The Rurikids were the reigning house of Rus’, its principalities and,ultimately the Tsardom of Russia, for seven centuries: from the IX to the endof the XVI century. According to the Primary Chronicle , the main chronicle of Rus’, the Rurik dynasty was founded by theVarangian prince Rurik, invited to reign in Novgorod in 862, but still there isno direct genetic evidence of the origin of the early Rurikids. This research,for the first time, provides a genome-wide paleogenetic analysis of boneremains belonging to one of the Rurikids, Prince Dmitry Alexandrovich(?–1294), the son of the Grand Prince of Vladimir Alexander YaroslavichNevsky (1221–1263). It has been established that his Y chromosome belongsto the N1a haplogroup. Most of the modern Rurikids, according to theirgenealogies, belonging to the N1a haplogroup, have the most similar variants ofY chromosomes to each other, as well as to the Y chromosome of Prince DmitryAlexandrovich. Genome-wide data of the medieval and modern Rurikidsunequivocally indicates that they belong to the N1a haplogroup of the Ychromosome, starting at least from the XI century (since the time of PrinceYaroslav the Wise). All the other alleged Rurikids, both ancient and modern,being carriers of other haplogroups , possess high heterogeneity ofthe sequence of Y chromosomes, meaning that we cannot confirm their commonancestry. The most probable ancestors of Prince Dmitry Alexandrovich in themale line were the men who left the burial ground Bolshoy Oleny Island on thecoast of the Kola Peninsula about 3,600 years ago. The reconstruction of thegenome of Prince Dmitry Alexandrovich indicates the contribution of threeancestral components to his origin: (1) the early medieval population of theeast of Scandinavia from the island of Oland, (2) representatives of the steppenomadic peoples of the Eurasian steppes of the Iron Age or the early medievalpopulation of central Europe (steppe nomads from the territory of Hungary), and(3) the ancient East-Eurasian component. Reliable statistics were also obtainedwhen the Scandinavians were replaced with the Medieval Russian Slavicpopulations of the XI century. Thus, for the first time, we have shown thecomplex nature of interethnic interactions in the formation of the nobility ofmedieval Rus’ on the example of the ancient Rurikid. The application of paleogenetic methods when studying the genetic identity andorigin of the medieval Russian nobility is one of the most productive amongmany modern scientific approaches capable of expanding and verifying theexisting knowledge about the Medieval Russian society, its ethnic composition,and political organization. Meanwhile, the remains of the Rurikids, the mostancient reigning family whose members were the major actors in the history ofRussia in the IX–XVI centuries, remain almost untouched by paleogeneticresearches. The XI– XII centuries Rurikids’ haplogroups werereconstructed based on genetic materials of modern individuals whose genealogy,according to historical data, ascends to Rurik with different degrees ofreliability . The accThe existence of a “blind spot” in the study of the genomes of theMedieval Russian nobility in many aspects is due to the complexity ofpersonally identifying the remains of the Rurikids and other aristocraticfamilies in the necropolises of the X–XIV centuries. It is well knownthat the names of the buried were not indicated in any way on funerarystructures, sarcophagi, or tombstones until the beginning of the XV century.The location of princely burial grounds is established by annalistic messages,synodics of the XVI–XVII centuries, taking into account the latertradition of church veneration of many representatives of the princely family.Archaeological research of burial grounds in Medieval Russian churches and theanthropological study of bones are the main ways of identifying noble burials,but the conditions of necropolises do not always allow for such identification.The long use of necropolises, the practice of placing new burials over oldones, moving the revered remains during their examination in the XV–XIXcenturies, and, finally, the removal of relics in the course of theanti-religion campaigns in Soviet times led to part of the princely remains ofthe XI–XIV centuries from the burial places in the Medieval Russianchurches getting lost, or the remains not being reliably matched with certainhistorical persons whose graves were disposed in these necropolises. Oneexample of the use of bone remains in order to conduct the genetic analysis ofthe Rurikids whose membership in the princely ruling family cannot be verifiedby archaeological data is the study of the presumed remains of Prince GlebSvyatoslavich from the Chernigov Transfiguration Cathedral – a skullfound during repair works at the temple without archaeological documentation.Thus, the few burial places of the Rurikids with bone remains that can bereliably attributed to the princely family, based on the archaeological data,anthropological definitions, and a set of historical evidence acquire specialsignificance. To such trustworthy burial places belongs the burial site ofprince Dmitry Alexandrovich, found in the southern altar apse in thesouth-eastern part of the Transfiguration cathedral in Pereslavl-Zalessky(Supplementary 1).DNA isolation and genomic library preparationAll experiments with aDNA were carried out in “a clean room”– a room specially equipped for these purposes at the Federal ResearchCenter “Fundamentals of Biotechnology” of the Russian Academy ofSciences (Skryabin Institute of Bioengineering).DNA was isolated from the bone remains found in the ruined sarcophagus of theTransfiguration cathedral in Pereslavl-Zalessky. According to the historicalinformation on the burial place, archeological data and anthropologicaldefinitions, these remains belong to the son of Prince Alexander YaroslavichNevsky – Prince Dmitry Alexandrovich (Supplementary 1). The remains arecharacterized by good preservation of bone tissue, which is typical for remainsfound long after burial out of contact with the ground, suggesting a ratherlate episode of destruction of the sarcophagus. To isolate aDNA from thesamples provided for genetic analysis, we obtained three portions of bonepowder weighing 20, 50, and 80 mg from the metacarpal bone of the hand, rotula,and navicular bone of the foot, respectively. DNA was isolated by magneticseparation using buffer D – that of the Dabney method , isopropanol, 0.12 M sodium acetate, and 0.05% (v/v)Tween 20) and silica-coated magnetic beads [The resulting DNA was used to prepare libraries of single-stranded DNAfragments using the ACCEL-NGS 1S Plus DNA Library Kit according to the original protocol but with minor modifications: for the stepsproviding strand elongation and sample indexing, uracil-tolerant polymerase was used. To assess the content of endogenousDNA, test sequencing of the constructed libraries of lowcoverage DNA fragmentswas carried out, approximately 3–4 million single reads per sample (50 bplong). For the sample with the best preservation of the genetic material (highendogeneity and the presence of C > T substitutions at the 5’ ends ofDNA fragments), an additional library was prepared from the same DNA extractand pre-treated with a mixture of uracil-DNA glycosylase (UDG) and endonucleaseVIII . The mixThe MyBaits Expert Human Affinities Prime Plus Kit (Daicel Arbor Biosciences)was used for subsequent enrichment of the genome regions of interest.Biotinylated single-stranded DNA probes from the kit cover single nucleotidepolymorphisms (SNPs) from the panel “1240K capture” , 46,000 Bioinformatics analysisTo remove contaminating DNA reads from the sequencing data, we used the BBDuksoftware  includedAligned reads were filtered to ensure a minimum display quality of 20 usingsamtools view (version 1.9) .IndexinPostmortem DNA damage patterns were analyzed using the MapDamage2 software, which oK) in the range of 4–12; the run with the lowestcross-validation error was selected to plot the graph of population admixture.To determine the genetic clustering of the NEV2.3 sample among the ancientsamples known at the time of the study presented in the Allen Ancient DNAResource (AADR) panel , theADMTable 1 ofSupplementary 2. The following parameters were set by default:lsqproject: YES, numoutlieriter; 0, shrinkmode; and YES for the smartpcaanalysis. Mitochondrial haplotypes were determined using the HaploGrep program[For principal component analysis (PCA), the smartpca tool from the EIGENSOFTpackage was used. Ancient samples were projected onto the first two componentsof the modern samples. A list of samples is presentedin  program. DetermiThe HIrisPlex-S online tool , 22, 23DNA isolation and genomic library preparation from a modern human bloodsampleTo avoid the bias caused by the method of library preparation, we sequenced asample of a modern Rurikid (sample with an identifier Olgovich3), according tohis genealogy. Informed consent to participate in the genetic research wasobtained from the modern Rurikid before the start of any research procedures.Genomic DNA was isolated from 200 μl of blood using a Magen DNA blood minikit according to the protocol. Overall, 1 μg of genomic DNA was used tofragment the sample to an average DNA fragment size of 200 nucleotides on aCovaris platform. The library for subsequent whole genome sequencing wasprepared according to the instructions in the NEBNEXT DNA UltraII (NEB) kit.Sequencing was carried out on a HiSeq1500 platform . In total,115,564,028 reads of 150 nucleotides were generated. Mapping was performedusing BWA (v0.7.17) on the hg19/GRCh37 reference human genome, followed by theremoval of PCR duplicates. For further identification of SNPs, we used104,109,318 reads generated on the bcftools software.Genetic testing of the alleged modern Rurikids was carried out in thecommercial laboratory FamilyTreeDNA in Houston (USA), in the laboratory ofhuman population genetics at the Research Centre of Medical Genetics namedafter the Academician N.P. Bochkov (Moscow) and in the laboratory ofevolutionary genetics at the Research institute of medical genetics (Tomsk).The results of the genetic testing were provided by Seslavin A.N., Volkov V.G.and Maksimov N.G., the leaders of the international research project“Rurikovichi. The genome of Russian princes.” Some of the resultswere presented as bam files ,part as a list of SNPs of the Y chromosome (Olgovich2 and Olgovich5). Allparticipants donated their genetic data to be used in this project.Discovery of the remains of Prince Dmitry Alexandrovich in theTransfiguration Cathedral in Pereslavl-Zalesskyth Novgorod chronicle, the Moscow chronicle of thelate XV century, Voskresenskaya and Nikonovskaya chronicles) contain directreferences to the fact that he was buried in the Transfiguration Cathedral ofPereyaslavl-Zalessky  [The architectural and archaeological team of the Institute of Archeology of theRussian Academy of Sciences, led by V.V. Sedov, examined the alleged burialsite of Prince Dmitry Alexandrovich in Pereslavl-Zalessky. Prince DmitryAlexandrovich (?–1294) was the second son of Prince AlexanderYaroslavich, who inherited the Principality of Pereslavl after hisfather’s death 1263). At different times Prince Dmitry possessed theNovgorod and the great Vladimir principalities. He died on Volok Lamsky on hisway to Pereslavl from Tver, and he was buried in Pereslavl . At the same time, a number of chronicles(including the 43. At dif 170 p.) ,28. ThePaleogenetic analysis of bone remains from the sarcophagusTo isolate aDNA, bone samples  of an adult individual, presumably Prince Dmitry Alexandrovich(identification number Nev2), were collected. Bone powder weights were obtainedfrom the samples with the corresponding identification numbers Nev2.1, Nev2.2,and Nev2.3, from which DNA was isolated and libraries of single-strandedfragments were prepared for the initial shotgun sequencing in order to assessendogeneity. Sequencing results are presented in Supplementary 3.The Nev2.3 sample was characterized by the highest proportion of endogenous DNAand the frequency of cytosine to thymine substitutions at the 5’ ends ofaDNA fragments and was selected for further in-solution enrichment andsequencing. The frequency of C to T substitutions around the 5´ ends ofthe sequences are presented in the figure in Supplementary 4.Table 1).As a result of assessing thelevel of contamination of the sample according to such parameters as the degreeof heterozygosity of mtDNA and X chromosome(Table 1 andTable 2of Supplementary 5), no contamination of the sample was detected.As a result of genome-wide sequencing of the library of Nev2.3 aDNA fragments,more than 15 million reads were generated and 532,154 single nucleotidepolymorphisms (SNPs) were identified. The data suggested that the genomebelonged to a man, his mitochondrial haplogroup was F1b, and the Y chromosomehaplogroup was N1a (Prediction of the phenotypic traits of Prince Dmitri Alexandrovich basedon genetic dataP = 0.962), hair colorwas dark (P = 0.810) or brown (P = 0.555),and skin tone was intermediate (0.635), that is, neither light nor dark.Prediction results for the phenotypic properties of sample NEV2.3 are shown inTable 2.We have investigated the sample NEV2.3, presumably belonging to Prince DmitryAlexandrovich, son of Prince Alexander Yaroslavich Nevsky, and managed toidentify the single-nucleotide polymorphisms that allow us to predict hisphenotype with a reasonable probability: hair color, skin color, and eye color.Eye color was most likely to be brown  and threeancient descendants of Rurik , 31, 32.For the phylogenetic positioning of the Y chromosome, we used all the samplesavailable in the Allen Ancient DNA Resource (AADR) database  carryingFig. 1)originating from various noble families:Mstislavich2 (M), Mstislavich3 (M), Mstislavich4 (M), Mstislavich5 (M),Yurievich1 (Yu), Yurievich2(Yu), Yurievich3 (Yu), Olgovich2 (O), Olgovich3 (O),and Olgovich5 (O). M stands for the Mstislaviches, heirs of the branch of theprincely Monomakh family descended from Prince Mstislav Vladimirovich(1076–1132); Yu, for the Yurieviches, a branch of the Rurikids derivedfrom the great prince of Kiev Yurii Dolgorukii († 1157); and O, for theOlgoviches, the heirs of the middle line of Chernigov princes, the descendantsof Oleg “Gorislavich” Svyatoslavich († 1115). Hereinafter wewill use the symbols M, O, and Yu (these branches are reproduced inSupplementary 7) in order to attribute the samples to the genealogic branch ofRurikids. The detailed genealogy of the Rurikids is provided in Supplementary 8.As a result of phylogenetic positioning, the Y chromosome of Prince DmitryAlexandrovich was clustered together with the Y chromosomes of alleged modernRurikids , published under theidentification number VK542 . The Y cFig. 2).It turned out that the Y chromosome of the alleged prince IzyaslavIngvarevich Lutsky, although it belongs to the haplogroup R1a, does not clustertogether with the samples of contemporary representatives of the Rurikids: thesamples of Mstislavich1 (M), Olgovich1 (O), and Olgovich4 (O). Moreover,Mstislavich1 is clustered separately from Olgovich1 and Olgovich4. We shouldmention that these samples do not cluster with other “Vikings” withhaplogroup R1a whose remains were found in Gnezdovo (VK466), Staraya Ladoga, and Kurevanikha (VK160) [For carriers of the haplogroup R1a of the modern representatives of theRurikids and samples from the AADR database, an analysis was carried out usingthe same algorithm as for carriers of the haplogroup N1a belonging to the N1a haplogroup and Prince Dmitry Alexandrovich havehighly similar Y chromosomes. The aggregate of genome-wide data on the medievaland modern Rurikids unequivocally indicates that they belong to the N1ahaplogroup of the Y chromosome, starting at least from the XIth century (sincethe time of Prince Yaroslav the Wise). All the other prospective Rurikids, bothancient and modern, being carriers of other haplogroups , possesshigh heterogeneity of the sequence of Y chromosomes; we cannot, therefore,confirm their common ancestry.Search for archaeological samples with the Y chromosome sequences closestto Prince Dmitry Alexandrovichmd millennium BC, located in the Kola district of the Murmanskregion .Previously, using these samples as anexample, the gene flow of the peoples of Siberia (East Eurasian component) tothe North and East of Europe was shown [The Y chromosome of Prince Dmitry Alexandrovich, in addition to the modernRurikids, is clustered in the same branch with the ancient people from BolshoyOleny Island (Russia_Bolshoy), a burial ground dating back to the middle of the2as shown .A high Fig. 3 andin Table 1 of Supplementary 9.We studied the contribution of the genome of people from Bolshoy Oleny Islandto the formation of the medieval population living in the Baltic territories ofmodern Finland, Denmark, Sweden, and Norway. For this purpose, we used thegenomes of the “Vikings” published in the 2020 Margaryan article indicated a significant flow of East Eurasian genes into the“Vikings” with the N1a haplogroup . Theresults are presented in Table 1 ofSupplementary 10). To achieve a successful modeling, a fourthsource was added, which was represented by the eastern samples of the XiongnuIron Age (about 100 BC–50 AD) or samples of the Bolshoy Oleny Island. Itturned out that the Scandinavian populations were modeled equally effectively(p > 0.05) using both the Xiongnu[Table 2 Supplementary10). Their genetic contribution to these populations wasas follows: to Ladoga – 4.7% Xiongnu and 4.7% Bolshoy Oleny Island; andto EstoniaIA – 6.5% Xiongnu and 8.4% Bolshoy Oleny Island.The hypothesis that the people from Bolshoy Oleny Island are one of the optimalproxy populations for four-ways admixture was tested by repeating the modelingthat was performed by Margaryan et al .Some Vi Xiongnu and BolsThus, it is clear that the gene pool of medieval “Vikings,”representing a significant part of Northern Europe (island and mainland), cameinto being partly through a flow of genes from Siberia, and the male ancestorsof Prince Dmitry Alexandrovich were, with a high probability, men who left theBolshoy Oleny burial ground island on the coast of the Kola Peninsula about3,600 years ago.Analysis of mtDNA of Prince Dmitry AlexandrovichThe mitochondrial haplogroup of Prince Dmitry Alexandrovich was identified asF1b1. This haplogroup refers to the East Eurasian cluster, and itsrepresentation at different frequencies in the gene pools of most of thepreviously studied ancient and modern populations of the Baikal region andadjacent territories of Central Asia is noted , 37, 38.It is rather difficult to interpret the origin of the mitochondrial haplogroupof Prince Dmitry Alexandrovich, since for almost all historical epochs there isan increased variability and “diversity” of the mitochondrialcomposition of the female part of the groups of the ancient population. This isdue to the fact that marriages of an official and unofficial natureconcentrated representatives of completely different genetic lines in onegeographical locus. When examining the history of dynasties, it is important tokeep in mind that the attraction of women of various backgrounds as abeneficial or forced political move is a widespread phenomenon. Thus, the F1bmitochondrial group of Prince Dmitry Alexandrovich can be associated both withthe ancient northern flow from the territory of Siberia (East Eurasiancomponent)  and withResults of PCA analysisTable 1 ofSupplementary 2). A simplified version of these results is reproducedin Fig. 4 . The location of ancient and modern genomes on the PCA mapcorrelates with the geographical coordinates of the correspondingarchaeological sites (Pearson correlation 0.76). The PC1 axis mainly coincideswith the West– East direction; and the PC2, with the North–South.The genome of Prince Dmitry Alexandrovich  holds an intermediate position between the European and CentralAsian clusters. The ancient samples closest in time to Prince DmitryAlexandrovich belong to an early medieval population of Central Europe, theAvars steppe nomads of the late period; for example, the Hungary_ LateAvar (IDI16741) [The principal component analysis (PCA) was used to assess the geneticaffinities of the genome of Prince Dmitry Alexandrovich to other known ancientand modern populations. The results of the PCAanalysis for 740 samples areshown in the figure in Supplementary 11 . Archaeologists often define the Avars asCaucasoids, suggesting that only a small ruling stratum, the elite, retained apronounced Mongoloid feature. The recently published genomes of ancientindividuals of the Avar period demonstrate their genetic heterogeneity; on theprincipal component plot, the studied samples are scattered over the entirewedge from the populations of Western Eurasia to the populations of NortheastAsia . DespiteAdmixture analysisK from 6 to 12. The resultsof the Admixture analysis in simplified form with the number of ancestralpopulations equal to six (K = 6) are shownin Fig. 5.Representatives of ancient populations are shown whose genomes wereused for modeling the genetic origin of the prince.Analysis of the genetic origin of Prince Dmitry Alexandrovich was carried outusing the Admixture method. Supplementary 12 presents the results of anAdmixture analysis with parameters When decomposing the genome of Prince Dmitry Alexandrovich into ancestralcomponents, it should be noted its genetic similarity to representatives of theearly medieval population of the east of Scandinavia, the“Vikings,” which may militate in favor of the version of the“Vikings” (Scandinavian) origin of Rurik, the ancestor of theprincely family called Rus’, which the Chronicle directly indicates. Hereand below, we use the term “Vikings” in quotation marks to showthat this is a heterogeneous and complex European population in its historicalformation, united only by the way of life and habitat.K equal to6–12.Comparison of the genome of Prince Dmitry Alexandrovich with the genomes of theScandinavian populations of the “Viking” Age, including those fromthe territory of modern Russia ,indicatThus, based on PCA data, Admixture analysis, and information on mitochondrialDNA, it can be argued that Prince Dmitry Alexandrovich had a significanteastern component in his genome. This distinguishes him from the early medievalpopulation of the east of Scandinavia, the “Vikings,” and themedieval Slavic sample from Vladimir (Sungir6), but it makes him closer to theancient population of Finland, the Kola Peninsula, and the early medievalpopulation of central Europe, which includes a well-known component of steppenomads. Probably, this contribution came from both the male and female lines,which corresponds to the routes of ancient migration from Siberia to the northof Europe and migrations from Siberia in the first millennium BC – thefirst millennium AD along the Eurasian steppe corridor.Modeling the genome of Prince Dmitry Alexandrovich from the genomes ofancestral populationsAfter analyzing the results of the PCA and Admixture analysis, as well asavailable historical information, we selected populations that couldparticipate in the formation of the genome of Prince Dmitry Alexandrovich: thegenomes of the early medieval population of the east of Scandinavia;representatives of the Iranian-speaking nomads of the Eurasian steppes of theIron Age, and the population of the early Middle Ages of Central Europe, whichincludes a well-known component of the steppe nomads and samples of individualsrepresenting the ancient East Eurasian component. To assess the contribution ofthe Slavic component to the genome of Prince Dmitry Alexandrovich, samples ofthe Medieval Russian population of the XI century from the rural necropolis ofthe Shekshovo settlement in Suzdal Opolye and an individual of the XII centuryfrom the territory of modern Vladimir (Sungir6) were used, 44.SevModeling of the genome of Prince Dmitry Alexandrovich using the genomesof the “Vikings”. QpWave-analysis determined that aminimum of three ancestry sources were sufficient to model the genome of PrinceDmitry Alexandrovich (Supplementary 13). Exploring all possible combinations ofthree sourc es from the selected list was accomplished using the qpAdm tool.Results of the modeling of the genome of Prince Dmitry Alexandrovich(p greater than 0.05) are presented in Supplementary 14(Table 1).The genome of Prince Dmitry Alexandrovich can be successfullymodeled as a three-way admixture of the “Vikings,” steppe nomads,and ancient East Eurasian components. For example, one of such models includes46.6% of the early medieval population of the east of Scandinavia(Sweden_EarlyViking); 39.6% of the early medieval population of central Europe,which includes the known component of the steppe nomads (Hungary_LateAvar); and13.8% of the Russia_IA source as the third component (Iron Age sample from theterritory of the Republic Altai). Substitution of Russia_IA with Iron Agesamples from the territory of Finland (Finland_Levanluhta) or the Bolshoy OlenyIsland in the Kola district of the Murmansk region produced a reliablethree-way admixture model. These third components  have the ancient East Eurasian component incommon. According to recent studies, people of that ancestry arrived in theKola Peninsula more than 3,500 years ago from Siberia[Fig. 6A and inSupplementary 14 (Table 1). Siberia and mixeFig. 6B).The Sweden_ EarlyViking population is represented by threesamples from the village of Bode (Böde) on the island of Öland anddated around the period between the VII and VIII centuries AD . The analysis of strontium isotopes for these samples[Among “Vikings” Sweden_EarlyViking was the largest contributingpopulation to the genome of Prince Dmitry Aleksandrovich 46.6%). For other“Vikings” this proportion did not exceed 9%. The minimumcontribution is made by the Estonia_ EarlyViking population: 2.7%(6.6%. Forp= 0.64) modeled as a three-way admixture between Europeanhunter-gatherers, Neolithic farmers, and steppe pastoralists (Supplementary14 Table 2),while the Oland_Sweden_Viking group could not be modeled as amixture of these ancestral populations (p = 0.01). There arealso significant differences in the modeling of these two groups of“Vikings” using a single source group, which are Iron Agepopulations from the territory of Europe or a sample of the Medieval Russianpopulation of the XI century from the territory of modern Vladimir, Russia(Supplementary 14 Table 3).In an extended analysis, all “Viking” population groups were usedto test the single-source model for the Sungir6 sample.Those populations of “Vikings” thatprovide reliable values of F4-statistics for this model are concentrated in thenorthern part of Europe, Ireland and Iceland, while none of the southernpopulations fits into such a model. These results raised the question of therelationship of the Scandinavian population groups to the Slavs in the periodfrom the VI to the XI centuries.Our qpAdm analysis showed a significant genetic difference between theOland_Sweden_EarlyViking group and other samples from Oland Island, designatedas Oland_Sweden_Viking: the first one can be successfully.The model provides a fits onlywith early Iron Age nomads from the Tien Shan region, perhaps, due to theirgenetic profile: high proportion (70%) of the Late Bronze Age steppepastoralists, 25% of the South Siberian hunter-gatherer component, and 5% ofthe component associated with the Neolithic population of Iran[A statistically reliable genetic model of the genome of Prince DmitryAlexandrovich was also obtained by replacing the early medieval population ofCentral Europe (including a component of steppe nomads) with Iranian-speakingnomads of The Eurasian steppes in The Iron Age . The results are shownin  of Iran. This grModeling the genome of Prince Dmitry Alexandrovich using the genomes ofthe Medieval Russian Slavic populationFig. 4).We hypothesize that alternative models – replacing the population of“Vikings” with Medieval Russian Slavic populations – willlikely also provide a fit. Since the pagan Slavic tradition practiced cremationuntil the end of the X century, we used samples of the medieval Russianpopulation of the XI century from the rural necropolis of the settlement ofShekshovo9 in the Suzdal Opolye and an individual of the XI century from theterritory of modern Vladimir, Russia (Sungir6), 43.TheFig. 8 and in Supplementary14, Table 6).Replacing the Sungir6 with Shekshovo9 as a source, the proportionof the Medieval Russian Slavic population amounts to 18.7%; the nomadic IronAge steppe peoples (Kyrgyzstan_TianShan_Saka) – 78.2%; and Russia_IA_2– about 3%.In the slightly differentthree-source modeling with the Hungarian Avars, Slavic Medieval Russian andRussia IA – the proportion reached 76,2%, 10,8%, and 13,1%,correspondingly. The decrease in the Slavicproportion can be explained by the partial compensation of Slavic origin by theAvars individuals who were used for modeling. Their origin suggests 80% of thelocal East European source and only 20% of the Central Asian one (ID I16741)[We successfully modeled Prince Dmitry Alexandrovich’s ancestry as beingderived from populations related to the Sungir6 sample – 19.7%; Iron Agenomadic steppe peoples – 73.8% for Kyrgyzstan_ TianShan_Saka; and theIron Age from the territory of the present-day Altai Republic – 6.5% forRussia_IA( I16741).Thus, modeling of the genome of Prince Dmitry Alexandrovich indicates thecontribution of three ancestral sources to its origin: (1) the early medievalpopulation of the east of Scandinavia from the island of Oland, (2) the steppenomadic peoples of the Eurasian steppes of the Iron Age or the early medievalpopulation of central Europe (steppe nomads from the territory of Hungary), and(3) the East Eurasian component. Alternative models, replacing the populationof “Vikings” with the Medieval Russian Slavic populations(Shekshovo9 and Sungir6) also provide a fit.Paleogenetics and mathematical statistics provided an opportunity to discussthe origin of the Rurikids and design a reliable tool to attribute remains withdisrupted documentation to this ruling family.An analysis of the genealogical tree of the Rurikids showed that the modernindividuals of this family, who have a Y chromosome clustered with PrinceDmitry Alexandrovich’s Y chromosome, belong to three different branches– the Olgoviches, Mstislaviches, and Yuryeviches. Thus, the N1ahaplogroup of the Y chromosome characterizes all three branches of the tree,suggesting that their first common ancestor, Prince Yaroslav the Wise, was acarrier of N1a haplogroup also.The mitochondrial haplogroup of Prince Dmitry Alexandrovich was determined asF1b1, which may point to the contribution of eastern populations to his genome.This hypothesis is also supported by autosomal data (PCA and Admixture).Although the main genetic makeup of Dmitry Aleksandrovich can be attributed tothe Scandinavians/Slavic/European populations our results provide clearevidence of the input of the Eastern genetic component. This is in line withhistorical data: marriages of Russian princes with the daughters of thePolovtsian khans from the end of the XI century were a common practice thatcemented allied relations and political interaction , 47. DmiOur results raise some questions on Rurikids’ genetic history. The mostobvious of them are (i) how to explain the presence of the “Easterncomponent” in the genome of Dmitry Aleksandrovich and (ii) were there agenetic connection between Scandinavians and Slavs in the pre Rurik era? Theanswers may come after a systematic paleogenomic study of new, reliablydocumented paleoanthropological materials from the territory of Russia.


Silages may be preventive against lifestyle diseases, including obesity, diabetes mellitus, or metabolic syndrome. Fermented vegetables and legumes are characterized by pleiotropic health effects, such as probiotic or antioxidant potential. That is mainly due to the fermentation process. Despite the low viability of microorganisms in the gastrointestinal tract, their probiotic potential was confirmed. The modification of microbiota diversity caused by these food products has numerous implications. Most of them are connected to changes in the production of metabolites by bacteria, such as butyrate. Moreover, intake of fermented vegetables and legumes influences epigenetic changes, which lead to inhibition of lipogenesis and decreased appetite. Lifestyle diseases’ feature is increased inflammation; thus, foods with high antioxidant potential are recommended. Silages are characterized by having a higher bioavailable antioxidants content than fresh samples. That is due to fermentative microorganisms that produce the enzyme β-glucosidase, which releases these compounds from conjugated bonds with antinutrients. However, fermented vegetables and legumes are rich in salt or salt substitutes, such as potassium chloride. However, until today, silages intake has not been connected to the prevalence of hypertension or kidney failure. In the XXI century, epidemics of lifestyle diseases can be observed. These are disorders mostly resulting from a sedentary lifestyle and an unhealthy diet, rich in simple sugars and saturated fats. Obesity is the most common noncommunicable disease, which can lead to many dangerous conditions such as hypertension, atherosclerosis, diabetes mellitus, and dyslipidemia. Those disorders may also occur as a result of an unhealthy lifestyle without the presence of obesity. Lifestyle diseases account for more than 70% of early deaths worldwide. The undisputed major cause of this phenomenon is obesity . This coNot only obesity but also an unhealthy diet is one of the leading causes of the development of lifestyle diseases ,3. The WThe aim of this article was to summarize current knowledge concerning silages and find connections between fermented vegetables, their metabolism in the body, and related health complications. In the following sections, pickled vegetables and legumes and their influence on selected lifestyle diseases, including metabolic syndrome, obesity, non-alcoholic fatty liver disease (NAFLD), and diabetes mellitus, were described. Moreover, alternations in microbiota caused by these products and their impact on noncommunicable disorders were marked. Those products were chosen because there was a lack of information linking them to lifestyle diseases. Fermented dairy, fruits, cereals, and others were omitted. Other noncommunicable disorders, such as cancer, were omitted due to various changes in the human organism caused by a specific type of the disease . Moreover, the mechanisms by which these foods affect health were marked. The food products that were investigated are summarized in The analysis of the current state of knowledge about fermented vegetables and legumes was based on articles found in international databases: Scopus, ScienceDirect, and PubMed. The following expressions were used as search terms: “fermented vegetables”, “fermented legumes”, “microbiome”, “lifestyle diseases”, “obesity”, “metabolic syndrome”, “hypercholesterolemia”, “hypertension”, “NAFLD”, and “salt”. The most relevant literature sources were compiled in this review. Priority was given to articles published during the last five years.Bacteroides and Prevotella among obese people than in a healthy population .,86.85,86Lactiplantibacillus plantarum exhibited potential antihypertensive activity due to their high γ-aminobutyric acid content (from 6.8 to 10.6 mg/g) [Lactiplantibacillus plantarum DSM19463 produces 4.83 mmol/L of GABA in 72 h but Lacticaseibacillus paracasei NFRI 7415–60 mmol/L in 144h [Lactic acid bacteria are characterized by high efficiency in GABA synthesis ,89. This.6 mg/g) . Moreove.6 mg/g) ,91,92. T in 144h . There iSilages are considered unsuitable for hypertensive patients due to their high content of NaCl. However, studies did not find a link between increased consumption of pickled products and hypertension , which mBioactive peptides have been reported to show efficacy in modulating starch digestion and glucose absorption, such as the inhibition of α-glucosidase, α-amylase, and dipeptidyl peptidase IV (DPP-IV) . MoreoveSpecific processing methods may increase antioxidants’ bioavailability in food products. Fermentation by microorganisms is characterized by such effects.It was confirmed that fermented foods may decrease inflammation . HoweverCruciferous plants [The antioxidant potential of food products depends on the quantity and bioavailability of the compounds from the group of phenols, polyphenols, flavanols, and vitamin C. The influence of fermentation on the concentration of these substances in plants is mostly dependent on the fresh sample used, i.e., the type of vegetable . Howevers plants . These cs plants ,100. Nevs plants ,102. Mors plants . The divs plants . It is iFermentation of vegetables may have an adverse effect on the product’s composition. The most common unfavorable change after the fermentation of plant products is a decrease in vitamin C . SalinitThere are many unanswered questions about vegetables’ antioxidant capacity. First of all, without a standardized method, results from different studies should not be compared. However, this is the only way to compile findings from various food products . Antioxi2), and monosodium glutamate. However, these substances may also have adverse effects on human health. In this section, the influence of salt and salt substitutes on selected lifestyle diseases is described.Salt, or sodium chloride (NaCl), is an inherent ingredient of life. It is mainly used to improve the taste of a meal and the microbiological safety of food products. Unfortunately, along with the development of the food industry, overconsumption of NaCl can be observed. The average person is exposed to great concentrations of NaCl in almost every food product on the market’s shelves. Daily intake of NaCl should not exceed 5 g (sodium itself should not be provided above 2 g) according to FAO/WHO recommendations, which can be difficult to do in the XXI century with ubiquitous processed foods . OverconOverconsumption of salt is considered an independent risk factor for obesity ,115. IncIn XXI century, salt is ubiquitous. It is present in cereal products, dairy products, processed meat, and sweet and salty snacks, and it is used as an individual condiment by consumers. People should eat only unprocessed food in order to not exceed the recommended value. Otherwise, it is basically impossible. Fermented vegetables are another group of food products rich in salt. The influence of salty silages on human health was described in the following sections.Salt is one of the most important ingredients in fermented vegetables. Salt concentration has a significant effect on sensory, nutritional, and microbial quality and safety ,125.The sodium (Na) content of fermented food items in Korean products and its dietary intake were investigated. This macroelement was present in amounts ranging from 40 to 180 mg per serving size (100 to 450 mg NaCl). Specific amounts depended on the particular fermented product (various kimchi types). It was noted that the dietary intake of Na for an average Korean amounted to 698 mg (1745 mg of NaCl) only from fermented vegetables . Accordi2 was used, the concentration of these antioxidant compounds was the highest and amounted to 748.5 mg/L. For anthocyanins, the best composition of salty additive was NaCl with KCl (235.6 mg/L) or KCl with CaCl2 (238.4 mg/L). On the contrary, the lowest anthocyanins’ amount was when NaCl was used exclusively (205.3 mg/L) [2 may improve the antioxidant potential of fermented vegetables. However, more research is needed in order to confirm this hypothesis.The composition of salty substances has an influence on the antioxidant potential of the final product. It was proven that the use of NaCl in Şalgam fermentation as the only flavor is characterized by an average amount of phenols (686 mg/L), while in the product where a mix of NaCl and CaCl.3 mg/L) . It can Lactococcus spp. and Lactobacillus spp.) had a relatively low abundance in the final product if the NaCl concentration was higher than 5%. On the contrary, Pediococcus spp. and Weisella spp. were characterized by their best growth when NaCl concentration was 10% [0.5% of salt was a sufficient concentration to reduce sugar and accumulate more organic acid, and thus lowered pH and provided microbiological safety. That amount of NaCl allowed the best LAB growth and lactic acid production . Moreove was 10% ,128. It  was 10% . Studies was 10% ,130. If  was 10% . There iSalt is an important ingredient in silages. However, there are still too many unknowns. The influence of salt concentration on silages’ quality is analyzed mainly with regard to soy products and kimchi. Other fermented vegetables, such as roots, are omitted. That is a mistake, because these products are gaining popularity among consumers all over the world. Depending on the culture, consumption of NaCl can be various; these should be included in research, especially as they concern hypertension prevalence and the consumption of silages. There is a need to conduct studies investigating the impact of salt and its substitutes on the nutritional, sensory, and microbial quality of all fermented vegetables. Moreover, the best recipe should be created using as little salt or its substitutes as possible, while maintaining adequate nutritional and sensory properties.In Studies analyzing KCl intake from fermented vegetables also concern the prevalence of hypertension. It was noted that partial replacement of NaCl by KCl (30% KCl) is the best modification in order to decrease the risk of higher blood pressure . HoweverBeneficial influences on blood pressure and a decreased risk of hypertension related to the consumption of fermented vegetables may be connected to the bioactive peptides found in these products. Those are GABA and ACE inhibitors. However, no connection was found between peptides with ACE-inhibitory activity from fermented milk and changes in the renin-angiotensin-aldosterone system . GABA, wThere are plenty of mechanisms by which fermented vegetables may affect health. Predominantly, it is a beneficial effect. However, silages may have an adverse impact on blood pressure and other lifestyle diseases due to the high concentration of salt in these products. Nevertheless, the consumption of fermented products was not connected to the prevalence of lifestyle disorders. Moreover, it may exhibit a positive influence on the prevalence of lifestyle diseases . That isFermented vegetables are an important part of daily nutrition all over the world. After processing by microorganisms, they are characterized by various changes that differentiate them from fresh samples. These modifications mostly depend on the food product used for fermentation . Still, several patterns can be highlighted. Fermented vegetables are characterized by a higher antioxidant potential due to the greater bioavailability of phenols. These compounds are released from conjugated bonds with antinutrients such as tannins and phytates, through the enzyme β-glucosidase produced by fermentative microorganisms. Moreover, during this kind of processing, proteins’ breakdown products are formed. These are short-chain amino acids that exhibit bioactivity .One of the most popular and important features of fermented vegetables is their probiotic potential. It was repeatedly confirmed that consumption of silages affects the diversity of gut microbiota by increasing LAB in feces. There is a lack of research concerning how these changes affect patients with lifestyle disorders. However, knowing that microbiome changes occur in various lifestyle diseases, it can be assumed that modifications in microbiota diversity caused by consumption of fermented vegetables are beneficial in noncommunicable diseases. Nevertheless, research on lifestyle disorders and patients with these diseases should be conducted in order to confirm or deny these speculations.Despite all the beneficial changes caused by silages, they may also have an adverse influence on humans’ health due to their high concentrations of salt and its substitutes. Nevertheless, until today, fermented vegetables intake was not related to hypertension or kidney damage prevalence. Unfortunately, there is a lack of studies investigating patients with diagnosed diseases. Moreover, previous research has analyzed mostly Asian people and the influence of kimchi. People who live in this region of the world are characterized by a relatively healthy diet based on plant products. There is a need to perform research in western countries, where WD and processed food dominate. Additionally, other fermented vegetables have to be investigated. These studies are very important for patients. Without them, doctors are unable to give these people fine recommendations concerning the permitted amount of silages to consume.Fermented vegetables constitute a large open field of new research. There is also a need to determine the best fermentation conditions for obtaining the highest nutritional value of the final product. There are several patterns that can be recognized, such as the decrease in antinutrients during the process. However, considering the different effects of fermentation and its condition depending on the fresh sample used, more vegetables should be examined. Kimchi and fermented soy-based products are popular in Asian countries, and these foods are well known. There is an urgent need for deeper studies concerning vegetables consumed in other parts of the world.


Sus scrofa leucomystax) in Japan, we identified 15 clusters using 29 microsatellite markers, each structured within a range of approximately 200 km. This suggests that evolution was essentially driven by isolation by distance, and that the range of gene flow was limited. One cluster contained subpopulations located approximately 900 km apart, indicating the occurrence of past anthropogenic introductions. Moreover, we estimated effective migration to visualize the geographic genetic population diversity. This analysis identified six potential barriers, one of which involved large plains and mountainous areas in the Kanto region of eastern Japan. This barrier likely persisted in the two eastern clusters for an extended period, restricting migration to the neighboring areas. Overall, our study sheds light on the demographic history of wild boar in Japan, provides evidence of past anthropogenic introductions from distant areas, and highlights the importance of geographic barriers in shaping genetic diversity and population dynamics. This knowledge will be beneficial for forming informed wildlife management strategies toward the conservation of genetic integrity and ecological balance of wild boar populations in Japan.Geographical wildlife patterns reflect historical range expansion and connectivity and provide insights into wildlife population management. In our large-scale phylogeographic population analysis of wild boars ( Sus scrofa) is one of the most widely distributed terrestrial mammal species worldwide. These boars originated from islands in Southeast Asia and colonized various areas of Eurasia during several migration waves until the early Holocene2. Wild boars are classified into 16 subspecies based on their morphological characteristics and are found in Asia, Europe, and North Africa3; two subspecies exist in Japan, the Japanese wild boar (Sus scrofa leucomystax) and Ryukyu wild boar (Sus scrofa riukiuanus). These two subspecies are genetically distant and inhabit separate areas of Japan4. An ancestor of the Japanese wild boar spread from Southeast to Northeast Asia during the mid-to-late Pleistocene5 and expanded its habitats in Japan. In contrast, the Ryukyu boar migrated from Taiwan and elsewhere, unlike the Japanese boar, which migrated across the continent6.The wild boar , microsatellites (simple sequence repeats [SSRs]), and single nucleotide polymorphisms (SNPs) using whole-genome sequences, are commonly used to analyze the population structure16. However, the latest comprehensive study by Watanobe et al., which covered eastern Japan, dates back to 2003. Furthermore, as previously mentioned, the rapid expansion of the wild boar population’s range in Japan over the last four decades7 may have led to alterations or increased complexity in the population structure compared to the period of these earlier studies.Numerous extensive phylogeographic investigations of wild boars have been conducted in Japan, primarily employing the mtDNA control regionIn this study, we conducted large-scale sampling covering Japanese wild boar habitats, comprising a total of 1062 individuals, to elucidate regional patterns of genetic population structure using 30 microsatellite loci  and expected heterozygosity (HE) ranged from 0.16 to 0.78 and 0.22 to 0.92, respectively (Supplementary Data p < 0.05).Except for S0090, 29 loci in > 99% of individuals were genotyped. MICROCHECKER identified no loci with null alleles common to all populations. We identified no paired loci with a linkage disequilibrium  indicated that K = 15 was the most appropriate number of clusters for describing the dataset with the lowest Bayesian information criterion values . When the distance between individuals was greater than 200 km, they were no longer related   Fig. .Figure 3Because the estimated effective migration surface (EEMS) analysis method visualizes migration patterns and tests spatial genetic structures using genomic data and surrounding locations defined by the density of demes, we used 1000 demes, the maximum number allowed by the program. As all five Markov chain Monte Carlo runs of the program converged  and EEMS analyses, we identified 15 genetically meaningful clusters; individuals belonging to each cluster were closely distributed, except for Cluster 3. Additionally, the clusters were separated by geographical features such as forest discontinuity, plains, and sea.In this study, we gathered samples from 1062 wild boars inhabiting various regions across Japan where wild boar populations are found. We used 29 microsatellite loci to reveal the genetic population structure at high resolution and identify the potential geographic barriers preventing the migration of wild boars. We observed a significant deviation from the HWE for all loci, which is consistent with the findings of other studies23 also reported that the wild boar populations in this area were a single population. B1 was identified as a continuous gap in mountainous areas, stretching in the northern and eastern directions  located at distances of < 200 km  to evaluate the influence of specific topographical obstacles. Further studies with more samples are required to increase the analysis resolution and identify the specific obstacles acting as barriers to wild boar migration.However, caution should be exercised when interpreting the results of this study. First, even though we performed microsatellite analysis of a sufficient number of loci to identify their genetic clusters and elucidate geographical barriers to the distribution of wild boar populations, using SNPs or whole genome analysis could provide finer-scale population structures and evolutionary relationships among subpopulations than SSRs. Furthermore, the representation of genetic variation representation could have been biased based on the use of a reference SSR panel in a large-scale study. In addition, “size homoplasy”, specifically individuals with the same allele size but different ancestries, might have occurred in the microsatellite analysis. In fact, this phenomenon is quite common owing to the high mutation rate and the genotyping performed using fragment lengths that contain repetitive regions and flanking regions35. Thus, when farms have inadequate biosecurity, there is a risk that infectious diseases might also be easily transmitted. This underscores the necessity for cooperation beyond the municipal level for proper management. From the viewpoint of infectious disease control, the genetic boundaries identified in this study will be useful for planning or evaluating strategies to control the spread of infectious diseases particularly when inter-individual transmission is considered the main route of infection (i.e. in the early stages of an epidemic)38. In 2018, a CSF outbreak occurred in Japan and is still spreading, not only in domestic pigs but also in wild boars. Mitigating wild boar infection will be key to controlling CSF spread through the use of an oral vaccine or wild boar depopulation39. Additionally, the threat of the African swine fever (ASF) virus entering Japan is increasing, owing to the recent outbreak of ASF across East Asian countries. Hence, the results of the present analysis could provide valuable insights for considering measures to control the subsequent spread of infection, such as a possible ASF outbreak. Moreover, since this study was performed prior to the CSF outbreak, further validation, based on assessing changes in the population structure pre- and post-CSF outbreak, would also provide valuable information regarding the mechanisms of disease spread.In summary, our study demonstrates the population structure and migration patterns of wild boar in Japan. Generally, wild boar management in Japan is mainly conducted at the prefectural level, and their migration or population structure does not correspond to a specific administrative unit. Moreover, it is feasible for wild boar and domestic pigs to interbreed. In fact, the presence of hybrids has been reported in JapanAll wild boar sampling activities were conducted from hunted and dead wild boars as official surveillance activities by licensed hunters and the Prefectural Animal Hygiene Service Center located in each prefecture. Therefore, no sampling activity from living animals was included in this study and no ethical approval was required.7 . The PCR mixture included 10 ng genomic DNA template, 1 × final concentration of PCR buffer, 200 µM final concentration dNTP mix, 0.1 µM each of forward and reverse primers, and 1 unit Taq DNA polymerase in a final reaction volume of 20 µl. Thermal cycling parameters for multiplex PCR were set with initial denaturation at 94 °C for 9 min, followed by 40 cycles at 94 °C for 30 s, annealing at 55 °C for 30 s, and extension at 72 °C for 1 min. After the PCR, two to three multiple PCRs, ensuring that the amplicon sizes and dyes did not overlap, were mixed and diluted in 1/10 ratio, and then 1 µl of the multiplexed PCR mixture was dissolved in 9 µl HiDi-formamide . Typing was performed using capillary electrophoresis on an ABI 3130XL Genetic Analyser  with ROX400 as the internal size standard, and the allele size was determined using GeneMapper v4.0 .We analyzed 1062 blood or tissue samples (ear and tongue) from hunted and dead wild boars collected by licensed hunters throughout the habitat areas identified in the survey between 2014 and 20207 Fig. . We reco41, we excluded loci that could not be genotyped in > 1% of all samples. Potential errors of alleles were checked using the software MICROCHECKER v2.2.342, applying the Brookfield 1 index43, and retained loci with values < 0.245. We explored the linkage disequilibrium using the poppr package in R46, and excluded loci with a standard index of association (O), expected heterozygosity (HE), inbreeding coefficient (FIS), and deviation from the HWE were computed using the pegas and heirfstat packages48.As minimizing missing information yields the most reliable results49. Compared with other widely used clustering programs, such as STRUCTURE, which relies on an explicit population genetic model50, the DAPC method is a model-free method, particularly for determining deviation from the HWE in genetic clustering analysis. Additionally, the DAPC method is more robust and suitable for assessment when the genetic structure is more or less clinal and under complex dispersal scenarios, such as those represented by stepping-stone and hierarchical stepping-stone models49. The optimal number of retained PCs were determined using cross validation methods to avoid overfitting of the data and creating a artificially large separation between clusters. We identified the optimal number of genetic clusters (K) that minimizes within-cluster variance using k-means estimation, as determined using the Bayesian information criterion, and estimated the probabilities of membership to each cluster for each sample. To assess the spatial aggregation of the samples, we calculated the geographical distance between pairs of individuals within each defined cluster.We analyzed the population structure using DAPC implemented in the adegenet package of Rr-values for each class bin at a fixed distance of 20 km. We generated p-values using the permutational Mantel test (1000 permutations) and adjusted them using the Bonferroni procedure to correct the significance level for multiple comparisons.Generally, genetic and geographic distances are correlated. To evaluate the relationship between genetic and geographic distances across space, we assessed the extent of spatial clustering in the samples using the vegan package in R. We calculated shared allele proportions (Dps) and geographic distances between pairs of individuals using distHaversine. We estimated correlograms of 51. This method does not require the use of environmental variables and can therefore be evaluated independently of the hypothesis-driven resistance surface approach. In turn, a map of the inferred effective migration patterns can be used to visualize the spatial genetic structure of large, complex samples. We set the number of demes to 200, 500, and 1000, and ran five independent analyses with 1 × 106 burn-in Markov chain Monte Carlo steps and 1 × 107 iterations. We combined the results of the five runs and plotted them using the rEEMSplots2 package in R (https://github.com/dipetkov/reemsplots2). We superimposed the areas estimated as barriers with a probability of > 90% on the forest continuity polygon to assess the relationship between the natural habitat of wild boar and geographical barriers20.To detect potential geographic barriers and gene flow patterns, we used the EEMS analysis method, which assesses the bias in genetic structure due to isolation by distance and then produces a visual representation of effective migration ratesp < 0.05 (see “All statistical analyses are performed using either software packages as described or in R 4.0.2. All differences were considered statistically significant when Supplementary Figures.Supplementary Information.


Centaurea solstitialis by measuring neutral genetic differentiation (FST), and comparing it with phenotypic differentiation (PST), in a common garden experiment in individuals originating from regions representing the species distribution across five continents. Native plants were more fecund than non‐native plants, but the latter displayed considerably larger seed mass. We found indication of divergent selection for these two reproductive traits but little overall genetic differentiation between native and non‐native ranges. The native versus invasive PST–FST comparisons demonstrated that, in several invasive regions, seed mass had increased proportionally more than the genetic differentiation. Traits displayed different associations with climate variables in different regions. Both capitula numbers and seed mass were associated with winter temperature and precipitation and summer aridity in some regions. Overall, our study suggests that rapid evolution has accompanied invasive success of C. solstitialis and provides new insights into traits and their genetic bases that can contribute to fitness advantages in non‐native populations.Invasive species often possess a great capacity to adapt to novel environments in the form of spatial trait variation, as a result of varying selection regimes, genetic drift, or plasticity. We explored the geographic differentiation in several phenotypic traits related to plant growth, reproduction, and defense in the highly invasive  We then tested for associations between the phenotypic traits showing differentiation and the environmental variables at the sites of origin. In addition, we included samples from Australia – a region not considered in previous genetic and evolutionary studies of yellow starthistle, to have additional introduced populations with which to test for selection and trait differentiation.In this study, we used a combination of approaches to search for genetic signals of divergent selection in 22.1C. solstitialis populations were sampled in the wild between 2009 and 2014 spanning sites in the species ancestral range in Asia  and expanded range in Western Europe (Spain), as well as introductions in South America , North America , and south eastern Australia. Both the ancestral and expanded ranges are considered to be part of C. solstitialis native range  including (1) days to bolting; (2) days to first flower; (3) length of the largest spine; (4) final plant height; (5) capitula number; and (6) seed mass. These traits relate to plant growth, reproductive output and herbivore defense (flower spinescence) and were shown to be important to C. solstitialis invasive success  and seedlings were transplanted into 2 L square plastic pots (one plant per pot) filled with the same type of potting soil about 3 weeks after germination. Plants were kept in an insect excluded glasshouse where they experienced a Mediterranean climate  and were grown to maturity and senescence . We recorded six phenotypic traits on each individual that reached reproductive stage and survived through senescence (2.3www.worldclim.com/version2), at a spatial resolution of 2.5 arc‐minutes. The data consisted of 19 bioclimatic variables related to temperature (bio1‐–bio11) and precipitation (bio12–bio19) for different periods of a year. To quantify climatic differences among regions, we used radar plots and conducted a standardized and centered PCA  using the (prcomp) function in the package factoextra , and Mse1 (T^TAA), followed by the ligation of unique paired combinations of individual P1 and P2 barcoded adapters , a reduced representation sequencing and genotyping method that has previously been applied with success to this species , to visualize trait differences between the native and non‐native ranges of C. solstitialis by employing the prcomp function to compute principal component scores and plotted the PCA with the fviz_pca function in the factoextra package v1.0.3 . We assumed trait heritability to be 1 as is standard in these analyses  to reduce the 19 bioclimatic variables to two or three main principal components that captured at least 85% of the total variance. We then used these principal components to build generalized linear mixed models to quantify the relationship between the phenotypic traits and the environmental characteristics of the maternal field site, at the regional level (i.e. within each region). We tested the effects of the fixed variables using the dropterm function and Chisq test.2.5.3−m = 5), a maximum mismatch distance of two nucleotides between loci when processing a single individual (−M = 2), a maximum of two stacks at a single locus (−X = 2), and a mismatch distance of two nucleotides between loci (−n = 2), to account for the possibility of fixed differences at loci in individuals when creating the catalog of loci, according to Barker et al. (−m = 10), but this did not significantly change the results. The C. solstitialis individuals were grouped into six geographic regions by supplying the population map into the denovo pipeline. We obtained 526,533 variant sites (unfiltered) across 194 individuals. Fifty individuals had sequenced poorly with less than 500,000 reads each, so we decided to remove them and keep 144 individuals (24 per region × 6 regions) for all the subsequent analysis. VCFtools were used to filter the genotype data for the highest quality genotype calls by filtering out indels, including only bi‐allelic sites , including only a minimum genotyping proportion per population of 0.9, and retaining only sites with a minor allele frequency greater than 0.05 in the whole dataset. These stringent filtering steps retained high‐quality genotypes for 2138 variant sites (SNPs) across 144 individuals.Sequencing data was demultiplexed or assigned to each sample individual according to their unique paired barcode sequences, and reads were quality filtered using Stacks 2.2 , He (expected heterozygosity),AR , and FIS (inbreeding coefficient) using the function divBasic in the R package diveRsity  as Jombart,  and ade433.1χ2(1) = 9.84, p = 0.001) and seed mass (χ2(1) = 4.31, p = 0.03). Native plants produced 54% more capitula compared with their non‐native counterparts, whereas introduced individuals produced seeds that were on average 34% heavier than those of native individuals  = 11.52, p = 0.04); (2) days to first flower: (χ2(1) = 13.82, p = 0.01); (3) number of capitula: (χ2(1) = 27.87, p < 0.001); (4) length of largest spine: (χ2(1) = 11.17, p = 0.04) and (5) seed mass: (χ2(1) = 36.27, p < 0.001). Plants from California initiated bolting 7.7 days earlier than plants from Spain  (Table p < 0.005). In addition, individuals from Spain also presented 22% lighter seeds compared to individuals in Argentina (p < 0.005) (Table p > 0.05). In a global correlation analysis, several traits demonstrated moderate but significant correlations among each other  and days to first flower . Another negative correlation was observed between spine size and days to bolting  and days to first flower . Seed mass increased with the plant height  and spine size  and decreased with increasing the number of capitula  and days to first flower . Number of capitula produced was positively correlated with the plant height   Table . Individ5) Table . In the r Figure . Plant h) Figure .3.2The PCA on 19 bioclimatic variables showed distinct clustering among regions in terms of climatic space and explained 57.5% variation in the first two PCs axes Figure . Plants 3.3Of 2138 SNP markers, 163 were identified as putative outliers. Three outlier SNPs were identified by all three methods. Another three outliers were identified both by BayeScan and OutFLANK and one outlier both by BayeScan and PCAdapt. Lastly, 12 outliers were identified both by OutFlank and PCAdapt. Consequently, 19 SNPs in total were identified by more than one method. The remaining 144 loci were identified by one method only as following: BayeScan, 4 loci; OutFLANK, 18, and PCAdapt, 122 Figure .3.4e. Moreover, Spain, California and Australia demonstrated a trend towards an increase in inbreeding coefficients compared to the rest of the regions . Pairwise comparisons between Turkey and all other regions demonstrated the highest values of FST, whereas genetic differentiation between Spain and all the introduced regions was relatively low  exceeded neutral genetic differentiation at SNP loci (FST), for two traits related to reproductive success, including seed mass (PST = 0.18) and number of capitula (PST = 0.25)  as well as between California and non‐native Chile and Australia, with California showing considerably larger seeds compared with these regions. In addition, differences in seed mass were also found between Argentinean and Turkish populations  Table . These s5) Table . In the er Table . PST vans Table .4C. solstitialis and other invasive plant species more generally. We identified significant trait divergence for seed size and capitula number between the native and non‐native ranges. These traits also seem to be rapidly developing trait–climate associations across several of their non‐native ranges. We add to knowledge about the genetic diversity of this species across its global range, in particular through expanding sampling in Chile and new sampling in Australia. The genetic evidence supports Western Europe, represented by Spain, as a bridgehead source of other non‐native regions based on limited genetic divergence between Spain and all other non‐native regions studied here.Our results add to the evidence of the rapid evolutionary capacity of C. solstitialis non‐native range compared to the native range . Their study supports the hypothesis of a steady increase in seed mass as C. solstitialis expanded its distribution across the globe  found that populations in both ranges tend to occupy a similar climatic niche , resulting in the discrimination of two major genetic groups separating native populations in Turkey from the remaining regions. Compared to other short‐lived outcrossing species, these FST statistics fall towards the lower end of measures of highly homogenized populations of Lupinus texensis (FST from 0.0007 to 0.018) versus populations with an intact native structure such as Nigella degenii (FST from 0.03 to 0.07) and Clarkia xantiana ssp. xantiana (FST from 0.048 to 0.171)  and two introduced regions  found evidence of selection for increased plant size and leaf length in the non‐native range  or maternal effects under different environmental conditions used by the different studies  as well as compared to non‐native Chile and Australia and increased seed mass in non‐native Argentina compared to native Turkey. Our study focused only on seed mass of the more abundant pappus‐bearing seeds, but the fact that Hierro et al.  . Therefore, the findings and conclusions that we present here are limited to the regions sampled. More extensive sampling within existing regions would better detect variation at these scales. Despite the extra preparation, we recommend that future research should aim to measure glasshouse‐produced seed from controlled crosses to better control for potential maternal effects. Nonetheless, despite all these challenges, our significant results based on PST Bayesian 97.5% credibility intervals (Leinonen et al., Our results on trait evolution need to be regarded cautiously since we used seeds collected in the field, thus, we cannot exclude environment of origin effects on the phenotypic traits we measured. However, we argue that plant maternal effects are probably minor in C. solstitialis. Evidence consistent with adaptive rapid evolution in phenotypic traits in the introduced range exemplifies how different environmental conditions across world regions result in unique local evolutionary and ecological dynamics within each region. Our results might be relevant to the management of this invasive weed, indicating that management practices should try to prevent or reduce yellow starthistle seed production and seed bank, thereby limiting further local adaptation.Overall, our findings suggest that genetic drift is unlikely to be the sole force behind the observed biogeographic phenotypic variation in The authors declare that they have no conflict of interest.Appendix S1Click here for additional data file.


Correction: BMC Biol 20, 166 (2022)https://doi.org/10.1186/s12915-022-01367-3The original article  containsCorrection 1:“Further, the DAI had a high migration rate with HAN, and DEA had a high migration rate with M.Yunnan.West in the same region, including AHC and DAI.”The abbreviation of Achang in this sentence is incorrect. The “AHC” should be corrected to “ACH”.Correction 2:“Since our studied populations are different genetic backgrounds and WES data were less used in population genetic studies, we incorporated diverse populations from both whole-genome sequencing (WGS) and genotyping array data like references and designed different dataset panels based on the different analysis purposes.”In this sentence, “are different genetic backgrounds” should be corrected to “are from different genetic backgrounds”, and “like references” should be corrected to “as references”.Correction 3:TAS2R30 might be associated with a higher incidence of hypertension in Di-Qiang populations. Adaptive variants related to malaria and glucose metabolism were identified in the Dai population and indicate the adaptation to thalassemia and G6PD deficiency resulting from malaria resistance, while selection on PARS2 is likely related to the perception of bitterness.”“A few functional mitochondrial alterations and TAS2R30 was found in Dai population, and PARS2 was found in Jingpo population belonging to the Di-Qiang lineage. The “TAS2R30” and “PARS2” should be interchanged in this sentence.The selection signal