genotype that originated upstream of the barriers. However, taking into account their small numbers comparison with the entire population, this is not surprising. A recently published study51 indicated that in populations from the Imsa River in Norway that had been isolated by barriers for 25 years, the offspring of the resident form were less likely to choose an anadromous life strategy. This could indicate that, apart from the growth-density factor, genetic factors are also responsible for migration decisions. Hence, F0 migrants from upstream of the barriers probably preferred a resident strategy and did not migrate to the sea.The average number of hybrids in the hybridization zone was calculated at about 25% in each year. The number varied depending on the method, but the results obtained with SNP analysis were slightly higher than those based on microsatellites. This appeared to stem from differences in marker characteristics and because not all the individuals used in the microsatellite analysis were used in the SNP microarray analysis; insufficient DNA concentration in some samples from juveniles  precluded their use in the SNP analysis. However, the compatibility of the two methods was confirmed as that (1) all the fish detected with microsatellite data as hybrids in the assignment test were also detected with the Bayesian method, and (2) all the hybrids detected from the microsatellite data were also detected in microarray analysis. In addition to the hybrids, the analysis also showed there were recent upstream migrants (F0) among the samples collected downstream of the barriers. This indicated that some individuals from upstream of the barriers swim (or are carried) down and cohabit with the progeny of anadromous fish. It is possible that some of them spawned with anadromous females as precocious males48 on the effects of medieval dams on genetic divergence and the demographic history of brown trout populations in the Gudenå River suggested that the most important consequence of the dams was local adaptation and evolutionary potential where barriers imposed strong selection against anadromy. The results obtained in this study indicated a similar impact, and the isolated upstream part of the population became a reservoir of forced residency.The results of clustering indicated the consolidation of a small proportion of upstream genotypes among anadromous individuals. Thus, a certain level of gene flow occurred, which we would envisage as being a constant phenomenon. On the other hand, a certain proportion of anadromous genotypes was noted in individuals upstream of the first barrier , but they were absent upstream of the next barrier (18MUU). So what was the effect of the exchange of the gene pool with the isolated upstream population on the anadromous population? The analysis indicated that this exchange did not increase variability in the whole population, but indeed, it probably reduced it. Among individuals from the isolated part of the river basin, lower rates of polymorphism were found with a simultaneous lack of private alleles. A study from DenmarkSeveral conclusions can be drawn based on the results of this study. The brown trout population from the Parsęta River was slightly diversified internally in the area that was accessible to migration. At the same time, the isolated upstream part of the population was very different from that in the rest of the basin. The spawning areas of the anadromous form located downstream of the barriers were in a hybridization zone and unidirectional gene flow was observed. A few juveniles from blocked tributaries migrated downstream and shared nursery areas near anadromous spawning grounds, and genotypes typical of populations upstream of the barrier were admixed downstream in the population, but they constituted a small percentage. The lack of genotypes noted upstream of the barriers among anadromous adult individuals could have indicated that migrants of upstream origin and hybrids preferred residency.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.


The genetic basis of phenotypic variation across populations has not been well explained for most traits. Several factors may cause disparities, from variation in environments to divergent population genetic structure. We hypothesized that a population-level polygenic risk score (PRS) can explain phenotypic variation among geographic populations based solely on risk allele frequencies. We applied a population-specific PRS (psPRS) to 26 populations from the 1000 Genomes to four phenotypes: lactase persistence (LP), melanoma, multiple sclerosis (MS) and height. Our models assumed additive genetic architecture among the polymorphisms in the psPRSs, as is convention. Linear psPRSs explained a significant proportion of trait variance ranging from 0.32 for height in men to 0.88 for melanoma. The best models for LP and height were linear, while those for melanoma and MS were nonlinear. As not all variants in a PRS may confer similar, or even any, risk among diverse populations, we also filtered out SNPs to assess whether variance explained was improved using psPRSs with fewer SNPs. Variance explained usually improved with fewer SNPs in the psPRS and was as high as 0.99 for height in men using only 548 of the initial 4208 SNPs. That reducing SNPs improves psPRSs performance may indicate that missing heritability is partially due to complex architecture that does not mandate additivity, undiscovered variants or spurious associations in the databases. We demonstrated that PRS-based analyses can be used across diverse populations and phenotypes for population prediction and that these comparisons can identify the universal risk variants.The online version contains supplementary material available at 10.1186/s40246-021-00370-z. The prevalence of many phenotypes differs across populations. The causes of population variability, though not always well understood, can be partially due to different frequencies of common causative alleles that are shared among populations and/or variation in environmental exposure across these same populations. However, it is also possible that population-specific alleles affect prevalence. One way to increase our understanding of a trait’s genetic architecture and population differences in disease prevalences is to determine if variants associated with risk in one or a few populations can be extrapolated to the phenotypic burden for other populations across the world. For example, some variants that are extremely common in some populations are very rare in others despite having large phenotypic effects . It has Models of genetic architecture often assume that the effects of a trait’s genetic components are additive, without interaction, and highly similar across populations. The assumption of additivity disregards potential complexity, but can be implicitly tested by assessing how well a genetic model explains the genotype to phenotype relationship .One additive model used to predict phenotypic status is the polygenic risk score (PRS), that has, in general, been used to elucidate an individual’s risk of a specific phenotype , 61. A PCommonly, calculated individual PRSs also assume that a trait’s genetic architecture is additive and that neither gene–gene nor gene-environment interactions are important factors. This method has seen some success, but often fails to predict an individual’s disease status, especially at intermediate values of the PRS , possiblA polygenic risk score additively  incorporates some, or all, of the known risk associated loci for a phenotype, often identified by GWAS. On an individual level, polygenic risk scores (PRS) have been used to predict phenotypic status. This has been done with varying success. For some individuals in some diseases, a PRS can be a better predictor than monogenic markers in simple genetic diseases at the extremes of the PRS value distribution . For othPRSs have already been implemented for individual risk of many different phenotypes. For some diseases, a PRS can be used as an early indicator before other predictors present, allowing for preventative intervention. For example, individual risk of coronary artery disease can be stratified using an effect size weighted PRS . Jia et p < 5 × 10–8. This was deemed restricted-to-significant polygenic scores (rsPSs). In other studies, the PRS refers to scores that also incorporate non-significant SNPs with p-values ≥ 5 × 10–8. These were referred to as global extended polygenic scores (gePSs). A final score was proposed, process-specific polygenic scores (pPSs) that built the metric using variants grouped according to common biological processes or pathways . We. We58]. Oct-1 .Table 4PTERF1 and the AFAP1L2 loci does interact to affect risk of melanoma [Given the known impact of environmental exposure on the development of melanoma , the obsmelanoma .2 improves as we drop SNPs from the psPRS model. Indeed, a DDX39B variant interacts with allelic variants in IL7R exon 6 to increase MS risk [HLA locus to increase risk of MS [While the relationship observed with the full MS psPRS model was nonlinear, after filtering the SNPs, the resulting model was strongly linear. This might indicate that there is some genetic interaction in MS, especially given that the r MS risk . Interacsk of MS .While the relationship of the full model psPRSs to population average height shows a relatively weak, though significant, positive correlation, the result of the maximization shows a very strongly correlated relationship. Although height is highly heritable, this was not expected, given the foreknowledge of the impact of environment on height, especially in women . It may 2 sensitivity analysis is filtering out the SNPs that are not distributed as the population prevalence distribution of the phenotype in some, but maybe not all, populations. This is, in effect, similar to a previous method Evolutionary Triangulation [Fst distribution relative to disease prevalence. Our results showing that pruning the SNPs in the model improves performance may be revealing heterogenous effect sizes that may present due to context dependent effects, such as epistasis or gene X environment interactions, spurious associations, or other population-specific effects. This may explain why a filtered model is superior in some cases to a model with all associating SNPs included. This approach is, in essence, removing noisy data. psPRSs provide some explanation of population differences but are less effective when all SNPs are included. This indicates that PRSs have value but must be refined to improve prediction. Although the underlying basis for improving prediction with a reduced number of SNPs and the nonlinear relationships between psPRSs and prevalence for some of the traits are unclear, we present above what we think are reasonable hypotheses worthy of further exploration. These findings may provide a novel means to explore the true genetic architecture of complex traits.We infer from our results that the maximizing rgulation , where wp-value was not good at predicting which SNPs would end up in our pruned SNP set. As shown by our investigation of whether our model enriched for SNPs with a smaller p-value in the GWAS Catalog, we can conclude that GWAS p-value is not always the best indicator of the value of a SNP in the PRS model. This does justify, to some extent, our use of SNPs that were not genome-wide significant at 5 × 10–08 and indicates that some care should be used in determining the importance of SNPs in models based solely on significance of p-values, such as the pT method. Our results cast uncertainty on how effective this method is in capturing relevant SNPs. Plotting the genomic positions of the SNPs for each phenotype showed that LD is unlikely to be an issue as the SNPs were distributed relatively evenly across the genome [Our investigation as to whether GWAS significance was a useful threshold for inclusion indicated that e genome . In addiFor our analyses, we note that, of the SNPs we used to generate the psPRSs, the vast majority were discovered in populations of European descent, the exception being the SNPs for LP, where only two of the 11 SNPs were European determined. All SNPs associated with melanoma were discovered in European populations. For MS, two SNPs were discovered in non-European populations; 367 were from European descent populations. Out of 4208 SNPs associated with height, 3422 were discovered in European populations . Additionally, it may be beneficial to include SNPs that are less statistically significant than the ones that we did, especially considering our finding that the GWAS r2 maximization pruning at least for the more polygenic traits. That the more complex diseases show a significant difference in unpruned versus prunedallele frequency means is not unexpected because the SNP pool with less common variants may on average decrease statistical power to detect predictive alleles. Also, SNPs that are more common are most likely to be more broadly distributed.A comparison of the average allele frequency of the unpruned versus pruned sets of SNPs for each trait indicates that we are selecting more common SNPs through Our results help to identify the populations in which we are missing the most information regarding genetic foundations of trait variation. This is underlined by some of our results where the population PRSs do not match the population prevalences, i.e., where the prevalence is high or medium and the psPRS is low, as in the cases of height and LP in African populations. That using a reduced number of SNPs improves the psPRS likely indicates a certain portion of missing heritability is due to more complex architecture, i.e., genetic interaction, possibly differing by population and that there are still undiscovered variants. However, our method helps to define the areas of the genetic landscape where our knowledge of genetic architecture is relatively complete and universal, and where it is not.Additional file 1: Figure S1. LocusZoom chromosomal location plot of the full melanoma SNP set.Additional file 2: Figure S2. LocusZoom chromosomal location plot of the full multiple sclerosis SNP set.Additional file 3: Figure S3. LocusZoom chromosomal location plot of the full height SNP set.Additional file 4: Figure S4. Lactase persistence separated by super population. The data points are colored according to the super populations: AFR (orange), AMR (black), EAS (green), EUR (blue) and SAS (purple). A) Full model by super population: AFR , AMR , EAS , EUR  and SAS . B) Super populations after maximization: AFR , AMR , EAS (no data), EUR  and SAS .Additional file 5: Figure S5. Melanoma separated by super population. The data points are colored according to the super populations: AFR (orange), AMR (black), EAS (green), EUR (blue) and SAS (purple). A) Full model by super population: AFR , AMR , EAS , EUR  and SAS . B) Super populations after maximization: AFR , AMR , EAS , EUR  and SAS .Additional file 6: Figure S6. Multiple sclerosis separated by super population. The data points are colored according to the super populations: AFR (orange), AMR (black), EAS (green), EUR (blue) and SAS (purple). A) Full model super populations: AFR , AMR , EAS , EUR  and SAS . B) Super populations after maximization: AFR , AMR , EAS , EUR  and SAS  .Additional file 7: Figure S7. Male height separated by super population. The data points are colored according to the super populations: AFR (orange), AMR (black), EAS (green), EUR (blue) and SAS (purple). A) Super populations with full model: AFR , AMR , EAS , EUR  and SAS: r2 = 0.1162, p-value: 0.2812. B) super populations after performing maximization: AFR , AMR , EAS , EUR  and SAS .Additional file 8: Figure S8. Female height separated by super population. The data points are colored according to the super populations: AFR (orange), AMR (black), EAS (green), EUR (blue) and SAS (purple). A) Super populations with full model: AFR , AMR , EAS , EUR  and SAS . B) Super populations after maximization: AFR , AMR , EAS , EUR  and SAS .Additional file 9: Figure S9. Lactase persistence maximization analysis r2 values.Additional file 10: Figure S10. Melanoma maximization analysis r2 values.Additional file 11: Figure S11. Multiple sclerosis maximization analysis r2 values.Additional file 12: Figure S12. Male height maximization analysis r2 values.Additional file 13: Figure S13. Female height maximization analysis r2 values.Additional file 14: Script used for SNP pruning method.Additional file 15: Table S1. Lactase persistence full data set. SNP rs number and minor allele are included, as well as the r2 values from the sensitivity analysis. The columns headed with the 1000 Genomes population codes are the allele frequencies for each SNP in those populations. The SNPs are listed in order of removal in the sensitivity analysis. Table S2. Melanoma full data set. SNP rs number and minor allele are included, as well as the r2 values from the sensitivity analysis. The columns headed with the 1000 Genomes population codes are the allele frequencies for each SNP in those populations. The SNPs are listed in order of removal in the sensitivity analysis. Table S3. Multiple sclerosis full data set. SNP rs number and minor allele are included, as well as the r2 values from the sensitivity analysis. The columns headed with the 1000 Genomes population codes are the allele frequencies for each SNP in those populations. The SNPs are listed in order of removal in the sensitivity analysis. Table S4. Height full data set. SNP rs number and minor allele are included, as well as the r2 values from the sensitivity analysis. The columns headed with the 1000 Genomes population codes are the allele frequencies for each SNP in those populations. The SNPs are listed in order of removal in the sensitivity analysis. Table S5. 1000 Genomes superpopulations, populations and population description. Table S6. PRS values for each population, before and after maximization. Table S7. Female height r2 values from the maximization analysis. Table S8. Lactase persistence filtered data set. Table S9. Melanoma filtered allele frequency data. Table S10. Multiple sclerosis filtered allele frequency data. Table S11. Male Height filtered allele frequency data. Table S12. Female Height filtered allele frequency data.


For the East Asia-based A-InDel set  the CPD spanned from 1–1.32 × 10–23 to 1–9.42 × 10–24, and the CPE ranged from 0.999965 to 0.999997. In the Hainan Li group, the average heterozygote (He) was 0.4666 (0.2366–0.5448), and the polymorphism information content (PIC) spanned from 0.2116 to 0.3750 (mean PIC: 0.3563 ± 0.0291). In total, the CPD and CPE of 57 A-InDels were 1–1.32 × 10–23 and 0.999965, respectively. Consequently, the novel 6-dye direct and multiplex PCR-CE-based typing system could be considered as the reliable and robust tool for human identification and intercontinental population differentiation, and supplied additional information for kinship analysis in the 1KG populations and Hainan Li group.Insertion/deletion (InDel) polymorphisms, combined desirable characteristics of both short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs), are considerable potential in the fields of forensic practices and population genetics. However, most commercial InDel kits designed based on non-Asians limited extensive forensic applications in East Asian (EAS) populations. Recently, a novel 6-dye direct and multiplex PCR-CE-based typing system was designed on the basis of genome-wide EAS population data, which could amplify 60 molecular genetic markers, consisting of 57 autosomal InDels (A-InDels), 2 Y-chromosomal InDels (Y-InDels), and Amelogenin in a single PCR reaction and detect by capillary electrophoresis, simultaneously. In the present study, the DNA profiles of 279 unrelated individuals from the Hainan Li group were generated by the novel typing system. In addition, we collected two A-InDel sets to evaluate the forensic performances of the novel system in the 1,000 Genomes Project (1KG) populations and Hainan Li group. For the Universal A-InDel set  the cumulative power of discrimination (CPD) ranged from 1–1.03 × 10 Low muts (SNPs) . In addis (SNPs) . With ths (SNPs) , the dets (SNPs) .At present, the commercial and widely-used InDel kits present some issues, 1) they are not always suitable for East Asian (EAS) ancestry populations; 2) the insufficient utilization for the CE system; and 3) the time-consuming procedures for DNA extractions and/or purifications. The shortcomings for most InDel typing systems limit the promotion of forensic system effectiveness and the extension of forensic scenarios for EAS populations. Therefore, based on the underlying genome-wide data of the EAS populations from the 1,000 Genome Project (1KG) Phase 3  and the Bloodstain samples of 279 unrelated healthy Hainan Li individuals were collected after receiving their informed consents. The experiment was conducted in accordance with the guidelines of humane and ethical research of Xi’an Jiaotong University and Southern Medical University, and warranted by the Ethics Committee of Xi’an Jiaotong University (No. 2019–1231). To evaluate the universal applicability of the novel 6-dye direct and multiplex PCR-CE-based typing system, we collected the population data of global 1KG populations from five continents.2O. PCR cocktail was performed on the GeneAmp PCR System 9700 Thermal Cycler  based on the following parameters: initial denaturation at 95°C for 5 min; then 28 cycles of 94°C for 30 s, 60°C for 1 min, and 62°C for 1 min; and the final extension at 72°C for 10 min. Afterward, 1 μl of PCR production was added to the cocktail of 0.5 μl AGCU Marker SIZ-500 (AGCU ScienTech Incorporation) and 12 μl of HiDi™ formamide (Thermo Fisher Scientific). The mixture was denatured at 95°C for 3 min and then immediately chilled on ice for 3 min. Finally, the product was detected on the 3500xL Genetic Analyzer (Thermo Fisher Scientific) using 36-cm capillary arrays (Thermo Fisher Scientific) with the POP-4® Polymer (Thermo Fisher Scientific). The CE parameters were as follows: 10 s injection at 2 kV, and electrophoresis at 15 kV for 1,400 s at 60°C. Genotyping data for each sample were determined by GeneMapper® ID-X software v1.6 (Thermo Fisher Scientific). Control DNA 9948 and deionized water were used as positive and negative controls, respectively.The amplification of the novel 6-dye direct and multiplex PCR-CE-based typing system was performed in a single multiplex PCR reaction  using 10 μl of Reaction Mix , 1 μl of U-Taq Enzyme (AGCU ScienTech Incorporation), 5 μl of InDel 60 Primers (AGCU ScienTech Incorporation), and 9 μl of sdHAllele frequencies of InDels were calculated using SAS® 9.4 software . The forensic parameters, match probability (MP), power of discrimination (PD), polymorphism information content (PIC), power of exclusion (PE), typical paternity index (TPI), and heterozygote (He), were conducted by PowerStats software . The Hardy–Weinberg equilibrium (HWE) and linkage disequilibrium (LD) were evaluated by Arlequin v3.5 . The meaFST) and corresponding p-values between different populations were estimated by analysis of molecular variance (AMOVA) using raw data at Arlequin v3.5 (R (https://www.r-project.org/) based on insertion allelic frequencies. Additionally, phylogenetic relationships among different populations were depicted in Molecular Evolutionary Genetics Analysis 7.0 (MEGA 7.0) software  of LD analyses (data not shown) were not statistically significant in the 1KG populations and Hainan Li group.The direct and multiplex PCR-CE typing system  was studied based on the genome-wide data from the EAS populations. Thus, the East Asia-based A-InDel set  of the typing system would be performing well for forensic purposes in the EAS populations. The results of HWE tests for 57 A-InDels in the 1KG populations and Hainan Li group are presented in –14 (LWK) to 1–1.27 × 10–18 (CHB), and the CPE varied from 0.993634 (YRI) to 0.999908 (CHS) in the 1KG populations and Hainan Li group.Allelic frequencies of all 57 A-InDel loci for the 1 KG populations and Hainan Li group are illustrated in –18to 1.03 × 10–14 for 27 universal human populations satisfied the requirements for forensic human identification (10–15–10–14)  , which i5–10–14) , CPE for4A and –23, which demonstrated that the EAIS have good performances for individual identification and paternity test in the Hainan Li group  to 1–9.42 × 10–24 (CHB), respectively. The results revealed that the EAIS has sufficient system effectiveness for human identification and kinship analysis in the EAS populations.As shown in , C, the first, second, and third components  accounted for 24.28%, 17.01%, and 7.91% of the total variance observed within these populations, respectively. In the PCA diagrams , which can accelerate the speed of algorithm execution, improve the performance of the analysis model, and reduce the complexity of data at the same time, were conducted based on insertion allelic frequencies of 44 A-InDels, which are illustrated in diagrams , populatdiagrams , which adiagrams  were in diagrams . In brieFST and the corresponding p-values between the Hainan Li group and 26 worldwide populations from different continents are listed in FST were identified at CEU and GBR , and GWD and PEL . Phylogenetic relationships between the Hainan Li group and the other 26 reference populations are visualized in the neighbor-joining tree  and KHV , and CHB and CHS clustered together with JPT in another inner branch. They all belong to Southeast Asia from the perspective of geography. The pairwise genetic distances indicated by FST values and the phylogenic relationships based on neighbor-joining tree were consistent with the results of the abovementioned population genetic analyses (PCA and MDS), which manifested that the genetic distances of different populations were consistent with geographic scales in the present study to some degree.The results of pairwise ing tree . The EASIn general, from the perspective of population genetic analyses, compared with paternal Y-STR genetic markers , the nov–14 to 1–1.27 × 10–18, and the CPE varied from 0.993634 to 0.999908. For EAIS (57 A-InDels), the ranges of CPD and CPE values were 1–1.32 × 10–23 to 1–9.42 × 10–24, and 0.999965–0.999997, respectively. In addition, the CPD and CPE values of EAIS for the Hainan Li group were 1–1.32 × 10–23 and 0.999965, respectively. The population genetic analyses clarified the distinctions among the 1KG populations, and the Hainan Li group had close relationships with EAS populations. Consequently, the novel 6-dye direct and multiplex PCR-CE-based typing system should be considered as a reliable and robust tool for human identification and intercontinental population genetics, and supply additional information for kinship analysis in the 1KG populations and Hainan Li group.In conclusion, the direct and multiplex PCR-CE-based typing system was studied based on genome-wide EAS population data, consisting of 57 A-InDels, 2 Y- InDels, and Amelogenin. We collected two A-InDel sets (EAIS and UAIS) according to the numbers of A-InDels, which confirmed to HWE and evaluated the forensic system effectiveness for each set from the perspectives of EAS and global 1KG populations, respectively. For UAIS (44 A-InDels), the CPD ranged from 1–1.03 × 10