A), the effectivenumber of alleles (NE ) based on the locus, the rarified allelic richness (AR), the observed (HO ) and expected (HE )heterozygosity,and the fixation index (FIS) were calculated. The degree of genetic differentiation of the breeds wasassessed based on the pairwise values of FST and D. The analysis of the allelic and genetic diversity parameters ofthe local breeds showed that the maximum and minimum levels of polymorphism were observed in pigs of theUkrainian White Steppe breed  and in pigs of the Duroc breed , respectively. The highest level of genetic diversity was found in the Large White breedof the Republic of Belarus (HO = 0.707 and NE = 0.702). The minimum level of genetic diversity was found in pigs ofthe imported breeds – the Landrace  and the Duroc  – indicating ahigh selection pressure in these breeds. Based on the results of phylogenetic analysis, the genetic origin of LargeWhite pigs, the breeds, from which the Berkshire pigs originated, and the genetic detachment of the Landrace fromthe Mangalitsa breeds were revealed. The cluster analysis showed a genetic consolidation of the Black-and-White,the Berkshire, and the Mangalitsa pigs. Additionally, the imported breeds with clustering depending on the originwere characterised by a genetic structure different from that of the other breeds. The information obtained fromthese studies can serve as a guide for the management and breeding strategies of the pig breeds studied, to allowtheir better use and conservation.One of the main tasks of genetics and animal breeding is the assessment of genetic diversity and thestudy of genetic relationships between different breeds and populations using molecular genetic analysis methods.We analysed the polymorphism of microsatellites and the information on the state of genetic diversity andthe population structure of local breeds in Russia: the Kemerovo, the Berkshire, the Liven, the Mangalitsa, and theCivilian; in the Republic of Belarus: the Large White and the Black-and-White; and in Ukraine: the White Steppe, aswell as commercial breeds of imported origin of domestic reproduction: the Large White, the Landrace, and theDuroc. The materials used for this study were the tissue and DNA samples extracted from 1,194 pigs and DNA ofthe UNU “Genetic material bank of domestic and wild animal species and birds” of the L.K. Ernst Federal ResearchCenter for Animal Husbandry. Polymorphisms of 10 microsatellites  were determined according to the previously developed technique using DNAanalyser ABI3130xl. To estimate the allele pool of each population, the average number of alleles (N Currently, the industrial production of pork is based on theuse of a limited number of commercial breeds of importedpigs. These breeds are well adapted for use in intensive productionsystems, aimed at maximising the genetic potentialof productivity . Along with breeds ofimported origin, there are local breeds that are carriers ofunique forms of variability and constitute the national geneticresources of agricultural animal species. Despite their smallsize, local breeds have not lost their importance in the modernconditions of the development of animal husbandry. Havinga lower productivity compared to commercial ones, suchbreeds are characterised by a greater individual variability,constitutional strength, stress resistance and good adaptationto local climatic conditions .Nowadays, local breeds are considered irreplaceable geneticresources for the creation of geographically oriented systemsfor organic production of livestock products. Accordingto Stolpovsky (2013), due to the inclusion of transnationallivestock industries in the agriculture world, there is a dangerof a reduction of the national genetic resources, dependenceon food imports, and breeding achievements, and there is athreat of globalisation of the spread of infections and hiddengenetic defects. This implies an increasing importance notonly to study the gene pool of species of foreign origin of theanimals but also the conservation of genetic resources of thelocal breeds.According to the guidelines for the development of nationalplans for the management of farm animal genetic resources, FAO proposes an integrated global managementof farm animal genetic resources using microsatellite referencemarkers  . Todate, there are many publications that show the applied importanceof STR for characterising the genetic diversity andstructure of pig breeds for commercial  and local breeding. However, comparative studies of the entire variety of local and commercialbreeds of pigs bred in Russia have not yet been carried out.The aim of this study was to characterise the genetic diversityand population structure of eight local and three commercialpig breeds based on the analysis of microsatellites.The object of research was biological material obtained from1,194 pigs, which was stored in the UNU “Genetic materialbank of domestic and wild animal species and birds” of theL.K. Ernst Federal Research Centre for Animal Husbandry.Tissue samples (ear pinch) were used as biological material.The presented sample included eight local breeds bred inRussia: the Kemerovo , theBerkshire , the Liven , the Mangalitsa , the Civilian ;The Republic of Belarus: the Large White  andthe Black-and-White ; and Ukraine: the WhiteSteppe , as well as three commercial breeds ofimported origin of domestic reproduction, bred in the breedingand genetic centres of Oryol, Voronezh, and Lipetsk regions:the Large White , the Landrace ,and the Duroc .DNA isolation was performed using DNA-Extraction kitsfor genomic DNA isolation , in accordancewith the manufacturer’s protocol. Analysis of polymorphismsof ten microsatellites  wascarried out according to the previously described method. The results of the amplified fragmentswere visualised using fragment analysis by using GeneMapper v. 4 software .ST and D and the construction of phylogenetic trees using theNeighbor-Net algorithm were performed in GenAlEx 6.503 , SplitsTree 4.14.5 software  and R package ʻdiveRsityʼ, with subsequentvisualisation using the package ‘pophelper’ .Analysis of population genetic parameters, the degree ofgenetic differentiation based on matrices of pairwise values ofFThe genetic structure of the studied pig breeds was assessedusing principal component analysis (PCA) in R package‘аdegenet’  and was visualised using R package‘ggplot2’  and through clustering usingSTRUCTURE 2.3.4 software , usinga mixed model . For each value of K,10 iterations were performed. Structure Harvester  was used to determine the optimal number ofclusters (ΔK), according to the method proposed by Evannoet al. (2005). Source files were generated in Microsoft Excelformat and R 3.5.0 software environment (R Core Team).O) and expected (HE) heterozygosity were noted at the SW951 locus: 0.437 ± 0.067 and 0.482 ± 0.071, respectively.Locus SW857 had the maximum values of indicators:HO = 0.868 ± 0.018 and HE = 0.783 ± 0.018.In the analysis of genotypes of ten microsatellites for the entiresample, 69 alleles were detected, which exceeded the numberof alleles  detected in the molecular genetic analysisof the Chinese pig breed with a similar number of markers. Locus SW951 had the lowest number ofalleles . A similar trend for this locus was revealedin studies of pigs bred in Ukraine   and Thailand  .The greatest number of alleles (22) was found at the SO005 locus,which was consistent with the results of the studies byGuastella et al. (2010) and Šalamon et al. (2019), in whichthis locus exceeded the others in the number of alleles: 19 and17 alleles, respectively. The minimum mean values of bothobserved  showed significant deviations from the state of geneticequilibrium at individual loci in all the breeds studied. InLandrace pigs, deviations from the genetic equilibrium werefound at all loci, in Duroc and Large White pigs, at nine andeight loci, respectively. It should be noted that local breedsof pigs were inferior to commercial breeds in terms of thenumber of loci with significant deviations from the state ofgenetic equilibrium. The number of such loci varied from threein the Liven breed to seven in the Ukrainian White Steppebreed. These findings can indicate greater selection pressuresin commercial pig breeds, compared to local breeds. Of theten studied loci, highly significant deviations from the geneticequilibrium were established for the SO005 locus, accordingto Hardy–Weinberg ( p < 0.001).An interesting research result published by Kramarenko etal. (2018) showed that in pigs of the Duroc breed bred in theregions of Ukraine, eight out of twelve loci had insignificantdeviations from the state of genetic equilibrium.A = 4.875) were observed inthree breeds: CVL, MNG, and DUR, and the maximum values(more than 6.000) were observed in Landrace pigs  and in the Large White breed bred in the territoriesof our country  and in Ukraine . The values of the number of effective allelesper locus (NE) ranged from 2.119 (DUR) to 3.709 (LWUK).To assess the degree of genetic diversity of populationsand breeds, two main indicators are most often used – thelevel of polymorphism and the degree of homozygosity(heterozygosity) , whose results arepresented in Table 2. The minimum values of the averagenumber of alleles per locus (NR), which is considered as a strongindicator of the evolutionary potential of a population , and it hasbeen suggested that this indicator is of key importance for theconservation and management of the population . The minimum values of this indicator, whichwere corrected using the rarefaction method, were detected forDUR – AR = 3.821, and were maximal in LWUK – AR = 6.020.According to Greenbaum et al. (2014), a decrease in allelicdiversity may lead to a decrease in the population’s ability toadapt to future environmental changes. Moreover, accordingto Wagner (2008), there is evidence that a high allelic diversity,even of simple neutral alleles, increases the evolutionarypotential by making less genotypic space available formutational events.Another measure that characterises the level of polymorphismis allelic diversity (AO) and expected (HE ) heterozygosity. The HO in the studied breeds ofpigs ranged from 0.459 ± 0.095 for LDR to 0.707 ± 0.052 forBLW. According to some authors, a decrease in HO can leadto a decrease in the average fitness of individuals, and, therefore,this indicator has clear ecological consequences . Moderate levels of HE(above 0.5) were observed in nine pig breeds, ranging from0.545 ± 0.095 for MNG, to 0.709 ± 0.028 for LWUK. Pigsof the Duroc and the Lansrace breeds were an exception, inwhich this indicator had minimum values: 0.469 ± 0.070 and0.490 ± 0.073, respectively.To date, the most commonly used indicators of the geneticcharacteristics of populations presented in most studiesare the observed  with a variation ofpositive values of the indicator from 0.036 for LW, to 0.139for KEM. However, for these breeds, with the exception ofKEM and LWUK, the 95 % confidence interval (CI) of thefixation index included the zero value, which indicates nonsignificantdeviations in the number of heterozygotes fromthe theoretically expected, in these breeds. A slight shift inthe genetic balance towards an excess of heterozygotes wasnoted in four breeds: LIV, BLW, BBP, and DUR, in which thefixation index had negative values, which amounted to 0.060,0.002, 0.008 and 0.014, respectively.A = 6.500, NE = 3.709,AR = 6.020), and the maximum level of genetic diversity wasfound in BLW . At the same time, pigs of the Mangalitsa breed had minimum values for all theanalysed parameters: NA = 4.875, NE = 2.723, AR = 0.659,HO = 0.524, and HE = 0.545. However, in an study by Drumlet al. (2012), the values of genetic parameters characterisingthe level of genetic diversity of pigs of the Mangalitsa breed ofAustria and the National Reserve of Serbia were even lower:NA = 3.8, HO = 0.49, and HE = 0.54 and NA = 3.94, HO = 0.58,and HE = 0.54, respectively. When comparing the animals ofimported origin of domestic reproduction, the group of largewhite pigs exceeded the other two in all aspects: NA = 6.250,NE = 3.349, AR = 5.126, HO = 0.651, and HE = 0.672. Of all thestudied pig breeds, the minimum level of polymorphism andgenetic diversity was found in the Duroc breed: NA = 4.875,NE = 2.119, AR = 3.821, HO = 0.480, and HE = 0.469. A similartrend towards a relatively low level of genetic diversityof this breed was noted in the works of other authors. In acomparative analysis of local breeds of Brazil with pigs ofspecialised breeds , theminimum values of both the average number of alleles perlocus and the effective number of alleles were identified in theDuroc breed, which amounted to NA = 3.65 and NE = 3.01 . In his work, Szmatoła et al. (2016), whilestudying the genetic diversity of four commercial breeds andone local breed of pigs in Poland using five microsatellites,he revealed the lowest values of the average number of allelesper locus (NA = 4.6), the number of effective alleles per locus(NE = 2.78) and allelic diversity (AR = 4.6). However, thestudies by Kim et al. (2005), which focused on the descriptionof the genetic diversity and population structure of fourEuropean, two Korean and three Chinese pig breeds, showedthat Duroc pigs outnumbered others in these parameters. Atthe same time, local Korean pigs showed consistently lowlevels of allelic diversity and heterozygosity, while Chinesepig breeds, except for the Wuzhishan breed, had a relativelyhigh degree of genetic diversity compared to commercialand local Korean pig breeds. The lower values of populationgenetic parameters detected in our work, both in pigs of theMangalitsa and the Duroc breeds, possibly indicates a highselection pressure and a minimal or no migration of newgenes in the breeds.Among the local breeds, the maximum level of polymorphismwas observed in LWUK , as well as coordination analysis, using PCA .Despite the fact that the algorithm based on the values of ΔK revealed that the optimal number of clustersfor this sample is equal to 9, K = 9 (ΔK = 136.79), the resultsat K = 11 were also presented.The breeds LIV, BLW, and LW are characterised by amixed genetic origin. In addition, a similar genetic patternwas observed in the CVL and LWUK breeds. A clear genetic structure has been identified in black-and-white, Berkshire,and Mangalitsa pigs. The formation of several clusters ofbreeds of imported origin is explained by both different originsand different strategies of selection and the breeding workused in the enterprises.Principal component analysis, the key feature that enablesthe projection of samples onto orthogonal coordination axes,each of which consisting of a linear combination of allelicor genotypic values , revealed a genetic mixing and enabled the visualisationof a slight differentiation for most of the studied breeds.Independent cluster formed by representatives of commercialbreeds ; at the same time,local breeds formed overlapping arrays. According to Jolliffeand Cadima (2016), the lack of clear clustering does not meanthe absence of differences but may indicate the similarity ofthe largest source of variability. In addition, this analysis madeit possible to characterise the range of variability in threecomponents. The first component was responsible for mostof the genetic variability of the entire data set (4.3 %), whilethe second and third components reflected 3.7 and 2.8 % ofgenetic variability, respectively.ST andNei’s GST, and indicators of allelic differentiation, such asJost’s D and differential entropy . One of themost commonly used indicators in population genetic studiesis the standard method for estimating the FST fixation index,described by Weir and Cockerham (1984). However, whencalculating the genetic distances based on the variability ofhighly polymorphic markers, the values of the indicator maybe shifted .Therefore, we additionally performed calculations of theD index proposed by Jost (2008) that takes into account theproportion of allelic variations in populations (Table 3).To assess the degree of differentiation of populations, twomain classes of indicators that determine the quantitativestructure of populations are used: fixation indices FST = 0.037, D = 0.064. However,regarding the maximum values of the indices, differenceswere detected: the greatest genetic distance, according to theFST fixation index, is characteristic of the LDR/CVL = 0.244group, and the LW/DUR = 0.291 group, according to theD index.The greatest genetic affinity for both indicators was foundfor pigs of the Russian and Belarusian populations of the LargeWhite breed: LW/BLW FST and D, were visualised using the Neighbor-Netalgorithm and are presented in Figure 3. A separate massif wasformed by groups of root pigs of a large white breed  and adjacent branches of the Kemerovoand Liven breeds. A separate branch is a cluster of breedsin the creation of which pigs with the blood of the Berkshirebreed took part: BBP, BERK, and DUR. The Landrace andthe Mangalitsa animals are presented in a separate cluster.To visualise the genetic degree of closeness of the studiedpig breeds, the numerical matrices of the pairwise geneticdistances, FOur studies were aimed at analysing the genetic diversity andstudying the relationship between eight local pig breeds andthree pig breeds of imported origin of domestic reproduction.In general, the studied local breeds exceeded the groups ofimported pigs in both allelic and genetic diversity, whichis probably explained by the lack of a practical program ofcontinuous improvement of specific characteristics, to whichcommercial breeds are subject. On the contrary, the maximumpositive values of the fixation index were detected in localbreeds (Kemerovo and Ukrainian White Steppe), which canlead to a shift in the genetic equilibrium towards a lack ofheterozygotes. The analysis of the main components, carriedout on the basis of the allele frequencies of the studiedbreeds of pigs, made it possible to characterise the range ofvariability and trace the main patterns of population geneticdifferentiation of individuals of the studied breeds of pigs. The information obtained from these studies can guide themanagement and breeding strategies of the studied breeds inorder to better use and conserve them. At the same time, furtherstudies of pigs, both local and specialised breeds, with manymicrosatellites, using mitochondrial DNA and single nucleotidepolymorphism analysis, seem necessary. Future geneticprogress will mainly depend on the availability of sufficientgenetic variation, and a more holistic understanding of thestate of genetic diversity and the structure of pig breeds willbring tremendous benefits for the entire pig industry.The authors declare no conflict of interest.Allendorf F.W. Genetic drift and the loss of alleles versus heterozygosity.Zoo. Biol. 1986;5:181-190.Andras J., Kirk N., Harvell C. Range-wide population genetic structureof Symbiodinium associated with the Caribbean sea fan coral, Gorgoniaventalina. Mol. Ecol. 2011;20:2525-2542. DOI 10.1111/j.1365-294X.2011.05115.x.Caballero A., García-Dorado A. Allelic diversity and its implicationsfor the rate of adaptation. Genetics. 2013;195(4):1373-1384. DOI10.1534/genetics.113.158410.Charoensook R., Gatphayak K., Brenig B., Knorr C. Genetic diversityanalysis of Thai indigenous pig population using microsatellitemarkers. Asian-Australas. J. Anim. Sci. 2019;32(10):1491-1500.DOI 10.5713/ajas.18.0832.da Silva E.C., Dutra W.M., Jr., Ianella P., Filho M.A.G. de OliveiraC.J.P., de Moura Ferreira D.N., Caetano A.R., Paiva S.R. Patternsof genetic diversity of local pig populations in the State of Pernambuco.R. Braz. Zootec. 2011;40(8):1691-1699. DOI 10.1590/S1516-35982011000800010.Druml T., Salajpal K., Dikic M. Genetic diversity, population structureand subdivision of local Balkan pig breeds in Austria, Croatia, Serbiaand Bosnia-Herzegovina and its practical value in conservationprograms. Genet. Sel. Evol. 2012;44:5. DOI 10.1186/1297-9686-44-5.Earl D.A., von Holdt B.M. Structure Harvester: a website and programfor visualizing Structure output and implementing the Evannomethod.Conserv. Genet. Resour. 2012;4:359-361. DOI 10.1007/s12686-011-9548-7.Egito A.A., Paiva S.R., Albuquerque M.S., Mariante A.S., AlmeidaL.D., Castro S.R., Grattapaglia D. Microsatellite based geneticdiversity and relationships among ten Creole and commercial cattlebreeds raised in Brazil. BMC Genet. 2007;8:83-97. DOI 10.1186/1471-2156-8-83.Evanno G., Regnaut S., Goudet J. Detecting the number of clusters of individualsusing the software STRUCTURE: a simulation study. Mol.Ecol. 2005;14:2611-2620. DOI 10.1111/j.1365-294X.2005.02553.x.FAO: Measurement of Domestic Animal Diversity (MoDAD): OriginalWorking Group Report. Rome, FAO, 1998.Greenbaum G., Templeton A.R., Zarmi Y., Bar-David S. Allelic richnessfollowing population founding events – a stochastic modelingframework incorporating gene flow and genetic drift. PLoS One.2014;10(3):e0119663.Guastella A.M., Criscione A., Marletta D., Zuccaro A., Chie L., BordonaroS. Molecular characterization and genetic structure of theNero Siciliano pig breed. Genet. Mol. Biol. 2010;33(4):650-656.DOI 10.1590/S1415-47572010005000075.Hopper J.V., McCue K.F., Pratt P.D., Duchesne P., Grosholz E.D.,Hufbauer R.A. Into the weeds: matching importation history togenetic consequences and pathways in two widely used biologicalcontrol agents. Evol. Appl. 2018;12(4):1-18. DOI 10.1111/eva.12755.Huson D.H., Bryant D. Application of phylogenetic networks in evolutionarystudies. Mol. Biol. Evol. 2006;23(2):254-267. DOI 10.1093/molbev/msj030.Jolliffe I.T., Cadima J. Principal component analysis: a review andrecent developments. Philos. Trans. R. Soc. A. 2016;374(2065):20150202. DOI 10.1098/rsta.2015.0202.Jombart T. adegenet: a R package for the multivariate analysis of geneticmarkers. Bioinformatics. 2008;24:1403-1405. DOI 10.1093/bioinformatics/btn129.Jost L. GST and its relatives do not measure differentiation. Mol. Ecol.2008;17:4015-4026. DOI 10.1111/j.1365-294X.2008.03887.x.Jost L., Archer F., Flanagan S., Gaggiotti O., Hoban S., Latch E. Differentiationmeasures for conservation genetics. Evol. Appl. 2018;11(7):1139-1148. DOI 10.1111/eva.12590.Kaul R., Singh A., Vijh R.K., Tantia M.S., Beh R. Evaluation of thegenetic variability of 13 microsatellite markers in native Indian pigs.J. Genet. 2002;80:149-153. DOI 10.1007/BF02717911.Keenan K., McGinnity P., Cross T.F., Crozier W.W., Prodöhl P.A.diveRsity:an R package for the estimation and exploration of populationgenetics parameters and their associated errors. Methods Ecol.Evol. 2013;4:782-788. DOI 10.1111/2041-210X.12067.Kharzinova V.R., Karpushkina T.V., Deniskova T.E., Kostyunina O.V.,Zinovieva N.A. Populational-genetic characterization of WhiteLarge, Landrace, and Duroc pig breeds using microsatellites. Zootekhniya= Zootechnics. 2018;4:2-7. (in Russian)Kharzinova V.R., Kostyunina O.V., Zinovieva N.A. Сomparative characterizationof the allele pool of local pig breeds based on microsatellite analysis. Svinovodstvo = Pig Breeding. 2017;1:5-7. (in Russian)Khrabrova L.A., Kalinkova L.V., Zaitseva M.A. Guidelines for the Useof Horse DNA Analysis for the Assessment of Genetic ResourcesinHorse Breeding. Divovo, 2011. (in Russian)Kim T.H., Kim K.S., Choi B.H., Yoon D.H., Jang G.W., Lee K.T.,Chung H.Y., Lee H.Y., Park H.S., Lee J.W. Genetic structure ofpig breeds from Korea and China using microsatellite loci analysis.J. Anim. Sci. 2005;83:2255-2263.Kramarenko S.S., Lugovoy S.I., Kharzinova V.R., Lykhach V.Y., KramarenkoA.S., Lykhach A.V. Genetic diversity of Ukrainian local pigbreeds based on microsatellite markers. Regul. Mech. Biosyst. 2018;9(2):177-182. DOI 10.15421/021826.Meirmans P.G., Hedrick P.W. Assessing population structure: F(ST)and related measures. Mol. Ecol. Resour. 2011;11(1):5-18. DOI10.1111/j.1755-0998.2010.02927.x.Muñoz M., Bozzi R., García-Casco J., Núñez Y., Ribani A., Franci O.,García F., Škrlep M., Schiavo G., Bovo S., Utzeri V.J., CharnecaR., Martins J.M., Quintanilla R., Tibau J., Margeta V., Djurkin-Kušec I., Mercat M.J., Riquet J., Estellé J., Zimmer C., Razmaite V.,Araujo J.P., Radović Č., Savić R., Karolyi D., Gallo M., Čandek-PotokarM., Fernández A.I., Fontanesi L., Óvilo C. Genomic diversity,linkage disequilibrium and selection signatures in European localpig breeds assessed with a high density SNP chip. Sci. Rep. 2019;9:13546. DOI 10.1038/s41598-019-49830-6.Novembre J., Johnson T., Bryc K., Kutalik Z., Boyko A.R. Genes mirrorgeography within Europe. Nature. 2008;456:98-101.Patterson N., Price A.L., Reich D. Population structure and Eigenanalysis. PLoS Genet. 2006;2(12):e190. DOI 10.1371/journal.pgen.0020190.Peakall R., Smouse P.E. GenAlEx 6.5: genetic analysis in Excel.Population genetic software for teaching and research – an update.Bioinformatics. 2012;28:2537-2539. DOI 10.1093/bioinformatics/bts460.Pritchard J.K., Stephens M., Donnelly P. Inference of population structureusing multilocus genotype data. Genetics. 2000;155:945-959.pmid: 10835412.R Core Team. R: a language and environment for statistical computing.R Foundation for statistical computing. Vienna, Austria, 2012.Available at http://www.Rproject.orgReed D.H., Frankham R. Correlation between fitness and genetic diversity.Biol. Conserv. 2003;17:230-237.Šalamon D., Margeta P., Klišanić V., Menčik S., Karolyi D., Mahnet Ž.,Škorput D., Luković Z., Salajpal K. Genetic diversity of the Banijaspotted pig breed using microsatellite markers. J. Centr. Eur. Agric.2019;20:36-42.Stolpovsky Yu.A. Population genetics studies underlying preservationof domesticated animal species gene pools. Vavilovskii ZhurnalGenetiki i Selektsii = Vavilov Journal of Genetics and Breeding.2013;17(4/2):900-915. (in Russian)Szmatoła T., Ropka-Molik K., Tyra M., Piórkowska K., Żukowski K.,Oczkowicz M., Blicharski T. The genetic structure of five pig breedsmaintained in Poland. Ann. Anim. Sci. 2016;16(4):1019-1027. DOI10.1515/aoas-2016-0006.Szulkin M., Bierne N., David P. Heterozygosity-fitness correlations:a time for reappraisal. Evolution. 2010;64:1202-1217.Toro M., Fernández J., Caballero A. Molecular characterization ofbreeds and its use in conservation. Livest Sci. 2009;120:174-195.Vonholdt B.M., Stahler D.R., Smith D.W., Earl D.A., Pollinger J.P. Thegenealogy and genetic viability of reintroduced Yellowstone greywolves. Mol. Ecol. 2008;17:252-274.Vrtková I., Stehlík L., Putnová L., Kratochvílová L., Falková L. Geneticstructure in three breeds of pigs populations using microsatellitemarkers in the Czech Republic. Research in Pig Breeding. 2012;6(2):83-87.Wagner A. Robustness and evaluability: a paradox resolved. Proc. Biol.Sci. 2008;275:91-100.Weir B.S., Cockerham C.C. Estimating F-Statistics for the analysisof population structure. Evolution. 1984;38(6):1358-1370. DOI10.2307/2408641.Wickham H. ggplot2: Elegant graphics for data analysis. NY: Springer-Verlag, 2009.Yue G.H., Wang G.L. Molecular genetic analysis of the Chinese Erhualianpig breed. S. Afr. J. Anim. Sci. 2003;33(3):159-165.Zinovieva N.A., Kharzinova V.R., Sizareva E.I., Gladyr’ E.A.,Kostyunina O.V., Lugovoi S.I., Tapiha V.A., Gamko L.N., OvseenkoE.V., Shavyrina K.M., Ernst L.K. Evaluation of the contributionof different pig populations to the genetic diversity of the large whitebreed. Selskokhozyaystvennaya Biologiya = Agricultural Biology.2012;6:35-42. (in Russian)


It is known that animal populations might be affected by bottleneck phenomena, which reduce genetic variability, increase inbreeding and consequentially reduce evolutionary potential. Pedigree completeness, genetic variability and population structure were analyzed in the Sardinian Anglo-Arab horse (SAA) breed, and the analyzed results were compared with three other Italian horse populations: Maremmano, Murgese and Bardigiano (reported in bibliography). In addition, the maternal lineage was analyzed through mitochondrial DNA in order to preserve and improve the breed. The estimated parameters suggest that the SAA breed is well managed, especially when considering the female lineage because it still conserves a high number of founder mares. The role of breeding programs in the conservation of genetic diversity is essential. In fact, a breeding program might lead to fast genetic progress; however, it might also lead to a high level of inbreeding and consequently to a genetic drift. Analyzing both these parameters and the additional use of mitochondrial DNA might be an effective tool not only to verify the success of a breeding program, but also to be helpful for breeders on planning effective mating programs.e) computed by an individual increase in inbreeding (Ne_1) was 456.86, the Ne on equivalent generations (Ne_2) was 184.75, and this value slightly increased to 209.31 when computed by log-regression on equivalent generations (Ne_3). These results suggest the presence of crossbreeding and bottleneck phenomena, and they were compared with other Italian horses (reported in bibliography) to present the SAA among the Italian horse breeds scenario. Furthermore, the noteworthy mitochondrial variability reflects the use of a considerable number of founder mares; the contribution of L lineage was very important, probably because of the re-colonization from the Iberian Peninsula after the Last Glacial Maximum.The Sardinian Anglo Arab (SAA) is a famous horse breed in Italy, with a significant historical background in the island of Sardinia. The aim of the study is to perform an evaluation of genetic variability in SAA using pedigree and mitochondrial data. In the current population, pedigree completeness was observed to be close to 100%, while the inbreeding coefficient and the average relatedness were lower than 3%. The ratio of effective founders/numbers of ancestors was 3.68 for the whole pedigree. The effective population size (N The Sardinian Anglo-Arab (SAA) is a horse breed that is mainly used for equestrian sport. It originated by crossing indigenous mares with old Arabian lines  and Thoroughbred stallions, and the use of these two breeds was very different depending on the historical period. Arabian and Thoroughbred stallions were alternatively used in order to achieve different goals, such as racing, jumping and other equestrian disciplines . This maIn 1874, the Ozieri Army Remount Station was established to supply mounts for the cavalry units of the Italian Army. Indigenous Sardinian mares were crossed with oriental-bred stallions to produce resistant and fast horses that were suitable for the cavalry ,4.In the early 1900s, Captain Grattarola, the commander of the Ozieri Remount Station, continued the work by crossing the best available indigenous mares with Purosangue Orientale stallions which had been purchased directly from Bedouin tribes. Later on, Arab and Thoroughbred stallions started to be bred with the mares of mixed Sardinian and oriental ancestry .In 1967, when the studbook of this breed was officially established, blood percentages were defined and accurately settled. The breed was officially named “Sardinian Anglo-Arab,” and it was described as, “The product of the selection and crossbreeding between Arabian lines, Thoroughbred, Anglo-Arab Thoroughbred, Sardinian Anglo-Arab stallions and indigenous mares with a percentage of Arabian blood not less than 25% and not more than 75%.” From that moment, the main breeding goal became sport ,5.SAA reference populations were analyzed and compared with three other Italian horse breeds, Bardigiano, Maremmano and Murgese, which were studied with the same statistical approach. This horse breed lives in the isolated geographic context of Sardinia  which could have had a key role in shaping its population and genetic structure. Recent studies based on horse mitogenomes have defined a complex and detailed phylogeny, describing the mutational motifs of 18 different haplogroups  and the In this study, three reference populations (REF) were considered: population REF1 consists of all the 43,624 horses registered in the studbook kept by the Italian Ministry of Agricultural Policies; REF2 consists of 22,956 horses, including only Anglo-Arab, Sardinian Anglo-Arab, Thoroughbred, Arab and Sardinian Arab ; REF3 includes only living horses, for a total of 13,266 animals .The results obtained from these populations were compared with those observed in the reference populations of the following three other Italian breeds: Bardigiano with one reference population (REF_B) , MaremmaThe Agricultural Research Agency of Sardinia—Istituto Incremento Ippico della Sardegna (AGRIS) provided a pedigree file that included 43,624 horses  born between 1853 and 2013 which were registered in the studbook kept by the Italian Ministry of Agricultural Policies.a only in REF1, genetic conservation index—GCI) and population structure  were computed using “ENDOG v 4.8” software [t-test computed by the R Core Team [e value, because of the small number of individuals and the overlapping generations [e_1 , Ne_2  and Ne_3 . The ratio Ne_1/2 was also calculated because it is a parameter that can explain whether bottleneck phenomena occurs in the population. As reported in Giontella et al. [e_1/2 should be close to the value of fe.Several parameters related to pedigree completeness , genetic variability , Maremmano (n = 90) and Murgese (n = 32). The mtDNA control region was amplified by PCR  using forward 5′-AAACCAGAAAAGGGGGAAAA-3′ and reverse 5′-TGGCGAATAGCTTTGTTGTG-3′ oligonucleotides. PCR reactions contained 1× Buffer GoTaq , 2.5 mM of each dNTPs, 0.3 μM of each primer, 0.03 U/μL of GoTaq DNA polymerase , 30 ng of genomic DNA and H2O to a final volume of 25 μL. PCR amplification was carried out as follows: 95 °C for 2 min, followed by 35 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 120 s and then 72 °C for 5 min. The PCR fragment was purified using exonuclease I and alkaline phosphatase , and subsequently Sanger sequenced with the primer forward 5′-CACCCAAAGCTGAAATTCTA-3′. Sequences were aligned to the Equine Reference Sequence  for the haplotype annotation, and their evolutionary relationships were evaluated through a median-joining tree built using “Network” software v.10.0” [To provide a comprehensive overview of the SAA maternal genetic variability, we phylogenetically analyzed 56 mitochondrial DNA (mtDNA) control-region sequences  and comp v.10.0” .The completeness of pedigree information is presented in The plot represents the number of ancestral generations that can be traced.From the first to the fifth generation, the REF1, containing the ancestor of the breed, showed a completeness of 86.44%, 79.87%, 75.82%, 72.88%, 68.53%; in the REF2 population these values increased up to 92.65%, 89.72%, 88.33%, 87.28%, 85.30%; as expected, in the REF3 population, the values reached 100%: 99.96%, 99.01%, 99.01%, 98.96%, 98.72%.The mean of the maximum number of traced generations reported in the studbook (REF1) was equal to 11.15; the mean of the fully traced generations was 3.62; and the mean of the equivalent complete generations was 5.96. In the REF2, these values increased to 13.09, 7.30 and 4.00, respectively; in the living animals (REF3), these numbers increased to 15.40, 8.66 and 5.43.The average inbreeding coefficient (F) in the REF1 population was 0.012 and its trend increased by year of birth up to a maximum of 0.023 in 2010 .p ≤ 0.05) according to t-test. The average relationship coefficient (AR) in REF1 was 1.72% and it increased up to 2.48% in REF2 and to 2.83% in REF3. The effective numbers of founders (fe) of the investigated SAA populations were 287 in REF1, 190 in REF2 and 157 in REF3; nevertheless, only 316 individuals explained the 50% of the genetic variability in REF1, 26% in REF2 and 64% in REF3. The effective ancestors (fa) numbers were 78 in REF1, 48 in REF2 and 38 in REF3; the ratios of fe/fa were 3.68 in REF1, 3.96 in REF2 and 4.13 in REF3.The F was 0.017 in REF2 and 0.021 in REF3. The difference between REF1 and REF3 F values was statistically significant  was 26.17 ± 19.58, with a minimum of 0.90, a maximum of 119.22 and a coefficient of variation of 70%.e_1 was the following: 456.86 in REF1, 196.32 in REF2 and 187.02 in REF3; Ne_2 was 184.75 in REF1, 127.86 in REF2 and 88.34 in REF3; Ne_3 values in the three REF_SAA were, respectively, 209.31, 128.52 and 87.36.The effective size of SAA population Ne_1/2 in REF1 was 228.43 and decreased to 98.16 and to 93.51 in REF2 and in REF3, respectively. The mean of the generation interval (GI) estimated on the whole pedigree (REF1) was 10.98 ± 5.10 years with a range of 8.42 ± 5.19 (mother–daughter) to 12.24 ± 5.01 (father–son).The ratio NThe analysis of 610 base pairs of the 56 SAA control-region sequences showed a high haplotype diversity (Hd = 0.969), with a total of 31 haplotypes and 49 polymorphic sites detected (S).All the SAA haplotypes were classified into eight haplogroups , with a prevalence of L haplogroup (41%), followed by G and I haplogroups  .Network analysis of all mitochondrial control regions belonging to SAA, Bardigiano, Maremmano and Murgese showed the phylogenetic relationships between haplotypes and the haplogroup clustering .A total of 18 unique SAA haplotypes could be observed: one from haplogroup A, two from B, two from E, one from G, two from I, seven from L and three from M. Among the remaining haplotypes, six were shared only with Maremmano , while seven haplotypes were also shared with Bardigiano and/or Murgese breeds . The hapThe structure and the pedigree of the SAA were compared with those of three other Italian breeds in order to underline possible differences and similarities .The comparison with the REF3_MA , which rThe quality of a pedigree depends on its length and depth: the more a pedigree is complete in terms of the number of generations traced, the more it is possible to determine the real number of ancestors. Therefore, the degree of pedigree information can affect the average inbreeding coefficient. All parameters that describe the probability gene origin are affected by the pedigree depth; in fact, a suitable estimation of genetic variability widely depends on available and accessible pedigree information measured by pedigree completeness .Regarding the mean of the maximum number of traced generations and the mean of fully traced generations , REF3_MAConsidering the same parameters, the REF_M showed quite similar values  to those of REF4_MA, and therefore, lower ones than those of SAA.The average inbreeding coefficient in SAA was lower than those of the other breeds .The F in REF2 was 0.017; in REF3_MA it was 0.029 ; in REF_p ≤ 0.05) between all the horses registered in the studbook (REF1) and the living horses (REF3), which provides breeders and owners with important information because inbreeding control is fundamental for mating plans. It must be highlighted that the results for this parameter allow us to verify the efficiency of a breeding program. Nevertheless, SAA shows limited values for F and for AR which reveal the effort to control inbreeding through applied breeding strategies. This demonstrates that it is possible to maintain genetic diversity in SAA by mating animals with lower AR. Maremmano showed a rather low F value, but a higher AR value, which might reveal a possible lack of suitable mating in its breeding program. Conversely, Murgese showed the smallest population in terms of consistency, despite reaching values similar to the other breeds for both F and AR parameters. It is possible to observe that Murgese shows AR and F values lower than those of Bardigiano, which has a larger population, and the AR value is closer to that of the REF2 SAA. This was unexpected, so we can assume that better strategies have been applied to the Murgese breeding programs in order to avoid inbreeding.A comparison between the AR coefficients was carried out in SAA reference populations and in the populations registered in the official studbooks of the other breeds. AR is a very useful parameter since it allows breeders to preserve the genetic pool. Horses with lowest AR, used as stallions and mares, can reduce the inbreeding, balancing the gene contributions of the founders in the population, and consequently the genetic variability. In REF2, the AR was lower than those in REF3_MA and REF4_MA, which were respectively 5.52% and 6.13%, although it was similar to the REF_M (2.45%); in the REF_B, this parameter increased to 11.0% ,10,11. Te), which represents the number of equally contributing founders that are expected to produce the same genetic diversity of the population, was higher in SAA than in Maremmano . In addition, the effective number of ancestors (fa), which is the minimum number of ancestors, not necessarily founders, and which explains the whole genetic diversity of a population, was higher than those estimated in REF3_MA (30) and in REF4_MA (25) by Giontella et al. [e/fa in SAA was higher than that observed in the Maremmano population .The effective number of founders  [e/fa ratio.The ratio f/fa > 1) . In thisThe GCI computes the genetic contributions of all the identified founders; for this reason, it has been assumed that the animals, which have a good number of genes percentage from a large number of founders, get higher values of GCI . In the The population structure parameters in reference populations are reported in e is a key parameter for planning strategies to define and protect endangered animals. In other words, a population with low Ne has a higher probability of extinction [e_1 was equal to 68.10 in the REF3_MA and 71.20 in the REF4_MA; Ne_2 was 42.00 in REF3_MA and 36.60 in REF4_MA; Ne_3 was 42.20 and 36.90 in the same reference populations , thereby confirming that bottleneck phenomena may occur. The same scenario occurred in REF3_MA and REF4_MA (34 and 36 respectively), as referred to by Giontella et al. [As previously reported, the ratio Ne_a et al. .The SAA medium generation interval (GI) is 10.98 ± 5.10 years and it is similar to the GI in the Maremmano  wherein The analysis of SAA mitochondrial control region sequences suggests a high level of haplotype diversity, which is comparable to other continental Italian breeds and essentially identical to those from Maremmano and Murgese; nevertheless, it is higher than other typically Sardinian horse breeds (Giara and Sarcidano) , althougThe Sardinian Anglo-Arab horse represents one of the most important horse breeds in Italy, which has a significant historical background in Sardinia island. It originated from the crossbreeding between local mares and Arabian and Thoroughbred stallions, which strikingly affected its composition and demographic and genetic patterns. The comparison between the SAA population with three other Italian horse breeds  allowed us to define pedigree completeness, genetic variability and population structure of this Sardinian breed.The estimated parameters on SAA suggest that the breed is well managed, especially considering the female lineage, since it still conserves a high number of founder mares. The pedigree completeness of living animals is close to 100%; this value is similar to that observed in the Maremmano breed that includes a smaller number of individuals.The number of founders and ancestors is quite high, and their ratio shows that this population could be affected by a certain bottleneck effect and genetic drift, which are also confirmed by the effective numbers that are higher than those observed in other Italian horse breeds, thereby requiring special care to avoid these events. On the other hand, the average relationship coefficient is rather low, and the GCI is high in comparison with the values estimated on other Italian breeds; therefore, a good genetic variability can be presumed in SAA.This finding could also be observed from a female perspective, wherein the mitochondrial analysis shows a high haplotype diversity, indicating the lack of bottleneck phenomena along the maternal lines and the important contribution of L lineage into the mtDNA gene pool, which is probably due to the re-colonization from the Iberian Peninsula after the Last Glacial Maximum.


The occurrence of refugia beyond the arctic treeline and genetic adaptation therein play a crucial role of largely unknown effect size. While refugia have potential for rapidly colonizing the tundra under global warming, the taxa may be maladapted to the new environmental conditions. Understanding the genetic composition and age of refugia is thus crucial for predicting any migration response.2 area in northcentral Siberia on the southern Taimyr Peninsula by applying an assay of 16 nuclear microsatellite markers. For estimating the age of clonal individuals, we counted tree rings at sections along branches to establish a lateral growth rate that was then combined with geographic distance.Here, we genotype 194 larch individuals from an ~1.8 kmFindings reveal that the predominant reproduction type is clonal (58.76%) by short distance spreading of ramets. One outlier of clones 1 km apart could have been dispersed by reindeer. In clonal groups and within individuals, we find that somatic mutations accumulate with geographic distance. Clonal groups of two or more individuals are observed. Clonal age estimates regularly suggest individuals as old as 2,200 years, which coincides with a major environmental change that forced a treeline retreat in the region.We conclude that individuals with clonal growth mode were naturally selected as it lowers the likely risk of extinction under a harsh environment. We discuss this legacy from the past that might now be a maladaptation and hinder expansion under currently strongly increasing temperatures. The study features findings of clonal growth and potentially millennial‐long survival in refugia beyond the treeline in northern Siberia for the first time. We conclude that surviving individuals were naturally selected for the ability to spread clonally as it lowers the likely risk of extinction under a harsh environment. We discuss this legacy from the past that might now be a maladaptation and could restrict treeline migration under currently strongly increasing temperatures. However, for tree species the timing and rate depends mainly on dispersal potential and survival of the offspring under changed conditions  can grow adventitious roots from their branches if they touch the ground  or asexual clonal growth by genotyping with highly polymorphic nuclear microsatellite markers. Second, this information combined with the lateral growth rate and distances between individuals of clonal origin allows us to estimate since when genotypes have survived in the refugia. Based on the evidence, we discuss the consequences of historical genotypes for the current and future treeline in response to a changing climate.22.1Larix gmelinii (Rupr.) Rupr. growing at favorable sites .Following our sampling scheme for genetic analyses, a total of 227 needle samples from 194 individuals were taken from across an area of ~1.8 km2 Figure . At thes2.2Larix gmelinii (Rupr.) Rupr.) growing on the southern Taimyr Peninsula, a set of 16 markers have been selected, multiplexed in three groups, and applied to infer the population dynamics at the treeline ecotone  DNA extraction, (b) multiplexed PCR, and (c) microsatellite fragment length determination, follow our laboratory protocol established and applied earlier .PCRs of 10 μl were set up using the Multiplex PCR Master Kit (QIAGEN), containing ~1–5 ng template DNA, primer  and MasterMix  with a final 3 mM MgClPCRs were performed using the following reaction profile: 15‐min activation of polymerase and initial DNA melting at 94°C, followed by 20 cycles of 30 s 94°C, 90 s 60°C, and 15 s 72°C, a further 10 repeats for 30 s 94°C, 45 s 53°C, and 15 s 72°C and a final elongation step at 60°C for 45 min to minimize stutter of bands introduced by incomplete A‐tailing of products .https://doi.org/10.1594/PANGAEA.920680).Fragment length estimation was performed by SourceBiosciences , with 1 μl of each PCR product added to 9 μl HiDi formamide/size standard mixture LIZ 600®, dye set DS‐33, and run on an ABI 3730xl DNA Analyser. The resulting raw data were processed in Geneious  using the Microsatellite plug‐in , and the first automatically scored peaks were checked and adjusted manually. Subsequent binning of scored alleles was calculated in R using a locally weighted method and previously provided scripts Kruse, . FragmenFor samples of the same individual that showed differently binned alleles  was determined with the “genotype_curve”‐function from R package “poppr” by permuting loci for 100 times and counting the number of multilocus genotypes observed.F‐statistics  are calculated after Weir and Cockerham  with the “hw.test”‐function from the R package “pegas” version 0.13 Paradis, , which wockerham  via the IA) using the “ia”‐function from R package “poppr.” Following Goss et al. .Three separate groups  were selected in the field that had 2, 3, and 11 lateral branches. Each branch was cut into 10 cm‐long pieces, dried, and transported to our laboratory facilities in Potsdam for further analyses. From each branch, the oldest part and the middle piece were selected, but for the individual with the longest branch , we decided to analyze two further samples at distances half‐way between the oldest part and the middle and between the middle and the tip. Samples were sanded using sandpaper and decreasing the grain size until the surface was even and the tree rings clearly visible under a binocular microscope. All samples were scanned at 4,800 dpi on a flatbed scanner and tree rings recorded in the software Coorecorder (Cybis Elektronik & Data AB). The number of tree rings counted corresponds to the age of the respective section of the branch. The length along the branch between two age determinations was divided by the age difference to get the growth rate in cm/yr. All information is stored in PANGAEA  was significant for the data set with all individuals (p < .001), became less significant in the clone‐censored subset (p = .0849), and insignificant for the clone and somatic mutation‐censored subset . Excluding one outlier of 1,015 m, the largest distance is 35 m . The differentiation was least between the populations TY02‐VI and TY02‐VII compared to the larger region TY02 (FST ≈ 0.01).Pairwise genetic distances are small (44.1Larix Mill.) can produce adventitious roots from branches  and when growing clonally ; Data curation (lead); Formal analysis (lead); Funding acquisition (supporting); Investigation ; Methodology (lead); Project administration ; Validation (lead); Visualization (lead); Writing‐original draft (lead); Writing‐review & editing . Aleksey I. Kolmogorov: Conceptualization (supporting); Investigation ; Writing‐review & editing . Luidmila A. Pestryakova: Data curation (supporting); Funding acquisition (supporting); Investigation (supporting); Project administration ; Writing‐review & editing . Ulrike Herzschuh: Conceptualization ; Formal analysis (supporting); Funding acquisition (lead); Investigation ; Methodology (supporting); Project administration ; Validation (supporting); Visualization (supporting); Writing‐original draft (supporting); Writing‐review & editing .Appendix S1‐S5Click here for additional data file.


Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan's NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined  outperformed models where land cover was defined more coarsely . Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.Landscape genetic analyses allow detection of fine‐scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population  out performed those models that defined land cover using a binary classification scheme  Our analyses demonstrate that different land cover data sets classifications can result in disparate landscape–genetic associations. Land cover resistance surfaces are typically constructed using publicly available land cover data layers , Global Land Cover (GLC) database, and NOAA’s Coastal Change Analysis Program (CCAP) , USA. Our three main objectives were (a) define fine‐scale spatial genetic structure, (b) identify landscape features that impede or facilitate gene flow, and (c) compare genetic and landscape associations estimated using different land cover datasets to understand the influence land cover dataset selection and reclassification have on landscape genetic analyses.22.1N = 365) were collected from harvested bears registered at hunter check stations during the 2006 fall harvest (September‐October) in Michigan's Northern Lower Peninsula  is a section. Harvest locations reported to a section were georeferenced using the section centroid and converted to UTM coordinates.Tissue samples  following manufacturer protocols. DNA was quantified using a Nanodrop spectrophotometer (Thermo Scientific) and diluted to a 20 ng/μl working concentration. We used PCR to amplify 12 variable microsatellite loci including: G10X, G10L, G10D, G10B, G10M , observed heterozygosity (Ho), and expected heterozygosity (He) over all loci, and probability of identity (PID) using program GenAlEx  calculated using program GenAlEx  was calculated by correlating pairwise geographical and inter‐individual genetic distance. We used program STUCTURE v. 2.3.4  without geographic location information and the posterior probability of each individual belonging to each cluster. We performed 10 independent runs of K = 1–10 using simulations of 2 x 106 iterations after a burn‐in period of 5 x 105 Markov Chain Monte Carlo (MCMC) iterations. The most likely number of clusters was determined by the log likelihood of K and the posterior probability of K (P(K|X)) as determined by the method described in Pritchard et al. (K (Dk)  Michigan Department of Natural Resources (MDNR) Resource Information Systems (MIRIS) Integrated Forest Monitoring, Assessment, and Prescription Project  table that quantifies the agreement  and disagreement  of land cover classification between the CCAP and IFMAP land cover datasets. In our matrix, the columns of Table Following reclassification of our land cover datasets using Carter et al. , we geneWe also modeled resistance as a function of land cover according to two alternative hypotheses, using a binary classification of land cover: (a) bear habitat  or non‐habitat , and (b) forested areas  or non‐forested areas . Major rivers and all roads  were included as predictor variables as potential physical barriers to dispersal. We proposed 18 alternative landscape hypotheses Table .c values . We randomly selected 340 samples to meet the pre‐condition of the program, which only allows one genetic sample per surface pixel. We used the proportion of shared alleles between all pairs of individuals  following Burnham and Anderson  gradient  interstate 75 only, (b) roads only, and (c) land cover only (for both IFMAP and CCAP datasets) classified according to the habitat suitability model from Carter et al. (i = 0.55) indicating rivers + roads, in the absence of land cover, were good predictors of gene flow  when compared to roads alone . Interestingly, when adding land cover classified based on the HSI to roads resulted in poorer performing models despite land cover only models performing better than the distance and null models . One possible explanation is that land cover may not be a good predictor of gene flow overall. Thus, adding land cover to the model may have increased noise.Comparisons between competing landscape models parameterized using single and multiple variables found that inclusion of more than a single landscape predictor improved model performance when rivers and roads were included. This result indicates that rivers may act as a semipermeable barrier to black bear gene flow; however, inclusion of rivers only slightly improved model performance  or fine  land coNone declared.Hope Draheim: Conceptualization (lead); Data curation (lead); Formal analysis (lead); Funding acquisition (supporting); Investigation (lead); Methodology (lead); Project administration (lead); Writing‐original draft (lead); Writing‐review & editing (lead). Jennifer A Moore: Conceptualization ; Data curation ; Writing‐review & editing (supporting). Scott Winterstein: Funding acquisition ; Writing‐review & editing . Kim Scribner: Conceptualization (supporting); Funding acquisition (lead); Supervision ; Writing‐review & editing (supporting).Supplementary MaterialClick here for additional data file.


Picea abies) is a widely distributed conifer species across the northern hemisphere, and it consequently plays a major role in European forestry. Here, we use whole-genome resequencing data from 35 individuals to perform population genomic analyses in P. abies in an effort to understand what drives genome-wide patterns of variation in this species. Despite having a very wide geographic distribution and an corresponding enormous current population size, our analyses find that genetic diversity of P. abies is low across a number of populations . To assess the reasons for the low levels of genetic diversity, we infer the demographic history of the species and find that it is characterized by several reoccurring bottlenecks with concomitant decreases in effective population size can, at least partly, provide an explanation for low polymorphism we observe in P. abies. Further analyses suggest that recurrent natural selection, both purifying and positive selection, can also contribute to the loss of genetic diversity in Norway spruce by reducing genetic diversity at linked sites. Finally, the overall low mutation rates seen in conifers can also help explain the low genetic diversity maintained in Norway spruce.Under the neutral theory, species with larger effective population size are expected to harbor higher genetic diversity. However, across a wide variety of organisms, the range of genetic diversity is orders of magnitude more narrow than the range of effective population size. This observation has become known as Lewontin’s paradox and although aspects of this phenomenon have been extensively studied, the underlying causes for the paradox remain unclear. Norway spruce ( Lynx lynx to 8.01% in Ciona savignyi, but that this is likely many orders of magnitude smaller than corresponding variation in expected census population sizes among the different organisms. This long-standing issue in evolutionary biology is known as “Lewontin’s paradox”  is one of the most important ecological and economical conifers and has wide natural distribution that range from the west coast of Norway to the Ural mountains and across the Alps, Carpathians, and the Balkans in Central-Europe  covers ∼60% of the total genome size (12 Gb out of the 20 Gb), consists of >10 million scaffolds, and contains 70,736 annotated gene models of which 66,632 are at least partially validated by supporting evidence (ESTs or UniProt proteins)  . Here, wP. abies) spanning the natural distribution of the species, extending from Russia and Finland in the east to Sweden and Norway in the west and Belarus, Poland, and Romania in the south . Scaffolds shorter than 1 kb were removed because they generally contain few variable sites and hence do not produce reliable estimates of recombination rates (see below). All BAM files were then subsetted using the reduced reference genome with the “view” module in samtools v1.5  to eliminate artifacts introduced due to DNA amplification by PCR, which could potentially lead to excessively high read depth in some regions. If not addressed, this could bias the number of variants called and may substantially influence the accuracy of the variant detection  duplicates were marked in all data subsets using MarkDuplicates in Picard v2.0.1 . 2) In order to reduce the impact from reads mapping to repetitive regions that may be collapsed in the reference genome, we recoded genotype calls with a depth outside the range 6–30 and a GQ < 15 to missing data and filtered each SNP for being variable with an overall average depth in the range of 8–20 and a “maximum missing” value of 0.8 (max 20% missing data). 3) We removed all SNPs that displayed a P value for excess of heterozygosity <0.05, as SNPs called in collapsed regions in the assembly, likely containing nonunique regions in the genome, should show excess heterozygosities as they are based on reads that are derived from different genomic regions. For more detailed information on the genotype calling pipeline and the hard filtering criteria, please refer to In order to remove potential false positives in raw variant calls, we performed hard filtering for each of the 20 genomic subset VCF files using the following steps: 1) We only included biallelic SNPs positioned >5 bp away from an indel and where the SNP quality parameters fulfilled GATK recommendations for hard filtering , alternately described as a subspecies  on kinship matrix. We first calculated the pairwise relatedness matrix for all SNPs following the method described in bspecies  or sistebspecies  of P. abπ) (We used ANGSD v0.921  to estimπ)  per scafP. abies v1.0  using a custom-made python script . Separate VCF files were then generated for the different site categories from the original VCF files based on the genomic bed files using vcftools. The diversity at different functional sites was then calculated using tP per scaffold for both nonsynonymous (0-fold) and synonymous (4-fold) sites. The values were scaled by dividing by the number of sites available in each functional category per scaffold. Similarly, for the intronic and intergenic sites, we calculated tP per scaffold and scaled the values by dividing by the number of intronic or intergenic sites per scaffold. When computing the global SFS for all SNPs and for intergenic variants in the Sweden-Norway population, we randomly downsampled the number of SNPs to 50% to reduce the computational cost and enable ANGSD to perform analyses.We repeated the analyses to estimate pairwise nucleotide diversity at different categories of functional sites: 4-fold synonymous sites, 0-fold nonsynonymous sites, introns, and intergenic sites. Bed files for these different genomic regions were generated from the genome annotation for https://github.com/wk8910/bio_tools/tree/master/01.dadi_fsc, last accessed January 29, 2020) to minimize potential bias arising when determining ancestral allelic states using a custom-made perl script. Eight plausible demographic models were then tested. All models included three current-day populations that were derived from a common ancestral population that experienced an ancient population bottleneck. Following the bottleneck, the Central-Europe population was assumed to diverge from the ancestral population, followed by the divergence between of the Finnish and Swedish/Norwegian populations  of each population with different genomic features. The population-scaled recombination rate (ρ = 4 Ner) was estimated per scaffold for each population using a Bayesian reversible-jump Markov Chain Monte Carlo scheme under the crossing-over model as implemented in LDhat v2.2 . GC density was calculated at the scaffold level as the fraction of bases where the reference sequence (P. abies genome v1.0) was a G or a C using bedtools. Divergence was calculated between each population of Norway spruce with outgroup at 4-fold, 0-fold, intronic, and intergenic sites by measuring the number of fixed differences per scaffold. Pairwise correlations between the variables of interest were calculated using Spearman’s rank correlations and by linear regression in Rstudio.In order to understand the factors determining variation in genetic diversity across the hat v2.2 . We perfP. abies using Illumina HiSeq 2000/X with a mean sequence coverage of 18.1× per individual  explaining 6.2% and 4.7% of the total variation in the SNP data, respectively  fell outside all P.abies individuals and the dendrogram showed that the remaining P. abies samples clustered into four groups corresponding to the same groupings inferred in the PCA  B and C. P. abies B, althouP. abies  and haveP. abies . We haveectively C. Howevees e.g., . The ind samples  online:  the PCA A. In the the PCA A. Both tP. abies is subdivided into three main domains, the Alpine domain, the Carpathian domain, and the Fennoscandian  domain (P. obovata that is more apparent in the eastern (Finland) population  domain . Our poppulation .π) across the P.abies genome was relatively low  of 0.0047 (SD = 0.0034) based on 11 loci screened in 10 natural P. abies populations from across Scandinavia. More recently, P. abies to show that nucleotide diversity at 4-fold synonymous sites ranged from 0.0072 to 0.0079 in the three domains, whereas 0-fold nucleotide diversity ranged from 0.0027 to 0.0032. Our results, based on whole-genome data, produce slightly different estimates of nucleotide diversity but are of the same order of magnitude as earlier estimates. The reasons for the slight discrepancies between the different studies are likely explained by differences in sampling design and sequencing strategies.Earlier studies of nucleotide diversity in Medicago truncatula    (P. abies) is one of the dominant tree species in the boreal and temperate zones of Europe, with a wide geographical distribution. Piceaabies has a longitudinal range ranging from the French Alps (5°27′E) to the Ural mountains (59°E), a latitudinal range from the southern Alps to Northern Scandinavia (70°N) and an altitudinal range from sea level to well above 2,300 m in the Italian Alps  are slightly higher than that in the predominantly autogamous legume mays L.) , wild ri nivara) , and 3- uloides) . Our resian Alps . PiceaabP. abies has experienced sharp reductions in the effective population size due to environmental events, for example, climate change, in the past and have only recently become abundant again, observed levels of genetic diversity would be substantially lower than expected based on current distribution and population sizes.One possible explanation for the low levels of genetic diversity seen in Norway spruce is demographic history. If P.abies, we inferred the demographic history for all populations  using the joint SFS based on all SNPs using coalescent simulations implemented in fastsimcoal2 . This population went through a sharp bottleneck, reducing the effective size to 2,216–24,608 ∼45.6 Ma (12.7–62.7 Ma). This bottleneck may reflect the influence from the Paleogene, when the global climate showed a significant cooling trend in the beginning of the Oligocene epoch , which timing roughly corresponds to when several abrupt changes in temperature took place during the quaternary, including the last glacial maximum.To assess whether demographic factors are responsible for the low genetic diversity seen in simcoal2 . A mutat25 years  were ass25 years  online. 25 years  online. 25 years  online. ne epoch . The divP. abies have also been identified in earlier studies    between nucleotide diversity and GC density or repeat density, measured as the fraction of sites per scaffold that are annotated as repeats.Levels of nucleotide variation is expected to be proportional to the density of selected sites per genetic map unit , becausethaliana , Asian rfipogon)  or insecic sites  online. ic sites  online. ic sites . We therr2 < 1%)  online. r2 < 1%)  online, P. abies. Similar effects have been found in a wide variety of species, for example, Drosophila melanogaster  to show that this species has extremely low intraspecific genetic diversity  despite achieving extremely high census population sizes in nature due to high rates of clonal growth. They also estimated a very low spontaneous mutation rate (at least seven times lower than estimates in other multicellular eukaryotes). This is an example of a species showing a low level of genetic diversity despite having a wide current natural distribution and where selection may have reduced mutation rates to very low levels. By carrying out the first comparative genomic study of substitution rates and mutational patterns between North American conifer species (Picea sitchensis and Pinus taeda) and angiosperm species , Finally, natural selection is expected to favor lower mutation rates because most spontaneous mutations are deleterious, and thus species with larger population sizes tend to have lower mutation rates because larger populations experience stronger effects of natural selection . TherefoP. abies, using whole-genome resequencing data, support the view that genome-wide levels of nucleotide diversity are low and of the same order of magnitude as seen in earlier studies, even if these have been based almost exclusively on a limited set of short genomic fragments from predominantly coding regions.Observations have shown that the range of effective population sizes across species exceeds the observed range of genetic diversity by several orders of magnitude  and thisP. abies. Population structure in our sample is characterized by three relatively well-defined clusters that closely correspond to the geographic origin of samples from Central-Europe, Finland, and Sweden/Norway. The demographic history for all three populations involves an ancient bottleneck in an ancestral population as well as more recent bottlenecks that coincide with the last glacial maximum. Population size has only recently recovered, suggesting that reduction in effective population size throughout the history of the species is an important contributor to the low intraspecific diversity.We have focused on identifying potential factors that can help explain the low levels of genetic diversity seen in P. abies genome and such linked selection can also contribute to the species-wide loss of genetic diversity. A weak, but significant negative correlation between nucleotide diversity  and gene density is also observed, which could be interpreted as background selection only having small effects in Norway spruce. Variation in linked selection across the P. abies genome is thus also a likely contributing factor explaining the low genetic diversity in the species. Finally, the unusually low mutation rate, common to most conifers, is yet another factor that contributes to the low species-wide diversity in P. abies.We also observe a positive correlation between nucleotide diversity and recombination rate across both genic and intergenic regions, suggesting widespread action of linked selection across the P. abies. Our results suggest that both past demographic events as well as pervasive effects of linked selection have important influences on genome-wide levels of diversity, highlighting the need for analysis methods that allow for the joint estimation of demography and selection from genome-wide data so the relative importance of demography and linked selection for explaining patterns of genome-wide variation in genetic diversity in P. abies can be assessed.Our results provide the first insights into whole-genome levels of variation in a conifer species. This allows us to also assess patterns of variation with a substantially higher resolution than earlier studies and lays the foundation for more accurate population genomic inferences in Genome Biology and Evolution online.evaa005_Supplementary_DataClick here for additional data file.


Alternaria is a widely distributed major plant pathogen that can act as a saprophyte in plant debris. Fungi of this genus frequently infect cereal crops and cause such diseases as black point and wheat leaf blight, which decrease the yield and quality of cereal products. A total of 25 Alternaria sp. isolates were collected from germ grains of various wheat cultivars from different geographic regions in Kazakhstan. We investigated the genetic relationships of the main Alternaria species related to black point disease of wheat in Kazakhstan, using the inter-primer binding site (iPBS) DNA profiling technique. We used 25 retrotransposon-based iPBS primers to identify the differences among and within Alternaria species populations, and analyzed the variation using clustering (UPGMA) and statistical approaches (AMOVA). Isolates of Alternaria species clustered into two main genetic groups, with species of A.alternata and A.tennuissima forming one cluster, and isolates of A. infectoria forming another. The genetic diversity found using retrotransposon profiles was strongly correlated with geographic data. Overall, the iPBS fingerprinting technique is highly informative and useful for the evaluation of genetic diversity and relationships of Alternaria species.The genus  Specifically, PCR methods based on detection of transposable element insertion site polymorphisms include Inter-retrotransposon amplified polymorphism (IRAP) , REtrotrIn the case of retrotransposons, the PBS is either complementary to the 3′ end or to an internal region of the primer tRNA. The iPBS amplification method is based on the virtually universal presence of a tRNA complement as a PBS in LTR retrotransposons that utilize conserved PBS sequences as PCR primers for detection of polymorphism between different individuals, as well as polymorphism within transcription profiles . This meAlternaria species isolated from wheat seeds. However, the black point disease complex from wheat grains has numerous Alternaria species. No genetic diversity studies have been conducted on the most common species, including A. alternata, A. tenuissima, A. arborescens, and A. infectoria, using the transposable element insertion site polymorphism amplification method. Here, we investigated the genetic relationships of the main Alternaria species related to black point disease of wheat in Kazakhstan using the iPBS DNA profiling technique.Thus far, the application of the iPBS method for investigating the genetic diversity of fungal pathogens has been very limited . To dateAlternaria sp. were collected from wheat grains of various wheat cultivars from different geographic regions in Kazakhstan  using modified CTAB extraction buffer  with RNAse A treatment . A detailed protocol for DNA isolation was followed as described in Alternaria sp. isolates was analyzed by using 25 PBS primers designed by ® Hot Start II PCR buffer with 1.5 mM MgCl2, 1 µM primer, 0.2 mM each dNTP, and 0.2 µl Phire® Hot Start II DNA polymerase (Thermo Fisher Scientific Inc.). PCR amplification was carried out in a Bio-Rad Thermal Cycler T100 under the following conditions: initial denaturation step at 98 °C for 1 min, followed by 30 amplifications at 98 °C for 5 s, at 50−60 °C (depending on primer sequence) for 20 s, and at 72 °C for 60 s, followed by a final extension of 72 °C for 3 min. All PCRs were repeated at least twice for each isolate. All PBS primers were tested to assess the genetic diversity of Alternaria isolates using iPBS amplification for DNA profiling. Primers that generated few PCR products were excluded. Primers with a weak profile or that produced mainly monomorphic amplification products were also excluded. PCR products were separated by electrophoresis at 70V for 8 h in 1.2% agarose gel with 1xTBE buffer. A Thermo Scientific  GeneRuler DNA Ladder Mix (#SM0332) was used as a standard DNA ladder. The PCR products were visualized with a ChemiDoc-It2 Imaging System  and a PharosFX Plus Imaging System  with a resolution of 50 µm, after staining with ethidium bromide. PBS primers generated in the PCR yielded clearly distinct amplification products, showing considerable variability among the isolates belonging to different Alternaria species.The genetic variability of Alternaria sp. from wheat grains. Each band of a unique size was assumed to correspond to a unique locus. To construct a binary matrix, reproducible fragments were scored as present (1) or absent (0). GenAlex 6.5  were extremely useful compared to the other PBS primers. Among these, primer 2242 showed the highest PIC index value. DNA profiling using PBS primers was highly efficient for isolates belonging to different types of Alternaria isolates. Several PBS primers showed a high level of polymorphism and were used in further studies to investigate the genetic diversity of other fungal species. Primers with a weak profile or that produced mainly monomorphic amplification products were excluded.In total, 25 PBS primers were used to analyze the polymorphism of 25 Alternaria alternata isolates (160 polymorphic bands out of 198), which was the most abundant species among the isolates (15 out of 25). Notably, the level of detectable polymorphism detected in our study is lower than that reported in similar studies using the iPBS method on both plants , Alternaria tenuissima (59%), and Alternaria infectoria (47%).Of the 328 amplified fragments, 228 (69%) were polymorphic (see h plants  and otheh plants . The ampA. infection) to 1.310 . AMOVA revealed that 79% of the total variation was due to differences among isolates within populations, and the variation between populations reflected only 21% of the total variation. These results are also consistent with the low Shannon’s indices (0.198–0.315). The overall Shannon’s index (I = 0.266) suggests that more than 20% of the genetic diversity is explained by differences between isolates. Based on these results, we conclude that most of the genetic variation (79%) was distributed among isolates across the regions. It is worth mentioning that the fungal isolates are mostly similar at the genetic level despite long distances between different wheat growing zones in Kazakhstan.Analysis of molecular variance (AMOVA) was used to calculate the number of effective alleles per locus (Ne) based on three PBS primers  . Ne rangAlternaria alternata samples  between the three groups was high (0.206). GenAlex 6.5 software  was used samples . The PICAlternaria isolates at the species level. The UPGMA dendrogram grouped all 25 isolates (which represented three populations) into two major clusters  were allocated to a separate sub-cluster and had some genetic similarities. Isolates of A. infectoria clearly formed a separate cluster. This species is also very different morphologically from A. alternata and A. tenuissima. Isolates of A. alternata and A. tenuissima were located in the same sub-cluster, although they are separated from each other.Phylogenetic analysis showed that the iPBS markers were effective at grouping the 25 clusters . Among tAlternaria is a very challenging task because this genus is characterized by significant polymorphisms in morphological and cultural features, as well as biological properties. For example, A. alternata and A. tenuissima are the most morphologically similar; , causing losses by seedling blight, reduced seed germination, or seedling foot and stalk rot. Another potential risk is the presence of fungi toxins; not only do they contaminate cereals, but they could also result in harmful contamination of foods and feedstuffs. Microscopic fungi of the ttention .Alternaria species is important for the development of strategies to counter these fungi, i.e., for breeding programs. Various genetic markers and fingerprinting technologies  have been widely used to identify the genetic diversity of fungal populations. Much research has been carried out on other Alternaria species  that cause vegetable diseases. However, for species that contaminate wheat, the information is very limited. Methods used to study genetic diversity are technically complicated and costly or have low efficiency. PCR-based DNA profiling technologies based on interspersed repeat sequences such as retrotransposons (Identifying the genetic variability in populations within a particular type of nsposons  have beensposons . Thus, tThe structure of retrotransposons contains conserved sites that belong to typical retroviruses for all eukaryotes. In this regard, the iPBS method developed by Alternaria species in wheat grains. Interestingly, the genetic diversity found here using retrotransposon profiles was strongly correlated with geographic data. One explanation for this observation is that the scored retrotransposon polymorphisms in fungal genomes are related to ecological and environmental stresses. Moreover, retrotransposons in fungal genomes are usually clustered near genes, and thus most likely to be under selection. Finally, permanent changes in retrotransposon content dynamically change fungal genomes; even strains of a single fungal species can display a certain percentage of variability during cultivation in response to different environmental conditions. Abiotic and biotic stresses, including plant interaction, are well known to activate retrotransposons (Alternaria species isolates and identify various Alternaria species. This knowledge may be helpful in understanding host adaptation to this pathogen; knowledge of population genetic structure of a pathogen provides information about its potential to overcome host genetic resistance. iPBS markers could be a useful tool for studying population biology and genetics of this fungus at a global level. The results show rapid LTR retrotransposon evolution in endophytic fungal genomes through integration, losses, and transfers of retrotransposons in almost every species and strain (Alternaria species and giving insight into local factors that may be driving genome adaptation. In addition, DNA profiling based on retrotransposons can be an inexpensive way to establish genetic diversity and to determine the species of fungi.In conclusion, this study demonstrated the effectiveness of iPBS amplification for DNA profiling and identification of the endophytic fungi nsposons . The iPBd strain . The rel10.7717/peerj.9097/supp-1Supplemental Information 1Click here for additional data file.


Within the disciplines of modern medicine, P4 medicine is emerging as a new field which focuses on the whole patient. The development of Ayurgenomics could greatly enrich P4 medicine by providing a clear theoretical understanding of the whole patient and a practical application of ancient and modern preventative and therapeutic practices to improve mental and physical health. One of the most difficult challenges today is understanding the ancient concepts of Ayurveda in terms of modern science. To date, a number of researchers have attempted this task, of which one of the most successful outcomes is the creation of the new field of Ayurgenomics. Ayurgenomics integrates concepts in Ayurveda, such as Prakriti, with modern genetics research. It correlates the combination of three doshas, Vata, Pitta and Kapha, with the expression of specific genes and physiological characteristics. It also helps to interpret Ayurveda as an ancient science of epigenetics which assesses the current state of the doshas, and uses specific personalized diet and lifestyle recommendations to improve a patient’s health. This review provides a current update of this emerging field. Ayurveda is the ancient system of traditional medicine in India. The term Ayurveda comes from two Sanskrit words, “ayus”, meaning life or lifespan, and “Veda”, meaning knowledge or science. Ayurveda may be translated as “the science of life”, or more specifically, “the science of lifespan”. Ayurveda was originally an oral tradition of natural health, and it was much later that this knowledge was written down in books . Some ofThe Tridosha theory of Ayurveda explains that there are three fundamental principles or forces, called doshas, which govern the physiology of each individual. Vata is the dosha involved in transportation in the body; from the transportation of molecules to the transportation of nervous impulses. It arises from the elements of ether and air. Pitta is the dosha that governs the process of digestion, as well as all metabolic pathways inside each cell. It is formed from fire and water. Kapha is the dosha that governs structure and cohesion in the body. It is an expression of earth and water. Each individual is born with a particular combination of these three doshas; this is called Prakriti. There are seven basic types of Prakriti: Vata; Pitta; Kapha; Vata/Pitta; Pitta/Kapha; Vata/Kapha; and Vata/Pitta/Kapha.Is there a scientific explanation for Prakriti? The best description so far has come from the new field of Ayurgenomics, which attempts to describe Prakriti types in terms of modern genetics and physiology.HLA DRB1*02 allele in the Vata type and of HLA DRB1*13 in the Kapha type. Furthermore, HLA DRB1*10 had higher allele frequency in the Kapha type compared to the Pitta and Vata types .Furthermore, a 2010 study showed that within Kapha types, there was a down-regulation of CYP2C19 genotypes, a family of genes that is involved in the detoxification and metabolism of certain drugs and an up-regulated in Pitta types . In 2012A paper in 2012 studied rheumatoid arthritis patients and found that inflammatory genes were up-regulated in Vata types, while in Pitta and Kapha types there was an up-regulation of oxidative stress pathway genes . In 2015Another study done in 2015 identified DNA methylation signatures that distinguish the three major Prakriti types. The authors suggest that DNA methylation is probably coupled to chromatin regulation as a contributor to different Prakriti phenotypes, and that this research provides insight into the epigenetic mechanisms of the Ayurvedic personalized system of medicine .One final paper in 2015 extended an earlier study, and found the combination of derived EGLN1 allele (HAPE associated) and ancestral VWF allele (thrombosis associated) was significantly high in the Kapha Prakriti group compared to the Pitta Prakriti group. They also showed a genetic link between EGLN1 and VWF, which could modify thrombosis/bleeding susceptibility and outcomes of hypoxia .A paper in 2003 showed that the Kapha Prakriti groups had a number of biochemical differences from Pittas or Vatas. They showed higher digoxin levels, increased glycoconjugate levels, increased free radical production and reduced scavenging, as well as increased tryptophan catabolites and reduced tyrosine catabolites . In 2011Research reported in 2014 found a correlation between body mass index (BMI) and Prakriti type. It was reported that Kapha Prakriti subjects had a higher BMI than those with a Vata Prakriti .Several papers have also correlated disease conditions with Prakriti. A 2012 paper reported that Vata-Kapha types had significantly higher triglyceride, VLDL and LDL levels, as well as lower HDL cholesterol when compared with other constitution types. The researchers also found that Vata-Kapha Prakriti individuals had higher risk for diabetes mellitus, hypertension and dyslipidemia. In addition, Vata-Kapha types had higher levels of biochemical markers such as IL6, TNF alpha, hsCRP and HOMA IR. The Vata-Kapha and Kapha Prakriti groups were both correlated with higher levels of the inflammatory markers. The conclusion of the authors was that there is a high correlation of risk factors associated with the Vata-Kapha and Kapha Prakriti groups .Another paper in 2012 looked at diabetic patients and studied the effects of walking (isotonic exercise). There was a significant decrease in systolic blood pressure in Vata-Pitta, Pitta-Kapha and Vata-Kapha types after walking. There was also a significant decrease in mean diastolic blood pressure in Vata-Pitta types . AnotherIn addition to the research papers, there have been a number of theoretical and review papers. In 2008 and 2010, papers began promoting the idea of the connection between Ayurveda and genomics ,24. In 2Modern medicine uses a highly reductionist system to describe the fundamental basis of our physiology and health, using terms like genome, gene expression and epigenetics. Ayurveda uses an entirely different holistic system, which includes terms such as dosha and Prakriti.Unfortunately, modern medicine has not recognized many of the useful preventative approaches of Ayurveda, due to a cognitive bias against folk or traditional medicine. Even though traditional systems of medicine are still widely used in many countries around the world, more research on their preventative and therapeutic treatment programs is necessary .Up to now, the research has been primarily on specific herbal preparations, with the main outcome being an attempt to isolate one active ingredient which can then be used by a pharmaceutical company. Ayurgenomics offers a new bridge between traditional medicine and modern medicine by providing a rigorous scientific understanding of basic concepts, and at the same time incorporating the practical preventative approaches of Ayurveda into modern medicine.Over the last ten years, a new field has arisen in modern medicine, which is known as P4 medicine. The four Ps are: predictive; preventive; personalized; and participatory ,45,46,47Ayurveda and other systems of traditional medicine have been patient-oriented and predictive, preventive, personalized and participatory for many thousands of years.Long before the advent of epigenetic and other fields such as nutrigenetics, Ayurveda understood how diet and other lifestyle factors could affect our health. They recognized what modern medicine is only beginning to comprehend, that prevention is key to health. Improvements in diet, sleep, exercise and stress management are crucial for an effectively preventative system of medicine.The development of Ayurgenomics, as we have said, helps give credibility to Ayurveda and other systems of traditional medicine by describing their ancient concepts in terms of modern science. New approaches in Ayurgenomics, which use big data analysis and machine learning, may greatly facilitate this whole process ,37,39. OAyurveda and genomics can contribute to each other. Modern science can help Ayurveda as an evidence-based system of medicine, and Ayurveda can help modern medicine, particularly through its preventative approaches. This is especially true with P4 medicine, which is based on many of the same principles of Ayurveda. Having time-tested personalized preventative lifestyle recommendations would make it easy for each individual to participate in their own self-care see .Ayurveda could also contribute to modern medicine in terms of the diagnosis of disease. Many believed that the enormous progress in the field of genomics would quickly bring a personalized system of medicine that could predict and prevent disease. Progress has been made, but researchers now realize that this may take longer than expected due to the highly complex nature of gene expression in the development of disease conditions. Ayurveda could help by stratifying individuals into broader categories using the Prakriti system of classification, along with modern genomics. Can we consider Prakriti as being our Ayurvedic genome? Can Ayurgenomics be used to diagnose and treat diseases?These and many other questions will need to be answered by future research. We can only hope that a massive research effort is undertaken as soon as possible. The combination of Ayurveda and genomics promises to markedly improve many areas of healthcare throughout the world.