Harmonia axyridis Pall analysis. The complete dataset consists of 39 haplotypes, 16 of which we identified for the first time. The intra-population and geographical variability of the barcoding fragment was studied for seven populations of the western and eastern groups of the native range and in six invasive populations, in which 25 of the 39 haplotypes are found. Population structure inferred on base of molecular variability and haplotype frequencies showed a high level of differences between the eastern and western groups of native populations and confirm the hypothesis of the origin of all invasive populations from native populations of the eastern group. A comparative analysis of molecular variation indices testifies to various evolutionary scenarios of the formation of the western and eastern groups of native populations and confirms the hypothesis of the microevolutionary history of the species, previously suggested in morphological character based studies of the geographical variability of H. axyridis. A significant decrease in the molecular diversity of invasive populations confirms the hypothesis of a random nature of the primary invasion of this species in North America.Our study is focused on original and publicly accessible data on the intraspecific variability of the barcoding DNA fragment in ladybirds  Harmonia axyridis   is a wide-areal species that before the 90s. of the last century inhabited Central and Northeast Asia. The boundaries and features of the native range of this species based on the cadastral list of finds and their localization on the map were described in detail . In. InH. axom Japan , 43. Thed origin . In gened origin . Publishc coast) . A studyc coast)  in the pulations  also shoH. axyridis invasion scenarios based on the variability of the system of 18 microsatellite loci in native, invasive, and biocontrol populations showed that two bridgehead populations initially occurred as a result of two independent introductions in the east and west of the United States. The eastern bridgehead has become the likely source of four introductions–to Africa, South America (Brazil/Argentina and Chili) and Europe, and the western bridgehead–is the source of at least two independent introductions to Europe [CO1 gene. The eastern center, which became the “waystation” for global invasion, turned out to be phylogenetically associated with both the eastern and western groups of the native range populations, which clearly contradicts the above results and conclusions of studies on mitochondrial and morphological markers. Most likely, mitochondrial and nuclear molecular genetic markers are necessary for the final decision on the origin of the “bridgehead” population of H. axyridis in eastern North America, because their polymorphism marks more ancient evolutionary events than too variable microsatellite loci.Evaluation of different o Europe , 45, 46.H. axyridis with rapid continental settlement, the molecular genetic studies of native H. axyridis populations, in which preadaptations to invasions realized at the end of the XX century have not been carried out to date.Despite considerable efforts to determine the origin of invasive populations and biology features , which pH. axyridis populations from different geographical regions. Despite the fact that approaches based on new genotyping technologies are finding wider application in population genetics, we used a barcoding fragment (5'-end of the CO1 gene), since data on its structure are most widely represented in international databases, and data on the structure of other molecular markers are scarce.The main scope of our work was a comparative study of the genetic structure of invasive and native H. axyridis from 6 natural populations of the invasive range and from 7 populations representing the western and eastern populations of the native range of the species [The material for this work was the collection of  species , 14. CauTM, a highly purified deionized sterile water sorbent for molecular genetic studies. The concentration of the obtained DNA samples was measured on a NanoDrop8000 microspectrophotometer (Termoscientific). Isolated DNA samples were stored at—20° C  in accordance with the manufacturer's manual, but with a lysis time increased to 2 hours. The isolated DNA was eluted with NucleoSat—20° C .Cox1) gene (barcoding fragment) was carried out by PCR with a pair of primers that we developed specifically for H. axyridis: F3457 5'–GACATTGGAACATTATACCTTTTA–3' and R79133 5'–AATTTTTTTCCCTCTTTCTTGTGT–3'. In comparison to the traditionally used primers LCO1490 and HCO2198 [Amplification of the 5ʹ-fragment of the cytochrome oxidase 1 PCR success was evaluated by electrophoresis of 3 μl of the reaction mixture in 1.5% agarose gel stained with ethidium bromide. To clean the PCR product, kits were used to isolate DNA from agarose gel and Cleanup Mini reaction mixtures manufactured by Eurogen (Russia) in accordance with the manufacturer's manual.CO1 H. axyridis sequences from the GenBank nucleotide database, which fully include the fragment we are analyzing and that do not have ambiguous or N letters and / or stop codons in translation . The GenBank sequences were used only for assessing the general parameters of the intraspecific polymorphism of the barcoding fragment (CO1) .Sequencing of purified mtDNA amplicones in both directions was carried out at the IDB RAS CCU using the ABI 3500 Genetic Analyzer. For the sequencing reactions, the same primers were used as in the amplification reaction with Big Dye Terminator reagent kit . Processing of the obtained nucleotide sequences was carried out using the SeqMan software package Lasergene 11 . Ambiguously decrypted positions were validated manually. The resulting sequences were aligned using the MUSCLE algorithm and trimmed for further analysis to a length of 647 bp. in the MEGA X software package . In addiTo estimate the phylogenetic relationships of the mitotypes recovered, a Maximum Likelihood analysis was performed using the Tamura 3-parameter model, which was identified as the best replacement model based on Bayesian information criterion (BIC) in MEGA X . The staPhylogenetic reconstructions were also performed using the posterior probability (BI) estimation method in MrBayes  v. 3.2.5CO1 Harmonia quadripunctata sequences from the GenBank were used, with the following accession numbers (ID): KM446380, KM447142, KU906663, KU909968, KU914822, KU915124, KU917400.As an external group, seven To assess the intraspecific phylogenetic relationships of the detected haplotypes, the median network was built using the Network 5.0 program , the MJ AMOVA estimation of fixation indices (FST) and their P-value, haplotype (Hd) and nucleotide (π) diversity, as well as the average number of pairwise differences (Pi) were performed in the Arlequin v. 3.5.1.2 software package  with molCO1 gene fragments from 366 samples, with length of 672–740 bp, depending on read quality. All sequences translate without insertions, deletions, or stop codons. Aligned and trimmed to 647 bp sequences split into 25 haplotypes. One sequence of each haplotype is deposited into the nucleotide database of the GenBank with registration numbers (ID): MN509563 –MN509583. CO1 sequences of H. axyridis available from the GenBank nucleotide database, include 14 more haplotypes.We obtained Analysis of the molecular variability of all 39 identified haplotypes showed that this fragment is AT-rich, the average total nucleotide ratio is– 15.5%–C: 38.41%–T: 30.92%–A: 15.14%–G. The sequences of the identified haplotypes contain 35 polymorphic sites (5.4%), 36 substitutions, of which 24 are transitions and 12 transversions. In nine haplotypes at eight polymorphic sites, eight non-synonymous substitutions occur, leading to substitutions of amino acid residues in the protein sequence. Details of polymorphic sites and the nature of substitutions for each haplotype are shown in The results of the analysis of the phylogenetic relationships of the identified haplotypes by the Maximum Likelihood (ML) method and the estimation of the posterior probabilities (BI) are presented in The median haplotype network  shows a H. axyridis CO1 sequences (including 104 sequences from GenBank) are shown in Tables The results of the analysis of the occurrence and intraspecific distribution of haplotypes obtained by comparative analysis of 470 H. axyridis. Haplotype H1 is the most massive. Its share is more than 70% in the entire sample analyzed. The total share of the H3 haplotype is also significant >7%. Haplotype H2 occurs with a frequency of 9.8%, but is distributed mainly in the populations of Siberia (the western group of native range populations). Only 3 samples of this haplotype were found in China and it was not found in any Russian populations of the eastern group of the native range. The frequency of the H9 haplotype does not exceed 2%, although it is widely distributed over the range and found in 6 populations analyzed by us –common for the entire range, three haplotypes  were also found in native populations of the eastern group and one unique haplotype–H15. Another unique haplotype (H39) was found in Germany according to GenBank data .CO1 for H. axyridis from the GenBank database were excluded from population genetic analysis, since a significant part of these data has not yet been published, and it is impossible to establish the ratio of haplotypes in populations only according to GenBank.The sequences of CO1 barcoding fragment in seven native populations of H. axyridis belonging to different geographical groups and in six invasive populations. The results of this analysis of the studied populations/population groups are shown in We analyzed the variability of the To determine the hierarchical genetic structure in the groups of populations studied, specific fixation indices (Fst) , 57 wereTo clarify and visualize relatedness between populations, we also performed a cluster analysis of samples by the distribution of haplotype frequencies shown in H. axyridis at the moment is perhaps the most representative for this species, both in the geographical spread of the populations and in the sample sizes from each of them. The nucleotide composition revealed in H. axyridis corresponds to the average for this fragment in coccinellids [Our study of barcoding mtDNA fragment polymorphism in inellids .Hippodamia convergens sequences, 9 haplotypes among 71 Hippodamia parenthesis samples and 4 haplotypes among 18 Coleomegilla maculata individuals [Iberorhyzobius rondensis– 33 haplotypes among 104 samples [H. axyridis as a distinct species. This conclusion is concordant with the position of H. axyridis on the common phylogenetic tree of coccinellidae, constructed according to the results of multilocus typing [The total diversity of haplotypes that we found  is small in comparison with other species of ladybugs. For example, studies of the variability of the barcoding fragment in North American native ladybug beetles revealed 29 haplotypes among 149 ividuals . The max samples . Values s typing .H. axyridis. The general geographical distribution of these haplotypes also confirms this assumption. The frequencies of H1 and H3 haplotypes in western native populations are significantly lower than in eastern ones. Haplotype H2, on the contrary, is the most abundant in western native populations, meeting here with a frequency of 43%  without preliminary identification of groups  indicateaxyridis , 57. TheH. axyridis in the [A comparative analysis of molecular variability indices highlights various evolutionary scenarios population formation and their intraspecific groups . The rels in the , 13, 14.Differences in specific fixation indices (Fst) of native populations within the western and eastern groups  can probH. axyridis. All molecular diversity indices in the populations of this group are also significantly reduced, especially compared to native populations of the western group –common to the entire range, three haplotypes  were also found in native populations of the eastern group and one unique haplotype–H15. Another unique haplotype (H39) was found in Germany according to GenBank  that were released in the Zailiysky Alatau at the end of the last century [It should be noted that the conditional probability of the chosen scenarios for American bridgehead populations is very small– 0.4425 and 0.6242, respectively. At the same time, with the exception of two mitotypes H1 and H3) common to populations of all groups, not one of the mitotypes found in the western group of native populations was found in invasive populations. Data on the phenotype of invasive populations also show their high similarity with native populations of the eastern group , 29, 41. common taxyridis , 34 as iH. axyridis are necessary. Of particular interest are the populations of China and Japan, for which data are currently not available in mass in scientific literature. However, the most intriguing question may be the fate of native populations of the Western group in the event of closure of the invasive range formed by natives of eastern populations. This question is not unfounded, since H. axyridis has already traveled east over 2,500 thousand kilometers from the place of the first find in Germany in 2001, and about 2100 km remained to go to the western border of the native range (the middle course of the Irtysh River in Western Siberia).Obviously, for a final answer to the questions of the native source (or sources) of bridgehead populations (ENA and WNA), further studies of the variability of various molecular and phenotypic markers in both invasive and native populations of S1 FigThe elytral ridge is a lateral chitin elevation at the distal ends of the elytra, inherited as a genetically determined monogenic trait. Absence of the ridge is determined by the recessive allele . The ely(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Birgus latro (coconut crab) is an edible crustacean that has experienced serious overharvesting throughout its whole habitat range; however, the negative effects of overharvesting on the genetic diversity within B. latro populations have not been elucidated. Here, we report sex ratio, body size, and genetic diversity in populations of B. latro in the Ryukyu Islands where large-male–biased overharvesting of B. latro has continued. In 2 of the study populations, the sex ratio was significantly skewed toward females, and in all of the study populations large males were rare, which we attributed to sex- and size-biased overharvesting. We found no differences in genetic diversity between small and large individuals, suggesting that genetic diversity, even among the large  individuals, may have had already been negatively affected by overharvesting. Continued monitoring of sex ratio, body size and genetic diversity are needed for effective management of the study populations. Once genetic diversity is lost from a population, it can be restored by genetic mutation or immigration of individuals from a population with high genetic diversity. However, the recovery of genetic diversity through mutation takes a long time, and although immigration from refugia is faster than mutation, the restoration of genetic diversity in isolated populations cannot be expected to occur through this means3. Therefore, conserving genetic diversity and increasing our understanding of gene flow patterns should be important goals for the effective management of fishery resources5.Overharvesting drives loss of genetic diversity6, although outbreeding depression has been shown to cause the correlation to become negative7. In addition, many papers have reported significant positive relationships between individual heterozygosity of allozyme or neutral microsatellite markers and fitness-related traits in taxa such as fish12, mollusks14, crustaceans15, marine and terrestrial mammals19, and terrestrial plants20. It is therefore important to investigate genetic diversity to assess the sustainability of fishery resources.Individual genetic diversity  likely plays an important role in population sustainability because it is correlated with fitness. For example, a meta-analysis has shown that the correlation between the level of individual heterozygosity and fitness is small but significantly positive21. Body size is a key factor that influences fitness in many taxa23. In males, larger individuals are stronger competitors for females24 and provide more sperm per ejaculation25, and in females, body size is positively correlated with number of eggs26, egg size27, and larval body size and starvation resistance28.High, long-term harvesting pressure often causes the average body size within a population to decreaseBirgus latro) is a terrestrial hermit crab with a marine larval dispersal stage distributed in subtropical and tropical regions of the Indo-Pacific30. Recent report showed that this species predates other animals including birds and mammals, and can have strong impacts on prey behavior, abundance and community composition31. However, this species has suffered severe resource depletion throughout its entire distribution range due to overharvesting and habitat destruction35. Therefore, B. latro is currently categorized as “data deficient” in the International Union for Conservation of Nature’s Red List, and “vulnerable” in the Japanese Ministry of the Environment’s Red Data Book36. In Japan, B. latro is an important resource not only for food but also for culture and tourism37. However, selective harvesting of large males has resulted in the sex ratio of local populations becoming skewed toward females and the average male body size becoming miniaturized in places that have experienced high fishery pressure38. Recent studies have revealed that large-male–biased harvesting negatively affects populations in several ways39: it decreases male size-dependent reproductive potential , reduces the pool of suitable males due to females refusing to mate with males smaller than themselves38, which results in reductions of number of spawned eggs43 and larval qualities44]. Thus, it is likely that decreases of average body size within a population will have negative impacts on the population’s genetic diversity through massive decrease of effective population size.The coconut crab , and genetic diversity  to elucidate the effects of overharvesting on population structure and genetic diversity as well as gene flow pattern among 8 populations  genotyping by sequencing (MIG-seq)ons Fig.  of B. laIn all study populations except Kurima (MK), the number of females was higher than that of males, although the difference was only significant in Ishigaki (IS) and Hatoma (HT) Table . Female Correlation test with Spearman’s rank correlation coefficient showed no significant correlation between cumulative human population density and, body size and proportion of female individuals Fig. .We sequenced a 466-bp region of the mtDNA COI gene sequence in 154 individuals and detected 53 haplotypes. Four dominant haplotypes were found in most of the study populations Fig. . PopulatCorrelation test with Spearman’s rank correlation coefficient showed no significant correlation between cumulative human population density and, haplotype diversity and nucleotide diversity, Fig. .FST = 0.074–0.104, P < 0.01; Table R2 = 0.042, P = 0.232; Fig. F = 0.294–0.532; Table We used 495 neutral genetic markers for the genetic analysis. TM and IS were significantly differentiated from both MK and YG  were very rarely seen except for TM, even in the populations where the sex ratio was less, or not, skewed toward females. A lack of large males promotes resource reduction through negative impacts on life history parameters 39. Our findings suggest that urgent implementation of an effective management policy is needed in Ryukyu Islands.In Japan, large males of tro Fig. , suggestBirgus latro can live for 60 years47 or longer48. At 10 years of age, males and females generally have a thoracic length of around 30 mm and 25 mm, respectively49. Overharvesting of B. latro likely started in Japan after the end of the Second World War37, and although the need for conservation efforts was noted as early as the 1970s, we were told by residents of Okinawa that overharvesting has continued and even become more severe in the past two decades . Thus, most small individuals analysed in the present study  would have been recruited after the intensification of overharvesting, whereas some of the largest individuals could have been recruited before. This suggests that since overharvesting reduces genetic diversity within a population3, the genetic diversity of small B. latro individuals should be lower than that of large individuals. However, in the present study, we did not find a correlation between thoracic length and population- or individual-level genetic diversity, as estimated by using COI gene and MIG-seq data. The average thoracic length of large individuals used in the COI gene analysis was 40 mm and 25 mm for males and females, respectively; therefore, based on the growth curve of B. latro, most of these individuals were recruited in the last 15–20 years49. The thoracic length of most individuals used for the MIG-seq analysis was smaller than 40 mm for both sexes; therefore, based on the growth curve of B. latro, most of these individuals were recruited within the last 20 years for males and within the last 30 years for females. It is therefore, suggests that not a few individuals included in the genetic analyses were likely recruited after the intensification of overharvesting and may have already suffered the impacts of overharvesting on genetic diversity. Growth rate can, however, vary among individuals based on resource availability and levels of social competition . Further monitoring and analysis using more samples from each population with wide range of body size are needed to evaluate the effect of overharvesting on genetic diversity of B. latro.3. Once genetic diversity is lost, recovery by genetic mutation takes many generations, and as both the coastal and oceanic environments are being rapidly changed by human activity52, mutation-based recovery is not realistic. In contrast, immigration-based recovery can occur relatively quickly3. In the COI analysis, no significant genetic differentiation between the study populations was found, and in the MIG-seq analysis, most population pairs showed no significant genetic differentiation. The pelagic zoeal larval stage of B. latro lasts for 18–23 days54, and megalopae settle at coastal areas and find a gastropod shell for migrating to land by around 10 days post settlement56. Therefore, the whole larval period lasts 4–5 weeks, which is similar to that of the coral Acropora digitifera57. It has been reported that population connectivity is generally high among A. digitifera populations in the Ryukyu Islands58, and the sampling locations in that study were similar to those in the present study. This suggests that the larval period of B. latro is long enough to allow sufficient larval dispersal among the study populations. This assumption is supported by the results of the Mantel test, which showed a non-significant correlation between genetic distance and geographic distance, which suggests that migration via larval dispersal frequently occurs among Japanese populations of B. latro.To conserve genetic diversity, genetic mutation or immigration from other populations with high genetic diversity are neededB. latro populations in the Ryukyu Islands, MIG-seq analysis detected significant genetic differentiation between four population pairs: TM–MK, TM–YG, IS–MK, and IS–YG. This is consistent with the divMigrate results showing that immigration to TM and IS from other populations is limited and IE, MK, HT, YG, and IR have a role as core populations within the overall population in the Ryukyu Islands. Local water current can work as a dispersal barrier in Ryukyu islands59. In addition, TM is far from the large islands  that are important for the maintenance of the genetic diversity of the other populations via immigration through larval dispersal. Low genetic diversity can affect the fitness6 and ability of individuals to survive and adapt in future environments3. To conserve the genetic diversity of B. latro populations in Japan, and therefore to conserve B. latro as an important fishery resource, we recommend long-term monitoring of genetic diversity, sex ratio, and body size composition. In Okinawa prefecture (the administrative division that includes the Ryukyu Islands), several municipal governments have implemented regulations that prohibit the catch of B. latro of certain sizes; we hope that such regulation will be implemented soon across the whole region.Here, we report COI- and MIG-seq-based genetic diversity, sex ratio and body size distributions in 8 Japanese populations of B. latro were randomly collected by hand and the sex and thoracic length (as an index of body size) of each individual were recorded. For DNA analysis, part of the third pereiopod was also collected. Tissue samples were fixed and preserved in 99.5% ethanol. All crabs were released at the sampling site after tissue sampling. Field surveys and sample collection were conducted in accordance with the regulations of local governments in Okinawa Prefecture, Japan.Field surveys were conducted in Ie (IE), Miyako (MY), Kurima (MK), Minna (TM), Ishigaki (IS), Hatoma (HT), Iriomote (IR), and Yonaguni (YG) in the Ryukyu Islands, Japan Fig. ; Table 12-test. In addition, we compared thoracic length among populations for each sex by using Tukey’s test. All analyses were performed using R v3.3.063.We examined whether the sex ratio of each population is skewed toward either sex by using the χB. latro. We therefore tested correlation between cumulative human population densities and, sex ratio, median body size, and genetic diversity indexes of B. latro in each island population with Spearman’s rank correlation coefficient. Data of every 5 year human population densities (1955–2015) of each island was obtained from Okinawa Prefecture64. We cumulated the human population density of 1955–2015 . The PCR conditions were as follows: initial denaturation at 94 °C for 2 min; 35 cycles each at 94 °C for 30 s, annealing at 47 °C for 30 s, and extension at 72 °C for 1 min; and a final extension step at 72 °C for 2 min. The PCR products were purified using Exo-SAP IT  and sequenced using an Applied Biosystems 3730xl DNA Analyser and the same primers as used for the PCR.DNA was extracted from tissue samples by using a DNeasy Blood and Tissue Kit . Partial sequences of the mtDNA COI-encoding region were amplified by polymerase chain reaction (PCR) with the universal primers LCO1490 and HCO219845. In brief, MIG-seq was used to amplify a few hundred to a few thousand genome-wide SNPs around ISSRs by using eight universal pairs of multiplex ISSR primers (MIG-seq primer set 1) for the first PCR. Then, DNA libraries with different indexes were pooled and sequenced by using a MiSeq system  and a MiSeq Reagent Kit v3 (150 cycle) (Illumina). A total of 83 individuals were analyzed in the MIG-seq analysis.Genome-wide single nucleotide polymorphisms (SNPs) were obtained using the protocolhttp://hannonlab.cshl.edu/fastx_toolkit/index.html) with a fastq-quality-filter setting of –Q 33 –q 30 –p 40. We removed adapter sequences for the MiSeq run from both the 5′ end (GTCAGATCGGAAGAGCACACGTCTGAACTCCAGTCAC) and 3′ end (CAGAGATCGGAAGAGCGTCGTGTAGGGAAAGAC) by using Cutadapt v1.1366, and then excluded short reads less than 80 bp. The quality-filtered sequence data were demultiplexed and filtered through the software Stacks v1.4668. We used Stacks v1.468 to stack the reads and extract SNPs. First, we used the U-stacks program with the following settings: ‘minimum depth of coverage required to create a stack (m)’ = 3, ‘maximum distance allowed between stacks (M)’ = 1, ‘maximum distance allowed to align secondary reads to primary stacks (N)’ = 1, with the deleveraging and removal algorithms enabled. Then, we used the C-stacks program with the option ‘number of mismatches allowed between sample loci when building the catalog (n)’ = 4, followed by the S-stacks program. Finally, we used the Populations program in Stacks v1.4 by restricting the data analysis to the criteria  49, and calculated genetic diversities for the two size classes. Mean ± standard deviation of the thoracic length of each group is 23.6 ± 4.0 mm (N = 25), 35.1 ± 5.6 mm (N = 29), 23.2 ± 2.5 mm (N = 22), and 31.6 ± 3.9 mm (N = 62) for small male, large male, small female, and large female, respectively. Population pairwise PhiPT values were estimated using the Analysis of Molecular Variance (AMOVA) method and GenAlEx 6.572 and tested for significance based on 999 permutations. Statistical significance levels for all pairwise tests were 0.05 after adjusting for multiple comparisons using false discovery rate (FDR) correction73.Haplotype networks were constructed using the haploNet function in the R package ‘pegas’ v0.1172. Pairwise population FST values were estimated by using the AMOVA method and GenAlEx 6.5. Statistical significance levels for all pairwise tests were 0.05 after adjusting for multiple comparisons using FDR correction68. Isolation by distance was tested using Mantel’s test based on 9999 permutations from the comparison of all pairwise FST/(1 − FST) values with pairwise geographic distances in kilometers (straight-line distance) using GenAlEx 6.572. In addition, individual heterozygosity was estimated using GENHET v2.374 with R v3.3.063.Based on 495 SNP markers, ratio of the number of observed alleles, observed heterozygosity, expected heterozygosity, and fixation index were estimated using GenAlEx 6.575 (https://popgen.shinyapps.io/divMigrate-online/). This program produces a migration network graph with relative values for gene flow among populations scaled to the largest magnitude estimated. For the analysis, we selected 61 MIG-seq markers that were detected in at least 68% of the samples. We used NM as a measure of genetic distance. The significance of asymmetrical gene flow among populations was tested using 1000 bootstrap iterations.The gene flow among populations was estimated by using divMigrate-onlineFigure S1.Table S1.Table S2.Table S3.Dataset1.


African populations are underrepresented in medical genomics studies. For the Somali population, there is virtually no information on genomic markers with significance to precision medicine. Here, we analyzed nearly 900,000 genomic markers in samples collected from 95 unrelated individuals in the North Eastern Somalia. ADMIXTURE program for estimation of individual ancestries revealed a homogenous Somali population. Principal component analysis with PLINK software showed approximately 60% East African and 40% West Eurasian genes in the Somali population, with a close relation to the Cushitic and Semitic speaking Ethiopian populations. We report the unique features of human leukocyte antigens (HLA) in the Somali population, which seem to differentiate from all other neighboring regions compared. Current study identified high prevalence of the diabetes type 1 (T1D) predisposing HLA DR-DQ haplotypes in Somalia. This finding may explain the increased T1D risk observed among Somali children. In addition, ethnic Somalis were found to host the highest frequencies observed thus far for several pharmacogenetic variants, including UGT1A4*2. In conclusion, we report that the Somali population displays genetic traits of significance to health and disease. The Somali dataset is publicly available and will add more information to the few genomic datasets available for African populations. Yet, many ethnic populations in Sub-Saharan Africa are not represented in medical genomics studies found in the literature, mainly because of the generally lower level of medical research conducted in Sub-Saharan Africa compared with developed countries due to insufficient research infrastructures or resources. As a result, most of the human genetic variation present in Africa is yet unexplored. Some efforts have been made in recent years to better understand African genetic variation. These include studies that examined genetic markers for diabetes8 and those investigating the genetic selection in Africa as a result of disease exposure or environmental adaptation9. However, the need for more genomic data from African populations still persists, notably for underrepresented ethnolinguistic groups, including the Somali population, where there is still lack of information on genetic markers for drug responses, immunity and diseases.Africa harbors the largest human genetic variation in the world13. Rock art paintings in multiple sites of Northern Somalia show Neolithic human activities in this part of East Africa15. Later Himyarite and Sabaean inscriptions found in the same Somali region suggest existence of socio-cultural mingling with the ancient Axumite-South Arabian sphere15. Due to the geographic location of Somalia, the Somalis have both African and non-African ancestries17. Most of the genetic studies involving ethnic Somalis in recent years have mainly focused either on forensic genetic markers used for legal issues21, or on anthropological genetics for assessment of ancestry and ancient demographic history16, using DNA samples collected from the Somali diaspora outside Africa. While such studies have provided interesting information on the genetic history of Somalia, there is scarce information on genomic data of importance to health and disease for the Somali population, apart from some single-gene-disorder case reports involving Somali patients living in Western countries. As an example, there are no datasets available concerning pharmacogenetics of drug metabolizing enzymes and transporters (DMET), HLA alleles, or disease markers for the Somali population. This lack of genomic information can be partly, but not wholly, explained by the political situation in the country, which has discouraged local and international researchers to conduct medical research during the turbulent years of Somalia. The few genetic case reports available in the literature, nevertheless, suggest that Somalia harbors some unique human genetic traits with implications to health and disease. These include the exceptionally high prevalence (93%) of the human leukocyte antigen (HLA) DRB1*03:01 allele found in Somali children with type 1 diabetes (T1D) in Minnesota22, the high frequency (75%) of the T1D predisposing HLA DR3-DQ2 haplotypes identified in Somali children with T1D in Finland23, the founder effect in the Horn of Africa of the insulin receptor mutation p.Ile119Met identified in unrelated Somali families living in the United Kingdom24, and the high incidence rate of mutations in adenosine deaminase (ADA) causing ADA-deficiency observed in Somali immigrants living in Denmark25. In the present study, we have tried to address the aforementioned needs for a Somali genomic data and for the first time created a genome-wide, publicly accessible, dataset containing genomic markers with significance to precision medicine for this ethnic population.Somalia is located in the Horn of Africa , a historic site for human migrations into either direction of Africa or EurasiaA total number of 95 samples and 1 Quality Control sample (included in the PMRA reagent) were analyzed on the GeneTitan MC Instrument. All samples passed the Dish QC (DQC) test. There were 888,799 markers analyzed in the study. Average genotype quality control call rate (QC CR) was 99.6%, which generated 849,267 high-quality markers that were included in the downstream analyses. Gender call counts  matched perfectly with the documented gender identity for study participants. Results for QC analyses are available in Supplementary Fig. 26 as well as with our previous O blood group genotype assessments for ethnic Somalis27. We also examined the distribution of Apolipoprotein E (ApoE) gene variants in the Somali population using the SNPs rs429358 and rs7412. ApoE is a genetic and anthropological marker that shows geographical and ethnic variations28. Our analysis showed that the most common ApoE gene variant in Somalia is ApoE-ε3 (79%), followed by ApoE-ε4 (16%), and ApoE-ε2 (6%). Homozygosity for the ε4 variant (ApoE-ε4/ε4) reported to show the strongest association with Alzheimer’s disease in western countries29 was found at 2% in Somalia. These ApoE gene frequencies are close to those previously identified in the neighboring Ethiopia31.To validate the genotype data, we examined markers known to associate with ethnic and geographic variations that were included in the PMRA analysis. First, we estimated the distribution of ABO and Rh blood groups in our Somali population using a set of 8 SNPs: rs8176719, rs8176746, rs505922, rs8176749, rs7853989, rs8176740, rs56392308 (ABO blood groups), and rs590787 (Rh blood type). The most common ABO blood group in Somalia was found to be O blood (60%), followed by A blood group , B blood group (14%), and AB blood group (4%). The proportion of Rh+ and Rh- were 88% and 12%, respectively. These results were consistent with the previously reported Somali phenotype blood group dataet al.16 and shows approximately 60% East African and 40% West Eurasian (25% West Asian and 15% North African) ancestry in the Somali population. The similarity of individuals is apparent, which presumably reflects very ancient admixture events and a unification process through endogamy. In addition, the non-African components in the Somali population seem to show relatively little inter-individual variations  (Supplementary Table https://gnomad.broadinstitute.org/) and the 1000 genomes project , a set of 9 pharmacogenetically relevant SNPs was found with unusually high prevalence in Somalia when compared with all other world populations  were homozygous to this variant in our Somali cohort, which along with 20 heterozygotes suggested that 24% of the Somalis carry UGT1A4*2 with an allele frequency of 14% (Table VKORC1 Asp36Tyr (rs61742245) previously reported to be associated with warfarin resistance in Ethiopians34. In our Somali population, 16 individuals (17%) were heterozygous to VKORC1 Asp36Tyr corresponding to a minor allele frequency (MAF) of 8% , followed by VKORC1*3 (32%) and VKORC1*4 (14%). The VKORC1*1 haplotype previously reported to be the ancestral haplotype38 had a frequency of 11% in Somalia . Of the 94 HLA alleles identified in Somalia, 61 were available on the AFND for all populations compared, and these 61 HLA were used for the analysis , mirroring the geographic location of Somalia. Ethnic Somalis, approximately 30 million worldwide, are unified by a shared culture, language and religion. Our data reveal a remarkably homogeneous Somali population with roots from ancient mixtures of populations from Africa and the Middle East. We observed that a vast majority of our study individuals carried similar proportions of genes from those ancient populations, which possibly can be explained by the fact that ethnic Somalis have a strong genetic unification by endogamy, due to the custom of marrying only within the limits of their ethnic group, in addition to consanguineous practices46. It is worth to mention that most of our samples were collected in the North Eastern region (Puntland) and that results in this study may reflect the genetic makeup of the population in this part of Somalia. However, recent evidences based on Y-STR haplotypes studied on Somalis from diverse geographic locations and clans suggest that ethnic Somalis are largely homogenous17, supporting the representativeness of our samples for the larger Somali population. In addition, frequencies of ABO and Rh blood groups estimated in this study are consistent with those found in an earlier study performed in 1987 by the Somali Red Crescent Society and the Finnish Red Cross Blood Transfusion Service who analyzed 1,026 blood samples of Somalis from the entire country26. Although ABO/Rh systems tend to show similar distributions for all neighboring populations, our genotypic blood group estimation is remarkably very close to the previously reported Somali phenotypic blood group data, again supporting a representative Somali population in our cohort.Numerous genetic variants associated with drug response and disease susceptibility have been identified in the past in different populations. Unfortunately, African populations are underrepresented in these studies despite most of the human genetic variation being present in AfricaVKORC1 Asp36Tyr (rs61742245), a variant identified to be associated with warfarin resistance, initially among Ethiopians34 and later in other populations in the Middle East, with an allele frequency peaking in Ethiopia47. The 8% MAF for this variant that we found in our Somali population is the second highest frequency of VKORC1 Asp36Tyr reported so far in the world, suggesting that this variant presumably arose in the Horn of Africa and spread into other neighboring regions. This study also found that VKORC1 haplotypes in Somalia are more similar to those found in West Eurasians rather than in other Sub-Saharan Africans, probably reflecting the considerable West Eurasian gene components found in the Somali population. The high frequency of VKORC1 Asp36Tyr and the type of VKORC1 haplotypes found in Somalia have consequences for precision medicine. Although novel oral anticoagulants, or non-vitamin K antagonist oral anticoagulants, (NOACs) are increasingly replacing warfarin for treatment and prophylaxis of venous thromboembolism (VTE)48, warfarin is still in use and may likely remain in use for years to come. Thus, Somali VTE patients undergoing warfarin treatment could benefit from genotyping for VKORC1 Asp36Tyr variant as well as for VKORC1 haplotypes to establish a proper warfarin dosing regimen for avoiding unwanted fluctuations in PT/INR during the initiation period of warfarin.We identified a number of pharmacogenetic gene variants with unusually high prevalence in the Somali population compared with all other populations, although studies involving Somali patients will be required to evaluate the significance of these as well as other variants for drug response in this population. Current study, nevertheless, confirms the Horn of African origin of 49 presumably have been more affected by historical changes in environment and disease burden in the Horn of Africa. Exceptions include DRB1*13:02 and DRB1*01:02, which show Horn of African signatures with frequencies peaking in Somalia and Ethiopia. It is, however, likely that local adaptations as a result of exposure to differing infectious diseases have resulted in a differentiated HLA architecture in Somalia. There are considerable environmental and geological differences between Somalia and Ethiopia. Whereas Somalia and the Eastern Somali region of Ethiopia are largely arid or semi-arid, there have been historically more favorable conditions for vegetation and farming in larger parts of Ethiopia. Recent studies carried on African populations observed differentiations in genes involved in immunity as a result of altered environment and exposure to diseases9. As an example, Gurdasani et al. previously reported that HLA-DRB1 is differentiated in some African populations as a result of historical exposure to Lassa fever9. Overall, HLA alleles in Somalia appear to diverge from those in proximal geographic locations as can be seen in the hierarchical heatmap plot . In our study, the Somali population appears to host the highest frequency in the world for this HLA haplotype. In contrast, African American-specific HLA haplotypes identified by Onengut-Gumuscu et al.8 are not prevalent in Somalia. This may be explained by the fact that most African Americans have their African ancestry in West/Central Africa, in populations speaking Niger-Congo languages who are not ethnolinguistically related to the Cushitic-Semitic speaking ethnic groups in the Horn of Africa. Another SNP, rs9273363 (HLA-DQB1-AS1), in the MHC gene with strong association with T1D8  was also found to have the highest allele frequency (AF) in the world in Somalia, with an AF of 0.35 in our cohort. Corresponding AF for SNP rs9273363 in other populations is 0.091 in other Sub-Saharan Africans, 0.331 in Native Americans, 0.319 in East Asians, 0.262 in Europeans, and 0.257 in South Asians, according to the 1000 genomes reference panel . There is no genomic data for Horn of African populations in the 1000 genomes project. This highlights the considerable genetic diversity existing among populations of African ancestry and the need to include more African ethnic groups in DNA sequencing studies, as we previously stressed2. At least 140 million Cushitic and Semitic speaking ethnic groups in the Horn of Africa are not represented in the international genomic projects such as the 1000 genomes and the HapMap.Remarkably, several HLA alleles with high prevalence in Somalia have been found to be associated with T1D in other studies. In particular, the genotypic combination DRB1*03:01 – DQA1*05:01 – DQB1*02:01 (DR-DQ haplotype) has been found to significantly increase T1D risket al.23 found that DR3-DQ2 was the dominating HLA haplotype in Somali children with T1D in Finland. In another study, Sunni et al.22 studied Somali children with T1D in Minnesota (US) and found that most of them (93%) carried the T1D susceptibility HLA allele DRB1*03:01. Another HLA allele, DRB3*02:02, which was frequent in our Somali study population  with 1,666,051 children of native Swedish parents and found that children with Horn of African ancestry had significantly increased risk for T1D. The risk was highest among Eritreans, followed by Somalis and lowest among Ethiopians. Unfortunately, we could not find enough HLA data for Eritreans in the literature to include in our analysis and it is therefore not inconceivable that T1D predisposing HLA alleles are also prevalent in Eritrea. These evidences suggest that Somali children are genetically predisposed to juvenile autoimmune diabetes, with HLA as an important contributing factor. Thus, physicians and other healthcare workers should be aware of the genetic predisposition to T1D among Somali children when performing differential diagnosis, warranting inclusion of blood sugar tests even in cases in which classical diabetes symptoms cannot be clearly observed. In addition to T1D, a study by D’Souza et al. identified an unusual form of autoimmune hepatitis observed in Somali men living in the United Kingdom53. Although the authors emphasized their study to be underpowered, they speculated that HLA types among Somalis appear to differ from those seen in other ethnic groups, an observation we corroborate in this study.The predisposition of HLA DR-DQ haplotypes to diabetes among Somali children was indeed previously reported by two studies. First, Oilinki et al.7 will be useful to employ.This study has some limitations. One major limitation is the absence of cases in the analyses. For investigating the role of HLA DR-DQ haplotypes in T1D risk among Somalis, a case-controlled study with sufficient power will be required. Such study should preferably also include neighboring Horn of African populations, including Djibouti and Eritrea, for whom there are no or little genomic data available. To explore mechanisms involving disease alleles or pharmacogenetic variants, approaches recently reported by Adeyemo In conclusion, this study provides the first publicly available Somali genomic dataset with significance to precision medicine. We report that ethnic Somalis are an East African population with unique HLA composition and that the Somali population hosts high frequencies of genetic variants with implications to T1D and pharmacogenetics. These variants should be validated in Somali patients in the future.All methods were carried out in accordance with relevant guidelines and regulations.The study was granted with ethical permissions from the National Ethics Review Authority in Sweden (registration number: 2019–00926) as well as from the East Africa University in Bosaso, Somalia (registration number: JBA-XG-0424). Written informed consent was obtained from all study subjects.The study recruited 95 unrelated individuals living in Bosaso city in the Northeastern region (Puntland) of Somalia. Birth places were confirmed with available documents or based on questionnaires. All participants were identified as ethnic Somalis belonging to the recognized clans of the Somali lineage. Age ranged between 18 and 65 years . Most of the study participants (96%) were born in Puntland. Two individuals were born in South-Central Somalia, another person was born in Northwestern Somalia , and one individual was born in the Somali region of Eastern Ethiopia.PGG.Population database (https://pggpopulation.org/). For HLA analysis, the Allele Frequency Net Database  was used to collect data on HLA frequencies for other world populations. Populations with close genetic and geographic distance were merged into a single group, e.g. Oromo, Amhara, Ethiopian Jews (=Ethiopia), Kongo Kinshasa, Central African Republic, Rwanda, Uganda , Tunisia, Algeria, Morocco (=North Africa), Saudi Arabia, United Arab Emirates (=South Arabia), England, Germany, Sweden, Norway (=North Europe), Native Americans (=AMR).For genome-wide principal component analysis (PCA), the Somali genotype data were projected onto African – West Eurasian PC1 and PC2 data from populations in the Blood was collected into PAXgene Blood DNA Tubes  and DNA was extracted using QIAamp DNA Blood Midi kit (Qiagen). Subsequent to DNA quality control, samples were genotyped using the Axiom Precision Medicine Research Array (PMRA) at the Array and Analysis Facility, Uppsala University, Sweden. The PMRA enabled genotyping of nearly 900,000 genomic markers, including >1,200 pharmacogenetic variants, >9,000 HLA markers, >2,000 blood phenotypes, >23,000 variants in ClinVar, as well as other disease or immunological markers. The analysis was performed according to the protocol described in the Axiom 2.0 Assay Manual Workflow User Guide . Briefly, DNA amplification was followed by fragmentation and DNA precipitation. The next steps included drying, resuspension, denaturation and hybridization of the DNA onto the arrays, which were followed by ligation, staining and scanning of the arrays. The array processing was performed using the GeneTitan MC Instrument, the GeneTitan Instrument Control software and the AGCC Portal 4.0 software (Thermo Fisher).http://zzz.bwh.harvard.edu/plink/)54. Maximum likelihood estimation of individual ancestries from the Axiom PMRA multilocus SNP genotypes was evaluated using the ADMIXTURE 1.3.0 software (http://software.genetics.ucla.edu/admixture/)55. Hierarchical clustering and PCA for HLA were carried out with R software (https://www.R-project.org/). Identity by descent (IBD) analysis was performed using PLINK 1.9 and the gdsfmt as well as SNPRelate R packages56. Haplotype analysis of vitamin K epoxide reductase complex subunit 1 (VKORC1) gene was performed with the Haploview 4.0 software (https://www.broadinstitute.org/haploview/haploview)57. Hardy–Weinberg equilibrium was evaluated using Chi2 test with a significance level of P < 0.05.For genotyping, raw data was analyzed with the Axiom Array Analysis Suite, version 4.0.3.3 (Thermo Fisher), using the Best Practices Workflow with default settings. To infer HLA types of the Somali samples from the Axiom PMRA genotype data, the Axiom HLA Analysis 1.2 software (Thermo Fisher) was utilized with default settings. Genome-wide PCA analysis was performed using the PLINK 1.9 software (Supplementary Information 1.Supplementary Information 2.


Biodiversity is under threat worldwide. Over the past decade, the field of population genomics has developed across nonmodel organisms, and the results of this research have begun to be applied in conservation and management of wildlife species. Genomics tools can provide precise estimates of basic features of wildlife populations, such as effective population size, inbreeding, demographic history and population structure, that are critical for conservation efforts. Moreover, population genomics studies can identify particular genetic loci and variants responsible for inbreeding depression or adaptation to changing environments, allowing for conservation efforts to estimate the capacity of populations to evolve and adapt in response to environmental change and to manage for adaptive variation. While connections from basic research to applied wildlife conservation have been slow to develop, these connections are increasingly strengthening. Here we review the primary areas in which population genomics approaches can be applied to wildlife conservation and management, highlight examples of how they have been used, and provide recommendations for building on the progress that has been made in this field. These threats are pervasive and global, so that an estimated 1 million species of plants and animals are at risk of extinction within the next few decades IPBES, . ThreatsSeveral years ago, spurred by technological advances in high‐throughput sequencing, a set of reviews and perspective articles assessed the potential for the field of conservation genomics  – chromosomal regions that have few or no heterozygous nucleotide sites because both chromosome copies derive from a single copy in a relatively recent common ancestor  individuals from Robinson et al. (Felis concolor) individuals. Size classes of ROH correspond to the expected number of generations since the individual’s maternal and paternal lineages shared a common ancestor for that chromosomal region , and it can also provide a backbone for creating a reference genome assembly for the focal species. The growing number of reference genome assemblies is facilitated by large collaborative initiatives focused on taxonomic groups, such as Australian mammals , birds  or all eukaryotes  from water or other environmental samples. Although some genomics techniques such as WGS require DNA samples of relatively high concentration or molecular weight, a growing range of techniques can be applied to low‐quality DNA samples , defined as the size of an ideal, panmictic population that would experience the same loss of genetic variation, through genetic drift, as the observed population. eN is usually smaller than the observed “census” population size (cN), due to a number of factors common in natural populations, particularly wildlife taxa, including fluctuating population size, variance in reproductive success and overlapping generations, although there is wide variation in the eN/cN ratio  and the site frequency spectrum (SFS). They found that RADseq data are effective for precisely estimating eN and for detecting declines in eN over contemporary timescales (20 generations), and that LD‐based methods are superior, provided a sufficient sample size of individuals. If a reference genome assembly with data on recombination rate is available, methods to estimate Ne based on LD among linked loci may be even more effective  in Switzerland and found markedly reduced LD‐based estimates of eN in reintroduced populations compared to the source population or the closely related Iberian ibex (C. pyrenaica)  data from two salamander species  agreed with population size changes inferred from mark–recapture data; because this study included ddRAD sequencing on samples collected decades ago, temporal trends in eN could be estimated for these two species using both mark–recapture and ddRAD. Jensen et al. (Chelonoidis duncanensis) samples from a single island in the Galápagos Island from before and after a bottleneck that reduced eN to just 150–200 in the mid‐20th century. They found that the extent and distribution of genetic variation in the historical and contemporary samples was very similar, which they attributed to a successful ex situ head‐start and release programme.Population genomics approaches can be used to estimate ) Figure . Nunziat) Figure  also foun et al.  comparedNe are also affected by historical population structure and migration  uncovered a long‐term decline of the population from an eN of 10,000 well before the last glaciation to <500 after this period, indicating that this population has been declining for thousands of years. Two subspecies of gorilla also provide an illustrative contrast: in Graur's gorilla (Gorilla beringei graueri), population declines have led to loss of genetic diversity and increased inbreeding, while the mountain gorilla (G. beringei beringei) population has remained small but genetically stable over the past century  that are predicted to result in energy deficit, muscle weakness, skeletal and cranial anomalies, and reduced aggressiveness. Arguably the strongest evidence for inbreeding depression comes from studies that show a negative correlation between fitness and inbreeding coefficients. Huisman et al. (Cervus elaphus) by examining the relationship between several different fitness metrics and inbreeding coefficients estimated using SNPs. In contrast, inbreeding coefficients calculated from a deep and fairly complete pedigree in the same population found evidence for inbreeding depression for fewer traits  across populations. Given that gene flow infuses new genetic variation into populations, there is also a strong interest in wildlife and conservation biology in understanding the amount of gene flow among populations, particularly those isolated in fragmented landscapes  in eastern Washington state using WGS of representative individuals, which has important implications for conservation of this imperiled species . In another example, mitogenomic  among island populations, which suggests recent human movement of foxes. In these examples, genetic and genomic data confirmed the expected delineation of populations by geography, but also quantified the differentiation among them.The first step in inferring population structure using genetic or genomic data is to delineate populations. What constitutes a population is not always obvious for natural populations, and it is important to distinguish demographic and genetic connectivity  populations in southern California, and the barrier effect of major highway corridors can be seen in the genetic separation of wildlife populations  in the Rocky Mountains of Glacier National Park, Montana, USA. Their analyses supported divergence with gene flow among three genetic clusters since the end of the Pleistocene , which they interpreted as the result of south‐to‐north recession of ice sheets.In other cases, geographical delineation of populations is not as clear. Landscape genetics combines population genetics, landscape ecology and spatial statistics to understand the effects of landscape and environmental heterogeneity on gene flow, genetic variation and microevolutionary processes, and to identify barriers between populations , Jones et al.  allele that has swept to high frequency in parts of the snowshoe hare range with milder winter climates. Adaptive introgression into this species may have allowed it to expand its range following Pleistocene glaciation  and the widely introduced rainbow trout (O. mykiss) in western North America reduces fitness of the native species  is listed as endangered under the U.S. Endangered Species Act (ESA), but recent hybridization with coyotes (Canis latrans) as well as historical hybridization with coyotes and other wolf taxa has resulted in substantial controversy. Nonetheless, Waples et al. (Canis lycaon), also has a complex history including recent hybridization. Heppenheimer et al. . Assuming a heritability of migration timing of 0.5, they predict that adult migration timing will advance by ~10 days in response to a 2°C increase in temperature and that quasi‐extinction risk will only be 17% of that faced by populations with no evolutionary potential. Many wildlife species that are the focus of long‐term studies have pedigree data that can be used to estimate heritability of phenotypic traits  is the ability of a population to evolve genetically based changes in traits in response to changing environmental conditions , an endangered New Zealand passerine  by analysing ddRAD data with GEA. Incorporating this climate‐adaptive potential into forecasts of range changes under climate change reduced projected range reductions, highlighting the importance of taking adaptive potential into consideration in climate change vulnerability predictions. The Figure shows this conceptual framework, reprinted from Razgour et al. (Setophaga petechia). They found that populations that will require the greatest shifts in allele frequencies at these adaptive loci to keep pace with climate change have already experienced the most severe population declines, suggesting that inability to adapt to a changing climate may already be causing declines.Another approach for assessing adaptive potential, particularly in the face of climate change, is to examine patterns of local adaptation to climate conditions across the current species range, and then project future climatic changes and species’ responses  – by applying RADseq to samples collected both before and after the disease appeared in three independent populations that were the focus of long‐term field studies. Signatures of selection in this case are shifts in allele frequency and LD at specific genomic locations, and concordant signatures across populations are evidence for an adaptive response. Similarly, Bi et al. (Tamias spp.) spanning a century and identified significant shifts in allele frequencies. Neither of these studies specifically included phenotypic data on potential adaptive traits; nonetheless, both identified specific candidate genes with known function that may affect fitness under changing selection regimes in natural populations.Within populations, adaptive variation and genomic signatures of selection can be detected if samples are available over multiple generations Gompert, . This isi et al.  applied A complementary approach to determine the genetic basis of adaptative variation in natural populations is genome‐wide association studies (GWAS)  comprise a small, isolated population in Italy. Benazzo et al. , such as evolutionarily significant units (ESUs) and management units (MUs). We define an ESU as a classification of populations that have substantial reproductive isolation and adaptive differences so that the population represents a significant evolutionary component of the species  listed under the U.S. ESA did not actually merit listing as a discrete population, while a second  harboured previously unknown genetic differentiation. Genomics studies have more power than previous microsatellite studies to quantify overall (genome‐wide) population differentiation; for instance, McCartney‐Melstad et al. (Rana boylii) and found five extremely differentiated clades that can serve as management units for this species of conservation concern. Barbosa et al. (Microtus cabrerae): ESUs on the basis of overall differentiation, MUs on the basis of differentiation at neutral loci and adaptive units (AUs) on the basis of outlier loci  and steelhead (O. mykiss). Thompson et al. , and hands‐on training in population genomic analysis, including the ConGen workshop at the University of Montana's Flathead Lake Biological station (http://www.umt.edu/sell/cps/congen2019/), the Genomics of Disease in Wildlife workshop at Colorado State University (https://gdwworkshop.colostate.edu/), and a variety of workshops given across Europe by the G‐BIKE (Genomic Biodiversity Knowledge for Resilient Ecosystems) programme (https://sites.google.com/fmach.it/g‐bike‐genetics‐eu/home).Another recommendation for improving the translation of population genomics into improved wildlife conservation and management is training for both aspiring population genomics students and conservation practitioners, ideally together to foster direct interaction between these groups. Population genomics workshops, for example, not only provide technical training in the ever‐expanding field of genomics; they can also provide opportunities for conservation practitioners to gain exposure to the field to give them a better appreciation of the capacity of population genomics, the steps involved, and how to apply it to the species they manage and the questions they face. Fortunately, several genomics workshops now provide venues to discuss the latest developments in population and conservation genomics, such as the annual Population and Conservation Genomics workshop at the International Plant and Animal Genomes Conference  since genomic data have been applied to population genetic questions in nonmodel organisms, population genomics has already helped answer a wide variety of questions in the biology of wildlife species. There has been a relatively slow uptake of population genomics results in influencing policy decisions and wildlife management actions (Shafer et al., Population genomics research is by nature intensive and focused on one or a few species. It has, therefore, been applied to wildlife species that are high‐profile or of significant economic interest, such as captive populations or salmonid fish (Waples et al., A future need in conservation is to understand how population genomics tools can be applied more broadly beyond single focal species, for instance at the ecosystem level (Breed et al., The field of population genomics continues to change rapidly, with technological and analytical advances expanding the tools that are available in wildlife biology at the same time as the need for conservation knowledge and action becomes more urgent. While it may be very difficult to keep up to date with all of the changes, it is critical for both researchers and wildlife professionals to maintain a broad understanding of the population genomics tools that are available and to foster communication between wildlife scientists and practitioners.All authors contributed to writing the manuscript.


Kajikia audax). Assessment of over 4,000 SNPs resolved spatiotemporal patterns of genetic connectivity throughout the species range in the Pacific and, for the first time, Indian oceans. Individual‐based cluster analyses identified six genetically distinct populations corresponding with the western Indian, eastern Indian, western South Pacific, and eastern central Pacific oceans, as well as two populations in the North Pacific Ocean (FST = 0.0137–0.0819). FST outlier analyses identified a subset of SNPs (n = 59) putatively under the influence of natural selection and capable of resolving populations separated by comparatively high degrees of genetic differentiation. Temporal collections available for some regions demonstrated the stability of allele frequencies over three to five generations of striped marlin. Relative migration rates reflected lower levels of genetic connectivity between Indian Ocean populations (mR ≤ 0.37) compared with most populations in the Pacific Ocean (mR ≥ 0.57) and highlight the importance of the western South Pacific in facilitating gene flow between ocean basins. Collectively, our results provide novel insights into rangewide population structure for striped marlin and highlight substantial inconsistencies between genetically distinct populations and stocks currently recognized for fisheries management. More broadly, we demonstrate that species capable of long‐distance dispersal in environments lacking obvious physical barriers to movement can display substantial population subdivision that persists over multiple generations and that may be facilitated by both neutral and adaptive processes. Importantly, surveys of genome‐wide markers enable inference of population‐level relationships using sample sizes practical for large pelagic fishes of conservation concern.Genomic methodologies offer unprecedented opportunities for statistically robust studies of species broadly distributed in environments conducive to high gene flow, providing valuable information for wildlife conservation and management. Here, we sequence restriction site‐associated DNA to characterize genome‐wide single nucleotide polymorphisms (SNPs) in a broadly distributed and highly migratory large pelagic fish, striped marlin ( HoweverOncorhynchus spp., Salmo spp.]; Ayllon et al., Thunnus albacares) from the Atlantic, Pacific, and Indian oceans were either consistent with genetic homogeneity or offered only preliminary evidence for population subdivision  is a large pelagic fish broadly distributed in temperate, subtropical, and tropical waters of the Pacific and Indian oceans . For example, a single ocean‐wide stock is presently recognized in RFMO assessment and management efforts in the Indian Ocean due to insufficient information on population structure. Additionally, uncertain population structure in some regions of the Pacific Ocean is further complicated by continued use of stock boundaries inconsistent with populations evident from available genetic and biological information. Such mismatches may be especially problematic for striped marlin because this species is estimated to be overfished or experiencing unsustainable levels of fishing effort in regions across the Indo‐Pacific IATTC, , 2018. SHere, we employ NGS of restriction site‐associated DNA , Kenya (KEN), and northwestern Australia (WAUS) in the Indian Ocean, and eastern Australia (EAUS), New Zealand (NZ), Japan (JAP), Taiwan (TAI), Hawaii (HAW), southern California (CAL), Baja California (BAJA), Ecuador (ECU), and Peru (PERU) in the Pacific Ocean Table  and main2.2PstI and SphI was selected based on the size of the representation and the fraction of the genome selected. Custom proprietary adapters used in ligation reactions were similar to those described by Elshire et al.  score for each K scenario and selecting those scenarios with the lowest BIC scores to assess with DAPC.We employed multivariate analyses and Bayesian‐based simulations to infer the number and geographic extent of genetically distinct populations of striped marlin represented in our dataset. Multivariate methods were selected for exploring population structure because these methods are computationally efficient and unconstrained by assumptions of Hardy–Weinberg equilibrium  using a dataset in which loci not conforming to the expectations of HWE were removed. Observed and expected heterozygosities were calculated in the R packages FST outliers were removed prior to calculating pairwise levels of genetic differentiation and population‐level inbreeding coefficients. Levels of genetic differentiation among populations were determined by calculating pairwise measures of FST in Arlequin. Statistical significance of FST values was assessed based on 10,000 permutations and a critical value corrected by a modified false discovery rate  of relative migration estimates were calculated based on 10,000 bootstrap iterations.A putatively neutral dataset was also used to infer the degree of genetic connectivity among populations and to identify populations that serve as sources or sinks , spanning approximately five generations of striped marlin. We also evaluated striped marlin sampled off eastern Australia in the year 1994  and in the years 2010, 2011, 2012, and 2015 , the latter of which were pooled to facilitate the valuable comparison of sample collections spanning 16–21 years (approximately three to five generations of striped marlin). Multigenerational stability of allele frequencies for temporally spaced sample collections was assessed by performing multivariate analyses (PCoA and DAPC) using the full dataset as described above, except only a scenario of K = 2 was evaluated with DAPC for each geographic region. This clustering scenario comprised groups of samples corresponding with each sampling period. FST values were also calculated between temporally spaced collections as described above using a dataset in which loci not conforming to the expectations of HWE and selective neutrality were removed.To evaluate the temporal stability of allele frequencies within geographically distant regions, we used collections with sample sizes ≥15 individuals per sampling period, and for which temporally spaced collections spanned at least one generation of striped marlin  were used for population assignment. Analyses were repeated five times for each marker subset, and final results were produced by averaging across iterations.We assessed the ability of SNPs resolved in this study to accurately assign individuals to populations and to identify a minimum subset of loci for population assignment, using the R package 33.1The original DArT PL dataset consisted of 61,908 SNP loci Table . A total3.2Multivariate analyses and Bayesian simulations were used to delineate populations of striped marlin using the full dataset and a dataset in which loci not conforming to the expectations of HWE were removed, respectively. At least five distinct clusters were resolved on PCoA axes one and two, which collectively explained 6.61% of total genetic variation Figure . These cK values of one through twelve ranged from 1,279.44 to 1,307.55 and were lowest for the scenario with K equal to two , but were also low for K equal to three through six . In the scenario with K equal to two, one cluster comprised all Indian Ocean collections plus EAUS and NZ, and a second cluster comprised all remaining Pacific Ocean collections. The scenario with K equal to three also resolved a cluster comprising EAUS, NZ, and Indian Ocean collections, as well as clusters corresponding with striped marlin from the North Pacific Ocean  and from the eastern central Pacific Ocean . These clusters were again resolved in the scenario with K equal to four  and Hawaii  comprised a fifth cluster also apparent in results from PCoA  was resolved, though in some iterations of K‐means clustering this cluster was not apparent until K increased to seven . The four putative migrants sampled off Hawaii and three putative migrants sampled off Ecuador consistently assigned to the cluster comprising striped marlin from the western South Pacific Ocean (EAUS and NZ).Results from DAPC of SNP genotypes revealed hierarchical relationships among multiple genetically distinct groups of striped marlin. BICs generated for  Figures  and S2. n Figure . Scenaril Figure b, exceptK equal to two through seven .The groups of striped marlin resolved in multivariate analyses were also apparent in results from STRUCTURE. Testing of SNPs for conformance to HWE identified 41 loci that violated the expectations of HWE in more than one sample collection; these loci were excluded from STRUCTURE analyses. For STRUCTURE analyses performed with all sample collections, multiple distinct clusters were consistently resolved across scenarios of n Figure . These in Figure . ResultsK for this dataset as four. STRUCTURE analyses limited to samples collected from the Indian Ocean and western South Pacific Ocean resolved at least two distinct clusters , the eastern central Pacific Ocean , and two regions of the North Pacific Ocean , eastern Indian Ocean , western South Pacific Ocean , and eastern central Pacific Ocean  were resolved by PCoA, DAPC, and STRUCTURE analyses, and these regions were recognized as distinct populations in subsequent analyses. Striped marlin from the North Pacific Ocean  were also consistently identified as genetically distinct, as was a second group in the North Pacific Ocean (NPO2) corresponding with JAP2 and HAW2. Population‐level calculations were performed using these six populations of striped marlin. The eight fish identified as putative migrants were retained with their original sample collections so that biologically realistic assemblages of striped marlin could be characterized; however, some calculations were performed a second time with these samples excluded (described below).3.3FST values associated with these SNPs ranged from 0.086 to 0.461, and all loci were candidates for divergent selection (α = .49–2.97). Results from outlier detection analyses performed using Arlequin included the identification of 229 SNPs (5.49%) as putatively under selection  and 155 were candidates for directional selection (per locus FST = 0.089–0.679). Fifty‐nine of the SNPs identified as FST outliers by BayeScan were identified by Arlequin as SNPs likely experiencing directional selection; these SNPs comprised a final list of outlier loci. Six of these loci produced BLASTn hits with expect values <10–10, but annotations for specific genes or gene functions were not returned (results not shown).From the 4,165 SNPs remaining after quality filtering and HWE testing, a genome scan performed using BayeScan identified 61 loci (1.46%) as outliers putatively under the influence of natural selection Figure . FST van Figure . Of thosn = 59) resolved populations that displayed comparatively large degrees of genetic differentiation based on the full dataset , but failed to resolve weakly differentiated populations . These analyses resolved populations that were separated by comparatively large degrees of genetic differentiation based on the full dataset . Rarefaction allelic richness and expected heterozygosity were highest for NPO2 ; these populations correspond with the smallest sample sizes employed in this study . Measures of genetic diversity for EIO were comparable to those for WIO , which comprised a larger sample size (n = 38). This result indicates that the low levels of genetic diversity observed for EIO may reflect low genetic diversity for striped marlin across the Indian Ocean, rather than an artifact of the small sample size for this population. High levels of genetic diversity estimated for NPO2 may accurately reflect elevated genetic heterogeneity for this population, including relative to NPO ; however, a larger number of samples for NPO2 are necessary to evaluate this hypothesis. Additional samples for this population are also necessary to assess mechanisms underlying the large difference between observed and expected heterozygosities . We did not observe any private alleles indicating fixed differences between populations, regardless of whether putative migrants were excluded from calculations. Genetic diversity metrics were also calculated for each sampling location  were excluded to produce a putatively neutral dataset prior to calculating pairwise measures of genetic differentiation, inbreeding coefficients, and relative migration rates. Pairwise FST values ranged from 0.0137 between EIO and WSPO, to 0.0819 between WIO and NPO2  were similar across all three metrics of genetic differentiation  corresponded with gene flow in both directions between ECPO and NPO. High levels of genetic connectivity were also observed in both directions between NPO and WSPO (mR > 0.80), and from WIO to WSPO (mR = 0.71). Relative migration rates between WIO and EIO  were lower than migration rates between most Pacific Ocean populations. Additionally, relative migration rates between WIO and WSPO (mR ≥ 0.58) were higher than those between EIO and WSPO (mR ≤ 0.54), despite the closer geographic proximity of EIO to WSPO. Accurate inference of relative migration rates is difficult under scenarios of high gene flow . Migration rates calculated between Indian Ocean and Pacific Ocean populations of striped marlin indicate that genetic connectivity between ocean basins is primarily facilitated by WSPO. Relative migration rates calculated with putative migrants excluded from analyses produced relationships similar to those described here . These levels of genetic differentiation are considerably lower than those observed for population‐level comparisons (FST = 0.0137–0.0819), in several instances by a full order of magnitude. Additionally, multivariate analyses performed with all samples resolved temporally spaced collections as comprising single groups within the eastern central Pacific and western South Pacific oceans. Collectively, results from comparisons of temporal collections are consistent with the stability of allele frequencies for a minimum of three to five generations of striped marlin in the eastern central Pacific and western South Pacific oceans.Genetic differentiation calculated between temporally spaced collections using a dataset in which loci not conforming to the expectations of HWE and selective neutrality were removed resulted in 3.6SE ± 2.29–4.55) when subsets of ≥200 SNPs were used to assign individuals to populations. Within populations, ≥90% assignment success was possible for ECPO (SE ± 1.40–2.60), WSPO (SE ± 0.00–1.80), and WIO (SE ± 0.00–6.12) across all SNP subsets  for NPO and 60 to 80% (SE ± 10.00–18.71) for NPO2. Low assignment success and high standard errors for NPO2 may result from a comparatively high degree of genetic heterogeneity for this population (see results above). A larger sample size for NPO2 is necessary to improve assignment accuracy for this population.Overall assignment success was ≥90% . Striped marlin larvae have been collected from both western and eastern regions of the Indian Ocean during seasons that at least partially overlap between regions  revealed the presence of genetically distinct populations in the western Indian, western South Pacific, central North Pacific, eastern North Pacific, and eastern central Pacific oceans  identified a single population spanning eastern and western regions of the Indian Ocean; a second population spanning the western South Pacific, central North Pacific, and eastern central Pacific oceans; and a third population in the South China Sea  were observed for five comparisons between sample collections within a population. An additional statistically significant comparison was observed within NPO2 , but in this case, the level of genetic differentiation was nearly three times higher than other statistically significant comparisons within populations. Low but statistically significant FST values were also observed within temporal collections . Aside from the comparison within NPO2, FST values associated with statistically significant comparisons within populations or temporal collections were less than half of those calculated between populations of striped marlin. This observation suggests that such low levels of genetic differentiation may not be biologically meaningful and may instead represent random sampling error due to the high level of statistical power associated with surveying a large number of genome‐wide molecular markers, but comparatively low power corresponding with relatively small sample sizes per sample collection.The populations of striped marlin resolved in this study were separated by levels of genetic differentiation that were also highly statistically significant. However, in some instances, comparatively low levels of genetic differentiation were also statistically significant. Low but statistically significant Genetic differentiation between populations of marine fishes is expected to be lower than in freshwater and anadromous fishes  for many marine fishes are very large relative to terrestrial or freshwater species and may also correspond with comparatively large effective population sizes , but those populations were not apparent in analyses based on putatively neutral loci . Further investigation is necessary to characterize mechanisms  underlying the relative capabilities of neutral genomic variation and variation influenced by natural selection to resolve populations of highly migratory large pelagic fishes. Such studies will ultimately inform our presently limited understanding of ecological and environmental variables, phenotypes, and loci involved in regional adaptive processes for large pelagic species.Population census sizes  are small and may contribute uncertainty to estimates of population‐level relationships . Similarly, temporal replicates for striped marlin from some locations (EAUS and ECU) facilitated evaluation of the multigenerational stability of allele frequencies for populations in these regions (WSPO and ECPO). However, a lack of replicate collections for other regions prohibited exploration of temporal patterns for additional populations. Though these limitations reflect persistent challenges to implementing biologically representative sampling designs for large pelagic fishes, surveys of genome‐wide variation enable robust assessments of population‐level relationships using sample sizes that are more tractable than those required of traditional markers. Additionally, results from genomic surveys such as that implemented in this study can inform targeted spatiotemporal sampling efforts for future studies of large pelagic fishes.4.5e and corresponding losses of genetic diversity (Allendorf, Berry, & Ryman, A prerequisite for sustainably managing wild populations is delineating management units that correspond with biologically distinct assemblages (Reiss, Hoarau, Dickey‐Collas, & Wolff, In the case of striped marlin, considerable mismatch exists between stocks recognized for fisheries management and what is known regarding the life history and spatial genetic structure of this species Figure . In the Results from this and other similar studies equip resource managers to incorporate information on genomic variation into evolutionary‐based management plans for highly migratory large pelagic fishes. Preservation of both neutral and adaptive genomic variation is essential to maintaining the adaptive potential of populations and their ability to withstand fluctuations in ecological and environmental conditions (Funk, Forester, Converse, Darst, & Morey, 4.6This and other recent surveys of genome‐wide variation in highly migratory large pelagic fishes present some of the first genomic resources for these species, particularly for those found at comparatively lower frequencies or of lesser commercial value. Results from this study provide evidence for six genetically distinct populations of striped marlin in the Indo‐Pacific and are valuable for improving rangewide conservation and management efforts for this species. Additional work that employs fine‐scale spatiotemporal sampling is necessary for identifying regional stock boundaries and whether these boundaries are seasonally dynamic. Such efforts will also help determine the contributions of distinct stocks to mixed‐stock fisheries. Studies that implement tagging technology capable of monitoring detailed movements over periods longer than 1 year are presently lacking, but necessary for elucidating seasonal movement patterns within and between ocean basins, and for determining the degree to which populations of striped marlin display spawning site fidelity. Equivalent genomic and tagging efforts across large pelagic fishes will provide valuable insights into broad‐scale patterns of connectivity within and between ocean basins, and ecological and evolutionary factors influencing genetic connectivity in pelagic communities (Hand, Lowe, Kovach, Muhlfeld, & Luikart, None declared. Click here for additional data file.