Aedes aegypti , is a highly invasive and medically significant vector of dengue, yellow fever, chikungunya and Zika viruses, whose global spread can be attributed to increased globalization in the 15th through 20th century. Records of the invasion history of Ae. aegypti across Southeast Asia are sparse and there is little knowledge regarding the invasion routes that the species exploited to gain a foothold in the Indo‐Pacific. Likewise, a broad and geographically thorough investigation of Ae. aegypti population genetics in the Indo‐Pacific is lacking, despite this region being highly impacted by diseases transmitted by this species. We assess 11 nuclear microsatellites and mitochondrial COI sequences, coupled with widespread sampling through the Indo‐Pacific region to characterise population structure at a broad geographic scale. We also perform a comprehensive literature search to collate documentation of the first known records of Ae. aegypti at various locations in the Indo‐Pacific. We revealed additional spatial population genetic structure of Ae. aegypti in Southeast Asia, the Indo‐Pacific and Australasia compared with previous studies and find differentiation between multiple Queensland and Torres Strait Islands populations. We also detected additional genetic breaks within Australia, Indonesia and Malaysia. Characterising the structure of previously unexplored populations through this region enhances the understanding of the population structure of Ae. aegypti in Australasia and Southeast Asia and may assist predictions of future mosquito movement, informing control strategies as well as assessing the risk of new invasion pathways.The dengue mosquito,  Belonging to a species group known as the Aegypti Group, whose diversity is centred in the islands of the Indian Ocean, it is thought to have initially reached mainland Africa less than 100,000 years ago  blood‐fed on non‐human hosts and inhabited tropical forests  in 1879 and later in Rockhampton, QLD in 1885  Garment quoted by Edwards , 0.1 μL 10 μM M13 tagged forward primer, 0.2 μL 10 μM reverse primer, 0.2 μL M13 tagged fluorescent dye , 0.01 μL (1 U) MyTaq polymerase and 1 μL of 1:10 DNA template. Subsequent PCR involved denaturation at 96°C for 3 min, followed by 13 cycles of denaturation at 95°C for 30 s, annealing at 56°C for 40 s (with a gradient decrease of 0.5°C/cycle) and extension at 72°C for 30 s. This was followed by a further 25 cycles of 95°C for 30 s, 50°C for 40 s and 72°C for 30 s. Then a final elongation step of 5 min at 72°C before cooling to 4°C. Amplification was confirmed by running 1 μL of the PCR product on a 2% agarose gel stained with MidoriGreen . Successfully amplified samples were sent to Macrogen Inc. (Republic of Korea) for fragment analysis on an ABI 3730XL DNA analyzer .Samples were screened for 11 microsatellite markers Table . These mRaw microsatellite data were processed using the standardization run wizard  in GeneMarker v.2.4.2 , number of effective alleles (Ne), observed (Ho) and unbiased expected values of heterozygosity (uHe) and global FST with and without the exclusion of null alleles using Genepop v.4.2  to assess significance. We tested for isolation by distance with a Mantel test, implemented in the adegenet 2.1.1 package  present in the dataset and to explore the effect of models using the admixture and population prior settings. Based on these preliminary analyses, the final analysis was run with the admixture model and using sampling locations as a prior, with K ranging from 2 to 22  with a burn‐in of 100,000 followed by 1,000,000 iterations. The output from the STRUCTURE run was processed in STRUCTUREHARVESTER  methods. In addition, we analysed subsets of the dataset based on these STRUCTURE results . For these sub‐analyses, we used the same settings and run time, but the value of K ranged based on the number of populations being analysed. Final plots were made using pophelper  to retain as that being associated with the lowest root mean squared error . The K‐means clustering was performed using the adegenet package . We used the lowest BIC to infer the optimal K value. Inferred group memberships were plotted against actual group (population) membership. We additionally performed this using regional definitions (Table To further explore clustering in our dataset given no prior population information (i.e. assuming populations are unknown), we used ns Table ; Region 2.3COI was amplified using previously used , 0.2 μL 100 μM forward primer, 0.2 μL 100 μM reverse primer, 0.2 μL MyTaq polymerase and 1 μL DNA template. For PCR, we used an initial denature of 94°C for 3 min, 35 cycles of denaturation at 95°C for 30 s, primer annealing at 45°C for 40 s, and primer extension at 72°C for 30 s. Final elongation was 5 min at 72°C prior to storing at 4°C. Amplification was confirmed using gel electrophoresis (as described previously) and PCR products were purified by adding 2.5 μL per sample of a mixture containing 1.4 μL H2O, 1 μL Exonuclease I and 0.1 μL Shrimp Alkaline Phosphatase  before incubation at 37°C for 20 min and denaturation at 80°C for 10 min. Samples were sequenced in both the forward and reverse directions by Macrogen Inc. (Republic of Korea) using Sanger sequencing. An automated workflow was used in Geneious v.11.1  using 1000 iterations.Additional 33.1Ho = 0.428) in the Waiben population despite it having the highest mean number of alleles (15.667) within the Australian region and the highest unbiased expected heterozygosity (0.592). Overall, SE Asian populations exhibited high allelic diversity  while most mainland Australian populations showed comparatively lower measures. Within Australia, Cairns showed the highest mean observed heterozygosity , while all other QLD populations had lower, although similar, values of unbiased heterozygosity.We observed the lowest heterozygosity (Na) was highest in Kuala Lumpur, Cambodia and Honiara, whereas Port Moresby exhibited the lowest , but the correlation is weak (R2 = 0.009), implying the influence of other factors shaping the genetic structure of Ae. aegypti in the study region.The loci AC4 and A9 were removed due to the high frequency of null alleles >0.2) and the number of populations that violated HW . Overall, significant FST ranged from 0.029 to 0.249 (p < 0.005) for the entire dataset. Genetic differentiation was lowest within Indonesia and Australia. Within Queensland, FST ranged from 0.035 to 0.161 , whereas more southerly populations showed signs of higher genetic differentiation . The New Guinean populations of Port Moresby and Timika displayed high differentiation with most other populations in pairwise comparisons.There was no major difference in the relationships observed using multiple measures of genetic distance  and Indonesian/Malaysian (blue) clusters with several other populations showing varying degrees of potential admixture between these two clusters . An upper K value of 10 , the Solomon Islands, Papua New Guinea, Indonesia, Malaysia, Southeast Asia and the USA.For the whole dataset, ∆n Figure . At K = 0 Figure  was suggK = 2 on the whole dataset. The Australian cluster (not including Waiben) is made up of approximately 3–6 genetic subgroups , Central (Longreach) and Central‐Eastern  Queensland divisions. However, there is significant admixture between many of these populations and further differentiation when K = 6. Finer population substructure was also uncovered in Holloways Beach (K = 2) and Charters Towers /Sulawesi and Bali/Sumba. Malaysia was comprised of three genetic clusters when sub‐analysed  were analysed together, the optimal K‐value ranged from 4 to 7. When K = 4, genetic clusters correspond to broad geographic groupings: Torres Strait, Papua New Guinea/Solomon Islands, Southeast Asia and the USA.Within the blue cluster Figure , there ad Figure , correspDiscriminant analysis of principal components of the full dataset Figure  is somew3.3COI did not reveal a strong genetic structure, but some patterns regarding the distribution and diversity of haplotypes are worth noting. The 13 haplotypes plotted in Figure COI sequences analysed of which 111 COI new sequences were generated by this work (GenBank accession numbers OK285076–OK285186). However, additional haplotypes (n = 99 haplotypes) are plotted in a TCS network in Figure Using the mitochondrial marker 44.1Ae. aegypti across our entire study region are consistent with those of global studies  populations. When Asian‐Pacific regions have been sub‐analysed , Gloria‐Soria et al.  was weak, implying that other drivers such as human movements play an important role in shaping the genetic structure of Ae. aegypti in Queensland. In a previous study, Endersby et al.  and Mount Morgan (MT). Based on what we know about the biology of Ae. aegypti, it is likely that there is real population genetic structure between these sites, but that relatively small sample sizes reduced our ability to detect significant FST between these sites. Low genetic differentiation between these sites may be due to either gene flow or a recent shared ancestry between them.Within Queensland, we found three clear genetic divisions that represent northern, south‐eastern and south‐central Queensland clusters Figure , with suy et al.  found thy et al. . UnsurprAe. albopictus . Waiben would likely have higher gene flow with other island populations in the Torres Strait and Southern Papua New Guinea regions, which appears to be the case for COI haplotype H14 was prevalent on Waiben but was not detected in mainland Australia. This is probably reflective of the Torres Strait region differing in invasion and demographic history from mainland Australia, reinforcing earlier findings by Beebe et al.  was highly distinct from Amamapare (MIM) and other parts of Indonesia. The population in Timika is situated further from the coast and migration from coastal populations connected by shipping may be less frequent, but it may be more genetically similar to other unsampled inland Papuan populations. The signature from the mitochondrial COI dataset does not support this difference, with all Indonesian populations sharing similar haplotypes, many of which are also found across other countries, especially within Asia . This is likely due to regular migration of FST measures with the population from Honiara. Human‐mediated movements during World War II would have had a significant impact on the distribution of mosquitoes in the Pacific area and could have led to admixture between geographically disparate populations  found moderate genetic differentiation between island populations (FST = 0.05–0.24) and that populations from more isolated islands were more genetically distinct than those from major towns, which showed a higher degree of mixed ancestry  used in their study revealed some geographic patterns of relatedness between populations, but certain haplotypes were more widespread than others. Their study highlighted that different regions likely had multiple introduction origins, both historic and contemporary. We suspect that populations from the Solomon Islands probably share some similarities to populations from other Pacific Islands  and that similar demographic processes may have occurred in the Solomon Islands. Other studies have shown that at low values of K, Australian populations (Townsville and Cairns) cluster with Pacific Island populations , however, further analysis at higher values of K  population appeared more genetically distinct from the nearby population of Port Moresby than expected and is somewhat unique within our study area. This is potentially due to mixed ancestry from Australia, the USA and parts of Asia  which share the smallest pairwise Ae. aegypti from Southeast Asia, Sri Lanka and Saudi Arabia belong to the same genetic cluster. This could suggest a similar introduction source, potentially revealing India as an important, yet understudied historical invasion source for Southeast Asian populations, given its importance in early trade and the early history of dengue , French Guiana (South America), SE Asia  and Ae. aegypti formosus from Western Africa and several Indian Ocean Islands. They found potential structure between South Pacific Island and Asian/American populations of Ae. aegypti, positioning African populations as more divergent. Additionally, they showed evidence of strong genetic differentiation between French Polynesian populations, which was more obvious than that between populations from Vietnam, Cambodia and French Guiana. This may have been the result of past major bottlenecks , a region mostly under Portuguese and Spanish control, via Southern Africa (the Cape Route) and the Indian Ocean. Later in the 17th and 18th centuries, this was overrun by British and Dutch enterprises, with the Dutch monopolising the Indonesian region (under the Dutch East India Company) and possessing a widespread trade network centralised in Batavia (now Jakarta), Java. New imperialism strongly shaped trade in the 19th century, which became more globalised and began to approach today's form. Due to the frequency of human movements  at the time when Ae. aegypti colonised Asia and Australasia rapidly, and it occurred in major trading ports at a similar time  suggests that population removal may be possible using the incompatible insect technique. This has recently been shown to be an effective tool for suppressing Ae. aegypti populations in north Queensland towns (Beebe et al., Importantly, our study demonstrates the need to analyse populations of Authors have no conflict of interest.Figures S1‐S8Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.


Ocypode ceratophthalmus populations in the East and South China Seas based on two genetic markers. The results showed that O. ceratophthalmus had a high genetic diversity among all collected populations, and an insignificant population structure was observed by a hierarchical analysis of molecular variance and fixation index. Additionally, Migrate-n revealed high historical gene flow and migration rates among populations. The results of this study could inform the construction and management of marine protected areas in the East and South China Seas.This study investigated the genetic diversity and connectivity of 15 Ocypode ceratophthalmus in the East and South China Seas, 15 populations from or near 15 marine protected areas in the two seas were studied using COI and D-Loop as genetic markers. The results showed that O. ceratophthalmus populations had high diversity, and the results of a hierarchical analysis of molecular variance and fixation index found that there were no significant genetic structures among these populations. High historical gene flow and high migration rates were further observed among populations by Migrate-n. Furthermore, the COI sequences further showed the asymmetric migration rate with a higher migration rate from south to north than from north to south. This information could provide recommendations for the management of marine protected areas in the East and South China Seas.The East and South China Seas are rich in marine resources, but they are also under great pressure from climate change and human activities. Maintaining diversity and connectivity between communities is thought to be effective in mitigating these pressures. To assess the diversity and connectivity among the populations of  The East and South China Seas are important areas for the utilization of marine resources and marine conservation. Currently, 12,933 tropical and subtropical species in the East China Sea (ECS) have been recorded, of which half 48%) were endemic 8% were e. Most of The Convention on Biological Diversity has recognized connectivity as a fundamental principle in the planning of the MPA network. Additionally, it was requisite to determine the boundaries of ecosystems based on connectivity in the ecosystem-based marine reserve management system [Protecting biodiversity as a whole required not only the design of protected areas with reasonable functions, but also the consideration of species migration among different habitats on a larger scale, such as the establishment of several protected areas with the same functions to form an MPA network. Previous studies have also revealed that the MPA network could effectively prevent or mitigate the negative impacts of biodiversity , which rt system . TherefoAtrina pectinate [Nibea albiflora [Octopus ovulum [Parasesarma affinis [Periophthalmus modestus [Siphonaria japonica [Thamnaconus hypargyreus [Chelon haematocheilus [Eleutheronema tetradactylum [Epinephelus akaara [Pagrus major [Scomber japonicus [The ECS belongs to the East China Sea Ecoregion in the Warm Temperate Northern Pacific Marine Biogeographic province, and the SCS belongs to the Gulf of Tonkin Ecoregion, Southern China Ecoregion and South China Sea Oceanic Islands Ecoregion in the South China Sea Marine Biogeographic province . Whetherectinate , Nibea albiflora , Octopuss ovulum , Parases affinis , Periophmodestus , Siphonajaponica  and Thamargyreus  had highocheilus , Eleuthedactylum , Epinephs akaara , Pagrus us major  and Scomaponicus . These iOcypode ceratophthalmus), a species widely distributed in the East and South China Seas, has a planktonic larval stage and a settled adult stage like most marine invertebrates. Furthermore, O. ceratophthalmus is expected to suffer less human disturbance due to its low economic value, and it is therefore considered to be better to reflect the diversity and connectivity patterns among protected areas [COI and D-Loop, were employed in this study to assess the genetic connectivity among horn-eyed ghost crab populations in or near marine protected areas in the two seas. The results from this study were expected to inform the construction and management of marine protected areas in China.Horn-eyed ghost crab , and the DNA was stored at −20 °C and used as a template in the PCR reactions. PCR was implemented to amplify COI and D-Loop using the following primers, LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HCO2198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) for COI, 13,323F (5′-GCGAATGCTGGCACAAACAT-3′) and 14,378R (5′-AGGGAGTGGTGCAATTCCAT-3′) for D-loop. The PCR amplification reaction included 25 μL 2× Hieff® PCR Master Mix, 2 μL of upstream and downstream primers (10 μM), about 55 ng of template DNA, and double-distilled water was added to the total volume of 50 μL. Then, the PCR thermal cycling program was set as follows: denaturation at 94 °C for 5 min, followed by 35 cycles of denaturation for 94 °C 30 s, annealing at 55 °C for 30 s, elongation at 72 °C for 1 min; and the final extension step for 10 min at 72 °C. All PCR products were detected by 1.5% agarose gel electrophoresis. Finally, the PCR products were purified and sequenced by Tsingke Biotech Company .The obtained samples were morphologically identified according to the previous study , samplesAll sequences were checked and edited using Geneious. Multiple sequences were aligned using the Geneious Alignment (Identity 1.0/0.0), and all alignments were checked visually. MEGA11 was used to evaluate the composition of the nucleotides. DnaSP v.4.0 was used to calculate the genetic diversity indices such as the number of haplotypes (h), haplotype diversity (Hd) and nucleotide diversity (π).STF) was employed to assess the pairwise genetic divergence between different populations and the significance was obtained by 10,000 permutations.A map of haplotypes distribution was constructed using ArcGIS, R 4.2 and Arlequin 3.5, and minimum spanning networks were constructed using PopART 1.7. Hierarchical analysis of molecular variance (AMOVA) of the different levels was used to further examine the population structure. The AMOVA based on three levels included population level , region level  and ecoregion level . The significance of AMOVA was analyzed by 10,000 permutations. The fixation index  and mutation-scaled population size θ . The runs were recorded every 100 steps for a total of 1,000,000 long-chain Markov chain Monte Carlo (MCMC) steps and a burn-in of 1000. To improve the efficiency of the MCMC search, a static heating scheme with four different temperatures  was used. We inspected histograms of estimated θ and M posterior values to assess convergence. We calculated historical gene flow (Nm) by using the equation Nm = θ × M .COI sequences  and 95 D-Loop sequences  were obtained from 15 populations of horn-eyed ghost crab. For COI sequences, the average contents of A, T, G and C were 26.6%, 34.6%, 16.8% and 22.0%, respectively. A total of 50 polymorphic sites were detected, including 24 singleton variable sites and 26 parsimony informative sites. Haplotype diversity ranged from 0.80 to 0.98, and nucleotide diversity ranged from 0.0022 to 0.0054 , including 24 shared haplotypes and 38 exclusive haplotypes. The frequencies of 62 haplotypes varied widely; the combined frequencies of Hap 1 and Hap 2 were as high as 49.40%, with the highest frequency of Hap 2 (26.48%), followed by Hap 1 (22.92%), while the remaining haplotypes were less frequent. Two haplotypes (Hap 1-2) were shared by 15 populations . There were few shared haplotypes between diverse populations and no dominant haplotypes  was accounted for by differentiation within populations, with a further −0.80% accounting for variation within ecoregions among populations, and the remainder (0.76%) partitioned among ecoregions (p > 0.05). The pairwise population STF showed that the genetic differences between populations ranged from −0.0456 to 0.0602. All pairwise STF values were not statistically significant  was accounted for by differentiation within populations, with a further 0.04% accounting for variation within ecoregions among populations, and the remainder (−1.31%) partitioned among ecoregions. At the population level, −0.82% of the total variance was found among populations, whilst 100.82% of the variance was found within populations , except for the significant differences between MZD and FYLD, and between MZD and HA  to 248.595 (QA-DH), while the estimated historical gene flow varied from 10.738 (SYH-DT) to 334.675 (CW-FYLD) based on D-Loop sequences. Two markers revealed the high historical gene flow among populations of horn-eyed ghost crab. The historical gene flow from MZD to most other populations was lower than that of most other populations to MZD based on two markers.Based on COI sequences, the estimated migration rate ranged from 134.7 (QA-FYLD) to 390.1 (MZD-QA), while the estimated migration rate varied from 159.3 (PT-SYH) to 284.1 (SYH-NJLD) based on D-Loop sequences. The analysis consequences of two markers exhibited the high migration rates among populations. The COI sequences further showed the asymmetric migration rate with the higher migration rate from south to north than from north to south. Meanwhile, the migration rate from SYH to most other populations was higher than the migration rate from that of most other populations to SYH based on two markers  and high nucleotide diversity (π > 0.005). In contrast, the higher genetic diversity was revealed by D-Loop marker, which may be related to the mutation rate of the fragment [D-Loop is the high mutation region, located on either side of the central conserved region, which evolves two to five times faster than mitochondrial protein-coding genes [COI marker [High haplotype diversity (Hd > 0.5) and low nucleotide diversity (π < 0.005) were observed based on rassipes  and Portrculatus . D-Loop,fragment . D-Loop ng genes , and thuI marker . This diI marker .Because the sampling sites of horn-eyed ghost crab were located in or near MPAs, the results of genetic diversity may provide some reference for the management effectiveness of MPAs. Species and populations are adversely affected by anthropogenic activities, such as habitat modification, which are a global phenomenon . These nCOI sequences showed that the most common Hap 1 and Hap 2, accounting for almost 49.40% of samples, were found in all 15 populations. Shared haplotypes may be related with the large population sizes and high rates of migration [COI haplotypes, shared haplotypes existed between distant populations (up to 1000 km), which indicated that dispersal of this species may occur over long distances. In contrast, the existence of lower-frequency haplotypes was related with large population sizes, and this is because these haplotypes were not eliminated by the selection when in a large stable population [D-Loop revealed few shared haplotypes among populations, it may not represent low connectivity among populations, but be due to DNA superdiversity [D-loop sequences [COI might provide more reliable results on the spatial distribution and connectivity.The spatial distribution of haplotypes and the haplotype network based on igration . Among tpulation . Whereasiversity . This reequences . TherefoSTF for two markers, which was also consistent with the findings of Collichthys lucidus [Larimichthys crocea [Pampus chinensis [Thamnaconus hypargyreus [COI marker in this study. It was also in line with the results of an analysis of seven mangrove species on the southern coast of China [D-Loop sequences, which may be due to the fact that the higher mutation rate of the D-Loop marker was not conducive to the prediction of gene flow or less number of D-Loop sequences [In this research, the high level of connectivity among 15 populations, between ECS populations and SCS populations and among three ecoregion populations of horn-eyed ghost crab was observed based on the results of AMOVA and  lucidus , Larimics crocea , Pampus hinensis  and Thamargyreus . The higargyreus , which cargyreus . On the argyreus . During argyreus , which mof China . Neverthequences . In addiequences , leadingCOI sequences in this research. Consequently, the hydrodynamic characteristics of the East and South China Seas should be considered in constructing a MPA network. In the near-shore areas of the East and South China Seas, the Guangdong coastal current (GDCC) and the coastal currents of Zhejiang and Fujian (MZCC) are the main currents [Although it is not clear whether marine protected areas in the East and South China Seas could form a network, the results of this study may provide some suggestions for the construction of such a network due to the sampling sites of horn-eyed ghost crab being located in or near protected areas. The higher migration rate from south to north related to the current in summer was revealed based on currents . In summcurrents . These scurrents , especiaAcropora solitarvensis, Epinephelus akaara and Epinephelus bruneus spawn during summer [Anguilla japonica and Penaeus japonicus also spawn exclusively during fall–winter [Many protected species such as g summer . The sumg summer ,54 contrg summer . This hag summer . Additiog summer . Howeverg summer . For exal–winter . So, flel–winter . MoreoveOcypode ceratophthalmus in the East and South China Seas based on wide geographic sampling and two genetic markers. The results showed that the horn-eyed ghost crab had high genetic diversity. The AMOVA analysis revealed no significant genetic structure and high genetic connectivity among the populations. All the STF values obtained from COI sequences were inapparent. The STF values based on D-Loop sequences were only significant between MZD and FYLD as well as between MZD and HA. Migrate-n revealed the high historical gene flow and connectivity among 15 populations based on two markers. The COI sequences further showed asymmetric connectivity with higher connectivity from south to north than from north to south. The outcomes from this study provide suggestions for the construction of marine protected areas.This study investigated the genetic connectivity among the populations of


Vernonia amygdalina, a fodder, vegetable and medicinal species of Africa and some parts of Yemen. Most empirical studies demonstrate that populations that are separated by geographic or ecological factors may experience genetic differentiation resulting from restricted gene flow between populations. A total of 238 individuals were sampled from two populations: i) Lake Victoria crescent (LVC) and ii) Southern and Eastern Lake Kyoga basin (SEK) agroecological zones of Uganda and genotyped using DArT platform. Of the two populations, the overall mean observed heterozygosity (Ho) was low to medium (Ho = 0.07[silicoDArTs] and 0.2[SNPs]). Inbreeding levels were also very low (-0.04 to -0.08) suggesting the presence of random mating. Partitioning of genetic structure in the two populations indicated that SEK exhibited a higher genetic diversity than LVC. The principal coordinates analysis (PCA) showed no geographical structuring, consistent with the low genetic differentiation (Fst = 0.00) and the low Euclidean genetic distance (1.38–1.39) between the LVC and SEK populations. However, STRUCTURE analysis with admixture models revealed weak possible genetic clusters with very small genetic distance among them. Overall, the results suggest low genetic diversity and weak genetic differentiation between the two populations. One possible explanation of the results could be the presence of human assisted gene flow over long distances.Determining the extent and distribution of genetic diversity is an essential component of plant breeding. In the present study, we explored the genetic diversity and population structure of  Population genetic theory predicts that geographic distance will cause genetic differentiation among populations on the landscape, implying that populations that are near each other will often be more genetically similar, while distant populations are often more divergent , 2. Geoghttp://www.diversityarrays.com/) have improved the rate of genotype calling and the ability to sequence more samples for less cost [Molecular markers have increasingly become popular as neutral tools for measuring genetic diversity and population structure , 8. Genoess cost . Genotypess cost –12. The ess cost . Single ess cost –18. TherVernonia amygdalina Del (Asteraceae) (2n = 40) is a small perennial shrub growing predominantly in tropical African countries and some parts of Yemen on the Arabian Peninsula. It has dark green leaves and a rough bark, with height of up to 10 m. Its flowers are bisexual, regular, numerous, and strongly exserted ). The flower of Vernonia is considered to exhibit protandry, which imposes allogamy on this plant [Vernonia amygdalina leaves, bark and roots are harvested for treatment of amoebic dysentery, gastrointestinal disorders, antimicrobial and antiparasitic activities [Vernonia amygdalina is especially important in the treatment of malaria [Vernonia amygdalina has also been used to treat syphilis, ulcers, liver problems, tuberculosis, cough, abdominal pain, wounds, hernia and headache [is plant . Vernonitivities , 21. It tivities . In Ugan malaria , 22. VerVernonia amygdalina occurs naturally in forest margins, woodlands and grasslands. It often occurs in disturbed localities such as abandoned farmland and can be found growing spontaneously in secondary forests [V. amygdalina is the most preferred species for malaria treatment in most parts of Uganda, including the study areas [V. amygdalina can help to establish appropriate conservation, management, and sustainable utilization strategies [In Uganda,  forests . Althougdy areas , 24. Thidy areas . Howeverrategies –28.Vernonia amygdalina among rural communities due to its therapeutical action on both bacterial and protozoal parasites [Vernonia amygdalina in Uganda. The objectives were: 1) to assess genetic diversity in the two geographically isolated populations using SilicoDArT and SNP markers; 2) to investigate spatial genetic structure of V. amygdalina. We hypothesize that the geographical and ecological separation of V. amygdalina may cause genetic differentiation due to restricted gene flow between populations.In spite of the high interest for arasites , little Leaf samples were collected from trees in two agroecological zones; the Lake Victoria crescent  and Southern and Eastern Lake Kyoga basin , with specific coordinates, where samples were collected in DNA extraction was done on individual samples using Nucleomag plant genomic DNA extraction kit (Macherey-Nagel). The genomic DNA extracted was in the range of 50–100 ng/ul. DNA quality and quantity were checked on 0.8% agarose gel.V. amygdalina. DNA samples were processed individually in digestion/ligation reactions using a combination of PstI and HpaII Restriction Enzymes (RE) as described ealier [DNA was sent to Diversity Arrays Technology Pty Ltd laboratories in Canberra, Australia for sequencing using the HiSeq 2500 following the protocol optimised for d ealier , 12. BriAfter PCR, equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina) bridge PCR followed by sequencing on Illumina Hiseq2500. Sequences generated from each lane were processed using proprietary DArT analytical pipelines. SNP markers were aligned to the reference genomes of Chickpea_ICC_v2, Grape_v8 in the National Centre for Biotechnology Information (NCBI) in order to identify chromosome positions. The BLASTN algorithm with an e-value ≤ 5e-7 and an identity percentage of > = 90% was used. SilicoDArTs were scored as "dominant" markers, with "1" = Presence and "0" = Absence of a restriction fragment with the marker sequence in genomic representation of the sample. SNPs were scored as codominant markers with 0 for the homozygous allele aa, 1 for the heterozygous allele Aa and 2 for the homozygous allele AA. Finally, identical sequences were collapsed into “fastqcoll files”. The markers were tested for reproducibility (%)–the proportion of technical replicate assay pairs for which the marker score exhibited consistency; call rate (%)–the success of reading the marker sequence across the sample; polymorphism information content (PIC)—the degree of diversity of the marker in the population and the usefulness of the marker for linkage analysis; and one ratio–the proportion of the samples for which genotype scores equalled ‘1’.The data were filtered using the dartR v 1.9.9.1 package  in R to o) and expected heterozygosity (He), total gene diversity (Ht), genetic differentiation (Fst) and population inbreeding coefficient (Fis). Marker allele frequency–the frequency at which the second most common allele occurs in a given population [Analyses were done on the silicoDArT and SNP markers that were retained after the filtration above using the two populations (LVC and SEK). All genetic diversity indices were estimated using the R package “ADEGENET” . The R ppulation , was alsTo explore the geographical structuring of the genetic variation among individuals, a supervised principal coordinate analyses (PCoA) using dartR was used. PCoA was performed separately on the SNP and SilicoDArT datasets. To further examine the genetic structure of the populations that could be independent of geographical location, an unsupervised model-based Bayesian clustering was conducted using STRUCTURE 2.3.4 software using only SNP markers. The STRUCTURE program uses a Markov Chain Monte Carlo (MCMC) algorithm to cluster individuals into genetic populations on the basis of multilocus genetic data . The anaV. amygdalina. The call rate of the silicoDArT markers (average = 0.97) was higher than that of SNP markers (average = 0.78). Similarly, the reproducibility of the silicoDArT markers was higher (averaged to 1.0) than that of the SNP markers (average = 0.98). However, the average one ratio estimated for silicoDArTs (0.22) was similar to the SNPs (0.21)  .The PIC value for SilicoDArT markers ranged from 0.02–0.5 (average = 0.08) and was lower than that of SNPs  for unfiltered data. The proportion of informative markers was higher for SNPs than silicoDArTs .After filtering the data, all individuals and 5084 (62%) of silicoDArT markers were retained. For SNPs, 234 individuals and 1722 (31.7%) markers were retained. These were used for the proceeding analyses. The mean minor allele frequency (MAF) based on SNPs ranged between 0.002–0.5 with an average of 0.13. Only 43% of the SNP markers had minor allele frequency less than 0.05. MAF was not estimated for the dominant silicoDArT markers. After filtration, the PIC estimates ranged between 0–0.5 (average = 0.04) for silicoDArTs and 0–0.5 (average = 0.23) for SNPs.Based on the two populations (LVC and SEK), the estimates for genetic diversity and differentiation were generally low for both markers. The genetic diversity calculated as expected heterozygosity (He) in the populations was 0.02 for silicoDArTs and 0.20 for SNPs . BetweenGenetic relationships among the individuals from LVC and SEK explored by principal coordinates analysis (PCoA)  indicateBased on unsupervised clustering of SNP markers in STRUCTURE, the optimal K-value showed the possibility of having two subpopulations, consisting of 45% and 55% of individuals . HoweverV. amygdalina with potential consequences on the species ability to recover from demographic, environmental and genetic stochasticity [Based on silicoDArT and SNP markers, the results indicated; (i) low to medium genetic variation in asticity . (ii) noV. amydalina. The silicoDArTs PIC value was lower than what has been established for tropical plants [Trema orientalis (average PIC = 0.27) [V. amygdalina should hold more genetic variation than the small populations (Amos and Harwood 1998) such as T. orientalis, which was not the case [V. amygdalina populations [The low genetic diversity estimates observed with silicoDArTs could have resulted from the very low PIC values (0.08) exhibited by these markers. PIC is often underestimated for dominant markers. Informativeness of markers based on PIC can be low (0 to 0.10), medium (0.10 to 0.25), high (0.30 to 0.40) and very high (0.40 to 0.50) , 39. In l plants . For SNP = 0.27) , suggestulations  indicatiV. amygdalina could be due to reduction in the occurrence of sexual reproduction, resulting from the frequent removal of young twigs for medicinal use and forage by domestic and wild animals. Removing young twigs reduces flower production that consequently reduces the effective population available for crossing. Under a neutral model, effective population size is a key factor that determines population’s genetic diversity [V. amygdalina is frequently harvested in the wild, the plants tend to reproduce through root vegetative reproduction. Asexual reproduction is predicted to experience reduced genetic variation as a result of the absence of segregation and genetic recombination [The low to average genetic variation in iversity . Additiobination .V. amygdalina is expanding its range as a result of enlargement of disturbed localities and secondary forests, where the plant prefers to grow, or introductions by humans into previously unoccupied areas as they transport medicine from one place to another. Populations that are the result of range expansions into previously unoccupied areas may have lower levels of genetic diversity as a result of repeated founder events [The very low inbreeding coefficient estimated based on geographical location is consistent with the possible lack of flowering mentioned above. The reduced genetic variation could also be explained by the premise that r events . This isr events .st; SNPs = 0.0008, silicoDArTs = 0.0005) suggesting that the 353 km distance and other geographical barriers such as river Nile between the study sites is not a constraint to gene flow in this species. Ideally, Fst values below 0.05 indicate low genetic differentiation, while values between 0.05–0.15, 0.15–0.25, and above 0.25 indicate moderate, high, and very high genetic differentiation respectively [is estimate based on geographically determined populations signifies the presence of random mating also known as Hardy–Weinberg equilibrium, and that the available parents experience a high level of outcrossing leading to homogenization of the populations. Concerning the mode of fertilization, the flower of Vernonia is considered to exhibits protandry, which imposes allogamy on this plant [In this study, the supervised PCOa did not clearly separate the two populations. This was consistent with the very low genetic differentiation coefficient . Although pollen flow is commonly skewed toward short distances of just a few meters, reflecting insect behaviour and the spacing of the plants, there is increasing evidence that small flying insects can disperse over large distances [V. amygdalina occurs naturally in mostly open places; forest margins, woodlands and grasslands or disturbed localities such as abandoned farmland and secondary forests makes it prone to frequent visits by pollinators, and hence the weak spatial structure. There also seems to be a constraint on self-pollination in V. amygdalina, where the plant seems to be allogamous [V. amygdalina populations where, geographical distinctness with a possible effect of plant isolation by distance and restricted gene flow were observed among the accessions [The spatial scales for causing genetic differentiation in plant species vary greatly, dependent on factors such as biology of the species, geography among others. Durka, Michalski  for examistances . Given tlogamous . Self-inlogamous . In addicessions .V. amygdalina, based on the level of bitterness, which ranges from very bitter to less bitter, with the ‘bitter’ type possessing a deep green coloration and a deep bitter taste and the ‘less bitter’ type possessing a fairly light green coloration with little or no bitter taste [V. amygdalina, they were not considered during the sampling for this study. Future studies possibly need to genetically characterise these ‘bitterness’ groups separately for further understanding of genetic relationships among V. amygdalina. There was, however, still low genetic differentiation between/among the different clusters, which allows us to assume that the two sampled populations constitute a single genetic population cohesive probably by the movement of pollen by insects and by the use that the local population gives to this species. The high positive FIS values and small genetic distances suggest presence of high inbreeding between and among individuals of different clusters. One possible explanation of this observation could be that insect mediated mating occurs among specific relatives, possibly over short distances to cause inbreeding, but human assisted gene flow causes homogenisation of genetic structure over long distances. It is also possible that the individuals in both LVC and Southern and SEK are remnants of a single population of much greater density, where related individuals had greater opportunity to interbreed.The unsupervised Bayesian clustering algorithm implemented in STRUCTURE clustered individuals into different numbers of clusters, possibly suggesting presence of short-scale genetic differentiation. It is also possible that the clustering results from sampling of different ‘varieties’. Elsewhere, some variations have been observed in er taste . AlthougV. amygdalina populations could decrease their ability to cope with anthropogenic and other environmental threats, leading to species endangerment or even extinction. Deliberate efforts to reduce overuse, as well as enhancing seed production for example through controlled crosses could improve genetic diversity. However, characterizing the genetic diversity of other populations, that could be more genetically diverse as well as considering potential phenotypic differences of ‘varieties’ will be important for making core collections for ex-situ management. It is acknowledged that where there is significant genetic differentiation among populations or groups of populations, they should be managed as distinct entities. In our case, Lake Victoria Crescent (LVC) and Southern and Eastern Lake Kyoga basin-SEK populations can be treated as the same population, for making core collections and for conservation. This study also suggests that geographical distance between populations should not necessarily be considered as the sole or best determinant of gene flow levels among populations of plants.The low levels of genetic diversity in the studies S1 Fig(DOCX)Click here for additional data file.S2 Figa). Principal coordinates analysis plot to infer group structure of V. amygdalina based on SNP markers. The populations were defined by clusters identified in STRUCTURE, where K = 2. Pink = individuals placed in cluster 1, blue = individuals placed in cluster 2, grey = individuals not significantly placed in either cluster b) estimated population structure of V. amygdalina individuals on K = 2. Accessions in blue were clustered into cluster 1 and cluster 2. b) estimated population structure of V. amygdalina individuals on K = 2. Individuals were clustered into cluster 1(red) and cluster 2(green).(DOCX)Click here for additional data file.S3 Figa) Principal coordinates analysis plot to infer group structure of V. amygdalina based on SNP markers. The populations were defined by clusters identified in STRUCTURE, where K = 3. 1 = individuals placed in cluster 1, 2 = individuals placed in cluster 2, 3 = individuals placed in cluster 3, 4 = individuals placed in clusters 1 & 2, 5 = individuals placed in clusters 2 & 3, grey = individuals not significantly placed in either cluster. b) estimated population structure of V. amygdalina individuals on K = 3. Individuals were clustered into cluster 1, 2 and 3 .(DOCX)Click here for additional data file.S4 Figa) Principal coordinates analysis plot to infer group structure of V. amygdalina based on SNP markers. The populations were defined by clusters identified in STRUCTURE, where K = 3. 1 = individuals placed in cluster 1, 2 = individuals placed in cluster 2, 3 = individuals placed in cluster 3, 4 = individuals placed in clusters 1 & 2, 5 = individuals placed in clusters 2 or 3 different clusters. b) estimated population structure of V. amygdalina individuals on K = 3. Individuals were clustered into cluster 1, 2, 3  and 4 .(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TablePopulations in this case were defined by STRUCTURE. Individuals that were not significantly placed in either cluster were discarded during these analyses See –S4 Figs.(DOCX)Click here for additional data file.S3 TableIndividuals that were not well placed in the different clusters were excluded during the estimations. Number of loci = 1722. Individuals that were not significantly placed in either cluster were discarded during these analyses.(DOCX)Click here for additional data file.S4 TableThe results are based on SNP markers.(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


Sudanese populations from different ethnicities have been studied for HLA-A, -B, -DRB1 and -DQB1 antigens by a standard microlymphotoxicity method. Results found show that Nubians are genetically related with African Sub-Saharan populations and distant from other Sudanese tribes, who are closer to Mediterranean populations than to Sub-Saharan ones. This is concordant with other authors and meta-analysis data. Our present work is, to our knowledge, the first and only one HLA research that studies Sudanese people according to different Sudan ethnic groups: samples were collected before Sudan partition between North and South. A prehistoric genetic and peoples exchange between Africa and the Mediterranean basin may be observed and is supported with the results obtained in this Sudanese HLA study. However, demic diffusion model of agriculture and other anthropological traits from Middle East to West Europe/Maghreb do not exist: a more detailed Sahel and North African countries ancient and recent admixture studies are also being carried out which may clearer explain pastoralists/agriculture innovations origins in Eurafrican Mediterranean and Atlantic façade.The contribution of migrated people from once green Sahara (about 10,000–6000 years  Samples studied in this work are from 13 different ethnicities of Sudan and were collected from Khartoum before Sudan split that was and still is being a big immigration city; it is placed at the convergence of Blue and White Nile River. Main local tribes or ethnic groups which participated in this study are defined by languages and depicted in Fig. 3 and www.joshuaproject.net). Also, South Sudan was somewhat neglected and stayed backwards between the Anglo (UK)-Egyptian mandate during 1899 and 1956. UK and Egypt centred benefits in North part of Sudan, particularly in Khartoum area [1.2]. Ethnicities admixture makes it difficult to assess individual genetic ancestry, although it was established mainly by language and place of origin. The term “Nubian” was used at the time of this study sample collection for African people with some Ethiopian phenotype characters close to Ethiopians or not, but coming from Nuba Mountains, which have blurred limits with South Sudan  part of Sudan against South (Christian). Two different countries arose in 2011: Sudan at North, mostly of Muslim religion and capital at Khartoum, and South Sudan, mostly of Christian and ancestral autochthonous religions and capital at Juba5. The findings of the same types of Lineal Megalithic or more recent scripts and megalithic structures in the Sahara and throughout the Mediterranean Basin and the Eurafrican Atlantic coast, including Canary Islands, support a common Ancient Megalithic culture that probably was born in the Humid Sahara period before 6000 bc5.Sahara desertification became more intense by 10–6000 years ago. Saharan people underwent then a strong migratory pressure and started to emigrate. In the context of a drying Sahara the desert settled populations there began to disperse in all directions, including North Africa and the Mediterranean basin. This gave rise to a mixture of people and genes and influenced the Ancient Classic Mediterranean cultures creating a cultural circle that includes Mediterranean populations and Guanches or Canary Islands First Inhabitants4; (3) to ascertain genetic differences found between Sudanese Nubians from Nuba Mountains and Sudanese from other Sudan ethnicities  to compare a mixed sample of different Sudanese ethnicities with other surrounding populations; (2) to clarify their origin according to HLA genetic background and to ascertain their relationships with Mediterranean and Sub-Saharan populations. HLA genes are the transplantation genes in humans and, for strictly biomedical purposes, they are useful compared to other genes for studying human populations and ethnic groups origins because of the HLA very high polymorphism and correlation of HLA allele frequencies follows a geographical gradient to compa6. Blood samples were collected at Ibn Sinna Hospital, in Khartoum State, and used for HLA typing and phylogenetic calculations. Other world populations were considered for genetic comparisons and are detailed in Table Ninety-eight healthy unrelated individuals from all Sudan territory have been included in this study belonging to different ethnicities  and class II (DRB1 and DQB1) typing was done by two-step microlymphotoxicity technique on T or B lymphocytesTwo types of analyses were done to compare HLA frequencies of Sudan with those of Mediterranean and other populations: (1) with low-resolution DRB1 and DQB1 data; and (2) with low-resolution DRB1 data. This latter analysis was performed in order to compare Nubians from Sudan and other Sudanese ethnicities in a proper way by using the same level of HLA allelism distinction, who have been typed/transformed for HLA-DRB1 generic but lack HLA-DQB1 typing.20. In summary, this program calculates the linkage disequilibrium between two alleles at two different loci. Their level of significance (p) for 2 × 2 comparisons and also their relative linkage disequilibrium were calculated as previously described21. Computer program calculated the most frequent HLA extended haplotypes that were further analysed as follows; the most frequent complete extended haplotypes were tentatively deduced from: (1) the 4 HLA loci haplotype frequencies were obtained by software22; (2) the previously described haplotypes in other populations were also taken into account21; and (3) haplotypes if they appeared in two or more individuals and the alternative haplotype was well defined were also studied22. In order to compare phenotype and haplotype HLA frequencies with other populations, the reference tables of the 11th and 12th International HLA Workshop were used12 (Table 23 and genetic distances between populations (DA)24, by using DISPAN software which contains the programs GNKDST and TREEVIEW26. Also, correspondence analysis in three dimensions and its bidimensional representation was carried out using Vista v5.05 computer program27. Correspondence analysis consists of a geometric technique that may be used for displaying a global view of the relationships among populations according to HLA (or other) haplotypic  frequencies. This methodology is based on the genetic distances (DA) variance among populations  and also a statistical visualization of these differences.Statistical analysis was performed with Arlequin v3.0 softwareThis study was conducted according to the Declaration of Helsinki by the Ethics and has been approved by Complutense University Ethical Commitee (EC-0117.AK).All subjects included in the study were adult unrelated blood donors who signed an informed consent to participate in it.The expected and observed genotype frequency values for HLA-A, -B, -DRB1 and -DQB1 loci do not significantly differ, and the population sample is in Hardy–Weinberg equilibrium. Table 1628 and with other genetic markers30. Our Sudanese studied group is placed especially close to most western Mediterraneans: North African (Algerians and Moroccans) and Iberians  and class II (DRB1 and DQB1) allele frequencies but also two-loci and extended haplotypes for the first time in a mixed sample of different Sudanese ethnicities, allowing their comparison with previously reported results in other populations.12. Also, this B-DRB1-DQB1 combination has an Iberian-Mediterranean background because is present with similar frequency in Portuguese, Italians, French, and African Americans from the US12. The second most frequent haplotype found in present work  has been found to be specific of the Sudanese sample studied, although its partial B-DRB1-DQB1 haplotype can be found in Italians12. Other Sudanese-specific HLA extended haplotypes with frequencies above 1% have been found in our Sudanese sample: A*02-B*51-DRB1*08-DQB1*03 (1.54%), A*03-B*52-DRB1*11-DQB1*03 (1.54%), and A*01-B*51-DRB1*13-DQB1*03 (1.02%). All other most frequent haplotypes are also shared with Mediterraneans (Table The most frequent HLA extended four-loci haplotypes (HLA-A-B-DRB1-DQB1) Table  are mark18), Egyptians, Iranians, Algerians, Italians and Iberians. This is concordant with other North African, Middle East and Iberian HLA compilation study31 by using another methodology (high-resolution HLA typing). On the other hand, Greeks were found initially close to Sahel populations ; it disconcerted us, and an allele-by-allele comparison was carried out between Greeks and Sub-Saharans to finally show that they both share some HLA alleles in common, confirming our phylogenetic results16. Typing of these populations has been done by many participant laboratories in the 12th Histocompatibility International Workshop under the direction of Julia Bodmer (Oxford)6 and computer calculations were made by us. Also, another autosomal marker (3120 + 1 G → A cystic fibrosis marker) was found in Africans and also only in Greeks out of all other European populations tested30. It was written by Aeschylus that African peoples (Danaids) emigrated northwards firstly to Egypt, where they were rejected and then fled to Peloponnesus32. This may give us a sort of historical reference for these genetic results that have been independently confirmed by other groups and other genetic markers30.Our present study shows that Sudanese population is related to Mediterraneans like Turks  and green Sahara Desert under the dunes34. Currently it is in a dry period, but the Sahara is expected to turn green again in about 15,000 years. Due to these desertification-greening cycles, it is possible that very different human populations inhabited in the last humid stage. The Tassili N’Ajjer National Park and Ahaggar Mountains in Algeria show a record of rock art that proves human settlement in the Sahara33. However, when the desertification began around 10,000 bc, the populations that inhabited the Sahara emigrated to other more habitable northern and other areas.Sahara Desert climate has undergone sharp variations over time, ranging from wet to dry over the past hundreds of thousands of years39, being a secluded population between European North Ices and Desert. On the basis of our present day genetic and linguistic studies, we have postulated that many people coming from what is nowadays the Sahara Desert started to move towards East, West, North, and also South, being an important part of the primitive people stock of Sumerians, Egyptians, Guanches (Canary Islands), Iberians, Etruscan, Minoans, Anatolians (currently named Turks on only linguistic bases because they show a clear Mediterranean HLA profile)4, Kurds, and other islanders or northern Mediterraneans39. In the present work, HLA genetic background of Sudanese people from different ethnicities is studied, and results obtained inpart confirm these North African-Mediterranean peoples (genes) exchange.It is well established that North Africans and southern Europeans are genetically related, and this may be due to a long lasting circum-Mediterranean cultural and genetic flow particularly during the last glacial peak40. Traces of incised lineal writing have recently been found in human sites in the central Sahara  in what would be the first discovery of “megalithic lineal writing” in continental Africa35. This finding supports the theory that incised lineal writing was originated in fertile Sahara populations, which moved to the North when desertification began, carrying this type of lineal writing to Canary Islands (Guanche culture), the Mediterranean Basin and Europe. It is probable that these populations of the green Sahara moved towards the Mediterranean and the Canary Islands, originating common cultural and genetic traits among all the descendant populations . The present study and other ones34, support this theory of movements from central Africa to other regions; the appearance of Mediterranean alleles and semi-haplotypes  in different African populations endorse bi-directional population movements from the Sahara to the Mediterranean and South Europe in prehistoric and more recent times. In some cases, Lineal Megalithic Scripts and later classical letters (or syllables) are found together; it is the case of naviform scripts that are found mixed with Iberian-Tartessian semi-syllabary signs in the same context both in Iberian Peninsula and in Canary Islands. An evolutionary pathway of writing may be observed from primitive Lineal Megalithic Scripts to other more recent and complex alphabets or syllabaries like those found in Sitovo41, Gradeshnitsa42 or Iberian scripts widespread in the Iberian Peninsula, Canary Islands and continental North Africa43. Also, other common archaeological elements have been found in Europe and Africa (Canary Islands), including Western Sahara territory45. The findings pointed out in this study in relation to Sudanese HLA profile support this emigrational theory from the Sahara (and Sub-Saharan areas) to the Mediterranean Basin.Finally, there are common traits in Mediterranean, Iberian and North African cultures Circle, which may be conjointly named the Saharo-Canarian Circle46. Also, East Sahelians admixture with North Africans is confirmed with other studies47, and Fulani nomads, Tuaregs and other Arab speaking Sahelians show admixture with North Africans like most of Sahelian groups do48.However, other authors with different techniques and genetic markers (SNPs) put forward that admixture with Arabs in recent times has occurred in Sudan and that this area has been subjected to significant population movements in ancient African /Eurasian history


In this study, we present a NGS-based panel designed for sequencing 1993 SNP loci for forensic DNA investigation. This panel addresses unique challenges encountered in forensic practice and allows for a comprehensive population genetic study of the Chinese Korean ethnic group. To achieve this, we combine our results with datasets from the 1000 Genomes Project and the Human Genome Diversity Panel.We demonstrate that this panel is a reliable tool for individual identification and parentage testing, even when dealing with degraded DNA samples featuring exceedingly low SNP detection rates. The performance of this panel for complex kinship determinations, such as half-sibling and grandparent-grandchild scenarios, is also validated by various kinship simulations. Population genetic studies indicate that this panel can uncover population substructures on both global and regional scales. Notably, the Han population can be distinguished from the ethnic minorities in the northern and southern regions of East Asia, suggesting its potential for regional ancestry inference. Furthermore, we highlight that the Chinese Korean ethnic group, along with various Han populations from different regional areas and certain northern ethnic minorities , exhibit a higher degree of genetic affinities when examined from a genomic perspective.This study provides convincing evidence that the NGS-based panel can serve as a reliable tool for various forensic applications. Moreover, it has helped to enhance our knowledge about the genetic landscape of the Chinese Korean ethnic group.The online version contains supplementary material available at 10.1186/s40246-023-00541-0. In recent decades, short tandem repeats (STRs) have emerged as highly efficient and well-established DNA markers in forensic analyses because of their high genetic polymorphisms, particularly for individual identification and parentage testing cases , 2. The In addition to forensics, advances in NGS technology have also facilitated major initiatives to genotype diverse global populations, including the Human Genome Diversity Panel (HGDP) , 1000 GeHerein, we presented a new panel designed to enable the concurrent amplification of 1993 SNP loci in a single assay for various forensic applications. This new panel is built on the MGISEQ-2000RS platform using targeted sequencing technology, which employs DNA nanospheres and rolling cycle amplification for sequencing, thereby minimizing genotypic errors typically caused by PCR , 19. ParThe Ethics Committee of Southern Medical University and Xi'an Jiaotong University reviewed and approved this study (No. 2019-1039). Additionally, it adhered to the guidelines presented in the revised Declaration of Helsinki from 2000. Before the sample collection, all participants gave their written informed consent.A total of 161 unrelated individuals were recruited from the Yanbian Korean Autonomous Region, China, to participate in this study. The participants were identified as Chinese Koreans by referring to the following criteria: (1) Residency in this region for at least three generations and their parents being Chinese Koreans; (2) Absence of intermarriage events with individuals from other ethnic groups (including the Han population) in their family histories; (3) Proficiency in the Korean language as their native tongue. To ensure that all participants were biologically unrelated to each other, we performed data quality control on the genotyping results of all samples by PLINK (version 1.9) software . SubjectDNA of the 161 Chinese Koreans was extracted from bloodstains on FTA cards using the Magbead Blood Spots DNA Kit from CWBIO in Beijing, China, following the manufacturer's instructions. The MGIEasy Pa-SNPs panel  was used to prepare the libraries. We added 10 ng of DNA to a centrifuge tube and mixed it with Elution Buffer to make a final volume of 20.5 μL for the experiment. The library preparation process involved two rounds of PCR, which has been detailed explained in a previous study . Followihttp://clavius.bc.edu/~erik/freebayes/).DNA nanoballs were prepared according to the manufacturer’s instructions with the final concentration being ≥ 8 ng/μL. Then, DNB sequencing was conducted on the MGISEQ-2000RS platform, configured with FCL SE50 + 10 settings. We used the SoaPnuke2 software to analyze the raw data. All reads were aligned to hs37d5 reference sequences using Burrows-Wheeler Aligner software. SNP genotypes were called using the Freebayes software (obs) and genetic diversity (GD). Besides, the Hardy–Weinberg equilibrium (HWE) test for each SNP locus were also estimated using both PLINK and STRAF, adjusting the p (p > 0.000025) values for HWE tests following Bonferroni’s correction. The linkage disequilibrium (LD) tests among pairwise SNP loci were assessed using the Haploview (version 2.1) software [r2 > 0.2. These parameters were used to evaluate the genetic diversities of the 1993 SNPs in the Chinese Korean ethnic group. Additionally, we computed the cumulative probability of discrimination (CPD) values and the cumulative probability of exclusion (CPE) values to confirm the effectiveness of this new panel for individual identification and parentage testing. We used the Familias (version 3.01) software [1) compared to the values for the unrelated hypothesis (H2) were calculated and compared. We used the allele frequencies of the SNPs in the Chinese Korean ethnic group to simulate genotype data for PC, FS, HS and GG relationships and further generated the LR distributions of these kinship cases. For these SNP loci, the mutation rate of 1.29 × 10–8 was determined based on a previous study [We used the STRAF (version 2.1) software  to calcusoftware , with LDsoftware  to simulIn addition to the data generated in this study, we assembled previously published public datasets, including the 1 KG and the HGDP datasets, to construct a comprehensive dataset for population genetic analyses. The composite dataset contained genotype data of present-day human populations representing eight continents, namely Africa, Europe, East Asia, Central South Asia, South Asia, America, Middle East, and Oceania. To ensure the robustness of our estimates, the method requires an SNP set with a minor allele frequency higher than 5%, and further mandated that the SNPs under consideration exhibit independence from each other. Therefore, we pruned the SNP loci using the PLINK software, which resulted in 1706 SNP loci available for subsequent population genetic analyses. The combined dataset consisted of genotype data from 1706 SNPs spanning 76 populations located across eight major global regions. For convenience, the Chinese Korean ethnic group was abbreviated as Korean_C in the data analyses. Detailed information on the geographic origins, names and abbreviations of the 75 reference groups can be accessed in Additional file Population-level principal component analysis (PCA) was performed utilizing the smartpca program in the EIGENSOFT package  for the Rstudio software was used to visualize PCA results using the first three PCs.To perform the individual-level PCA, we utilized the GraphPad Prism (version 9.4.1) software to compute the PCs that explained the majority of variances within the compiled dataset. Notably, the individual-level PCA was performed stepwise to distinguish between different population groups. Initially, all 76 populations from eight intercontinental geographic regions were employed to construct the principal component space on which we projected all individuals. Subsequently, we retained populations from Asia , along with those from the Middle East, to generate a more distinct distribution of these geographically closed populations. Lastly, we divided these populations, all of East Asian ancestry, into Han, Northern minority, and Southern minority groups for PCA to show their distribution patterns. The ggplot2 package in K) with the lowest cross-validation (CV) errors, we ran ADMIXTURE with K values ranging from 2 to 10. We conducted 20 algorithm iterations for each K value. The population structures were visualized using the pophelper packages in Rstudio and plotted the replicate of each K.We used the ADMIXTURE (version 1.3.0) software  to analyFST matrix among different populations was evaluated using the smartpca function in EIGENSOFT (version 7.2.1) software [In the combined dataset, the software . The "fsf3 and f4 statistic tests using the “q3pop” and “qpDstat” commands in the AdmixTools2 packages [R program, respectively. The outgroup-f3 statistic was computed as f3  to explore the shared genetic drifts between the Chinese Korean ethnic group and the reference populations, where X represented various populations from the combined dataset (except for the Korean_C and Mbuti populations). Higher f3 values indicated that more genetic drifts had occurred between the Korean_C group and X, suggesting their higher genetic similarities. The pairwise outgroup-f3 results were then presented with a heatmap using the R package ggplot2.The f-statistic framework was used to explore the genetic relationships between the Chinese Korean ethnic group and the reference populations by computing the outgroup-packages  in the Rf4 statistics were measured using the ordered set , where X represented the East Asia Han populations  and Y represented the non-Han populations  in East Asia. In this test, a significant positive f4 statistic indicated gene flow between Korean_C and Y, while significant negative f4 statistics suggested potential gene flow between Korean_C and X.The FST values between pairwise populations using the MEGA software (version 11.0) [https://www.chiplot.online/) and the iTOL (https://itol.embl.de/) tools, respectively. Besides, we also used the TreeMix (version 2.1) software [https://github.com/carolindahms/TreeMix) and were kindly provided by the developers. We used the 'OptM' package in R to estimate the optimal number of admixture events.The phylogenetic relationships within these populations were inferred based on different algorithms. First, a population-level unrooted neighbor-joining (NJ) tree was constructed with the on 11.0) . Next, ton 11.0) . These tsoftware  to reconWe manually checked the sequencing data quality to ensure a reliable estimation of the forensic efficiencies for the analyzed SNP loci in the Chinese Korean ethnic group. This process involved a thorough evaluation of the newly generated dataset, including both per-sample and per-marker examinations. We removed samples with mapping rates < 100% and excluded SNP loci with allele calling rates < 100% to generate a comprehensive dataset for subsequent analyses. Eventually, four samples and 47 SNPs were excluded, leaving a total of 157 samples and 1946 SNP loci  for further forensic analyses. These 1946 SNP loci spanned across the 22 autosomes in the human genome . The results of LD tests for pairwise SNP loci showed that only 14 pairs (14/1048575 = 0.0013%) of SNP loci deviated from LD in the Chinese Korean ethnic group.For the HWE tests of the 1946 SNPs, 13 diallelic SNPs disconformed to HWE after Bonferroni correction  distribution for first-degree kinship (including PC and FS) and second-degree kinships (encompassing HS and GG). For the first-degree kinships, the log10(LR) values for PC and FS were in the range of 110.46 to 155.90 (mean of 132.48 ± 6.90 standard deviation) and 87.02 to 149.38 (mean of 117.31 ± 9.62 standard deviation), respectively. For the second-degree kinships, the Log10(LR) values for HS and GG kinships were distributed from 10.65 to 41.95 (mean of 26.71 ± 4.66 standard deviation), and 10.81 to 42.88 (mean of 26.71 ± 4.74 standard deviation), respectively. The distributions of Log10(LR) values for FS, HS and GG kinships were entirely distinct from those of unrelated individuals in the simulated kinships  statistics to measure the ancestry information content of these SNP loci in distinguishing among different populations. The In values were calculated to distinguish populations at different levels. In_1 denoted the capacity of the SNP loci to differentiate among the overall 76 intercontinental populations. In_2 quantified the effectiveness of the SNP loci in distinguishing populations from East Asia, South Asia, Central South Asia and Middle East. In_3 specifically gauged their efficiencies in distinguishing East Asian populations. We also generated the scatter plots to provide an intuitive distribution of the forensic efficiencies for these SNP loci, with the X and Y axes demonstrating the In and PD values of each SNP locus. As shown in Fig. In > 0.1) in Additional file By incorporating the 75 reference populations into this study, we were able to conduct a comprehensive evaluation of the ancestry inference power for this panel. Initially, we visualized the allele frequencies of the 1706 overlapping SNPs across all 76 populations through a heatmap Fig. A. These FST) was used to estimate pairwise genetic differentiations among the overall 76 populations, encompassing the Chinese Korean ethnic group genotyped in this study and 75 worldwide populations selected from the 1 KG and the HGDP. First, we computed the pairwise FST among different intercontinental populations and visualized the FST values using a heatmap. Then, we extracted the FST between the Chinese Korean ethnic group and its reference populations from different continents to visually present genetic relationships among these populations. Our observations unveiled significantly lower genetic distinctions among populations residing within the same continents, whereas higher genetic differences were evident when comparing populations from different continents. The African populations were relatively more genetically far from other intercontinental populations  could be separated into different genetic clusters based on pairwise shared genetic drift. The Chinese Korean ethnic group shared the most genetic drifts with the East Asia populations, followed by South Asia, Central South Asia, Middle East, Europe, America, Oceania and Africa populations. Furthermore, we extracted the outgroup-f3 statistics calculated in the East Asian populations to determine the relationships between the Chinese Korean ethnic group and the other East Asian reference populations. As shown in Fig. FST and PCA conclusions. We also used the f4 statistics to assess the shared genetic ancestries or admixtures among the East Asia populations. The f4 statistics showed significantly more genetic exchanges between the Chinese Korean ethnic group and the Northern Han, Beijing Han, and Southern Han populations compared to Dai, Tu, Vietnam, Naxi, Hezhen, Yakut, and Cambodian populations  , suggesting that the Chinese Korean ethnic group might share more genetic affinities with Han populations across different regions and with the Daur, Tujia, Japanese, Mongolian, Miao, She, Yi and Xibo populations.Compared to previous reports, this study allowed us to better examine ADMIXTURE and the recent gene flow within the Chinese Korean ethnic group and the reference populations through computation of the f statistics by enlarging the sample size and the number of SNP loci. As indicated in Fig.  2) Fig. C–E. HoweFST values  were generated for 20 iterations by the TreeMix software. Based on the distributions of ∆M, the optimal number of admixture events for the Chinese Korean ethnic group and the reference populations was indicated to be ∆M = 4. The ML tree, considering a total of 76 populations along with four admixture events (∆M = 4), was shown in Fig. We also applied a model based on population allele frequencies to account for variances introduced by secondary migration events to test whether admixture was confounding the phylogeny. The maximum likelihood (ML) trees with one to seven admixture events ∆M = –7 were gThe development of NGS technology has revolutionized forensic DNA investigation. The advantages of NGS to forensic DNA analyses can be summarized as follows: (1) increased sensitivity, (2) simultaneous analysis of multiple markers, (3) enhanced discrimination power, (4) expanded data output, (5) adaptability to degraded samples, (6) improved efficiency, and (7) the ability to provide comprehensive genetic profiling. The Illumina sequencing platform has gained significant popularity and acceptance in forensic applications, primarily due to its high throughput, accuracy, and a broad range of sequencing capabilities. In recent years, the MGI has independently developed a sequencing platform that builds libraries based on DNA nanospheres and applies a rolling loop replication strategy for sequencing. Studies have shown that the MGI sequencer has many advantages such as high accuracy, low duplicate sequence rate and low label jump rate, rendering it a powerful tool for forensic DNA practice. Additionally, a previous study has indicated that the 1993-SNP panel, developed on the MGI sequencer, could identify complex relatives in the Han Chinese population . HoweverBased on the above considerations, we further verified the efficiency of the 1993-SNP panel for various forensic applications in the Chinese Korean ethnic group from the Yanbian Korean Autonomous Prefecture, China. First, the genetic diversities of the 1993 SNP loci and the potential applications of this new panel for individual identification and parentage testing were investigated by calculating a series of forensic parameters. Then, we integrated the typing data of the Chinese Korean ethnic group with data derived from reference populations representing eight different continents worldwide, to provide an extensive exploration of the genetic background of the Chinese Korean ethnic group. To the best of our knowledge, the number of Chinese Koreans and the SNP loci covered in this study surpasses barely all previous studies on the genetic characterization of the Chinese Korean ethnic group. This study was conducted to further expand the genetic diversity profile of the Chinese Korean ethnic group, which will not only serve as a valuable resource for future genome-wide association studies concerning the Chinese Korean ethnic group but also extend insights into its neighboring populations across East Asia.We have confirmed that this new panel can be effectively used for individual identification and parentage testing in the Chinese Korean ethnic group. Considering the potential challenges of SNP locus detection in aged or degraded DNA samples, we evaluated the trend of 1-CPD and 1-CPE values as the number of SNP loci increased. The results showed that even with only 10% of the loci detected (200/1993), the threshold of statistical determination required for individual identification and paternity testing can still be reliably met. By simulating different degrees of kinships separately, we confirmed that this panel could effectively determine parent–child, full-sibling, half-sibling and grandparent-grandchild kinships from unrelated individuals, indicating that this panel was potentially useful for complex kinship determinations. However, this conclusion should be further validated with real cases in future studies.FST among different populations also supported that the genetic affinities of populations decreased with increased geographic distances, consistent with the isolation-by-distance model [This study expanded the population analyses to a global scale by incorporating 75 reference populations from eight different geographic regions. In the population genetic analyses, we showed that the Chinese Korean ethnic group was genetically more related to the East Asian populations in comparison to groups outside the East Asian context. More specifically, the Chinese Korean ethnic group has been shown to be closely related to the Han Chinese populations and several ethnic minorities residing in northern China. The influence of the geographic location in the differentiation of different intercontinental populations was observed in the PCA, which defined major clusters coincident with Africa, Europe, East Asia, South Asia, Middle East, Central South Asia, and Oceania populations. Pairwise ce model . This geIn recent years, the genomic characteristics of the ethnic Korean population in South Korea has been well characterized by a series of genome-wide studies. The Koreans in South Korea were discovered to share relatively close genetic connections with other East Asian populations, including the Japanese and the Han Chinese –35. In 2To summarize, the genomic data and findings yielded in this study enhance our understanding of East Asia population histories and contribute to the broader knowledge of global genetic history. Importantly, our data have provided a crucial resource and biomedical reference that will facilitate understanding of rare and common genetic variants in the Chinese Korean ethnic group. Meanwhile, we also admitted that given the limited number of participants included in this study, there may be some uncertainty in the conclusions drawn when exploring the genomic background of the Korean ethnic group in China. In future research endeavors, we intend to enhance our analysis of the genetic structure of the Chinese Korean ethnic group by including a more extensive cohort of Korean individuals, as well as its neighboring ethnic groups that may engage in significant genetic exchanges .Additional file 1. Fig. S1: Sequencing depths and allele coverage ratios of the SNP loci in the Chinese Korean ethnic group. A Distribution of the 1993 SNP loci in the 22 autosomes; B Histogram of the average sequencing depths for 1993 SNP loci in 161 Chinese Korean individuals; C Boxplot of the sequencing depths for SNP loci with a sequencing depth less than 500×; D Histogram of the average allele coverage ratios for the heterozygous SNP loci in the Korean ethnic group. Fig. S2: Forensic efficiencies of the 1946 SNPs in the Chinese Korean ethnic group. A Forensic statistical parameters, encompassing gene diversity (GD), Hobs (Observed heterozygosity), PD (Power of discrimination), PE (Power of exclusion) and PM (Probability of match) of the 1946 SNPs; B Distribution of 1-CPD and 1-CPE values estimated with the increase of SNP loci. Fig. S3: ADMIXTURE results for K = 2 ~10, with the K denoting the pre-assumed ancestry components represented by different colors. The ancestry composition of each population is proportional to the height of different colors. Fig. S4: Principal component analyses (PCA) of the Chinese Korean ethnic group and the reference populations. Each dot represents a single individual and is colored according to its continental origin. A PCA of the Chinese Korean ethnic group and all the reference populations from eight major geographic regions worldwide. The Chinese Korean ethnic group and East Asian populations are marked with black box; B PCA of the Chinese Korean ethnic group and the East Asian reference populations. Fig. S5: Phylogenetic reconstruction of Treemix results for one to seven (except for four) migration events between the Chinese Korean ethnic group and the reference populations. Fig. S6: Pairwise residuals of Treemix results for one to seven (except for four) migration events between the Chinese Korean ethnic group and the reference populations.