Microphysogobio rapidus, an endemic cyprinid fish species found exclusively in Korea, has been identified in only two tributaries of the Nakdong River. The species predominantly occupies the near-gravel bottom waters within shallow sections of the middle and lower reaches of the river, characterized by swift currents. M. rapidus is currently recognized as a critically endangered species due to its distinct habitat preference, as well as the negative impacts of stream dam development and water environment pollution. In this study, we used 10 microsatellite markers to examine the genetic diversity of M. rapidus in the upper Nam (UN), lower Nam (LN), and Deokcheon Rivers (DC) in Korea, with a specific focus on assessment of the impact of dam development. Fish sampled from the UN and LN showed a greater average number of alleles and allelic richness  compared to those from DC . The observed heterozygosity among the fish examined ranged from HO = 0.748 (LN) to 0.766 (DC). All three fish groups exhibited a significant departure from Hardy–Weinberg equilibrium (HWE) (p < 0.05). Despite having the largest effective population size , the fish sampled from UN and LN showed the highest inbreeding coefficients , which were highly significant (p < 0.01). In contrast, the fish sampled from DC exhibited the smallest effective population size (Ne = 61) and showed an inbreeding coefficient close to zero (p > 0.05). BOTTLENECK analysis and estimated M-ratio values (0.341–0.372) revealed indications of past population size reduction in all fish groups examined. No significant genetic differentiation (FST < 0.05) was detected using the DAPC, STRUCTURE, and AMOVA among the fish studied. However, pairwise comparisons of FST between fish sampled from the Nam and Deokcheon Rivers revealed significant values (p < 0.001) ranging from 0.013 to 0.014. In addition, the closest genetic distance (0.026) was observed between UN and LN, while the greatest distance (0.087) was found between UN and DC. Analysis of gene flow rates among the fish examined indicated asymmetrical gene exchange within the Nam River, which was 31.51% in the downstream direction (from UN to LN), with a minimal gene flow rate (0.41%) in the upstream (from LN to UN) direction. The opposite trend was recorded between DC and LN, with a higher gene flow rate (29.74%) in the upstream direction compared to the downstream direction (0.12%). Our study highlighted the importance of implementing long-term conservation efforts focused on maintaining river integrity by removing water barriers such as weirs that impede fish migration and implementing active protection measures, such as aquaculture breeding and reasonable stocking practices, to preserve M. rapidus in the study area. A total of 34,012 weirs have been constructed in rivers in Korea to ensure an adequate supply of agricultural water, specifically for rice cultivation. However, structures such as dams, weirs, and other manmade obstacles in streams significantly impact the migration patterns of fish species due to habitat fragmentation, resulting in reduced fish population size, restricted gene flow, and a consequent decrease in genetic diversity, ultimately leading to increased levels of homozygosity . The redMicrophysogobio rapidus is an endemic cyprinid fish species found exclusively in Korea, which inhabits fast-flowing shallow waters with gravel bottoms in the middle and lower reaches of the Nam and Deokcheon rivers, tributaries of the Nakdong River [M. rapidus is currently classified as a “critically endangered” species [M. rapidus faces significant fragmentation, with 35 weirs installed in the Nam River and an additional 39 weirs in the Deokcheon River, contributing to the substantial division of its habitat. Furthermore, the presence of Jinyang Lake acts as a further barrier separating the two rivers [M. rapidus, it is important to conduct studies focused on genetic diversity and structure. Obtaining such data will be crucial for the development of a systematic conservation strategy, which is urgently required to safeguard the future of this species.ng River ,8. M. ra species  due to i species . At preso rivers . Given tDespite a few molecular phylogenetic studies, the lack of efficient molecular markers and comprehensive population genetic studies has hindered the comprehensive evaluation of the genetic diversity and genetic structure of this species ,14,15,16M. rapidus population in the Nam and Deokcheon rivers using microsatellite markers. The obtained genetic data can be utilized to develop an effective conservation strategy for the existing shoal population.This study was performed to analyze the genetic diversity and structure of the M. rapidus has been designated as Class I endangered wildlife by the Ministry of Environment, proper permissions for capture were obtained from the Nam River  and Nakdong River Basin Environmental Offices of the Ministry of Environment in 2012 and 2013. The study area consisted of one sampling site in the Deokcheon River (DC) and two sites along the Nam River, i.e., the upper Nam (UN) and lower Nam (LN), both of which are tributaries of the Nakdong River before it reaches Jinyang Lake (2) and a scoop net with a mesh size of 8 mm (covering an area of 1.35 m2) were employed, and we collected a total of 120 specimens, i.e., NAM_UN01–48 from UN, NAM_LN01–48 from LN, and DC_Rv01–24 from DC. After capture, individuals were anesthetized with MS-222 , following which a portion of the pelvic fin was sampled and preserved in absolute ethanol for further analysis in the laboratory. Subsequently, the captured fish were immersed in 50 ppm oxytetracycline solution and released back into the natural habitat.As ang Lake . To collAfter transportation to the laboratory, the fin samples were washed thoroughly with ethanol, rinsed with distilled water, and treated with TNES-urea buffer containing 8 M urea, 10 mM Tris-HCl (pH 7.5), 125 mM NaCl, 10 mM EDTA, and 1% SDS for DNA isolation, according to the methodology described previously by Asahida et al. . The quaRsaI  for 10 s according to the manufacturer’s instructions. The resulting DNA fragments were treated with mung bean nuclease (New England Biolabs) for 30 min to obtain blunt ends and dephosphorylated using calf intestinal phosphatase (New England Biolabs). DNA fragments of 200–800 bp were separated using electrophoresis on 1.5% agarose gels and recovered using a QIAquick gel extraction kit . The recovered DNA fragments were ligated to adapters (SNX/SNX reverse linker) by combining with 60 μM SNX adapter, 5 μL of NEB #2 buffer, 0.5 μL of 100× BSA, 1 μL each of NheI (New England Biolabs) and XmnI (New England Biolabs), 50 mM rATP , and 2000 units of ligase (New England Biolabs) in a total volume of 50 μL.Microsatellite DNA was isolated as described previously . The ext10 and (CT)10. After the addition of 50 μL of hybridization solution  together with the probes to the linker-ligated DNA, the mixture (100 μL) was heated to 95 °C for 15 min and then incubated at 60 °C for 12 h. Hybridization was performed using Streptavidin paramagnetic particles (Promega) in accordance with the manufacturer’s instructions. Single-stranded DNA was eluted at 95 °C for 15 min in 100 μL of low-TE buffer containing 10 mM Tris-HCl (pH 8.0) and 0.1 mM EDTA. PCR was performed in mixtures consisting of 10 μL of DNA, 4 μL of 10 μM SNX primer, 0.3 μL of Vent (-exo) polymerase (New England Biolabs), 4 μL of dNTP mix, 5 μL of 10× Thermopol buffer, and 26.7 μL of sterilized distilled water with denaturation at 96 °C for 5 min followed by 40 cycles of 96 °C for 45 s, 62 °C for 1 min, 72 °C for 2 min, and a final elongation step at 72 °C for 5 min.The microsatellite DNA regions of interest were selectively recovered using a Magenesphere magnetic separation kit (Promega) and biotin-labeled probes (GT)NheI, and the pUC18 vector was cut with XbaI. After dephosphorylation with calf intestinal phosphatase, aliquots of 100 ng of each DNA fragment and vector were mixed with 2 μL of NEB #2 buffer, 0.2 μL of 100× BSA, 1 μL of NheI, 2 μL of 10 mM rATP (Promega), and 400 units of ligase (New England Biolabs) in a total volume of 20 μL. The reaction mixtures were incubated at 16 °C for 30 min and 37 °C for 15 min for a total of 40 cycles. The ligated vector was transformed into XL1-blue MRF strain  using cold shock, spread on LB agarose plates containing 100 μL of 10 mM IPTG and 100 μL of 2% X-Gal, and incubated overnight at 37 °C. White colonies, expected to contain microsatellite regions, were subjected to PCR using the two probes and the M13 forward/reverse primers. The amplified products were confirmed using 1.5% agarose gel electrophoresis. The respective positive colonies were cultured in LB medium, and the microsatellite-containing vector was isolated using a QIAprep Spin Miniprep kit (Qiagen) and sequenced by Macrogen . Finally, primer sequences suitable for PCR were generated from the upstream and downstream sequences of the microsatellites using PRIMER 3 software ver. 0.4.0 [The double-stranded DNA fragments were cut with r. 0.4.0 .® PCR premix kit  (Six previously reported microsatellite markers  and fourf Korea) . The PCRThe precise size of all alleles was determined using Peak Scanner™ software ver. 1.0 (Applied Biosystems). The genotypic data were validated using MICRO-CHECKER software ver. 2.2.3 , which fA) and observed heterozygosity (HO) and expected heterozygosity (HE) were calculated using Cervus software ver. 3.0 [FIS) within the fish sampled from LN, UN, and DC were calculated using Arlequin software ver. 3.5 [The number of alleles  among the examined fish groups was conducted using Arlequin software ver. 3.5 [K), from 1 to 5 clusters were simulated under an admixture model. Moreover, a non–model-based genetic clustering method, Discriminant Analysis of Principal Components (DAPC), was conducted using the R package ADEGENET ver. 2.1.3 [Analysis of genetic differentiation  compared to those from DC . The observed (HO) and expected heterozygosity (HE) among the fish examined ranged from HO = 0.748 (LN) to 0.766 (DC) and from HE = 0.775 (DC) to 0.809 (UN), respectively.The results of the analysis based on the 10 microsatellite markers are shown in p < 0.05), mainly due to heterozygosity deficiency. The microsatellite markers with the greatest and significant (p < 0.05) HWE departure were MRms245-1 and GBms157 in UN, MRms245-1 in LN, and GBms157 in DC.Analysis using Fisher’s exact test indicated that all three fish groups exhibited significant departure from HWE , the fish sampled from UN and LN showed the highest values of the inbreeding coefficient . In contrast, the fish sampled from DC showed the smallest effective population size (Ne = 61) and inbreeding coefficient values close to zero (p > 0.05). BOTTLENECK analysis and estimated M-ratio values (0.341–0.372) revealed signs of past population size reduction in all fish groups examined (Despite having the largest effective population size (examined .FST) between fish sampled from the Nam and DC Rivers revealed very low but significant values (p < 0.001), ranging from 0.013 to 0.014. The closest genetic distances (0.026) were observed between UN and LN, while the greatest distance (0.087) was found between UN and DC , with a minimal gene flow rate (0.41%) in the upstream (from LN to UN) direction. Opposite trends were recorded between DC and LN, with a higher gene flow rate (29.74%) recorded in the upstream direction compared to the downstream direction (0.12%). A near lack of gene flow (0.06–0.16%) was recorded between UN and DC .M. rapidus. The average number of alleles recorded in the present study was A = 22.7, which was comparable to or greater than the average number of alleles detected in Pseudopungtungia nigra (A = 14.4), Pseudopungtungia tenuicorpa (A = 19.2), Gobiobotia macrocephala (A = 22.4), Gobiobotia brevibarba (A = 18.8), and Gobiobotia naktongensis (A = 21.5) [G. brevibarba and G. macrocephala, but lower than those recorded in P. nigra, P. tenuicorpa, and G. naktongensis [M. rapidus has not significantly impacted its genetic diversity. However, the species remains at risk of extinction due to human activities other than weir construction [M. rapidus is at risk [p < 0.001) inbreeding index of the entire population (0.044) indicated that genetic drift in the population could lead to rapid loss of genetic diversity.In this study, 10 microsatellite markers were used for population genetic analysis of the critically endangered fish species,  = 21.5) . The aveongensis . These otruction . The efftruction . In construction . One imptruction . Based o at risk ,37. In aThe fish in the DC group exhibited lower genetic diversity, reduced average number of alleles, and lower allelic richness compared to those in the LN and UN groups. Moreover, the average effective population size for the DC subpopulation was 61, which falls below the threshold size of 100 required for the maintenance of the population size in the short term [M. rapidus, there are three sites but only one population. Therefore, although genetic diversity is high, it is likely to become extinct due to habitat destruction or climate change, considering that the population has decreased to the point where it cannot actively respond to environmental changes evolutionarily.In the analysis of genetic differentiation among sampled fish, the DC group exhibited small but significant genetic differentiation when compared to the UN and DC groups. However, the results of STRUCTURE, DAPC, and AMOVA strongly supported that DC, LN, and UN represent a single genetic cluster. The existence of a small number of subpopulations resulting from low genetic differentiation is considered a significant risk to the continued survival of a species . Having Thymallus thymallus, Salvelinus leucomaenis, and Lethenteron sp., it has been reported that manmade structures such as weirs, sluice gates, and dams installed in rivers impede migration [M. rapidus due to its high slope. Fish frequently migrate during the spawning season, but the spawning season of M. rapidus is in the dry season (4–5 months), such that the low water level will hinder the migration of fish in the Nam River  to those in the Deokcheon River (DC) via Duin weir. Genetic flow from upstream to downstream is common. Genetic flow from the DC to LN population was 29.74%, indicating an upstream direction in short generations. This confirmed unidirectional genetic flow from DC to LN based on the detection of some genotypes in the gene structure. LN and UN showed little gene flow over short generations, despite the absence of artificial structures blocking gene flow. However, given the genetic diversity, there may have been gene flow in the past rather than recently, but this was not confirmed in this study. Therefore, further analyses of genetic flow are necessary.BayesAss-based genetic flow analysis, conducted to estimate genetic flow over the short term (5–10 generations), revealed a lack of gene exchange between the DC group and the UN and LN groups. In the case of fish such as igration ,4,5. A fM. rapidus, leading to a high risk of extinction and confirming its critically endangered status.The fact that the DC, LN, and UN groups constitute a single population implies that there is only one remaining population of The upstream genetic flow between DC and LN has a flow rate of 29.74%, but the upstream flow between LN and DC appears to be almost nonexistent (0.12%). The Duin weir is located between LN and DC. Considering the results of genetic flow analyses, Duin weir  appears to be blocking the movement of fish upstream of the shoal. A terraced fishing ground (4.5 × 36 × 1.5 m) was completed on the right side of Duin weir in 2011, but it is estimated that its use is limited. There are many weirs in DC. In particular, the Duin weir is judged to have intensified the blockage of gene flow. To ensure the effective conservation of the species, measures should be taken to facilitate genetic exchange across the study area.M. rapidus in the study area.Our study highlighted the crucial importance of implementing long-term conservation efforts focused on maintaining river integrity by removing water barriers such as weirs that impede fish migration and implementing active protection measures, such as aquaculture breeding and reasonable stocking practices, to preserve


Ovis aries) are maintained here. However, the genetic diversity and maternal origin of these local breeds have not been comprehensively investigated. In this study, we describe the diversity and phylogeny of Russian sheep breeds inhabiting different geographical regions based on the analysis of complete sequences of mitochondrial genomes (mtDNA). Complete mtDNA sequences of the studied sheep were obtained using next-generation sequencing technology (NGS). All investigated geographical groups of sheep were characterized by high haplotype (Hd = 0.9992) and nucleotide diversity (π = 0.00378). Analysis of the AMOVA results showed that genetic diversity was majorly determined by within-population differences (77.87%). We identified 128 haplotypes in all studied sheep. Haplotypes belonged to the following haplogroups: B (64.8%), A (28.9%), C (5.5%), and D (0.8%). Haplogroup B was predominant in the western part of Russia. A high level of mtDNA polymorphism in the studied groups of local sheep indicates the presence of a significant reserve of unique genotypes in Russia, which is to be explored.Eurasia is represented by all climatic zones and various environments. A unique breed variety of farm animals has been developed in Russia, whose territory covers a large area of the continent. A total of 69 local breeds and types of dairy, wool, and meat sheep ( O. aries) is one of the first domesticated, adaptable, and widespread livestock species [O. orientalis) is proposed as the ancestor of domestic sheep. Several studies reported that most of the functional genetic diversity in sheep came from mouflon during progressive domestication [Domestic sheep  . Chessa Ovis genus had occurred repeatedly from Africa and Central Europe to the Iberian Peninsula. At the same time, there was a settlement of Northern Europe through the center of domestication through the Caucasus and Russia [After approximately 1000 years of domestication, domestic animals and plants expanded and replaced the foraging lifestyle in larger parts of Southwest Asia . Furtherd Russia ,13.According to references, the colonization of Asia began from the Middle East to the Mongolian plateau and the Indian subcontinent, from where sheep migrated to the north and southwest of China ,15. SheeThe territory of Russia covering a large part of Eurasia is in the arctic, subarctic, temperate, and partly in the subtropical climatic zones. Thus, the country is characterized by a wide variety of climatic conditions. A total of 69 different sheep breeds have been registered on its territory , which pThe genetic diversity of farm animals plays an important role in the more complete performance of economically significant, fitness-related traits . The conCytB gene [Mitochondrial DNA (mtDNA) is inherited only through the maternal lineages and has great variability within the same species. In this regard, mtDNA polymorphism analysis is widely used to investigate phylogenetic links and genetic structure of various livestock species ,23,24,25ytB gene ,27.O. gmelini anatolica Valenciennes), was identified [CytB allowed for the detection of the fifth haplogroup E [Based on the analysis of sheep mtDNA control region polymorphism, seven haplogroups were identified. Haplogroups F and G disappeared, and five others  are present in modern breeds ,28. The entified ,33,34. Uogroup E . The latogroup E ,12,34, Eogroup E ,36, Tibeogroup E , and Iraogroup E . These hogroup E .CytB gene [Thus, a comprehensive study of mtDNA polymorphism allows unlocking the evolution events, which had occurred during the migration of domestic sheep populations throughout the history of their domestication. However, most sheep population studies are based on using the D-loop  or the CytB gene  sequenceIn this research, for the first time, an investigation of the genetic diversity and structure of Russian sheep populations based on the polymorphism of complete mitochondrial genomes is presented.Tissue specimens (ear fragments) of Russian sheep breeds were used as biological material for this research. Sheep samples were provided by the Bioresource Collection of Farm Animals of the L.K. Ernst Federal Science Center for Animal Husbandry (deposited to the collection in the period from 2003 to 2018).https://www.montana.edu/kalinowski/software/mL-relate/index.html (accessed on 7 December 2022) [Based on pedigree records, the unrelated individuals were chosen. Additionally, all samples were genotyped using STR markers. The matrix of STR-genotypes was analyzed in ML-Relate software [er 2022) . Only unThe final studied sample included 135 sheep inhabiting six territorial districts of Russia, corresponding to diverse geographical zones .DNA was isolated from ear fragments using commercial «DNA-Extran-2» kits  in accordance with the manufacturer’s guidelines. An integrity of DNA was checked by gel electrophoresis in 1% agarose gel via the colorimetric gel documentation system Uvitec FireReader V10 imaging System . The OD260/OD280 ratio and concentrations of DNA solutions were measured using a NanoDrop 8000  and a Qubit 4.0 fluorimeter , respectively. The isolated DNA samples were preserved at −20 °C until further laboratory manipulations.O. aries ARS-UI_Ramb_v2.0 sheep genome version using the online resource Basic Local Alignment Search Tool (BLAST) (https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 10 January 2023)) [For the next generation sequencing of complete mitogenomes, six mtDNA fragments with overlapping regions were amplified. Primer sequences for the amplification of target overlapping mtDNA fragments were selected based on the reference sequence of complete mitochondrial genome of sheep, which were presented in the National Center for Biotechnology Information NCBI  according to y 2023)) . The nuc2O, 2.5 µL dNTPs (1 mmol/L), 1 µL primer mix, 0.25 µL SmartTaq HF-FuZZ DNA polymerase , and 1 µL of DNA template.The final volume for PCR was 25 µL and contained 10 µL PCR-buffer (2.5½ HF Reaction buffer), 10.25 µL HQuality of PCR amplification was checked by gel electrophoresis in 1% agarose gel using the colorimetric gel documentation system Uvitec FireReader V10 imaging System .PCR products were purified using the «Cleanup Mini kit for the isolation and purification of DNA from agarose gel and reaction mixtures»  and were used to prepare sequencing libraries with NEBNext Ultra II DNA Library Prep Kit for Illumina  following the manufacturer’s recommendations. To check the quality, sequencing libraries were submitted to CJSC Syntol (Russia).Sequencing of the prepared libraries was performed by 300-bp paired-end procedure on MiSeq .https://www.megasoftware.net/history (accessed on 12 May 2023)) [MtDNA sequences were aligned and edited using the MUSCLE algorithm  as impley 2023)) .S), average number of nucleotide differences (K), number of haplotypes (H), haplotype diversity (Hd), nucleotide diversity (π), and standard error mean (±SEM) were calculated in DNASP 6.12.01 software (https://www.bioinformaticshome.com/db/tool/DnaSP (accessed on 7 June 2023)) [Measures of genetic diversity parameters including number of polymorphic sites (e 2023)) .http://www.mitotool.org/ (accessed on 15 June 2023)) [Haplogroups for Russian sheep were identified using the MitoToolPy software (e 2023)) .The best evolution models were determined using the PartitionFinder 2 program  using thhttp://nbisweden.github.io/MrBayes/ (accessed on 20 June 2023)) [http://tree.bio.ed.ac.uk/software/figtree/ (accessed on 20 June 2023)) [The phylogenetic relationship was established by the Markov chain Monte Carlo search running with four chains for 10,000,000 generations, with trees being sampled every 500 generations (the first 25% of the trees were discarded as ‘burnin’) in the MrBayes 3.2.7 program (e 2023)) .https://popart.maths.otago.ac.nz/ (accessed on 30 June 2023)) [To analyze evolutionary relationships, a median-joining network was constructed in PopART 1.7 software (e 2023)) .http://cmpg.unibe.ch/software/arlequin35/Arl35Downloads.html (accessed on 7 July 2023)) [Wright’s F-statistics  and analy 2023)) , were usThe demographic history of populations was assessed based on pairwise mismatch distribution analyses using the DnaSP 6.12.01 software , and tesTo determine single nucleotide polymorphisms (SNPs) in the complete mitochondrial genomes of sheep, genetic diversity indices were calculated .S = 468) was determined in sheep of the North Caucasian region, and the smallest number (S = 49) was identified in the Volga region. The lowest values of nucleotide diversity and the smallest average number of nucleotide differences were recorded in sheep from the Volga region . Sheep from the Far Eastern region were characterized by the highest values of these indicators .A total of 804 polymorphic sites were identified in 135 studied animals. The largest number of polymorphic sites , which are characterized by high mtDNA polymorphism, in these geographical regions.The results of Hd = 0.9992 ± 0.01). At the same time, the greatest haplotype diversity was determined in sheep from the Southern, Siberian and Volga regions .In all studied sheep, a total of 128 haplotypes were identified including 7 haplotypes in FAE, 5 in VOL, 46 in NCN, 17 in SIB, 38 in SOU, and 19 in CEN, respectively. The distribution of haplotypes of complete mitochondrial genomes of sheep is presented in We checked all identified haplotypes with those published worldwide by comparing nucleotide sequences using the BLAST tool. We did not find the 100% nucleotide similarity . In thisIn the studied groups of sheep four haplogroups were determined , using the MitoToolPy program.Analysis of median-joining network  and the STF values were calculated  and between Volga and Far East geographical groups (STF = 0.39243), respectively (STF = 0.00818), as well as the Southern and North Caucasian regions (STF = 0.00937) were characterized by the greatest genetic similarity.The greatest genetic remoteness was showed between Far Eastern and Central geographical groups   confirmeThe mismatch distribution (distribution of the number of pairwise differences between sequences) for all groups of sheep was multimodal . All stuOvis is formed from five major haplogroups  [The study of genetic diversity of sheep provides a better understanding of aspects of breeding and domestication processes. MtDNA is distinguished by its uniqueness and is one of the most informative genetic markers for phylogenetic studies, because mtDNA has a maternal inheritance pattern and allows determining introgression only in females. Here, we investigated complete sequences of mitochondrial genomes of Russian sheep originated from different geographic regions. The phylogeny of mitochondrial genome of the genus , and E) .Based on analysis of polymorphism of complete mtDNA nucleotide sequences, we identified four sheep haplogroups, including A, B, C, and D, in studied sheep populations.n = 38) was haplogroup A, which was also found in all regions, but most of the animals were bred in the Siberian and Far Eastern geographical regions.According to previous studies ,28,29,56As in previous studies ,57,58, hHd = 0.999 ± 0.010) and nucleotide (π = 0.00378 ± 0.00027) diversity of Russian sheep were comparable to the values obtained earlier in African sheep  [Hd = 0.8502 ± 0.024, π = 0.00373 ± 0.012) [Haplotype (± 0.012)  and Indi± 0.012) .The results of the AMOVA determined higher genetic differentiation within the studied groups of sheep (77.87%), rather than among populations (23.13%). Our data were corresponded to the results, which were obtained in Indian sheep (variability among sheep groups is accounted for 11.1%) . Such a Tajima’s D neutrality test results were close to zero and statistically insignificant in all sheep populations, suggesting that the evolution of maternal lineages was rather random. However, high negative Fu’s Fs  values were found in several groups and suggest population expansion. Similar results were obtained in Indian  [Tajima’s D = −0.921; 3.715; Fu’s Fs = −24.003; −10.426) [−10.426) .Thus, based on the results, we may summarize that there is a significant reserve of unique genotypes in Russian sheep populations. In future we would continue our investigations of genetic diversity of these populations for better understanding of their developmental history, and genetic relationships.H = 128) was identified, of which 94.5% (H = 121) were unique. High nucleotide and haplotype diversity was revealed that corresponded to high variability of mtDNA. At the same time, the median-joining network of haplotypes formed joined clusters among the populations, which may indicate on a common maternal origin of the studied sheep. The results obtained in this study will be useful for improving strategies for the conservation of sheep genetic resources. Continuing research on large samples will provide a more complete pattern of genetic diversity and demographic history of sheep inhabiting different geographical areas in Eurasia.On the territory of Russia, there is a large population of domestic sheep with specific genotypes, the genetic potential of which has not been fully explored yet. In the present study, we analyzed the polymorphism of complete mtDNA nucleotide sequences of 135 Russian sheep from different habitats. A large number of haplotypes (


Biological production systems and conservation programs benefit from and should care for evolutionary processes. Developing evolution‐oriented strategies requires knowledge of the evolutionary consequences of management across timescales. Here, we used an individual‐based demo‐genetic modelling approach to study the interactions and feedback between tree thinning, genetic evolution, and forest stand dynamics. The model combines processes that jointly drive survival and mating success—tree growth, competition and regeneration—with genetic variation of quantitative traits related to these processes. In various management and disturbance scenarios, the evolutionary rates predicted by the coupled demo‐genetic model for a growth‐related trait, vigor, fit within the range of empirical estimates found in the literature for wild plant and animal populations. We used this model to simulate non‐selective silviculture and disturbance scenarios over four generations of trees. We characterized and quantified the effect of thinning frequencies and intensities and length of the management cycle on viability selection driven by competition and fecundity selection. The thinning regimes had a drastic long‐term effect on the evolutionary rate of vigor over generations, potentially reaching 84% reduction, depending on management intensity, cycle length and disturbance regime. The reduction of genetic variance by viability selection within each generation was driven by changes in genotypic frequencies rather than by gene diversity, resulting in low‐long‐term erosion of the variance across generations, despite short‐term fluctuations within generations. The comparison among silviculture and disturbance scenarios was qualitatively robust to assumptions on the genetic architecture of the trait. Thus, the evolutionary consequences of management result from the interference between human interventions and natural evolutionary processes. Non‐selective thinning, as considered here, reduces the intensity of natural selection, while selective thinning (on tree size or other criteria) might reduce or reinforce it depending on the forester's tree choice and thinning intensity. In natuMaintaining the potential for genetic responses is acknowledged as crucial for short‐ and long‐term adaptation because plastic responses may not be enough to keep pace with contemporary rapid environmental changes  are efficient tools to address these challenges. Defined as individual‐based population dynamics models with heritable trait variation and phenotype‐dependent interactions between individuals, DG–ABM proved to be useful for the exploration of fundamental questions in evolutionary ecology, and also as effective prospective and decision support tools to investigate the interactions between management and natural eco‐evolutionary processes  genetic model is used to reproduce observed within‐stand phenotypic variation and heredity. Although each of these components can be separately validated, assessing the validity of joint predictions when all processes are combined remains challenging for three reasons. First, introducing genetic variation, i.e., non‐stochastic and inherited inter‐individual variation, may affect the predictions of the forest growth and dynamics and progressively deviate from the previous calibration domain after several generations. Second, the coupled model aims at exploring complex eco‐evolutionary feedbacks that may result in non‐monotonic genetic trajectories hardly comparable to empirical data. Third, multiple levels of stochasticity in the demographic and inheritance processes may result in uncertain predictions. Multigeneration demographic and genetic records are available to validate predictions of such coupled models in some managed population systems , but other species can be considered. Full description of the model is available online in a user manual, regularly updated with new developments (https://capsis.cirad.fr/capsis/help_en/luberon2).To that aim, we developed Luberon2 by coupling a forest dynamics model with a model of disturbance regimes and a quantitative genetic model of trait inheritance Figure . Each an2.1.1The simulated forest stand consists of individual trees and geographic elements . The initial individual trees are defined in the inventory input file, and then new individual trees are created by the model during the regeneration process. In the initial inventory, if the individual tree diameters are not provided by the user, they are drawn from the expected distribution of diameters given tree age and stand density.2.1.2Simulations proceed at a 1‐year temporal resolution. Each year, the succession of processes is as follows: growth, selective mortality through self‐thinning, non‐selective mortality through disturbance, regeneration and non‐selective thinning at the end of the annual step Appendix .2.1.3In each pixel, the annual diameter growth of each individual tree is computed by correcting the baseline prediction of a distance‐independent tree growth model with an additive term representing the individual phenotypic value of vigor that accounts for genetic and environmental effects.Here, we used a generic and flexible tree growth model for pure even‐aged stands . The annual production of cones by a female tree is determined as an ordinal variable related to tree diameter and crown height with inter‐annual (for each tree) and inter‐individual  stochasticity; this relationship and the mean number of viable seeds per cone were calibrated on Atlas cedar Appendix . Individ2 at the center of the pixel from all mother trees of the stand, which ends in the range of seedling density values observed in Atlas cedar regeneration plots ). Once a seedling is established and its mother‐tree assigned, its father‐tree is determined among the fertile trees with a probability that may represent different types of mating systems, with or without selfing allowed. Here, the probability of each fertile tree to be the father of a seedling depends on its male fertility only. The genotype of each seedling is then derived from the genotypes of its parents, accounting for stochastic gamete sorting and recombination. Since this is a finite‐loci model to simulate short‐term evolution, i.e., only a few generations of trees, mutations are neglected.Each new seedling is initialized with a diameter drawn from the expected distribution of diameter at recruitment age given tree density, and it does not grow until reaching this age. Since the growth model is calibrated for even‐aged cohorts, adult trees that may eventually remain in the pixel are removed when recruitment starts to let the recruited seedlings grow. Self‐thinning operates to regulate the density of recruited seedlings, and the age of all accumulated seedlings over the regeneration period is set to the recruitment age. Note that the initial diameter of a new seedling is drawn at random, but its genotypic value for vigor is derived from the parents. Thus, the genotype only starts to influence tree diameter after the first step of growth. In other words, the inter‐individual variation resulting from pre‐recruitment processes is considered to be neutral with regard to the quantitative traits in the model and genetic variation only affects post‐recruitment processes.2.1.5In Luberon2, stochastic disturbances are simulated through their quantitative impacts on the demographic processes of growth, survival and/or reproduction. The disturbance regime of impacts is characterized by the distribution of impact intensities, their frequency of occurrence and, eventually, spatial patterns of impacts. The regime of disturbance impacts can be calibrated on empirical surveys. Here, we considered stochastic disturbances killing trees randomly, regardless of their individual characteristics. Each year, a potential mortality rate is drawn for the whole stand, which is then locally reduced in pixels where trees are fewer and/or smaller according to the sum of individual crown height. This type of density‐dependent stress level can be observed with abiotic stresses as well as biotic interactions. Additionally, the model allows further adjustment of the probability of death at the individual tree level based on genetic sensitivity to this disturbance, an option that is not used in the following simulations.Here, we considered three disturbance regimes of random mortality. We simulated a reference scenario with no disturbance at all. A medium disturbance regime was obtained by drawing the annual potential mortality rate from a unimodal Weibull distribution resulting in an average annual mortality rate of around 2%, which corresponds to observed rates in other tree species . Thinning criteria can be defined at the stand level based on target tree density or target basal area with thinning from below , or thinning from above (removing bigger trees), or at random thinning. Alternatively, criteria can be defined at the individual tree level, by age, size, spatial criteria or any combination thereof. In this study, all interventions were random and thus selectively neutral.U‐long and U‐short for long or short cycles, respectively), we only applied the seeding cut and the final harvest to allow regeneration, control the cycle length and align the number of seed‐trees contributing to the regeneration with the other scenarios. As baseline scenarios for long and short cycle management (B‐long and B‐short), we followed silviculture guidelines for Atlas cedar in France , delayed (E‐delayed) and relaxed (E‐relaxed). For E‐low, we performed all thinning with higher target density values to maintain a higher stand density than in the reference. For E‐delayed, we skipped the first three thinning and kept the remaining thinning with the same target density as in the reference. For E‐relaxed, we skipped the second to fourth thinning , the individual phenotypic values are computed as the sum of the genotypic value with an individual environmental term made of two components: a fixed component and an “inter‐step” component that randomly varies annually. The genetic mean and variance of the population are computed each year from the individual genotypic values.h2 = 0.3, which is usually found for growth‐related traits . Individual vigor, i.e., the individual deviation to the baseline prediction of individual diameter increment as previously defined, was the only variable trait. The response to selection depends on the initial genetic variance and, for a given initial variance, changes in genetic mean and variance depend on the genetic architecture of the trait, defined by the number of QTL, the distribution of allelic effects, and the allele frequencies. To test the sensitivity of the model to assumptions made on the genetic architecture of the variable trait , we fixed the initial additive variance of vigor and considered two contrasted genetic architectures, either 10 or 50 QTL, which are expected to result in slightly different micro‐evolutionary outcomes  are drawn from independent binomial distributions parameterized with the allelic frequencies. The fit of the resulting genetic mean and variance to their target value is evaluated and the successive steps are repeated until a proper fit between the realized and targeted values is reached. Because of the stochasticity involved at each of these steps, running the algorithm several times with the same target values results in different sets of allelic effects, allelic frequencies and genotype frequencies. We assume that the main driver of selection is the target variance rather than its underlying genetic setup. In the following simulations, we assessed the sensitivity of the model to this genetic initialization stochasticity by running five optimizations for each set of target genetic parameters for the scenario with non‐null genetic variance of vigor.Finally, we used 11 initial genetic backgrounds: one reference case without phenotypic variation and 10 cases with the same additive variance, either with 10 or 50 QTL, five different genetic setups of allelic effects and frequencies in each case, with linkage equilibrium at the start.2.1.8Selection proceeds through differential survival (viability selection) and contribution to regeneration . Natural mortality by competition, i.e., self‐thinning, is selective with regard to tree size. In this context, tree size is a key fitness component because it is the direct target of viability and fecundity selective processes. Therefore, indirect selection on vigor dynamically emerges from the model because vigor is a variable trait contributing to tree size through the growth model. The intensity of indirect selection on vigor depends on its correlation with tree size, which dynamically varies with age, competition and disturbances. More generally, in DG–ABMs, selection intensity is not a fixed parameter, it varies with population dynamics, disturbance regime and management interventions.By contrast, in the following simulations, mortality by disturbance and by silviculture were not selective because there was no variation in sensitivity to disturbance and thinning were random. Here, the only consequence of random disturbance and random thinning was to reduce the population density, hence reducing the intensity of indirect natural selection on vigor by competition.2.2The full‐factorial design combining 11 genetic backgrounds with seven silvicultural scenarios and three disturbance regimes resulted in 231 scenarios that were replicated 10 times to account for stochasticity in the regeneration compartment of the model, for a total of 2310 simulation runs.H0, defined by Gingerich , which includes the volume of dead trees. We then followed the evolution of the quadratic mean diameter (QMD) as a summary variable of the distribution of tree size  without disturbance. Over four consecutive cycles, the genetic mean of vigor continuously increased, both within‐ and across generations . We first characterized the process of natural density‐dependent soft selection on vigor that emerged from the model in the unthinned long cycle scenario  was systematically more pronounced with 10 QTL than with 50 QTL. The variation among 10 replicates with the same initial genetic setup, resulting from stochasticity in the reproduction process, was negligible except for the predicted changes in genetic variance with 50 QTL, and, in this case, it remained much less than the variation due to assumptions on genetic architecture  without disturbance by comparing simulations with vs without phenotypic variation in vigor. Stand productivity was higher with phenotypic variation than without. At the end of the first generation, total timber volume produced was 5% higher with phenotypic variation than without, due to the progressive elimination of the least vigorous individuals  without disturbance, selective self‐thinning was the only mechanism of mortality , the non‐selective decrease of tree density reduced the rate of mortality by self‐thinning , viability selection by competition was the main evolutionary force increasing population genetic mean in both cases. The short cycle induced shorter exposure to selective competition, concentrated in early stages when competition was maximum, but more opportunities for fecundity selection, finally resulting in a slightly lower genetic mean at most dates , random thinning impeded viability selection by competition, and fecundity selection remained the only driver of changes in population genetic mean  resulted in the highest evolutionary rates of vigor for each disturbance regime, ranging from 0.35 to 0.62 over all simulation runs when there was no disturbance, depending on the number of QTL and the genetic setup (Appendix E‐low) reduced the evolutionary rate by 9% on average over the disturbance regimes, and other scenarios resulted in higher reduction, in the following order: exploratory delayed thinning scenarios , unthinned short cycle scenarios , exploratory relaxed thinning scenarios , baseline long cycle scenarios  and finally baseline short cycle scenarios . On average over all the silvicultural regimes, the medium disturbance regime reduced the evolutionary rates by 46%. The severe disturbance regime caused a population collapse that did not allow any genetic evolution.More intensive random thinning  or more intensive disturbance regimes were associated with higher reduction in selective competition intensity and, consequently, with lower evolutionary rates Figure . The untAppendix . In comp4This study highlights the power of demo‐genetic models to investigate the interactions between evolutionary and demographic processes: how demographic processes including management drive predicted evolutionary rates and, vice‐versa, how evolutionary processes drive predicted population growth and dynamics. Here, we implemented this approach by integrating genetic variation of vigor in an individual‐based forest growth model, wherein a trait directly linked to the demographic processes also drives individual fitness components . This represents a situation of particularly strong interaction between evolutionary and demographic processes, and we expect less interaction when the variable traits are less tightly connected to demographic processes.4.1Considering a plausible level of initial additive variance and various scenarios of silviculture and disturbance regimes, the predicted evolutionary rates of vigor over four generations  were in the range of published empirical estimates for wild plant and animal populations: from 0 to 0.65  the mortality rate by self‐thinning progressively decreases while population density decreases, which in turn decreases the selection intensity; (ii) genetic variance in vigor progressively decreases due to selective mortality, which decreases the response to selection; (iii) the correlation between vigor and tree diameter, a major component of fitness, progressively increases with age, which increases the selection gradient on vigor. In our simulations, the third mechanism did not compensate for the other two, and the higher rate of change in the genetic mean and variance was observed at a very early stage, 1 or 2 years after the start of self‐thinning mortality.The model did not account for possible selection on vigor during the juvenile phase before recruitment age because the initial diameter was randomly distributed among trees regardless of their genotypes at this age. Selection on vigor only operated once a correlation between vigor and tree diameter was established and competition‐induced selective mortality started. However, it has been shown that selection can start during this juvenile stage, in particular, due to the elimination of inbred individuals . Thus, compared to the baseline unthinned scenario without disturbance, the rate of evolution was reduced by disturbance alone, depending on the disturbance regime, and reduced by management alone, depending on the silvicultural scenario. When both disturbance and management were combined, the rate at which vigor evolved was drastically reduced. There was no synergy but rather a negative interaction effect so that the impact of disturbance on the rate of evolution was reduced in the scenarios with management and vice‐versa.In reality, the negative impact of thinning on the intensity of natural selection for vigor can be reinforced or balanced by the forester's tree choice, i.e., when non‐random thinning is applied, either “from below”  or “from above” (bigger trees removed preferably), at each step. Therefore, the evolutionary impact of real forest management combines both effects: the regulation of natural evolutionary processes combined with direct or indirect anthropogenic selection, which can ultimately result in higher rates of evolution in human‐impacted contexts than in wild populations as observed in animal populations  and because vigor had no effect on competing ability before the recruitment age of 25. However, in reality, the evolutionary consequences of shortening the silvicultural cycle would be very sensitive to the intragenerational demographic dynamics, which are complex and multifactorial, depending on initial tree density, local site fertility, population vigor and thinning and disturbance regimes.4.4This study highlights the need to compare genetic mean and variances between generations at similar life stages, as the annual fluctuations in genetic mean and variances within each generation may not faithfully represent the effective evolutionary signal passed from one generation to the next. For the genetic mean of vigor, viability and fecundity selection mechanisms operated in the same direction, with a proportionally higher impact of the former in our stand density conditions, resulting in a positive correlation between within‐generation changes and inter‐generational evolution. The situation was radically different for genetic variance. The genetic variance was restored at each reproduction event after regular reduction due to linkage disequilibrium through the Bulmer effect during the viability selection process within the previous generation. This alternation of reduction and restoration phases resulted in weak inter‐generational evolution of variance despite intensive selection and a decrease of variance within each cycle. The restoration of variance at reproduction was more efficient with 50 QTL than with 10 QTL, which relates to a greater role of covariance among QTL with 50 than with 10 QTL as previously mentioned. The restoration of variance at each generation might be one of the reasons why standing variation is not a powerful indicator of the response to selection in wild populations . Through the same mechanism, this study also shows that non‐selective disturbances take over density‐dependent soft selection. Shortening the rotation length has a complex effect on selection.Extending the model to disturbance regimes with additional specific resistance traits independent from the target of density‐dependent selection would be an approach to investigate the trade‐off between resistance to disturbance and long‐term adaptation in long‐lived organisms (Schmid et al., All the co‐authors disclose any potential sources of conflict of interest.Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.Appendix S3Click here for additional data file.Appendix S4Click here for additional data file.Appendix S5Click here for additional data file.


This review highlights the effect of carcinomas on the results of the examination of autosomal genetic traits for identification and paternity tests when carcinoid tissue is the only source and no other samples are available. In DNA typing or genetic fingerprinting, variable elements are isolated and identified within the base pair sequences that form the DNA. The person’s probable identity can be determined by analysing nucleotide sequences in particular regions of DNA unique to everyone. Genetics plays an increasingly important role in the risk stratification and management of carcinoma patients. The available information from previous studies has indicated that in some incidents, including mass disasters and crimes such as terrorist incidents, biological evidence may not be available at the scene of the accident, except for some unknown human remains found in the form of undefined human tissues. If these tissues have cancerous tumours, it may affect the examination of the genetic traits derived from these samples, thereby resulting in a failure to identify the person. Pathology units, more often, verify the identity of the patients who were diagnosed with cancer in reference to their deceased tumorous relatives. Genetic fingerprinting (GF) is also used in paternity testing when the alleged parent disappeared or died and earlier was diagnosed and treated for cancer. In 1985, Jeffreys discovered DNA profiling, which is one of the most important and prominent developments in the security and criminal fields ,3. DNA pAmong the genetic sites, microsatellites (MS) are 1-6 base pairs of tandem repeats found within introns and are subject to insertion/deletion events . They arCancer arises from a combination of the loss of tumour suppressor genes and the activation of oncogenes. Most oncogenes are activated by simple missense variants that are unlikely to affect the fingerprint. Loss of tumour suppressor genes, especially DNA repair genes, may increase the risk of multiple mutations throughout the genome. The loss of chromosomal material, in which the second copy of a tumour suppressor is often lost, can delete information important for fingerprinting . BecauseWhat is GF, and what are the GF sites?What is the role of GF in detecting cancerous tumours?What are cancerous tumours, and what is the effect of carcinomas on GF?In writing this review, answers to the following questions were sought:The characteristics of all living organisms, including humans, are essentially determined by the information contained in the DNA they have inherited from their parents. The molecular structure of DNA can be thought of as a two-strand zipper, with nucleotides being the teeth of that zipper. Each tooth is represented by one of the four letters , and the opposing teeth form one of two pairs, either A-T or G-C. The letters A, C, G and T stand for adenine, cytosine, guanine and thymine, the basic building blocks of DNA. The information contained in DNA is primarily determined by the sequence of letters along the zipper. For example, the sequence AAT stands for different information than the sequence TAA, although they use the same letters.The process of GF was developed in 1985 by the geneticist Alec Jeffreys. He discovered that some sections of strands of DNA contained sequences of nucleotides repeated next to each other. He also discovered that these sequences are found in the same order of nitrogenous bases in all humans but differ from person to person regarding the number of times they are repeated . A repeaGF has evolved in two distinct stages. The first stage relied on the use of VNTR analysis to detect repeating sequences of medium length  and the subsequent use of the restriction fragment length polymorphism (RFLP) technique to analyse the genetic traits of the DNA in the genetic loci. The VNTR technique for GF analysis was exclusively used for approximately 10 years after its discovery . In the Genetic loci that contain short repetitive sequences are known as STR loci, STR markers or genomic fingerprinting loci. The human genome contains many STR markers scattered on all somatic and sex chromosomes, where they occur at a rate of 1 per 10,000 nitrogenous bases. The sites usually spread in the non-coding regions, and they are located either (1) outside the boundaries of genes, in the regions that separate genes on the DNA strand or (2) within the boundaries of genes, in what are called introns . Genes consist of two parts: exons and introns. Exons, which are the functional parts of each gene in which the process of gene expression occurs, produce the proteins responsible for genetic traits. Introns are the non-functional parts of genes in which no gene expression occurs and no type of protein is produced. Consequently, GF sites are named according to their locations. A genetic site located within a particular gene is designated by the gene’s name, while the genetic sites that lie outside the boundaries of known genes are named based on the chromosomal positions of the loci in the human genome. Each genetic locus on autosomal chromosomes contains two variants of the gene  because there are two copies of autosomes in each cell (one inherited from each parent). The allele at a DNA site is the number of repeats of the sequence of nucleotides at that site. If the alleles are different, they appear as two different numbers, such as the genotype ‘9,7’; in this case, the genotype is heterozygous. If the alleles are identical , the genotype is homozygous. As for the fingerprinting sites on sex chromosomes, each genetic locus contains only one allele because each cell contains one copy of the sex chromosomes; a female carries the X allele at the locus on her chromosome, while a male carries the Y allele at the locus on his chromosome. The main 13 STR loci used for DNA fingerprinting analysis—the Combined DNA Index System (CODIS) core and extra STR loci—are located on the autosomal chromosomes of the human genome ,24, in ap53 gene in the next stage, after which the cell cycle stops, allowing DNA repair; alternatively, the cell will die by apoptosis if the damage is not repaired. In some cells, the p53 gene may not be active or stimulated because of cellular damage. This may lead to the failure of the cell cycle to stop at the control point, causing the cell to continue for the rest of its life cycle with errors or damage to the DNA without repair or apoptosis occurring. In this case, the cell accumulates the types of mutations or chromosomal changes that lead to cancer development [A DNA fingerprint is the same for every cell, tissue, and organ in a person, unlike a traditional fingerprint, which is only found on the fingertips and can be altered by surgery. As a result, DNA fingerprinting is rapidly replacing other methods to recognise and distinguish unique people. An additional application of GF technology is the diagnosis of disorders and diseases in humans. Detecting disorders and diseases early allows the medical team to provide effective treatment. GF has been used in many medical applications, arguably the most important of which is the detection of cancerous tumours . Cancer elopment . Apoptosis is one of the vital processes necessary to maintain the positions of cells in tissues as well as their balance. During this programmed cell death process, a cell kills itself during growth via the shrinkage and decomposition of the contents of the nucleus and the disintegration of DNA into very small pieces, and the remains of the dead cell are consumed by the healthy neighbouring cells. Apoptosis can be inhibited by tumour-suppressing genes because of exposure to genetic mutations. This allows cells with damaged DNA to grow abnormally and become cancerous . MS regiThey are also important for studying the genetic changes in tumours since tumour tissues often display some type of MSI as a result of mutations and somatic changes. Another type of genetic instability, loss of heterozygosity (LOH), is accompanied by allelic losses. Cancerous tumours are usually categorised in terms of instability, and somatic genetic changes in the microsatellite regions are divided into three subgroups. First are tumours with a very high level of microsatellite instability (MSI-H), in which the genetic loci have a mutation rate of more than 30%. Second, are tumours with a low level of microsatellite instability (MSI-L). The third group comprises tumours with microsatellite stability (MSS). LOH is a common method of detecting genetic deletions in many tumours. LOH is indicated by severe allelic defects at one or more genetic loci in a single-source DNA sample; one allele is deleted, and the other allele remains at the heterozygous locus and appears as homozygous. In this case, LOH influences human identification when an archived clinical sample from a tissue biopsy is used as a reference sample for the identification of a person in mass disasters . In manySeveral studies have linked the incidence of various cancerous tumours (and the differences resulting from these tumours) to the alleles of the genetic fingerprints of affected individuals. However, other studies have described differences in patients’ genetic fingerprint analyses, indicating that cancerous tissue samples cannot reliably identify individuals or resolve questions about paternity. A study by Al-Harthi  was condAnother study by Much et al.  in the UA study by Pelotti et al.  was condAdditionally, Ceccardi et al.  evaluateA study by Alharbi et al.  investigIn addition, Al-Qahtani et al.  reportedA study explored the use of a forensic short tandem repeat (STR) kit to detect somatic hypermutational tumours in gastrointestinal (GI) cancers. The researchers tested 250 GI tumour samples using two commercial kits to determine microsatellite instability (MSI) and STR alterations. They found that 62.4% of patients exhibited STR alterations, including 100% of MSI-H cases and 60% of MSI-L/MSS cases. The researchers also identified three types of forensic STR alterations: allelic loss, Aadd, and Anew. The allelic loss was the most common alteration observed in GI tumours. The study suggests that the widely used forensic STR kit may have potential usage in identifying hypermutational status in GI cancers. This method could help in the selection of patients who can benefit from immunotherapeutic agents .Another study examined the genetic alterations and DNA damages caused by formalin-fixed tumour tissue in forensic identification. Tumour tissue samples from 25 patients who had undergone surgery for neoplasia were analysed. The DNA profiles of both fresh and formalin-fixed tumour tissue were compared to those of fresh and formalin-fixed normal tissue from the same patient. The results showed that only a quarter of the samples had the same genotypes in all tissue specimens, while the rest had at least one altered locus. The most common genetic alterations observed were partial loss of heterozygosity (pLOH), complete loss of heterozygosity (LOH), additional alleles, and substitution of new alleles. The frequencies of pLOH and LOH were higher than allelic addition or substitution. The study also identified specific genetic markers that were frequently altered in tumour tissue, including D5S818 and D18S51. These findings highlight the importance of considering genetic alterations caused by formalin fixation when using tumour tissue for forensic identification .A study by Chen et al.  investigAnother study looked at the clonal evolution of tumour cells and the potential implications for therapy selection. The researchers conducted an in vitro study using cultured tumour cell lines (Jurkat) and non-tumour cell lines (WIL2-S). They analysed the subclones of these cell lines using Short Tandem Repeats (STR) profiling to identify genetic aberrations. They found that new aberrations occurred more frequently in tumour cells than in non-tumour cells. They also found a significant correlation between the accumulation of aberrations and the rate of cell growth. These results suggest that tumour cells have a higher potential for genetic instability and clonal evolution. The researchers suggest that this approach could be used to analyse primary tumour cell cultures from patients to aid in therapy selection .In addition, a study investigated the reliability of using malignant tissue samples for forensic investigations by analysing Short Tandem Repeat (STR) loci in surgically removed samples of papillary thyroid cancer (PTC). The study found four types of changes in STR loci between normal and tumour tissue: partial loss of heterozygosity (pLOH), complete loss of heterozygosity, additional alleles, and new alleles not found in normal tissue. These changes were observed in 20 of the 23 STRs tested, with D2S1338 being the most affected locus and pLOH being the most common change. The study suggests that caution should be exercised when interpreting DNA typing results from malignant tissue, especially when reference samples from normal tissue are not available. Patients aged 40–59 years had the highest frequency of STR variation. The results contribute to the understanding of the genetic mechanisms underlying PTC and highlight the challenges associated with the use of tumour tissue in forensic casework .In addition, a review study reported that forensic short tandem repeat (STR) markers, commonly used for DNA profiling, could provide information beyond mere identification. The authors conducted a comprehensive search and identified 57 studies that reported an association between a STR-inclusive gene and a phenotype. These studies identified 50 unique traits associated with the 24 markers included in the analysis. The STR marker TH01 had the largest number of associations, including five studies that linked it to schizophrenia. However, none of the associations found were independently causative or predictive of disease. While forensic STRs have traditionally been considered non-coding and unrelated to phenotype, there is increasing evidence that STRs in non-coding regions may influence gene expression and contribute to specific traits. Further research is needed to explore the potential functional role of forensic STRs [The cancerous tissue is usually heterogeneous, with some having a high proportion of normal cells, and it is usually removed with normal tissues bordering it that can be used easily for genetic fingerprinting. If no normal material is available, areas with very low neoplastic content can be identified and, therefore, still may contain at high levels any markers used in fingerprinting; as such, low allele levels of a marker such as an SNP can be discarded in most cases as being due to low level of contamination with tumours. Even in biopsy samples containing high neoplastic content, available assessment of rare polymorphisms may still be possible. However, genetic instability and degradation in archived histology samples from cancerous tumours could affect the reliability of STR typing and its potential for human identification . FingerpThe above review has documented that GF became a precious method for solving crimes and protecting human rights, particularly by distinguishing people from one another, especially discovering the criminals and proving paternity in family conflicts or identifying the dead and the missing. The STR technique is used to employ a group of genetic sites on the DNA, as these sites are characterized by the fact that they spread on all the chromosomes that make up the human genome and that they are of high individual strength, as well as contain short-length tandem sections. This made it of great importance in cases of examining decomposing forensic samples. However, there is a possibility that the mutations in carcinomas affect the formation of GF sites spread on different chromosomes, which leads to different results of the genetic fingerprint examination for carcinoma samples. This may affect the examination of the genetic traits derived from these samples, which may result in a person not being identified correctly.


The genomic landscape of Stone Age Europe was shaped by multiple migratory waves and population replacements, but different regions do not all show similar patterns. To refine our understanding of the population dynamics before and after the dawn of the Neolithic, we generated and analyzed genomic sequence data from human remains of 56 individuals from the Mesolithic, Neolithic, and Eneolithic across Central and Eastern Europe. We found that Mesolithic European populations formed a geographically widespread isolation-by-distance zone ranging from Central Europe to Siberia, which was already established 10,000 years ago. We found contrasting patterns of population continuity during the Neolithic transition: people around the lower Dnipro Valley region, Ukraine, showed continuity over 4000 years, from the Mesolithic to the end of the Neolithic, in contrast to almost all other parts of Europe where population turnover drove this cultural change, including vast areas of Central Europe and around the Danube River. Genome-wide sequencing of 56 ancient hunter-gatherer and early farmer individuals from Stone Age Central and Eastern Europe reveals striking population continuity in the east in contrast to central Europe that displays extensive admixture. Before the agricultural transition that started ~8500 years ago5, Europe was inhabited by hunter-gatherer populations, roughly clustering into two groups (as defined by archaeogenetics); Western Hunter-Gatherers (WHG) in Western Europe and East European Hunter-Gatherers (EHG)8 in northeastern and in the extreme eastern frontier of Europe10. In between these core regions, the groups from the east (EHG) and from the west (WHG) probably met and admixed13. In Scandinavia, where ice coverage partially persisted until 10,000 years ago, the colonization of WHG groups took place from the south, whereas EHG groups entered from the northeast, likely following the Norwegian Atlantic coast from the north to the south, creating an admixture pattern that goes in the opposite direction to central/eastern Europe11. However, our knowledge concerning the history and dynamics as well as the time scale of genetic admixture and continuity of the Mesolithic populations across Europe is still limited.Modern humans started spreading into Europe some 50,000–40,000 years ago16 from the Near East, which were genetically closely related to the groups from the Neolithic Anatolia (AN)19 and more distantly to the hunter-gatherers from the Caucasus region also known as CHG20. The mode and level of population interaction in the initial and subsequent times of the European Neolithic farmers and hunter-gatherers has been a matter of debate for very long time. The current consensus points to geographically and temporally varying level of genetic admixture of the EN and WHG groups24 starting already at the early stages of the arrival of the former in central Europe24. Based on evidence from the archeological record, there may have been differences in the levels of cultural contacts between the farmer and hunter-gatherer groups in a west-east gradient of the widely spread Early Neolithic Central European Linear Pottery culture (LBK)25. However, the suggested differences in interaction levels may have been in the form of exchange of goods rather than genetic admixture.The population structure of Stone Age Europe experienced a large-scale change in the early Holocene. This change was driven by the migration of farming groups 26. A similar type of development took place in these communities as in the Neolithic farming groups. For instance, in some parts of Eastern and Northeastern Europe pottery was introduced but the subsistence was still mainly based on hunting and gathering28. Genetic data from some of these groups have shown that the genetic makeup before and after the European agricultural dawn remained similar in contrast to Central and Western Europe30.In addition to the variable contacts and interactions between the hunter-gather and incoming farmer groups, in some European regions  the hunter-gathering lifeway prevailed for much longer in comparison with the Southern and Western Europe. In Ukraine for example, the steppe and forest steppe zones of the North Pontic region were inhabited by hunter-gatherer communities still during the Neolithic sustaining mostly on aquatic resourcesTo improve our understanding of the level, character, and regional variability of contacts between the Central and Eastern European Stone Age groups, we sequenced and analyzed whole genomes of individuals who lived before and after the Neolithic transition  in the eastern frontier of Europe. The investigated area encompasses an area covering modern-day Romania, Poland, and the lower Dnipro Valley region in Ukraine over a time span of ~5000 years . Our investigation revealed that before Neolithic, the eastern frontier of Europe contained an admixture cline between genetically differentiated groups from Central Europe and Siberia. We also observe stronger genetic continuity and limited admixture in the Dnipro Valley region after the Neolithic transition while large-scale gene flow took place in populations further to the west.To investigate the genetic affinities in Stone Age Central and East Europeans, we generated genome-wide sequencing data from a collection of 56 individuals from Epipaleolithic/Mesolithic, Neolithic, and Eneolithic Poland, Romania, and Ukraine Fig.  and 2. T20) and the Upper Paleolithic Siberian Afontova Gora38 , the Baltic region (BHG), and Sweden & Norway (SHG) also fell within this cline. To gain further insight into the genetic composition of the studied groups, we inferred ancestry components31  for the dataset together with a collection of Stone Age and Bronze Age individuals across West Eurasia , where X was the test individual and Y the highest genome coverage Mesolithic individual from the same region. In addition, we employed the co-called Anchor Method to assess population continuity through time32 using Loschbour (WHG) as an anchor individual. These tests further supported strong genetic continuity of the lower Dnipro Valley region from the Mesolithic to the Neolithic , which indicates past effective population sizes. To get an indication of past population sizes, we compared CND trough time in each region. The magnitude of CND was very similar for the Mesolithic and Neolithic populations from the Dnipro Valley region, in contrast to Romania and Poland where the genetic diversity is much higher among the Neolithic individuals in comparison with any Mesolithic estimate  and the Human Origins overlap panel. The f4-values were negative for all test individuals indicating increased allele sharing with the Paleolithic Siberian AfontovaGora3, indicating gene flow between the WHG and Paleo-Siberian lineage. However, the tests were not significantly different from 0 for the Polish Mesolithic individuals, indicating genetic similarity to WHGs  from the 1000 genomes overlap panel and confirmed the significant contribution from the eastern lineage to all Central and East as well as North European Mesolithic individuals investigated  from the best single-source models in 19 cases  and in 15 cases both single-source models were rejected (tail probability < 0.05). The estimated admixture proportions of WHG-related ancestry ranged from 50.9%  for Sidelkino to 88% (76.2–99.8%) for SC1  forms a clade with the European Mesolithic individuals in respect to the AfontovaGora3  using f34) including representative groups from the gradient. The stepping stone-like graph ). Furthermore, as three other tested models without this admixture were inconsistent with the data  in the Paleo Siberian-WHG cline using a linear regression analysis of the level of allele sharing (f4-test) and distance from the approximate region occupied by unadmixed WHG population. The geographic coordinates of a set of samples assigned as WHG8 were used to approximate this so-called WHG core region  or the WHG distance median or total distance from all the WHG points (instead of minimum distance form WHG) were used in the modeling , we also found that a three-way population admixture model that includes the Caucasus Hunter-Gatherers (EHG-WHG-CHG) fits the genetic ancestry composition of this population  populations in addition to a chimpanzee outgroup , while the single-source models were rejected  archeologically classified as Serednyostogivs'ka (Sredny Stog) horse keepers  showed smaller level of allele sharing with other individuals from the same region Fig. . This in6, a significant increase in allele sharing with the local hunter-gatherers were detected in 16 out of 30 investigated Neolithic/Eneolithic individuals from Poland and Romania  share more alleles with the Romanian and Polish Neolithic/Eneolithic individuals when compared with early Neolithic Central Europeans. In comparison with the early Neolithic LBK individual from GermanyZ-score > 2, f4-ratio test) ranged from 9 to 20% in the Romanian Neolithic/Eneolithic individuals while it was 9–97% among the Neolithic/Eneolithic individuals from Poland . This resurgence of the local Mesolithic ancestry in the Eneolithic has also been found in previous studies in other parts of Europe36.The estimated ancestry deriving from the local Mesolithic hunter-gatherers  among the sequenced individuals  among the studied individuals within population using the READ software packageFrom the Curătești family, buk019 and buk023 were first-degree relatives, while buk019 and buk023 were second-degree relatives to buk022 . Both analyzed individuals were males who carried the same mt & Y haplogroups , pioneer animal husbandry 52, suggest a variety of different views and practices of biological and potentially non-biological kin relations.Our analysis of close genetic relatedness, on the one hand, revealed the role of genetic relatedness in burial practices in cultures across Mesolithic, Neolithic, and Eneolithic Europe. One the other hand, the results also pointed to a possibility of non-genetic connections such as in the Neolithic Late Lengyel culture Kruza Zamkowa case exemplified here. These observations, together with previous investigations of close kin relations in the Stone Age53. The sampling appropriateness of the material was overseen by the museum curators included in this study.The study has conducted following the ‘Ethical guidelines for good archaeological praxis’, including respectful handling of archaeological human remains, as described by the Swedish Archaeological Society14C was measured from tooth or bone specimens in Beta Analytic Carbon Dating Service in Miami, FL, USA (44 specimens) and in Poznań Radiocarbon Laboratory in Poland (7 specimens) or collected from the literature (3 specimens). For a subset of samples (see Supplementary Data δ13C) and nitrogen (δ15N) isotope ratios were measured using Isotopic Ratio Mass Spectrometry (IRMS). This was carried out at Beta Analytic Carbon Dating Service for most of the samples and the remining at the Department of Geosciences at the Faculty of Geosciences/Geography at the Goethe University in Frankfurt am Main, Germany. The dates were calibrated using Oxcal v4.4.4, IntCal 20 and freshwater reservoir effect (FRE) correction was applied sample specifically depending on the stable isotope-based dietary analysis. See Supplementary Notes A set of 59 prehistoric human bone specimens  that have been attributed to either the Mesolithic , the Neolithic or the Eneolithic periods, was collected from Poland, Romania, and Southeastern Ukraine , positive air pressure, and HEPA-filtered laminar flow hoods, either at Uppsala University, Sweden  or at Adam Mickiewicz University in Poznan, Poland (lbk- and poz-specimens). The laboratories were frequently cleaned with bleach (NaOCl) and UV-irradiation. Equipment and non-biological reagents were regularly decontaminated using bleach and/or RNase AWAY™ (ThermoScientific) and UV irradiation.2 on each side), wiping with 1 % bleach, a second round of UV-irradiation and finally by removal of the outer surface layer. Two strategies were used when sampling for DNA. Either we removed between 40–110 mg of bone- or tooth root powder (with a Dremel drill or by grinding down a bone piece with a Starbeater mill from VWR), or we removed a small amount of bone or tooth root of approximately the same weight using a Dremel drill with diamond cutting wheels. DNA was extracted using silica-spin column protocols, either following protocol C54 but with the SDS in the extraction buffer exchanged for 1 M Urea as in55 or by using a protocol targeting shorter fragments56 and finally eluted in 40–110 μl EB (Qiagen). When DNA was extracted from bone fragments, the protocols were modified to include an initial incubation step with 1 mL 0.5 M EDTA in a hybridization oven at 37 °C and on rotation for 30 min, followed by removing the solution. If the samples were not fully digested after over-night incubation in the extraction buffer, additional Proteinase K (50 µg/mL) was added, and incubation continued for 5–8 h at 55 °C57. Between one and three different bone elements were sampled from each individual, and one to three extractions were performed per bone element. We processed one or two negative controls for every eight samples extracted.Prior to sampling, bones and teeth were decontaminated through UV-irradiation -treated libraries, where post-mortem deaminated sites were cut with UDG and endonuclease VIII (endo VIII)59 (see Supplementary Data 58 were prepared and amplified following the supplier’s instructions (ThermoFisher Scientific). Libraries were then amplified in two to four reactions for 12–18 cycles with approximately one negative control for every four reactions. Blunt-end libraries were prepared and amplified as in11 and UDG-treated libraries as in60 using IS4 and indices from58. Amplified libraries were quantified either on a TapeStation using a High Sensitivity kit (Agilent Technologies) or using a Bioanalyzer 2100 and a High Sensitivity DNA chip (Agilent Technologies). All DNA libraries were sequenced at SciLifes SNP & SEQ Technology platform in Uppsala, Sweden, using either Illumina HiSeq 2500 with v. 2 paired-end 125 bp chemistry or HiSeq X Ten with v. 2.5 paired-end 150 bp chemistry. The negative controls processed did not yield any DNA and were not sequenced.Single-indexed blunt-end Illumina DNA libraries were prepared using P5 and P7 adapters following refs. 61 or MergeReadsFastQ_cc.py62. Trimmed and merged reads were mapped to human reference genome version hs37d5 using bwa aln Version: 0.7.17-r118863. The following non-default parameter settings were used in the mapping: -l 16500 -n 0.01 -o 233. PCR duplicates were detected and collapsed using a modified version of FilterUniqueSAMCons.py62, including a random sampling of variant selection in duplicate collapsing. We excluded reads with reference identity less than 90 % or read length shorter than 35 bp.The obtained paired-end sequence reads were first merged (minimum required overlap was set to 11), and adapters were removed using AdapterRemoval v. 2.1.711. The genetic sex for each sample was determined based on the ratio of reads mapping to sex chromosomes64 and X-to-autosome ratio65. For each specimen, we performed multiple rounds of sequencing. After an initial inspection of the data quality, we merged the mapped bam files from each library, including PCR duplicate removal, as described above. After the library merge, we combined all libraries from one individual into one bam file using samtools v. 1.5 merge option66. For each sampled individual, we inspected the read length distribution and signature of deamination at 5’ and 3’ read ends67 using MapDamage v.2.0.868. The read length and damage plots associated with the samples from this study are available from Figshare (10.6084/m9.figshare.22811240.v1).To evaluate each sequencing library’s quality and quantity, we calculated the number of reads, the proportion of reads mapping to the human genome, average read length, clonality, and mean depth of coverage as in70 and were applied to all samples. X-chromosome-based contamination estimation was applied to male individuals as implemented in ANGDS v.0.92172. We used the same parameter settings as in the ANGSD manual except –minQ was set to 30.To estimate possible contamination in our sampled individuals, we utilized three different methods exploiting variation in haploid chromosomes. Two methods were based on mitochondrial (mt) reads71 using the following command: angsd -i <inbam > -doFasta 2 -doCounts 1 -minQ 30 -minMapQ 30 -setMinDepth 3 -r MT: -out <outfasta > . Hence, the minimum number of bases per site was set to 3, minimum mapping, and base quality to 30. At polymorphic (heteroplasmic) sites, the most common base was retained.We generated a consensus mitochondrial sequence based on the reads mapping to the hs37d5 mt reference assembly. For doing so we retained the most common allele on each sites and each individual with ANGSD73. and the online version of HaploFind74 with default settings. For each Haplofind assignment, we checked the missing mutations and evaluated the assignment’s reliability using PhyloTree Build 1775. If all the defining mutations of the haplogroup at the lowest assigned level were missing due to lack of data or if any of them had the ancestral allele, we manually assigned the haplogroup to a higher-level clade. We also checked the presence of additional previously defined variants if the HaploGrep and HaploFind assignments differed. The detected mt base substitutions are available from Supplementary Data The generated fasta files were used to call mitochondrial haplogroups using the standalone version of HaploGrep v. 2.1.1651. In brief, the most likely haplogroups were assigned based on the genotype calls from 7773 transversion no-indel branch determining polymorphisms obtained from the International Society of Genetic Genealogy collection . For each sample, we followed its Y-lineage based on derived mutations starting from the Y-chromosome tree root. The individuals were assigned to the lowest level haplogroup linked to the most likely lineage that did not show ancestral mutations. The derived mutations not connected to this lineage were not taken into consideration. Y-haplogroups were not assigned if the individual’s mean genome depth was less than 0.1 X. The ancestral vs. derived state for accessible sites is available from Supplementary Data For individuals assigned as XY, we determined Y-chromosome haplogroups using the pipeline developed in83 as a comparative dataset. The full list of ancient individuals is available from Supplementary Data In addition to the newly produced samples, we utilized a selected set of previously published Eurasian ancient individuals84, and all sites were used in genotype calling. The alleles absent in the modern reference samples were coded as missing data for the specific ancient sample on the final dataset. The reference panels utilized in this study were (1) 594 024 autosomal SNP sites from the Affymetrix Human Origins fully public dataset34. (2) 1 938 919 autosomal transversions from the 1000 genomes project85 of which minor allele frequency in the Yoruba (YRI) population were at least 0.186.The ancient samples were overlapped with two different reference single nucleotide polymorphism (SNP) panels by randomly drawing a base from each SNP site on the given reference panel . We used only transversions for blunt-end libraries, while for UDG-treated libraries, all sites were used. For half-UDG treated libraries, we clipped off five bp from both read ends using BamUtil v. 1.0.14 trimBamhttp://popgen.dk/software/download/angsd/hg19ancNoChr.fa.gz . Each position’s genotypes were extracted using samtools faidx and written in tped format as homozygous for the reference allele. The dataset was converted to bed format and the genotype files were merged using Plink version 1.90b4.987–bmerge option. Of the original sites, 29,347 were triallelic and were removed from the final combined dataset.After the dataset overlap, we haploidized the modern samples to match the format of the ancient samples. For the overlap panels 2, we added chimpanzee genotypes from 37 to check for possible sample duplicates and kin pairs in our dataset. Since READ assumes no population structure within individuals explored, the following groups were analyzed in separate READ runs: (1) All Ukrainian samples from the current study and additional Mesolithic and Neolithic samples from nearby sites from Jones et al. 30  and Mathieson et al. 12 . (2) All Romanian and Polish Mesolithic samples from this study in combination with relevant samples from González-Fortes et al. 22  and Mathieson et al. 12 . (3) Romanian Neolithic/Eneolithic samples from this study, and samples I2,532 and I2,533 from Mathieson et al. 12. (4) The Polish Neolithic/Eneolithic samples from the current study combined with 11 age fitting samples from Fernandes et al. 36 .We used program READWe initiated the analysis by including all individuals. To confirm the detected kinship pairs, we rerun the analysis by excluding samples with little data (<0.1 X mean depth of coverage) and only including whole-genome sequence data. We used the 1000 genomes overlap panel for the kinship analysis except for the sample sets, including capture data, where we used the Human Origins overlap panel. We reported the kin pairs, whose standard error scaled normalized mean P0 score distance (Z-score) to the adjacent categories were >1.96.89. The eigenvectors were estimated twice. First, using 768 individuals from 81 West Eurasian groups from the Human Origins dataset, and the ancient samples were projected onto the vector space estimated from the modern samples with the following settings:The general population structure of the investigated individuals was characterized using principal component analysis (PCA) implemented in EIGENSOFT smartpcaaltnormstyle: NO, numoutlieriter: 0, killr2: YES, r2thresh: 0.7, numoutlierevec: 0, lsqproject: YES, shrinkmode: YES31. We first pruned the data for linkage disequilibrium using Plink version 1.90b4.987, and the following parameters–indep-pairwise 200 25 0.4 . We ran ADMIXTURE with a different number of clusters (k) ranging from 2 to 10, and for each number of clusters, we replicated the run ten times with different random starting points. For visualization, we used pong v. 1.4.790 to bundle together the membership coefficient matrices (Q) from different replicates and the different number of clusters.Additionally, we run model-based clustering of the 281 ancient individuals  Ukraine Mesolithic: Lower Dnipro Valley Epipaleolithic/Mesolithic ukr125, ukr102, ukr108 (this study) and Ukraine_HG/StPet1230. (2) Ukraine Neolithic: Lower Dnipro Valley Neolithic/Eneolithic . (3) Sweden Mesolithic1: Huseby Klev Mesolithic ble004, ble008 from Kashuba et al. 80. (4) Sweden Mesolithic2: Stora Förvar (Gotland) Mesolithic SF12 and SF9 from ref. 11. (5) Sweden Mesolithic3: Motala Mesolithic Motala-1, Motala-4, Motala-6, Motala-126. (6) Southern Norway Mesolithic: Hummervikholmen Mesolithic Hum1 and Hum2 from Güther et al. 11. (7) Romania Mesolithic: rom061 and rom066 from this study; SC1 & SC2 from ref. 22. (8) Poland Mesolithic: poz297 and poz503 from this study. (9) Latvia Mesolithic: Hunter-gatherers from Zvejnieki ZVEJ25 & ZVEJ3230.  Poland Neolithic/Eneolithic lbk102, lbk104, lbk138, poz120, poz177, poz236, poz252, poz275 from this study. We paired individuals in descending temporal order (except for kin pairs) to access the temporal patterns in diversity   following the procedure from ref. 3-outgroup statistics  and f4-statistics  for multiple different comparisons. A random sample of 20 Yoruban individuals were used in the f3-statistics calculation. The f-statistics were calculated using AdmixTools v. 2016080334 in an AdmixTools wrapper Admixr v. 0.7.191 in R v. 3.6.192. To test for genetic continuity, we used the f3-outgroup test and the highest coverage local Mesolithic individual as the group Y. Different admixture scenarios of the Mesolithic and Neolithic groups were tested using qpAdm v. 401, and qpGraph v. 6100 from the AdmixTools package34 with the following parameter settings:To test the different models of shared ancestry and admixture in our study group, we calculated fqpWave & qpAdm: allsnps: YES, qpGraph: outpop: NULL, useallsnps: YES, blgsize: 0.05, forcezmode: YES, lsqmode: YES, diag: .0001, bigiter: 6, lambdascale: 1For each qpAdm set of models, an adjusted set of left and right groups were used to optimize the confidence interval estimation. See Supplementary Data for the model information.34 test ran in Admixr to estimate admixture proportions in the Neolithic dataset as follows: (1) The Polish Neolithic/Eneolithic dataset: f4ratio. (2) The Romanian Neolithic/Eneolithic dataset: f4ratio.We used f4-ratio34 package was used throughout the study to covert the data from plink to eigenstrat format.Covertf program from the AdmixTools v. 2016080332. In short, we condition upon heterozygous ancestral/derived sites in a given so called anchor individual and count the proportion of derived alleles at those sites in other test individuals. The proportion derived at conditioned sites is expected to remain constant forwards in time along the branches of a phylogeny, but reduces backwards in time, providing a signal to detect population continuity and ghost admixture among a heterochronous sample . All-site vcfs were used as input to the Anchor method . Analyses used all sites for UDG-treated sequence data and were restricted to transversion sites for non-UDG treated sequence data.Continuity through time was tested using the Anchor Method8) to the selected Mesolithic sites. To calculate the path distance from the start sites, we created cost raster and surface raster as an input variable for the analysis94. Cost raster represents the cost-per-unit for moving through the cell, and surface raster the difficulty of moving from one cell center to another. Present Global Land Cover95 was used to produce a cost to travel across land and water areas travel in the Western Eurasia region with a resolution of 30 arc seconds. We used the percentage values of water coverage in each cell of the land cover type. Original percentage values have been reclassified into six cost value classes on a scale between 1 to 16. The cells with 0% water cover were reclassified to one, 1–20% to 2, 21–40% to 4, 41–60% to 6, 61–80% to 8, and 81–100% to 16. Values were assigned regarding the difficulty of moving across each land cover classes. Land areas were assumed the easiest for traveling and was therefore assigned a value one. As the percentage of water in the raster cell increase, we assumed that it would increase the friction of the movement. High percentages indicate large water bodies and wetlands. Noteworthy, the water cover does not restrain the traveling fully but it only increases the friction to cross the landscape, and the added cost is low if the water percentage is low. In this study, we assumed that points with high water percentage  acted as isolation barrier even though probably occasionally crossed. Waterways are commonly assumed to be the main routes during prehistoric times. In our analysis, this is also often shown to be the case as rivers and inland lakes become the preferred route since the slope along them is shallower than other areas. In addition, to consider the areas covered by the continental ice sheet 10,000 years ago97, we reclassified the original data by adding a value of 999 for those areas assuming that people would not be willing to travel across ice sheets. Overall, we assumed that people more apparently choose the easier landscape to cross before using the most difficult one. The final cost raster is created by adding together the reclassified datasets.We used Geographic Information System-based path distance and least-cost path (LCP) computation to determine the minimum accumulative travel cost from five different WHG sites  was set to 5 degrees, meaning that VRMA below or above 5° was set to infinity in the analysis. The Inverse Linear parameter favors movement gently sloping downhill. Path distance analysis produces a distance raster that contains information on the accumulative cost over a cost surface while compensating the actual surface distance that need to be traveled and for the vertical factors influencing the total cost of moving from one site to another. Analysis also produces backlink raster that identifies in which cell to move into on its way back to the source site. LCP was carried out with Cost path tool in ArcGIS pro using both distance and backlink raster.Global Multi-resolution Terrain Elevation DataThe least-cost routes obtained from this analysis were used to measure the geographical accessibility of a given point from the specified start points to test the relationship of genetic and spatial distance between datapoints. Hence, they should not be interpreted as suggested migratory routes per se. Furthermore, we acknowledge that there are several uncertainties in the accessibility of a given site, such as glaciation and sea-level variation in Northern Europe.We used linear regression analysis to investigate the dependence of admixture proportions in the Mesolithic East and North European individuals relative to geography distance between sites (admixture isolation-by-distance). The admixture isolation-by-distance model assumes that the proportion of contribution from each source population is inversely correlated with the geographic distance from the source population. Based on the descriptive admixture results (PCA & Admixture), we assumed two source populations closely related to two lineages; Western Hunter-Gatherers and Paleolithic Siberians (represented by AfontovaGora3). In a simple balanced isolation-by-distance admixture zone, the genetic contribution from a population A is assumed to be 100% at the site of the source populations while at this site the genetic contribution from the population B is assumed to be 0%. In the geographic midpoint, the admixture proportions from each source group are expected to be at 50%.4 calculations, we did not include individuals investigated by SNP-capture method in our set of X individuals. Additionally, we excluded individuals of which f4 calculations were based on less than 5000 sites. We fitted linear model y ~ ax +bz + c to the dataset using lm function in R v. 3.6.292. The lm diagnostic plots were inspected to detect highly influential points and evaluate if the dataset met the critical model assumptions.To test whether geographic distance explains the admixture proportions in the Mesolithic samples from Northern and Eastern Europe, the shortest least cost route from the WHG core region and AfontovaGora3 were used as explanatory variables for the admixture measure f4. To avoid possible batch effects on our fFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1–17Reporting Summary