We generate genome-wide data from 40 individuals from the seven islands, dated between the 3rd–16rd centuries CE. Along with components already present in Moroccan Neolithic populations, the Canarian natives show signatures related to Bronze Age expansions in Eurasia and trans-Saharan migrations. The lack of gene flow between islands and constant or decreasing effective population sizes suggest that populations were isolated. While some island populations maintained relatively high genetic diversity, with the only detected bottleneck coinciding with the colonization time, other islands with fewer natural resources show the effects of insularity and isolation. Finally, consistent genetic differentiation between eastern and western islands points to a more complex colonization process than previously thought.The indigenous population of the Canary Islands, which colonized the archipelago around the 3 Here, the authors use paleogenomic data from the indigenous people of the Canary Islands to shed light on the Prehistory of North Africa, and on how insularity and resources availability shaped the genetic composition of this isolated population. This autochthonous component descends from a population linked to the Upper Paleolithic population from Taforalt (present-day Morocco), dated to around 15,000 years before present (BP). Their genome-wide ancestry is consistent with a substantial Eurasian origin, suggesting a Paleolithic back migration to Africa from Eurasia as proposed before4. Later Early Neolithic genomes (7000 BP) were genetically similar to the Taforalt population, showing that the first stages of the Neolithic revolution in North Africa were driven by the acquisition of farming techniques by the local population and not by a population turnover5. However, the later phase of the Neolithic was characterized by the movement of people, as Late Neolithic genomes (5000 BP) showed admixture between the local populations and early European farmers5. Nevertheless, to our knowledge, no other genomic information has been obtained for the Western North African region until the late Medieval Period. From the 7th century after the Common Era (CE), the Islamic invasions from the Arabic peninsula changed the cultural and genetic background of most of the local populations6, making it difficult to disentangle the genomic history of the region from the Late Neolithic to the Antiquity.North Africa has a unique geographical situation that has favored demic diffusion between continents. The Sinai Peninsula is a land bridge that supports migratory routes between the African continent and Eurasia. At the north, the Mediterranean Sea has been the center of the cultural and economic trade that shaped the history of the surrounding human populations. Due to the effect of the warm and humid climate on human remains, ancient DNA (aDNA) from the North African region has remained largely understudied; only three prehistoric populations from the Upper Paleolithic to the Late Neolithic of the Western North African region have been reported so far Fig. . The curA unique perspective window into the past of North Africa can be accessed through the study of the indigenous people from the Canary Islands (CIP). The Canarian archipelago consists of seven main volcanic islands in the Atlantic Ocean, 100 km off the northwest African coast Fig. . This po7. Archaeological evidence indicates that later connections between the islands and the African coast were very limited, and that the islands remained practically isolated until contact with European seafarers and explorers in the 14th century8, who eventually conquered the archipelago in the 15th century and admixed with the surviving indigenous people. Previous genetic data pointed to a clear North African origin for the Canarian indigenous population, using uniparental markers14 and genome-wide data16. Spatial differences have been observed in the islands’ populations regarding both the diversity and composition of the mitochondrial DNA (mtDNA), with islands with more natural resources having higher genetic diversity9.Current radiocarbon dating evidence suggests that the Canaries were first populated between the second and fifth centuries CE20. In that sense, the Canary Islands can also be used as unique laboratories to study complex demographic processes from a genetic perspective, including colonization, isolation, or admixture with other populations. On that basis, we perform a study of the whole Canary Islands indigenous population at a genome-wide level to obtain information on the prehistory of North Africa and understand how isolation and insularity shaped their genetic structure.Given the tremendous biogeographic diversity that characterizes the archipelago, the first settlers were driven to develop different life strategies that resulted in different adaptive processes in terms of social complexity, subsistence practices, and demographic development, making the settlement of the Canaries an intriguing human colonization processth to the 15th centuries CE15, from a 19th century private collection conserved at the Anatomical Museum Edinburgh University with no archaeological information; and four from the Cendro site in Gran Canaria dated around the 12th century CE21. Here, we generated 9 medium to low-coverage genomes (5.82X–0.36X) by shotgun sequencing and genome-wide data from another 31 individuals by in-solution capture from the CIP 25 as the reference panel, the CIP are placed with Eurasian populations  using present-day data and then projected ancient individuals. When projecting using the Human Origins datasetons Fig. , and for26 , a source of Paleolithic/Early Neolithic Maghrebi ancestry , a source of steppe ancestry , and Mota as a proxy source of sub-Saharan ancestry  placed closer to Upper Paleolithic and Early Neolithic North Africans, and all eastern islands  cluster closer to ancient and modern Europeans. The only exceptions are one individual from Tenerife (CAN.039) from the period of contact with European explorers and one indigenous individual from La Palma (CAN.035).f3-statistics considering the amount of shared ancestry with populations in the Human Origins dataset for the total and the insular populations, we observe that shared drift between eastern Canarian people and Neolithic and Bronze Age populations from Europe is higher than those inferred for the western individuals 35 dataset, Gran Canaria, Tenerife, and La Palma show the highest heterozygosity estimations , close to Late Neolithic populations from Morocco (Supplementary Note pi = 0.193) and Fuervententura (pi = 0.184) . Based on our results, most indigenous individuals have 20–60 cM of their genome composed of shorter ROH segments of 4–8 cM . This relatively high level of parental relatedness is a sign of recent background ROH, indicative of low population sizes36. We also examined individuals having >20 cM ROH (sROH>20cM) segments greater than 50 cM, which is a signature of very small and isolated populations and consanguinity36. We identified five ancient individuals exceeding this long ROH threshold with total ROHs larger than 100 cM (Supplementary Data th century onwards), which can be indicative of the effect of isolation in the later centuries. Remarkably, sROH>20cM above 50 cM are very uncommon in the global aDNA record: only 3% of the ancient individuals analyzed met this feature36 and, notably, 20% of them are located on islands, such as Malta37. Although the first European written records reported strict rules against marriage among members of the same group in the indigenous populations38, our results indicate sporadic close-kin mattings, probably due to small population sizes and cryptic relatedness.To investigate parental relatedness and consanguinity in the Canarian indigenous population, we also performed an estimation of the presence of runs of homozygosity (ROHs), using hapROHset Fig. . ROHs loe) of the Canarian populations using hapROH , a slightly lower value than previously reported considering only ancient genomes from Gran Canaria and Tenerife39. In view of the indigenous population of each island, we observe that Fuerteventura  and Lanzarote  have the populations with the smallest Ne, followed by the population from El Hierro . However, we must consider that we only sampled two individuals from Lanzarote and Fuerteventura and the actual Ne values for these islands could be higher as these individuals might not be representative , followed by the population from La Palma  and Tenerife , agreeing with our heterozygosity estimations. Surprisingly, La Gomera has the second-largest Ne estimation of all the islands . However, for La Gomera, we have an individual from an earlier occupation phase of the archipelago  and two from a later period (12th–14th centuries), while individuals from Fuerteventura, Lanzarote, and El Hierro are all from the late occupation phase (12th century onwards). If effective population size had decreased over time, this difference might affect Ne estimations. When we removed CAN.027 from the analysis, we obtained a more reduced Ne for La Gomera , more in line with that observed in other islands with low heterozygosity values.Considering the elevated background relatedness in the indigenous people, we estimated the effective population size  as representative of the inbreeding level of the population at that time, we detect a steep increase in inbreeding levels over time for the islands of La Gomera, Lanzarote, Fuerteventura, and El Hierro. On the contrary, when individuals from Gran Canaria and Tenerife (the two islands with larger sample sizes) are considered independently, the Ne value remains stable for the entire indigenous occupation period.When exploring how Nded Fig. . Howeverote Fig. . If we c40. Genetic clustering of the CIP based on IBD-sharing shows three differentiated groups in the archipelago: one containing individuals from El Hierro, one with individuals from the remaining western islands, and the other with individuals from the eastern islands (Supplementary Note th century. Gun005 and gun008 (both dated around the 10th century) come from a private collection and were probably obtained from the same site, although it is impossible to know due to the loss of their archaeological context15. This analysis suggests the predominant driver in genetic relatedness is not extensive migrations between eastern and western islands and between islands within regions, as insular populations share more IBD with themselves than other islands.To determine if differentiated migration patterns are responsible for differences in the effect of genetic drift over time, we inspected for shared genomic segments that are identical by descent (IBD) using IBDseqe reductions in the islands’ different populations (bottlenecks or founder events) using ASCEND41  , while eastern individuals share an earlier event between the first and 7th centuries BCE (95% CI: 793 BCE–121 BCE)  and time range. Westwards, the population from Tenerife could have experienced a more drastic event (95% CI: 3.7%–4.7%) than the whole western region but in the same period (95% CI: 66 BCE–242 CE) as the ones observed in other island populations41. Finally, we observed that El Hierro population went through a strong bottleneck event between the 7th and 12th centuries CE, a few generations before the individuals included in the study lived  Fig. , comparaCE) Fig. . NeverthCE) Fig. . The besved Fig. . The int21. With this larger dataset, we have shed light on the prehistory of North Africa. Data from the CIP, who colonized the archipelago around the first centuries CE, indicate that this North African population was composed of four main ancestral components. First, as observed for Late Neolithic Moroccans5, the Canarian indigenous people have both North African Paleolithic/Early Neolithic and European Early Neolithic components. However, the contribution of the North African Paleolithic/Early Neolithic component is greater in the indigenous people than in the Kef El Baroud individuals, confirming that the impact of European Neolithic migrations was not homogenous in the North African region. In addition, the indigenous people show the presence of a steppe component, most probably associated with the migration of North Mediterranean populations into North Africa during the Bronze or the Iron Ages. Finally, we detect a small sub-Saharan African component implying the existence of trans-Saharan migrations in North Africa already before the first centuries CE, predating such gene flow inferred from modern DNA data42.We analyzed genome-wide data from 49 individuals from the CIP, including 40 newly generated samples and 9 whole-genome sequencing data from previously published studies44. While records of the Libyco-Berber language are found throughout all the islands, Lanzarote and Fuerteventura have produced inscriptions belonging to the so-called Latino-Canarian alphabet that are not present elsewhere in the archipelago44. Another example of differences between regions is the presence of fig trees (Ficus carica) exclusively in Gran Canaria45, suggesting that some differences in the cultural and biological background of the settlers were present since the start of the 1st millennium CE. However, it is important to note that, although our dataset spans from the 3rd to the 16th century CE, most of the individuals are dated after the 10th century and more data from the early colonization period would be needed to determine if populations differed in origin (the two regions being colonized by moderately different North African populations) or if asymmetrical migrations occurred at the beginning of the colonization phase.Our dataset of the indigenous people of the Canary Islands has also allowed us to start understanding the challenges of human dispersals into oceanic islands and isolated environments. Based on the analysis of ROHs, all individuals show signs of a relatively small effective population size, likely due to the initial founder effect. Within the archipelago, we observe that insular populations are heterogenous regarding both their genetic composition and diversity. As observed for the mtDNA, the western and eastern islands are differentiated, with the islands closer to the continent having a greater affinity with prehistorical populations from Europe, while the western islands are more akin to prehistorical individuals from North Africa. In fact, qpAdm modeling determines that the contribution of these two components is slightly different in the two regions. When combined with radiocarbon data, these differences appear to have existed since the beginning of the indigenous colonization period and remained unchanged. It is worth mentioning that the islands closer to the continent exhibit a more diverse corpus of alphabetic inscriptions and variations in rock art compared to the western islandsth–12th centuries CE, we observe that some individuals from these islands are the result of close-kin matings, as expected with the reduced population sizes at that time. These islands also exhibit low mtDNA diversities, with the partial or complete fixation of certain lineages, reinforcing the idea of strong genetic drift and the lack of gene flow. These results agree with these islands being isolated and inhabited by small populations. For the island of El Hierro, we detect a strong bottleneck around the 9th century, coinciding with the transition period between the Vandal Minimum and the Medieval Warm climate episodes46. However, radiocarbon dating indicates that this island was populated simultaneously with the rest of the archipelago47, so this bottleneck would evidence a strong population size reduction long after the initial colonization of the island. Considering that El Hierro is an island with limited resources, temperature and rainfall changes during the 9th century could have heavily affected the availability of natural resources and crop production, potentially producing a severe bottleneck. More data from the other islands would be needed to determine if this phenomenon was also experienced by La Gomera, Lanzarote, and/or Fuerteventura. Gran Canaria, La Palma, and Tenerife present a completely different scenario. In these islands, genetic diversity is higher than in the other sub-populations, both inferred from the mtDNA and genome-wide data. Estimations of effective population sizes are also higher, and, for Tenerife and Gran Canaria, Ne values remain constant over time. Furthermore, the detected founder effect coincides with the proposed colonization time, indicating that no additional bottlenecks occurred during the indigenous colonization period. These results evidence that these populations either had a comparably large size and could sustain genetic diversity over time or were not isolated, as proposed for Gran Canaria based on interpretations on the archaeological evidence48. However, IBD estimates and consistent differences in the ancestral contributions between Tenerife and Gran Canaria do not agree with having a significant gene flow between them. In this line, a constant Ne value is against the idea of a significant migration event within the studied period. Differences in the genetic diversity of insular populations can be explained as the result of differences regarding the islands’ size, their ecological diversity and their annual amount of rainfall. Gran Canaria, La Palma and Tenerife are medium size islands with a current average rainfall varying from c. 100 mm in the southern coasts to c. 800–1000 mm in the highest parts of the islands49. Those conditions provide higher availability of resources that could sustain larger and more diverse populations than in the smaller islands of El Hierro, La Gomera, and Lanzarote. Fuerteventura, despite having a larger size, currently receives c. 100 mm of rainfall per annum, being the most arid island of the archipelago49. Having into consideration the historical and archaeological records50, it has been estimated that Tenerife, La Palma and Gran Canaria could have sustained a population of around 30,000–60,000 people at the beginning of the Castilian conquest, while the remaining islands were probably populated by just 1000–3000 people.Island sub-populations also show differences in their genome-wide genetic diversity. El Hierro, La Gomera, Lanzarote, and Fuerteventura islands show the effects of strong genetic drift resulting in the reduction of their effective population sizes over time. By the 10Overall, this dataset has allowed us to better understand the prehistory of North Africa and to start producing a more detailed picture of the complex colonization process of the Canary Islands, where human resilience, isolation, and diverse insular environments led to the differentiation of their genetic landscape.53, (a) we only sampled the archaeological material strictly necessary to meet the objectives of this project, (b) we used less-destructive sampling methods such as the use of teeth and/or small bones, (c) we worked with local museums and institutions in order to secure the dissemination of our research to the general public in the Canary Islands, including science popularization talks, outreach activities for high school students and the inclusion of aDNA results in temporal and permanent exhibitions, and (d) we carried out this study involving international collaborations but led by a Canarian institution.Permissions needed to analyze ancient human remains were granted by the local authority  and local museums. To assure ethical treatment of the archaeological remains and proper heritage conservation and dissemination, and facilitate the building of local capacities5. Briefly, well-preserved teeth and bones were thoroughly decontaminated and pulverized using a mixer mill (MM Restch®). Bone or teeth powder was then extracted following a modified version of the protocol outlined in ref. 22, and DNA libraries were constructed following ref. 23. Ancient DNA libraries were then sequenced on an Ilumina’s NextSeq 500 platform . Samples with low endogenous DNA content (<10%) were also enriched in human endogenous DNA using whole-genome in-solution capture54, while samples with high endogenous content (>10%) were additionally sequenced to saturation. To improve the coverage on informative regions, samples were also captured using baits covering the SNPs contained in the Illumina Multi-ethnic Genotyping Array (MEGA)34.Sample collection was carried out in collaboration with the universities of La Laguna (Tenerife) and Las Palmas de Gran Canaria (Gran Canaria); the insular museums of Gran Canaria (El Museo Canario), La Palma (Museo Arqueológico Benehaorita) and La Gomera (Museo Arqueológico de La Gomera), and Tibicena Arqueología y Patrimonio Ltd. (Gran Canaria). In total, 40 human remains from 22 archaeological sites were selected for this project Fig. . Informa14C dates were then calibrated with the internationally agreed IntCal20 atmospheric calibration curve using the OxCal online software version 4.4 55. The two-sigma probability interval (95.4%), recommended by ref. 56, was used when discussing the 14C measurements.Radiocarbon dating was performed using Accelerator Mass Spectrometry (AMS) at Beta Analytic, Inc. (USA) and the Scottish Universities Environment Research Centre (SUERC) facilities. All individuals included in this study were radiocarbon dated, excluding those from which a date was already available  and duplicated mapped reads, as well as reads with alternative mapping coordinates were removed using SAMtools v. 0.1.1959. Lastly, BAM files from different runs were merged for each sample using SAMtools merge. All individuals were assessed for damage patterns using MapDamage v. 2.0.260  and read length (<30 bp), and adapters were removed using AdapterRemoval v. 2.134, previously curated following ref. 5. We performed a PCA based on the MEGA-HGDP panel and then projected ancient individuals using both LASER and the lsqproject option from smartpca66. For LASER, the BAM files trimmed 3 bp at both ends were directly used for PCA using the default specifications. For lsqproject, we performed SNP calling on the ancient trimmed bam files using SAMtools mpileup and filtering out low-quality bases (BASEQ > 30). Pseudo-haploid calls were then obtained by randomly selecting one allele from the mpileup output. Ancient Canarians were also compared to the Human Origins panel using the Allen Ancient DNA Resource curated by the Reich Lab 25. Previously published aDNA genomic data was also integrated into the Human Origins dataset  using the MEGA-Human Genome Diversity Project (HGDP) reference panel67. The global ancestry of ancient people was determined using ADMIXTURE v1.3.026. The analysis was performed in 10 replicates with different random seeds, and only the highest likelihood replicate for each value of K was taken into consideration. Alternatively, we performed an admixture modelling using qpAdm from ADMIXTOOLS following ref. 68. To account for sample size differences, we compared the qpAdm admixture values obtained at individual level by island 69. We used the MEGA-HGDP panel and considered only archaeological sites with at least two individuals  in the indigenous populations of the islands, we also used hapROH in combination with radiocarbon data. We used ASCEND41 to infer the time and the intensity of putative bottleneck events that occurred in the archipelago (Supplementary Notes n = 4) as all of the individuals have an average depth higher than 0.3X. For that, we also corrected for sample bias in the islands with n > 5 by performing 10 independent replicates using 4 individuals with similar characteristics to the sample from El Hierro. Finally, we inspected for shared genomic segments that are identical by descent (IBD) using IBDseq40. For that, we imputed genomes with an average depth >0.1X using GLIMPSE71. We first computed genotype likelihoods for each genome using the candidate variant sites contained in the 1000 Genomes Project phase 372, and filtering out for non-biallelic and singleton variants .Diversity estimations were performed by calculating the whole-genome heterozygosity at a population and archaeological site level, and inbreeding patterns at the individual level. First, we estimated heterozygosity from each island and archaeological site using popstats77. For that, we calculated admixture proportions using the mL estimator as in ref. 12, considering three main parental populations: the Canarian indigenous population, and the current Iberian and Sub-Saharan populations. Admixture estimator mL was calculated based on Y-chromosome haplogroup frequencies using the WLSAdmix program78. For the admixture proportions at genome-wide level, we built a dataset including the indigenous individuals, the Human Origins panel and the data generated by ref. 39. With that dataset, we applied qpAdm from ADMIXTOOLS as described for the ancient individuals, and considering the indigenous, Spanish and Yoruban populations as contributing sources.In order to compare the ancient population with the modern people of the Canary Islands, we first considered the Y-chromosome data and compared it to the modern DNA data from ref. Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-10Reporting Summary


Larimichthys crocea  is one of the most economically important marine fishes in China, and research on the ecology and genetics of this species is of immense significance. In this study, we performed restriction site-associated DNA sequencing (RAD-seq) of 54 individuals collected from four sites in China to analyze the genetic structure and diversity of large yellow croaker at the genome level. It revealed that the large yellow croaker populations in the Ningde and Zhoushan coastal waters can be clearly distinguished. Different genetic diversity indices were used to analyze the genetic diversity of the large yellow croaker, which showed that there was a differentiation trend between the wild and farmed populations in Ningde. Moreover, we identified genetically differentiated genomic regions between the populations. GO gene enrichment analysis identified genes that are related to fatty acid metabolism and growth. These findings enhance our understanding of genetic differentiation and adaptation to different living environments, providing a theoretical basis for the preservation and restoration of the genetic resources of the large yellow croaker. L. crocea) is an economically important marine fish species distributed in the coastal waters and estuaries of the northwestern Pacific Ocean, including areas from central Vietnam to Korea and Japan, as well as the southern Yellow Sea, East China Sea, and South China Sea [L. crocea aquaculture in China has surpassed that of any other domesticated marine fish species [L. crocea were successfully bred and released via artificial propagation. In the 1990s, the population genetic structure and genetic diversity of wild and farmed L. crocea in Fujian Province were analyzed using isozyme biochemical markers. The results revealed that both populations had relatively low genetic diversity levels [L. crocea. These studies have shown that the genetic diversity of domestically cultivated populations is generally lower compared to that of wild populations [L. crocea have relied primarily on mitochondrial DNA (mtDNA) sequences and offered limited resolution [The large yellow croaker  technique can detect widely covered genetic markers throughout the whole genome, and it has advantages such as high throughput and low cost ,8. It hareported ,16. HoweL. crocea [L. crocea populations based on the simplified genome sequencing technology (RAD-seq). Genetic diversity and population structure analyses were performed on three naturally wild populations captured from Zhoushan, Ningde, and Zhanjiang, as well as on a cultivated population. The identified candidate genes that differentiate the populations of L. crocea by using selective sweep analysis, and revealed their adaptive evolution mechanisms in different environments, providing scientific evidence for the recovery and protection of the resources of L. crocea.Currently, there are differences in phenotypes between the farmed and wild populations of . crocea , which cL. crocea from three wild populations  distributed throughout the coastal waters of China, and a farmed population (NDB) was collected from Ningde Fufa Aquaculture Co., Ltd. . . . A pieceHigh-quality DNA was used to build the restriction site-associated DNA (RAD) libraries. An equal amount of 1 μg of DNA from each individual was digested by two restriction enzymes (AvaII and MspI) and modified at the 3′ and 5′ ends, followed by sequencing adapter ligation to facilitate anchoring of the connecting products on the flowcell for bridge amplification. PCR amplification was performed to select appropriate fragments for library construction. Each sample was independently sequenced on the Illumina HiSeq X10 platform.L. crocea reference genome (GCA_000972845.2 version) using BWA [Raw sequence reads were processed to trim the adaptor and filter the low-quality reads using fastp . The trising BWA . The bamsing BWA  to ensursing BWA , with easing BWA  were useSnpEff  was usedFIS), for each population using PLINK [We calculated basic statistics, including observed heterozygosity (HO), expected heterozygosity (HE), and inbreeding coefficient  were excluded, and we retained only SNPs with known genomic locations within a 1Mb distance. This approach is important as it helps prevent the overrepresentation of loci affected by selection hitchhiking.In order to minimize the occurrence of unreliable genotypes, we used BCFtools  to elimi2 between all pairs of SNPs (--minMAF 0.05--Miss0.1) located within 100kb of each other and plot the LD decay curve.PopLDdecay  was usedThe effective population size was calculated for each group using SNeP . The parFST divergence statistic, with a window size of 100 kb and a step size of 10 kb for each population pair. We Z-transformed the distribution of FST and calculated the log value of θπ ratios [FST)and log2(π ratios) simultaneously served as the candidate outliers under strong selective sweeps. All outlier windows were assigned to their corresponding SNPs and genes. Selected genes enriched in gene ontology (GO) terms were determined using Metascape [https://metascape.org/gp/index.html#/main/step1 (accessed on 17 February 2023)), with a significance threshold of p < 0.05 for enriched terms.We used VCFtools  to estimπ ratios . The winetascape   of the populations were positive but not significantly different from zero. The ZSC group had the highest FIS value (0.179) than the other group, indicating higher levels of inbreeding.We estimated basic population genetic parameters to determine the status of each population . The obsL. crocea individuals could be divided into two different groups.The admixture analysis revealed clear evidence of clustering. Assuming clustering numbers  of 2–4, the cross-validation error was lowest at K = 2, suggesting that the population genetic structure of our samples is best modeled by considering the admixture of the two genetic components , and theA principal component analysis (PCA) recovered clusters similar to the admixture analyses. The first principal component (PC1) separated the NDC, which was consistent with the admixture result at K = 2. The second principal component (PC2) further separated the ZSB populations and was able to distinguish between the NDB and other populations b.The most compelling detail about the population substructure was generated by the fineRADstructure method, and the resulting cladogram and co-ancestry matrix c confirm2) in the NDC group dropped rapidly at approximately 1 kb, while ZSC and NDB showed a relatively slower drop. In addition, the average pairwise correlation coefficient (r2) was higher in the ZJC than in other reference populations (The patterns of linkage disequilibrium (LD) indicated that the mean of the correlation coefficient values  and log2(π ratio) values, the selected threshold was Z(FST) ≥ 1.69 and log2(πNDC/πNDB) ≥ 1.75 , nucleosidases activity (nt5c1bb), and cellular aldehyde metabolism  (We compared the genomes of ) ≥ 1.75 . A totalnd idh1) .impa1, ppip5k1b, and bpnt2), negative regulation of the cell cycle , and glycerophospholipid metabolism   and peptidyl-serine phosphorylation   and carbohydrate metabolism   . For theentified , with ansnk1g2a) . In addisnk1g2a) , with annd idh1) .L. crocea differs from that of wild populations. Domestication through farming may lead to changes in traits, such as body size, and exhibit clear signals of selection at the genomic level. However, studies based on early molecular markers have not thoroughly investigated the impact of farming domestication on the L. crocea genome and phenotype over the past few decades. Further research using whole-genome data may provide a more comprehensive understanding of the current status of the L. crocea genome and its relationship to phenotypic traits.Previous research has indicated that the genetic structure of farmed L. crocea in Zhoushan was relatively low. The unique and complex terrain of the Zhoushan nearshore area, along with the presence of numerous islands [L. crocea in this area and those from other regions, leading to reduced wild resources and bottleneck effects due to genetic drift. Before the 1970s, L. crocea had obvious fishing grounds and a fishing season. In 1974, the fishing ground in Zhoushan was heavily fished, with the fishing intensity far exceeding the level that its resources could bear, and the natural resources of L. crocea in the Zhoushan fishing ground in the north of the East China Sea rapidly declined and nearly disappeared [L. crocea in Zhejiang Province has increased, indicating that the wild population resources are gradually recovering [In this study, it was found that the genetic diversity level in the wild  islands , may havappeared . The higcovering .Previous studies have shown that the genetic diversity of cultured populations of the large yellow croaker in Ningde, Fujian Province has significantly decreased compared to wild populations . The resL. crocea populations in natural waters is also low due to scarce resources. This reflects the urgent need for genetic resource protection for this fish species.The decrease in genetic diversity of the breeding population is attributed to the genetic bottleneck effect caused by the small effective number of parents and the accompanying genetic drift and inbreeding depression. After the successful artificial breeding and reproduction of the large yellow croaker in 1985, the industry rapidly developed, particularly in the coastal areas of Ningde in eastern Fujian. It is not surprising to find that the genetic background of the cultured large yellow croaker in Ningde is relatively homogeneous due to continuous artificial selection, and artificial selection has been broadly reported to possibly reduce genetic diversity ,37. WhilAdmixture analysis revealed that at K = 2, genetic differentiation was observed between the wild (NDC) and farmed populations (NDB) in Ningde, whereas some individuals showed signs of admixture, possibly due to gene flow. To further investigate the population structure, a fineRADstructure analysis was performed. The clustered co-ancestry heat map, generated with fineRADstructure using genome-wide SNPs, identified that the single population in our dataset, within NDC, had a high level of intrapopulation co-ancestry. However, several individuals in two populations (NDC and NDB) presented similar genetic ancestry, which suggested that they might have escaped farm fish with a higher frequency discharge rather than the natural-wild population . In geneThere are significant differences between wild and farmed populations of large yellow croaker, such as body length, body shape, and the content of polyunsaturated fatty acids, which may be caused by potential SNP loci that drive their different traits. The farmed population in the Ningde region primarily comprises descendants of about 30 parent fish that were caught in the same sea area back in 1985–1986 and subsequently bred artificially . In thisslco1d1 is responsible for the ingestion of steroid hormone conjugations into liver cells, and changes in its expression may affect the amount of hormone conjugations, which may affect the embryonic development and morphological behavior of zebrafish [Gene ontology analysis revealed genomic adaptation to the cultivated environment via selective scanning. These results suggest that the population of the large yellow croaker has genomic features that adapt to different survival environments. This study has shown that the significantly enriched pathway was related to the transmembrane transport of thyroid hormones. Thyroid hormones regulate many important functions in teleost fish, such as growth, skin pigmentation, morphogenesis, and metamorphosis, during early development . The slcebrafish .Ddx4 is an important regulatory factor for determining germ cell development and is the first gene involved in primitive germ cell development [Ddx4 is widely used as a DNA molecular marker for fish gonadal development, gender determination, and differentiation [Brd3a is necessary for the differentiation of embryonic stem cells into blood platelets during early embryonic development [npffr1 genes located on chromosome 3 that have a strong selection signal. Npffr1 is involved in the control of feeding behavior in vertebrates and invertebrates [npffr1 gene may be associated with growth and weight in the large yellow croaker. This may be evidence of artificial selection affecting the developmental processes of the farmed large yellow croaker, which grows faster and tends to mature earlier than the wild population.Moreover, differences were observed in genes involved in early embryonic development. elopment . Ddx4 isntiation ,44,45,46elopment . We identebrates ,49. Therfads2) is a key enzyme in the biosynthesis of long-chain polyunsaturated fatty acids (LC-PUFA) in vertebrates, which is considered the rate-limiting step and is commonly used as an indicator of LC-PUFA biosynthesis [fads2 activity [n − 3)/(n − 6) LC-PUFA ratio is higher in the wild population [Fatty acid desaturase 2 (ynthesis . The difactivity . A compapulation . We specpulation ; as a re


N = 2216) representing Nakh-Dagestani, Altaic, and Indo-European language families revealed the presence of three components  that emerged in different historical periods. The Steppe component occurs only in Karanogais, indicating a recent medieval migration of Turkic-speaking nomads from the Eurasian steppe. The Iranian component is observed in Azerbaijanis, Dagestani Tabasarans, and all Iranian-speaking peoples of the Caucasus. The Dagestani component predominates in Dagestani-speaking populations, except for Tabasarans, and in Turkic-speaking Kumyks. Each component is associated with distinct Y-chromosome haplogroup complexes: the Steppe includes C-M217, N-LLY22g, R1b-M73, and R1a-M198; the Iranian includes J2-M172 and R1b-M269; the Dagestani includes J1-Y3495 lineages. We propose J1-Y3495 haplogroup’s most common lineage originated in an autochthonous ancestral population in central Dagestan and splits up ~6 kya into J1-ZS3114  and J1-CTS1460 (Avar-Andi-Tsez linguistic group). Based on the archeological finds and DNA data, the analysis of J1-Y3495 phylogeography suggests the growth of the population in the territory of modern-day Dagestan that started in the Bronze Age, its further dispersal, and the microevolution of the diverged population.The gene pool of the East Caucasus, encompassing modern-day Azerbaijan and Dagestan populations, was studied alongside adjacent populations using 83 Y-chromosome SNP markers. The analysis of genetic distances among 18 populations ( The East Caucasus, which spans modern-day Dagestan and Azerbaijan, is an important land bridge connecting Europe to West Asia. One of the gateways through the Caucasus is the Big Caucasian Pass, a strip of coastal land that runs between the East Caucasus mountains and the Caspian Sea all the way from Derbent to Sumgait.The early humans of the Oldowan culture dispersed to the East Caucasus about 2 million years ago . The ancIn the 7th–6th centuries BC, the south of the region was infiltrated via the Derbent Pass by the Iranian-speaking Scythians who formed a military-political alliance known as Ashguza on the lands of today’s West Azerbaijan and Northwest Iran. Later, this territory was annexed by the Median Kingdom. In the late 1st millennium BC, the state of Caucasian Albania emerged from a tribal confederation in the northern part of the region. It had its own writing system, and its population adopted Christianity in the 4th century AD. Caucasian Albania was largely made up of autochthonous Caucasian tribes that spoke the Lezgic languages of the Nakh-Dagestanian family, although its reign extended over some Iranian tribes too. The territory of modern-day Dagestan was a cradle to such early states as Lakz, Tabarseran, Haidak, Sarir, Gumik, etc., which came into being in the first half of the 1st millennium AD.During the first few centuries AD, other Iranian tribes, including Sarmatians, Maskut, and Alans, were arriving in the Caucasus. The South Caucasus was raided by Turkic-speaking Huns, Sabirs, and Khazars who came from the North via the Derbent Pass in the early Middle Ages (between the late 4th and 8th centuries) . In the After the Tatar-Mongol expansion that started in the 14th century, Islamic influence regained its primacy in Dagestan. The local medieval principalities of that time existed until the 19th century when they were absorbed by the Russian Empire. The southern regions of the East Caucasus were under Iranian influence during the 15th–19th centuries; they were incorporated by the Russian Empire in the aftermath of the Russo-Persian wars when the peace treaties were signed in 1813 and 1828. Later, these territories became the state of Azerbaijan.Thus, the gene pools of the autochthonous East Caucasus populations were influenced by the massive ancient migration waves of Iranian speakers and later migrations of Eurasian Turkic peoples. The rich history of the region is reflected in the linguistic diversity of the East Caucasus. The languages of the region include the Nakh-Dagestanian languages (spoken by over 30 ethnic and ethnographic groups), the Turkic languages of the Altaic language family , and the Iranian languages of the Indo-European language family  . This stIndeed, there is a wealth of publications describing the gene pools of East Caucasian populations ,24,25,26Samples of Azerbaijani populations were studied for Y-chromosome markers by ,11,12,16The Azerbaijani gene pool remains understudied due to the use of small genotyping panels or small samples ,10,11,12Dagestani populations have been studied for Y-chromosome markers much better ,22,24,28Thus, the aim of this study was to systematically analyze the gene pools of popula-tions inhabiting the historically connected territories of Dagestan and Azerbaijan and to reconstruct the genetic history of the East Caucasus using a broad panel of Y-chromosome markers. The results of this study could be used to model the dispersal of early humans across the Caucasus, West Asia and Eurasia, using the updated and expanded DNA data.Samples. We have analyzed 18 populations from the East Caucasus and the adjacent regions  array of 31 SNPs . The “CoStatistical analysis. The pairwise matrix of Nei’s genetic distances , Table  shows thNote. The red dots on the map represent the analyzed populations. Areas of grey indicate haplogroup frequencies below 1%, areas of green indicate low frequencies of 1–20%, areas of yellow are the zones of moderate frequencies (20–50%), and areas of red and purple are zones of high frequencies (>50%). The region with haplogroup frequency ≥20% in Legend:1—Karanogais, 2—Chechen, 3—Kumyks, 4—Tindi, 5—Avars, 6—Dargins, 7—Tsez (Dido) and Hinukh, 8—Laks, 9—Kaitak, 10—Azerbaijanis from Dagestan, 11—Kubachi, 12—Tabasarans, 13—Tsakhur, 14—Rutuls, 15—Lezgins, 16—Azerbaijanis (including Karapapakhs) from Azerbaijan, 17—Iranian-speaking peoples of the Caucasus , 18—Iranian-speaking peoples of the Caucasus (Yezidis).https://www.yfull.com/tree/J-Z1828/, accessed on May 31 2023] and 6.5 ± 1.5 kya according to [So far, subhaplogroup J1-Y3495 has not been described in detail in the literature, but its ancestral lineage J1-Z18375  is well known . We focurding to . This lirding to ). This lrding to . Its subrding to . The only lineage that represents haplogroup J1-Z1841 in the populations of the East Caucasus is J1-Y3495. We were able to identify 17 polymorphic lineages of this haplogroup ; frequenLinguistic data suggests that the Nakh-Dagestanian protolanguage split occurred at the end of the 3rd millennium BC (~5 kya) . This isThus, the main portion of the Y gene pool of Dagestan’s populations (carriers of haplogroup J1-M267(×P58) can be traced to the ancestral lineage J1-Y3495. We hypothesize that in the late Copper Age and the early Bronze Age (~6.5 kya), carriers of this lineage were settled in the central part of mountainous Dagestan, where the geographies of the main J1-Y3495 clades overlap E, and weThe main impact of the Avar-Andi-Dido J1-CTS1460 lineage is observed for its branch J1-Y6916  shape two opposite poles and contribute the most to the East Caucasus gene pool. The Dagestani component is largely shaped by lineage J1-Y3495 (6.5 ± 0.6 kya), whose emergence may have been preceded by a population decline followed by a population growth. The analysis of J1-Y3495 phylogeography allowed us to trace its origin to the central part of mountainous Dagestan, to an ancestral population that spoke the pan-East-Caucasian language. The split in the gene pool after ~6 kya can be linked to the population growth, dispersal of communities, and their long-lasting isolation in the mountains. This is confirmed by the split of this haplogroup in Avar-Andi-Dido populations ~4–5 kya.The Iranian component shows the genetic similarity of Azerbaijanis, the Tabasaran, and the Iranian-speaking populations of the Caucasus to the populations of West Asia that can be linked to a series of ancient migration waves. This pattern results from the contribution of haplogroups J2-M172 and R1b-M269 that may have been carried into the region by migrations of West Asian tribes, mostly Iranian speakers, throughout its history. Detailed analysis of the origin of this component and its dating will be conducted in the future in a broader genogeographic context.


E) of some of these populations and tested for signs of a recent population bottleneck in Lake Þingvallavatn.Lake Þingvallavatn in Iceland, a part of the river Ölfusá drainage basin, was presumably populated by brown trout soon after it formed at the end of the last Ice Age. The genetic relatedness of the brown trout in Þingvallavatn to other populations in the Ölfusá drainage basin is unknown. After the building of a dam at the outlet of the lake in 1959 brown trout catches declined, though numbers have now increased. The aim of this study was to assess effects of geographic isolation and potential downstream gene flow on the genetic structure and diversity in brown trout sampled in several locations in the western side of the watershed of River Ölfusá. We hypothesized that brown trout in Lake Þingvallavatn constituted several local spawning populations connected by occasional gene flow before the damming of the lake. We also estimated the effective population size  while the putative anadromous trout from River Sog and the spawning population from River Öxará, that flows into Lake Þingvallavatn, showed notably higher NE values . No signals of recent population bottlenecks were detected in the brown trout of Lake Þingvallavatn.Overall, the genetic relatedness of brown trout in the Ölfusá watershed reflected the connectivity and topography of the waterways. Ancestry proportion analyses and a phylogenetic tree revealed seven distinct clusters, some of which corresponded to small populations with reduced genetic diversity. There was no evidence of downstream gene flow from Lake Þingvallavatn, although gene flow was observed from much smaller mountain populations. Most locations showed low NThis is the first time that the genetic structure and diversity of brown trout in the watershed of River Ölfusá have been assessed. Our results point towards the presence of a metapopulation in the watershed of Lake Þingvallavatn, which has been influenced by restoration efforts and is now dominated by a genetic component originated in River Öxará. Many of the locations studied represent different populations. Those that are isolated in headwater streams and lakes are genetically distinct presenting low genetic diversity, yet they can be important in increasing the genetic variation in downstream populations. These populations should be considered for conservation and direct management. Salmo trutta) in subarctic freshwater systems, such as those found in Iceland. This species most likely colonized Iceland soon after the end of the last Ice Age 10–14,000 years ago. During this period of ice cap retreat, the newly formed watersheds were further shaped by geological forces, tectonic movements, active volcanism, and erosion. Apart from diverting some waterways, these processes often caused formation of new lakes and impassable waterfalls thus preventing gene-flow from downstream to upstream populations.Changes in key climatic factors are predicted to be more pronounced in higher latitudes , and orgi.e., they have become permanent freshwater residents, while in other cases trout still retain the anadromous life cycle.Presently, brown trout is found in both lakes and streams in all parts of Iceland. Several populations, especially those inhabiting headwaters, have become isolated above waterfalls. Many systems with access to the sea host populations that no longer migrate to the ocean, th century. They travelled to Iceland to experience angling of large trout at the outlet of the lake and in the outflowing river, Efra Sog, that runs in a 1.2 km long canyon into the smaller lake Úlfljótsvatn   . This haittatum) . It is aittatum) .i.e., in the Hengill volcano) in the Þverá/upper Ölfusvatnsá rivers  may have influenced the genetic composition of the brown trout of Þingvallavatn and Úlfljótsvatn, especially the spawning populations in the lower Ölfusvatnsá and the nearby Villingavatnsá. Hence, if the waterways of Þingvallavatn and Úlfljótsvatn had not been altered we would expect the genetic structure to reflect a metapopulation where several local spawning populations were connected by occasional gene flow   . A decadin 1984) . These din 1984)  after 19Several attempts were made to re-establish the trout in Þingvallavatn. In 1993, 10,000 one year old trout derived from Öxará spawners  were released at many locations in Þingvallavatn . Between3/s to flow on average through a bottom sluice to the natural outflow, i.e., the Efra Sog canyon  and at the outlet . Our results will shed light on the genetic relatedness and connectivity of these brown trout populations and assist authorities in their efforts of conservation and management.We hypothesize that the brown trout inhabiting Lakes Þingvallavatn and Úlfljótsvatn constituted a metapopulation before the damming of Þingvallavatn, with local sub-populations spawning in the tributaries . Sampling in other rivers was done within the Freshwater Institute monitoring regime of salmonids in Varmá, Sog, Ölfusá, Efra Sog, Villingavatnsá, Ölfusvatnsá, and Leirvogsvatn. The Hengill volcano streams were surveyed with Gísli Már Gíslason (professor emeritus). Samples from Hvítá were provided by Árni Guðmundsson, farmer at Arnarbæli, caught as by-catch in salmon nets.An anadromous population from Loch Slapin  was chosen as a reference group in our analyses. The samples from this population were kindly provided by Dr. Colin Adams from the University of Glasgow.g for 5 min and the supernatant was retrieved. A suspension of magnetic beads was added, mixed with the sample by pipetting, and allowed to incubate for 5 min at room temperature. After a series of washes, the mixture was incubated in elution buffer  for 10 min at 55 °C. The supernatant was separated using a magnetic plate and the concentration of DNA was measured via spectrophotometry (Nanodrop). The DNA quality was assessed via gel electrophoresis using 0.6% agarose gels run at 200 V for 45 min. Low quality samples were discarded to avoid preparation of libraries with highly sheared DNA, which would yield a low number of raw reads and possible PCR artifacts . Fin clips or muscle tissue samples were submerged in a solution containing proteinase K and lysis buffer and digested overnight at 55 °C. Immediately prior to removing the supernatant, a secondary digestion with RNase was performed for 5 min at room temperature. Samples were centrifuged at 5,600 × rtifacts .i.e., 30 or 50 μg μl−1) and distributed in different rows of 96-well plates. Sample DNA was digested with BamHI-HF  in CutSmart buffer (New England Biolabs) for 4 h at 37 °C . After adding NEB 3.1 buffer (New England Biolabs) to the first digestion mixture, a second digestion with ApeKI  followed immediately for 4 h at 75 °C . Eight adaptors for BamHI-HF cut sites were combined with 12 adaptors for ApeK cut sites to produce 96 unique combinations which were assigned to each sample (−1) and incubated for 12 h at 21 °C. The enzyme was inactivated by incubating the mixture at 65 °C for 10 min.The methodology is based on h sample . Adaptori.e., 360–440 bp) were isolated using a 2% agarose Pippin Prep gel cassette and an internal marker for enhanced accuracy . A PCR reaction to amplify the resulting fragments was performed using OneTaq reaction mixtures (New England Biolabs) in the following conditions: 72 °C for 3 min, 98 °C for 30 s, 11 cycles of 10 s at 98 °C, 30 s at 65 °C, and 45 s at 72 °C, and a final extension time of 5 min at 72 °C. A further clean-up step was performed with magnetic beads  and the final product was eluted in 5 mM Tris/HCl buffer (pH 8.5) and visualized via electrophoresis in a 2% agarose gel, run for 45 min at 80 V. The final DNA concentration was measured using Qubit fluorometric quantification with the dsDNA BR Assay . Library quality was assessed by sequencing on an Illumina MiSeq platform (2 × 150 bp paired-end reads) in four out of the total 13 libraries prepared. Data from MiSeq and HiSeq runs were later combined. Each library was sequenced on a single lane of an Illumina X Ten platform (2 × 150 bp paired-end reads) at the BGI Tech Solutions facility in Hong Kong.All 96 samples were pooled together and purified using magnetic beads . Fragments of the appropriate size . However, the results from SNPs obtained with a reference genome approach were very similar to the findings presented here  and n  before assembling putative loci, as suggested by n = 2, we ran the program ustacks to identify putative alleles using the parameter m = 3 . Before proceeding to the building of a catalogue of consensus loci with the program cstacks, samples with an average coverage <20× were discarded. The catalogue was built using three samples from 26 different locations (26.64× average coverage). The programs sstacks, tsv2bam and gstacks were successively run to match all samples to the catalogue, incorporate paired-end reads, and align reads per sample calling variant sites in the populations, respectively. The program populations was run with a minimum threshold of 66% for a locus to be present in the individuals of each population, a minimum minor allele frequency of 5%, and a maximum observed heterozygosity of 60%. A description of the function as well as the values assigned to parameters of each program used at this stage can be seen in After an initial quality check of the raw reads with the software fastqc , we demucks v2.4 . We foll Project , availabted here . We deteparalog-finder v1.0  within any of the sampling locations using the scripts from Next, R  and the number of alleles and loci present in every location. The function pi from the package radiator  by applying the linkage disequilibrium method described in E, calculating the linkage disequilibrium with the Burrow’s Δ parameter. A random mating scenario was selected, and singletons were excluded from the analysis, since they contribute the most to an upward bias in the estimation of NE when using the linkage disequilibrium method .Since it has been shown that this method works better when using at least 30 individuals per population/location , we relatimation . The funradiator , which rNPRelate , was runFST values across all samples following E. Hence, we excluded locations with a smaller sample size , Hengladalsá , Hvítá, Ölfusá, Varmá).To ensure that we had only neutral markers, we removed loci responsible for the highest 5% Bottleneck v1.2.02 , the former being much faster immediately following a bottleneck event. Hence a population that experienced a recent bottleneck will exhibit an apparent heterozygosity excess  is assessed as the proportion of iterations that yield exactly the number of alleles observed; (iii) two-phased model of mutation (TPM), which is intermediate to IAM and SMM and was run with a variance of 30 and a proportion of SMM set at 70%. Although originally designed for microsatellites, these models have been successfully applied to investigate bottlenecks using RADseq data in foxes, mosquitoes and serpentinophyte plants  , since the program had trouble dealing with a higher number of markers. Similarly, as no mutation rate estimates are available for brown trout, the SNP mutation rate of the lake whitefish  × 10−8)  was usedE estimation was also used in the population bottleneck test . However, this time the program populations was used to call haplotypes instead of single SNPs per locus. All subsequent filtering steps were run in a similar fashion . Establishing a minor allele frequency threshold of 5% is common practice and works under the assumption that the removed loci are not very informative at the population level. However, this strategy may be removing true rare alleles that could aid in the inference of a bottleneck .A total of 2,597 polymorphic loci from 317 individuals out of 555 originally analysed were retained after the stringent filtering steps outlined in Materials and Methods. These markers were used to address questions about the patterns of genetic differentiation between locations. We estimated population genetic parameters for each sampling site. Heterozygosity varied more than threefold, from 0.080 for Leirvogsvatn (LEI) and Þverá/upper Ölfusvatnsá (THV) to 0.289 for Hvítá (HVI) and Ölfusá (OLF) . In mosti.e., Öxará (OXA), lower Ölfusvatnsá (FUS), Villingavatnsá (VIL), and Efra Sog (EFR) showed similar but notably lower levels , but in Þverá/upper Ölfusvatnsá (THV), isolated from these locations by an impassable waterfall (see −4). Similar low genetic diversity was seen in the headwater population of Leirvogsvatn (LEI) (π = 2.58 × 10−4) , Miðdalur (MID), and Fremstidalur (FRE) displayed intermediate levels of diversity, despite being isolated from the lowland locations by waterfalls . Notably, broader variance in diversity estimates was seen in locations that showed evidence of admixture, i.e., Hestvatn (HES), Hengladalsá at the Miðdalur valley (MID), Ölfusá (OLF), Þingvallavatn (THI) and Úlfljótsvatn (ULF) samples.The average nucleotide diversity also varied more than threefold among samples . The putfall see , the nuc × 10−4) . Interesterfalls . The divFST, admixture analyses, and a phylogenetic tree gave congruent results on genetic differentiation. Individuals from many of the sampled locations separated along the first two PC axes , the southwest Hengill trout, i.e., in tributary streams of Hengladalsá at Innstidalur (HIN), Miðdalur (MID), and Fremstidalur (FRE) valleys, and the headwater local reference population in Leirvogsvatn (LEI). The samples from the putative anadromous populations in the lowlands of Ölfusá (OLF), Hvítá (HVI), and Sog (SOG) as well as those from Hestvatn (HES), a lake connected to Hvítá (HVI), formed the fourth cluster, suggesting a close relation between brown trout from these sampling sites. Interestingly, the fish caught in Varmá (VAR), which we expected to group with the putative anadromous samples because of the connected waterways, clustered with the southwest Hengill populations. Also noteworthy is the fact that the Scottish reference group (SLP) grouped with Ölfusá (OLF) and Varmá (VAR) on the first axis but only separated from the Icelandic trout on the second axis. The third component separated three clusters, one composed of samples from Hestvatn (HES), one with individuals from Ölfusá (OLF), Sog (SOG), Hvítá (HVI), with a few individuals from Hestvatn (HES), and one with the samples from the rest of the locations. The samples from Leirvogsvatn (LEI) separated along the fourth principal component , i.e., group ABOVE (I) in FST = 0.311. Similarly, fish in Hvítá (HVI), Sog (SOG), and Ölfusá (OLF)  suggesting that they stem from the same population or belong to sub-populations that have interbred in the recent past. Varmá (VAR) and Hestvatn (HES) are connected to Ölfusá (OLF) and Hvítá (HVI) without any obvious physical barriers to fish migration. Yet the fish from Varmá (VAR) and Hestvatn (HES) deviated strongly from fish caught in Hvítá (HVI), Sog (SOG), and Ölfusá (OLF) .The pairwise estimates of ocations . ConsistFST values were observed between Leirvogsvatn (LEI) and the rest of the samples from the drainage basin of Ölfusá , which curiously was almost two times higher than the average FST (0.298) between the Scottish population (SLP) and the Icelandic samples/populations.The highest i.e., FST = 0.054) despite being separated by a waterfall, suggesting downstream gene-flow from the Innstidalur valley (HIN).Finally, brown trout from Hengladalsá at Innstidalur (HIN) and Miðdalur (MID) were closely related , Miðdalur (MID), and Fremstidalur (FRE) valleys and downstream in Varmá (VAR) split into two groups , the samples from Hvítá (HVI), Sog (SOG), Ölfusá (OLF), and Hestvatn (HES) were grouped together in C3 while some individuals from Hestvatn (HES) split from the Hvítá (HVI) and Sog (SOG) cluster to form their own group (C4). Most fish caught in the watershed of Þingvallavatn and Úlfljótsvatn clustered together (C5), the exception being the fish from Þverá/upper Ölfusvatnsá (THV) plus a few fish caught in lower Ölfusvatnsá (FUS), Villingavatnsá (VIL) and Þingvallavatn (THI) that clustered separately (C6). Leirvogsvatn (LEI), and the Scottish reference population (SLP) formed distinct clusters, C7 and C8, respectively , Þverá/upper Ölfusvatnsá (THV), and Hestvatn (HES) into downstream locations. The data do not indicate downstream flow of genes from the Þingvallavatn/Úlfljótsvatn system into the putative anadromous populations in the lowlands.The admixture proportions varied per location K = 8, . The resca. 700 m to the south of the former (A unique genetic component from Þverá/upper Ölfusvatnsá (THV) appeared downstream in the fish caught in the lower Ölfusvatnsá (FUS)  and Villingavatnsá (VIL). The latter river runs parallel to Ölfusvatnsá in the lower reaches and has its mouth in Þingvallavatn e former . This gee former , but noti.e., FST = 0.054) despite being separated by a waterfall, and the ancestry plots suggested a downstream flow of genetic material from Innstidalur (HIN). The samples from the upper parts of Hengladalsá  showed a unique component that progressively decreased downstream. The samples from Varmá (VAR) clustered with individuals from Hengladalsá , Sog (SOG), Ölfusá (OLF)  and Miðdalur (MID) were closely related  . Note alsá (OLF) , reflectMixing or contribution of genes between Hestvatn (HES) and the anadromous fish in Hvítá (HVI), Sog (SOG), and Ölfusá (OLF) was also evident, but the data also indicate that some Hestvatn (HES) trout belong to a resident population. A genetic component from the lowlands, which was most predominant in the Sog (SOG) samples, appeared in some individuals caught in Hestvatn (HES).RAxML while largely congruent with the PCA revealed several extra observations , Öxará (OXA), lower Ölfusvatnsá (FUS), Þverá/upper Ölfusvatnsá (THV), Villingavatnsá (VIL), Efra Sog (EFR), Úlfljótsvatn (ULF) and those from the south-western slopes of Hengill, i.e., Varmá (VAR), Fremstidalur (FRE), Miðdalur (MID), Innstidalur (HIN) formed two distinct clades separated by a large phylogenetic distance. It should be noted that the brown trout in the southwestern slope of Hengill and the river Þverá are relatively close geographically  but the streams drain in opposite directions.A phylogenetic analysis with rvations . The samThe headwater population of Leirvogsvatn (LEI) and the reference group from Scotland (SLP) formed their own distinct clades . A well-i.e., Miðdalur (MID) and Fremstidalur (FRE) valleys) and a sub-branch with individuals from the upper part of the river, i.e., Innstidalur (HIN) as well as some from Miðdalur (MID).The clade distribution of individuals from the lowland putative anadromous populations was less clear, with individuals from Sog (SOG), Ölfusá (OLF), and Hvítá (HVI) landing on different poorly separated branches. Samples from Hestvatn (HES) grouped in two clusters on the same well supported branch. The samples from Varmá (VAR) connected to the stem of the well supported Hvítá (HVI)/Sog (SOG)/Ölfusá (OLF) branch. Samples upstream of Varmá (VAR) formed a well-supported clade containing a mix of individuals from the lower parts of Hengladalsá  . Notably, the estimate for Öxará (OXA) was the second largest  while that of lower Ölfusvatnsá (FUS), showed an NE = 14.9  . Brown tE = 14.9 . Recall,i.e., 3,019 polymorphic loci, 373 individuals) using haplotypes and excluding sampling locations with <20 individuals. For reference, other locations with n > 20, including the Scottish reference group, were also tested.To test for a bottleneck in the population of Þingvallavatn, we used a different dataset  yielded significant results for the Wilcoxon tests for heterozygosity excess, except for the reference group SLP with the IAM model. All other sampling locations showed significant heterozygote deficiency (p < 0.001).None of the three runs for the three different models  showed a pattern and degree of genetic structure that fit expectations based on the present topography and connectedness of waterways in the western part of the Ölfusá watershed. The headwater populations, which are located above impassable waterfalls, showed clear signs of isolation from the putative anadromous lowland populations, which may exchange genetic material more freely. Importantly, individuals from Úlfljótsvatn, Þingvallavatn, and its tributaries, i.e., Úlfljótsvatn (ULF), Þingvallavatn (THI), Öxará (OXA), Ölfusvatnsá (FUS), Villingavatnsá (VIL), and Efra Sog (EFR) clustered together and most of their pairwise FST values were low (range 0.005–0.134), implying that the populations of these lakes and tributaries are genetically very similar. As was expected, the Þverá/upper Ölfusvatnsá (THV) population, located above an impassable waterfall, was the only one forming a genetically distinct sub-group within this clade. The significant ancestry component from this population in lower Ölfusvatnsá (FUS), Villingavatnsá (VIL), and Þingvallavatn (THI), suggests significant downstream gene flow from Þverá/upper Ölfusvatnsá (THV). The observed pattern of admixture may stem from remnants of spawning populations in lower Ölfusvatnsá and Villingavatnsá, which, due to geographic proximity, would be affected by down-stream gene flow from the Þverá/upper Ölfusvatnsá (THV) population, an influence not seen in the Öxará spawners.The results FST values among the fish caught in these locations and the dominant admixture component from the Öxará population (OXA) seen at present may to a large extent be a consequence of the efforts to reinvigorate the trout of Þingvallavatn.Although written records of brown trout spawning sites are scant, it is generally thought that prior to the construction of the dam the trout of Þingvallavatn originated from several sub-populations of spawners. The best-known spawning locations are in the rivers Öxará, lower Ölfusvatnsá, Villingavatnsá, and close to and in the outlet at Efra Sog  and accoFST values ranging from 0.058–0.134). Moreover, they show the most definite genetic component from the isolated population in Þverá/upper Ölfusvatnsá (THV). The fish caught in lower Ölfusvatnsá (FUS) were more mixed in this respect, some showing a strong component from Þverá/upper-Ölfusvatnsá (THV) while others mostly had the Öxará (OXA) component. This suggests that the releases of parr and planting of eggs from the Öxará spawning population lead to mixing of these components in the trout presently spawning in lower Ölfusvatnsá (FUS). The planting of eggs and releasing of juveniles may have been crucial, as the hatched fish would have experienced the imprinting of the natal river conditions. As the Þverá/upper Ölfusvatnsá (THV) population consists of small trout, but the lake fish can reach very large sizes, it remains an open question whether this gene flow influences traits in individuals of mixed ancestry. When some Irish lakes were stocked with adults for a period of 7 years, the genetic material of the native populations remained intact because the introduced fish did not spawn in the streams, as they probably lacked the imprinting of the conditions of the natal streams at an early age (It is notable that the parr caught in Villingavatnsá (VIL), which had no releases of fingerlings from Öxará (OXA), were the most genetically distinct among the samples from Þingvallavatn, Úlfljótsvatn and its tributaries , isolated above impassable waterfalls), the data suggest that these isolated tributaries harbour a genetically distinct population. Below these waterfalls, Miðdalur (MID) and Fremstidalur (FRE) had 15/40 and 1/15 fish with an Innstidalur (HIN) ancestry proportion >0.5, respectively. Most fish caught in the tributary streams of Miðdalur (MID) (25/40) and Fremstidalur (FRE) (14/15) showed a different genetic component that, somewhat unexpectedly, was found dominant in the fish from Varmá (VAR). Notably, this component was also found in trout from the Hvítá (HVI)/Sog (SOG)/Ölfusá (OLF) system. As might be expected, all the fish from Varmá (VAR) had a small but significant ancestry proportion (0.08–0.25) from the Hvítá (HVI)/Sog (SOG)/Ölfusá (OLF) system. The reasons for the shift in genetic composition from Innstidalur (HIN) to Miðdalur (MID) are unclear but we hypothesize that this may be caused by differences in habitat. The Innstidalur (HIN) habitat only consists of cold springs whereas the fish from the Miðdalur (MID) and Fremstidalur (FRE) valleys were caught in streams influenced by geothermal activity which drastically changes the annual temperature regime and ecological cycles of these streams  of the phylogenetic tree. Although we only had three samples from the upper Hengladalsá , are close to the Þingvallavatn/Úlfljótsvatn fish on the first PCA axis and are placed together, on a well-supported branch, in the phylogenetic tree. This suggests a common origin and early split between these populations. Presumably the early isolation of trout in the Þingvallavatn/Úlfljótsvatn system resulted in strong selection against downstream migration and rapid adaptation to a resident lifestyle, a process that would have been greatly enhanced by high productivity of benthic invertebrate prey and opportunities for switching to piscivory in these lakes. Migratory behaviour is a quantitative trait with a genetic component (It is notable that fish from Leirvogsvatn (LEI), despite having the highest omponent . A highlomponent . Similarca. 30% of the genetic component from the lake and about 50% of the Hvítá/Sog/Ölfusá component (Although fish from the connected lowland waterways in Hestvatn (HES), Hvítá (HVI), Sog (SOG), and Ölfusá (OLF) scattered on the same branch of the tree, most individuals caught in the same water body tended to cluster together on well supported sub-branches. In Hestvatn (HES), most of the fish clustered on a well-supported (100%) sub-branch while a few fish had a high proportion (around 50%) of a genetic component from the Hvítá (HVI)/Sog (SOG)/Ölfusá (OLF) system. Some individuals caught in Hestvatn had omponent , indicatE estimates for several locations in the watershed of Ölfusá showed small values, the exception being the spawning trout from Öxará (OXA), and the fish sampled in Sog (SOG) which had larger NE, and the population in Leirvogsvatn (LEI) with a very small NE.The NE values may reflect habitat constrains that limit the number of sexually mature individuals coming to the spawning grounds.The observed variation in Ni.e., HIN, MID, FRE) and Þverá/upper Ölfusvatnsá (THV), resulting in the small NE values observed. This is in stark contrast to the situation in Þingvallavatn, which offers vast habitats and favourable conditions for growing immature fish. Presently, several thousand individuals enter Öxará each year to spawn  was almost four times larger than that of Þverá/upper Ölfusvatnsá (THV), it seems low considering the number of spawners. This strongly suggests a different type of habitat constraint, i.e., that NE is limited by the size of the spawning area. The number and quality of suitable spawning areas can have a direct impact on the effective population size , or something in between. For a species like brown trout (with overlapping generations and repeated spawning), Nb is expected to be lower than NE  and Þingvallavatn (THI) and the accessible part of its tributary streams but ca. 50% lower than that of the fish caught in the lowland rivers below the Írafoss and Kistufoss waterfalls.Our results revealed a clear pattern of genetic diversity by geography and waterway topography. The small headwater populations of Þverá/upper Ölfusvatnsá (THV) and Leirvogsvatn (LEI) showed the lowest diversity of the populations studied, with the average nucleotide diversity being O) and nucleotide diversity estimates (π) in most of the headwater locations sampled are probably the result of many generations in isolation. However, in the case of Leirvogsvatn (LEI) and Þverá/upper Ölfusvatnsá (THV), habitat constraints could also play a role, e.g., restricted spawning habitats  and restricted winter habitat and adaptation to the ecological effects of the thermal springs . Considering the low levels of genetic diversity in Leirvogsvatn (LEI), Þverá/upper Ölfusvatnsá (THV), and the populations of Úlfljótsvatn (ULF) and Þingvallavatn (THI) and its tributaries, the relatively higher genetic diversity in the mountain streams in Hengladalsá is odd. This may reflect admixture caused by recent stocking or population substructure. The six fish from Innstidalur (HIN) were sampled in cold springs but the fish from Miðdalur (MID) and Fremstidalur (FRE) were sampled in several very small, thermally influenced streams , Þverá/upper Hengladalsá (THV). Despite small population sizes, we did see evidence of downstream gene flow from the small streams in the Hengill volcano, but notably, no such signals were seen in populations downstream of the much larger Þingvallavatn/Úlfljótsvatn system. Most sampling locations displayed rather small effective population sizes, except for Sog (SOG) and Öxará (OXA). Only two of eight populations measured showed a NE > 50, which is a reason for concern  and Þingvallavatn (THI). This could be important as these fish may harbour variation related to a wider tolerance of increased temperature.Although the iconic Þingvallavatn trout seems to have made a drastic recovery in the last decade, better knowledge is needed to understand how its sub-populations in Öxará (OXA), Ölfusvatnsá (FUS), and Villingavatnsá (VIL), contribute to the metapopulation of trout in Þingvallavatn and Úlfljótsvatn.10.7717/peerj.15985/supp-1Supplemental Information 1de novo derived SNP’s. In total 2,597 putative neutral markers were ascertained after stringent filtering. (B) A dataset obtained from mapping to a reference genome, that resulted in 2,946 putative neutral markers using the same filtering criteria. Both show individuals and the two components that explained most of the data variation. See (A) PCA on Click here for additional data file.10.7717/peerj.15985/supp-2Supplemental Information 2(A) PC1 = 21.2% and PC2 = 7.7%; (B) PC3 = 4% and PC4 = 3.6%. See Click here for additional data file.10.7717/peerj.15985/supp-3Supplemental Information 3Estimated with the function snmf from the R package LEA (version 2.0).Click here for additional data file.10.7717/peerj.15985/supp-4Supplemental Information 4snmf from the R package LEA (version 2.0).Note, not all the samples from each location grouped in the same cluster. Estimated with the function Click here for additional data file.10.7717/peerj.15985/supp-5Supplemental Information 5BamHI-HF adapters were combined with 12 ApeK. adapters to generate 96 unique combinations.The eight Click here for additional data file.10.7717/peerj.15985/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.15985/supp-7Supplemental Information 720X: samples that were below the average coverage threshold of 20X; NKIN: individuals removed due to a high relatedness score; NMIS: individuals missing more than 60% of data. NDNA denotes samples used in most data analyses. N15X: number of individuals added back in after relaxing the coverage threshold to 15X in order to increase the number of samples for the effective population analysis and the detection of population bottleneck signals. Note that not all extra samples were used since the filters for missingness and kinship were re-applied.NClick here for additional data file.10.7717/peerj.15985/supp-8Supplemental Information 8E: expected heterozygosity; HO: observed heterozygosity; π: nucleotide diversity. A star indicates pairs of HE/HO that showed significant differences .N: sample size; HClick here for additional data file.10.7717/peerj.15985/supp-9Supplemental Information 9i.e., C1: upper Hengladalsá river including Innstidalur, Miðdalur and Fremstidalur; C2: lower Hengladalsá river including Miðdalur, Fremstidalur as well as Varmá ; C3: Sog, Hvítá, Ölfusá and Hestvatn; C4: Hestvatn; C5: Þingvallavatn, Öxará, lower Ölfusvatnsá, and Villingavatnsá as well as Efra Sog and Úlfljótsvatn; C6: Þverá/upper Ölfusvatnsá and lower Ölfusvatnsá, Þingvallavatn, Villingavatnsá, and Úlfljótsvatn; C7: Leirvogsvatn; C8: reference group from the Isle of Skye, Scotland).The cluster description corresponds to that of Click here for additional data file.10.7717/peerj.15985/supp-10Supplemental Information 10E values obtained with a dataset without linkage disequilibrium (LD) correction , and with a second dataset obtained after LD correction . The function filter_ld from the R package radiator was used to make sure that only one SNP was present in each read, and that a SNP’s filter was applied based on Long Distance Linkage Disequilibrium.NClick here for additional data file.10.7717/peerj.15985/supp-11Supplemental Information 11Pending submission of raw sequencing reads to SRA.Click here for additional data file.


Non-recombining regions of the Y-chromosome recorded the evolutionary traces of male human populations and are inherited haplotype-dependently and male-specifically. Recent whole Y-chromosome sequencing studies have identified previously unrecognized population divergence, expansion and admixture processes, which promotes a better understanding and application of the observed patterns of Y-chromosome genetic diversity.Here, we developed one highest-resolution Y-chromosome single nucleotide polymorphism (Y-SNP) panel targeted for uniparental genealogy reconstruction and paternal biogeographical ancestry inference, which included 639 phylogenetically informative SNPs. We genotyped these loci in 1033 Chinese male individuals from 33 ethnolinguistically diverse populations and identified 256 terminal Y-chromosomal lineages with frequency ranging from 0.0010 (singleton) to 0.0687. We identified six dominant common founding lineages associated with different ethnolinguistic backgrounds, which included O2a2b1a1a1a1a1a1a1-M6539, O2a1b1a1a1a1a1a1-F17, O2a2b1a1a1a1a1b1a1b-MF15397, O2a2b2a1b1-A16609, O1b1a1a1a1b2a1a1-F2517, and O2a2b1a1a1a1a1a1-F155. The AMOVA and nucleotide diversity estimates revealed considerable differences and high genetic diversity among ethnolinguistically different populations. We constructed one representative phylogenetic tree among 33 studied populations based on the haplogroup frequency spectrum and sequence variations. Clustering patterns in principal component analysis and multidimensional scaling results showed a genetic differentiation between Tai-Kadai-speaking Li, Mongolic-speaking Mongolian, and other Sinitic-speaking Han Chinese populations. Phylogenetic topology inferred from the BEAST and Network relationships reconstructed from the popART further showed the founding lineages from culturally/linguistically diverse populations, such as C2a/C2b was dominant in Mongolian people and O1a/O1b was dominant in island Li people. We also identified many lineages shared by more than two ethnolinguistically different populations with a high proportion, suggesting their extensive admixture and migration history.Our findings indicated that our developed high-resolution Y-SNP panel included major dominant Y-lineages of Chinese populations from different ethnic groups and geographical regions, which can be used as the primary and powerful tool for forensic practice. We should emphasize the necessity and importance of whole sequencing of more ethnolinguistically different populations, which can help identify more unrecognized population-specific variations for the promotion of Y-chromosome-based forensic applications.The online version contains supplementary material available at 10.1186/s40246-023-00476-6. Human genomics studies in the whole-genome sequencing era have updated our understanding of the patterns of genetic diversity among ethnolinguistically diverse worldwide populations, fine-scale population structure, variation spectrum of various kinds of genetic variations of single nucleotide variations (SNV), structural variations (SV) and complex mobile elements –3. The pY-chromosome-based phylogeny was used to explore the population origin, admixture, and evolutionary history at the era of the first genome sequencing era. Zerjal et al. genotyped over 32 markers among 2123 males and identified the genetic legacy of the Mongol western expansion , 14. TheThe deeper understating of Y-chromosome variations in human evolutionary research further promoted its wide applications in forensic science , 24. TheWe generated genotype data of 639 Y-SNPs from 322 unrelated individuals from six Chinese populations belonging to three different language families using our developed 639-plex Y-SNP panel (Method), including three Mongolic-speaking Mongolian populations in North China and one Sinitic-speaking Hui in Northwest China, two Tai-Kadai-speaking Gelao in Southwest China and Li in southernmost China Fig. A. To proWe explored the similarities and differences based on the haplogroup frequency spectrum (HFS) among 33 investigated populations at level 4 Fig. A, B. O1aWe additionally explored the genetic similarities and differences among 33 Chinese populations using principal component analysis (PCA) based on the HFS on the fourth level. PC1, extracting 51.45% variance from total variations, separated the northern and southern Chinese populations and PC2, extracting 10.15% variance, separated Tai-Kadai-speaking Li from other Chinese people . O2a2a1/b1, O2a1a1, and O2a1a1/b1 contributed to Mongolian, Hui and Gelao. We could also identify some star-like expansion of some dominant lineages, such as C2a2b1a1a1 in Mongolian populations.Han Chinese is the largest ethnic group in the world and has a significant influence on the formation of the gene pool of Chinese populations , 46–48. The reconstructed merged Network confirmed the complex admixture and expansion events among Han Chinese and other non-Han Chinese populations Fig. . The MonAncient DNA from Island Southeast Asia (Nagsabaran Site) and Mainland Southeast Asia  has demonstrated that both ancient Southeast Asian Hòabìnhian hunter-gatherers and Neolithic southern Chinese rice farmers contributed to the complexity of genetic diversity of Southeast Asia , 50. LarTo comprehensively investigate the population interaction between East and Southeast Asians, we aggregated our data with publicly available haplogroup information from 3094 people. We obtained one aggregation dataset that included 4728 individuals from 114 ethnolinguistically populations from China and Southeast Asia, including 5 Altaic-, 6 Austroasiatic-, 5 Austronesian-, 62 Sino-Tibeto-, 10 Hmong-Mien-, 26 Tai-Kadai-speaking populations , O2a1b1a1a1a1a1a1-F17 (43), O2a2b1a1a1a1a1b1a1b-MF15397 (26), O2a2b2a1b1-A16609 (24) and O2a2b1a1a1a1a1a1-F155 (23) have experienced population expansion events recently, which was consistent with population expansion among Han Chinese populations inferred from the whole-genome sequencing project. Admixture-introduced rare lineage Q1a1-F746/F790 was observed in 30 samples from Mongolian, Han, and Gelao people, and steppe pastoralist-related R1a1a1b2-Z93 was observed in 17 Mongolian and Hui people. Our results provided genetic evidence for extensive admixture between northern East Asians and surrounding populations. Similar patterns were also illuminated in the paternal genetic history reconstruction by Wang et al., who concluded that multiple ancestral sources contributed to the formation of the paternal gene pool of Mongolian people . BesidesWe also identified paternal genetic structure among Chinese populations in the clustering patterns via HFS, PCA and MDS. These population stratifications were in accordance with the language or geographical categories. Island Li people shared their specific paternal genetic structure and clustered far from other Chinese populations. Similarly, Mongolian people clustered together and have a relatively close genetic relationship with northern East Asians. We also should note that the Y-chromosome-based population structure in China is rougher than that inferred from the genome-wide SNPs. Our recent genetic studies have identified five population substructures correlated with languages and geography in China. Mongolic and Tungusic people in northeastern China harbored the highest Ulchi or ancient Boisman/DevilsCave-related ancestry , 41. TibThe forensic community has noticed that whole-genome sequencing in a forensic case needs to overcome specific infrastructure of the specialist, platform and genomic statistical methods, as well as the experiment method focused on the forensic case samples. Besides, the cost of one sample is another important obstacle to the wide application of whole-genome sequencing technology in forensics. Evolutionary genetic scientists have conducted many vital projects to explore the complete anthropologically-informed phylogeny , 19, 20.Forensic pedigree search can help trace possible crime suspects based on the shared Y-chromosome mutations. Lineages informative Y-SNPs were usually used together with Y-STR markers. Many prior works provided relatively high-resolution forensic phylogenic trees and presented the corresponding scientific examination and analysis strategies. They promoted the advances of forensic Y-chromosome applications in pedigree search and biogeographical ancestry inference based on the customized SNaPshot and NGS technologies , 58–60. WCH (100 K genome sequencing of rare disease), can provide more unreported LISNPs for forensic application of Y-chromosome.The complete landscape of human Y-chromosome variations and the gradually updated Y-chromosome phylogenetic tree with more population-specific LISNPs formed the fundamental for forensic application and evolutionary study. To overcome the shortness of the whole Y-chromosome sequencing in forensic science at the initial stage of the genome sequencing era, we developed one high-resolution 639-plex Y-SNP panel that included 639 LISNPs defined 573 terminal Y-chromosomal lineages. We generated the population data from 1033 individuals from 33 populations, including Han, Hui, Mongolian, Li and Gelao and investigated the forensic features, HFS and evolutionary processes via multiple statistical models. We identified 257 terminal Y-chromosomal lineages with several common founding lineages of O2a2b1a1a1a1a1a1a1-M6539, O2a1b1a1a1a1a1a1-F17, O2a2b1a1a1a1a1b1a1b-MF15397, O2a2b2a1b1-A16609, O1b1a1a1a1b2a1a1-F2517 and O2a2b1a1a1a1a1a1-F155. Patterns of HFS and corresponding geographical distribution illuminated that some Siberian or western Eurasian-originated paternal lineages contributed to the formation of the paternal gene pool of Mongolian, Hui and northern Han Chinese populations. Network and our reconstructed forensic phylogenic topology further illuminated the complex population divergence and expansion of different paternal lineages, which also found some ancestral lineages shared by geographically or linguistically different Chinese populations. PCA and MDS clustering patterns showed that the paternal genetic structure was correlated with the geographical and linguistic categories, which provided the basic genetic background for forensic paternal biogeographical ancestry inference. Our reconstructed revised phylogeny and comprehensive population genetic investigation based on this Y-SNP panel can provide the highest resolution of the terminal lineage and genetic diversity, which provides one panel with high marker coverage and lineage representation. Ethnolinguistically diverse Chinese populations had the highest genetic diversity. Thus, anthropologically-informed Y-chromosome whole-genome sequencing will promote the further development of higher-resolution Y-SNP NGS panels and corresponding population-specific dataset construction. We also emphasized that the large-scale population cohorts, such as 10,000 Chinese Person Genomic Diversity Project (10K_CPGDP) and 100 K-GSRDWe collected peripheral blood samples from 1033 unrelated Han, Tibetan, Hui, Gelao, Manchu and Li individuals from 33 geographically different regions  and Yfull databases focused on Chinese populations were included. Second, key mutations included in our previously developed panel and validated in the population genetic studies were included [We chose the final included markers based on the following five rules to present a full-scale Chinese Y-chromosome diversity and high resolution of each terminal lineage. First, all major clade lineages recorded in the International Society of Genetic Genealogy (ISOGG) Y-DNA Haplogroup Tree 2019–2020 version 15.73  to conduct quality control on the merged dataset based on the missing SNP rate and missing genotyping rate with two parameters (–geno: 0.1 and –mind: 0.1) [We sequenced 639 Y-SNPs in 322 samples from Mongolian populations in three geographically different regions , Hui people from Wuzhong , Gelao people from Daozhen  and Li people  from Hainan, using our developed 639-plex Y-SNP panel on the MGISEQ-2000RS sequencing platform. In each sequencing run, positive control of 2800M  and negative ddHnd: 0.1) . We finaWe manually classified the NRY haplogroups for sequencing data based on the predefined phylogenetic tree with the chosen mutation markers. And then, we merged the sequencing data and the chip-based data and used the python package of hGrpr2.py instrumented in HaploGrouper to classify the haplogroups . All braWe calculated the HFS at different levels of the terminal haplogroups. We estimated the geographical distribution via a pie chart in the map and a heatmap based on the HFS matrix. Followingly, we conducted PCA based on the HFS matrix. We used the top three components extracted from the total variations to cluster our studied populations. We also calculated the pairwise Fst genetic matrix based on the HFS and conducted the MDS to explore the genetic affinity between included populations.We used the LineageTracker to construct the phylogenetic topology of all individuals . Tools fhttps://github.com/rubenAlbuquerque/fasta_nexus_converter/blob/master/fasta_to_nexus/Main.py) was used to generate the nexus files. We used popART to construct the Network relationship. Here, five Network models were used to build the phylogenetic relationship among different lineages, including Minimum spanning network, Median Joining Network, Integer NJ Net, Tight Span Walker and TCS Network [Python package of fasta_to_nexus_Main.py ( Network . AMOVA w Network .Additional file 1: Table S1 Haplogroup distribution of all studied populations. Table S2 Haplogroup frequency of 33 Chinese populations. Table S3 Genetic distances among 33 Chinese populations. Table S4 Haplogroups of 4727 people from 114 populations.