Origanum vulgare subsp. hirtum (Link) Ietsw. in Bulgaria using simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers and gas chromatography/mass spectrometry (GC/MS) analysis of flower volatiles from individual plants. Two regions, including the Kresna Gorge and Eastern Rhodopes, typical for the species comprising eight populations and 239 individual plants were included in this study. An analysis with 11 SSR markers and eight SRAP primer combinations showed that SRAP markers were substantially more informative than the SSR markers and were further used for genetic diversity analysis. The results showed low-range to mid-range genetic differentiation between the populations with pairwise fixation index (Fst) values ranging between 0.0047 and 0.11. A total of 10 genetic clusters were identified. An analysis of the flower volatile diversity identified a total of 63 compounds with the vast majority of plants belonging to the carvacrol chemotype and just a single plant to the thymol chemotype. Large deviations were observed for individual compounds within each region as well as within the populations. Hierarchical clustering showed a clear sample grouping based on the two different regions. In addition, an in-depth analysis identified six major and 23 minor metabolite clusters. The overall data set and cluster analysis were further used for the development and testing of a simple and straightforward strategy for the selection of individual plants for the development of a core collection representing the sampled natural populations for this species in Bulgaria. The proposed strategy involves precise genetic clustering of the tested plants followed by the selection of a minimal set from each genetic cluster representing the different metabolite clusters. The selected core set was further compared with a core set extracted by the PowerCore software. A comparison of the genetic and metabolic affiliation of the members of both sets showed that the reported approach selected representatives from each genetic cluster and minor metabolic cluster, whereas some metabolic clusters were unrepresented in the PowerCore set. The feasibility and efficiency of applying the pointed strategy for the development of a core collection representing both the genetic and metabolite diversity of natural populations in aromatic and medicinal plants toward subsequent steps of selection and breeding are discussed.We studied the genetic and flower volatile diversity in natural populations of  Origanum vulgare subsp. hirtum (Link) Ietsw. also known as Greek oregano, which has high essential oil content  from epidermal glands of O. vulgare. The set of developed SSR primers has not yet been applied in population genetic studies, and its application for intraspecific differentiation of natural populations needs to be evaluated. As an affordable alternative to the SSR markers, sequence-related amplified polymorphism (SRAP) markers, developed by Li and Quiros  analysis of flower extracts from individual plants were applied for the characterization of the genetic and metabolic diversity of the populations. The obtained data were used for separate genetic and metabolic clustering of the studied plants and subsequent application of the cluster affiliation data for setting up a simple strategy for the selection of a core collection of individual plants well representing both the genetic and metabolic diversity of the sampled populations. The content of the selected core set was compared with that of a core set extracted by the PowerCore software. The application of such a strategy for the routine characterization and sampling of natural populations from medicinal and aromatic plants is discussed.The present study focuses on the analysis of the genetic and flower volatile diversity in natural populations of O. vulgare subsp. hirtum (Link) Ietsw. in Bulgaria—two in the Kresna Gorge region (July 2018) and six in the Eastern Rhodopes region, near the town of Ivaylovgrad (July 2019)  . The plaThe frozen leaf samples were ground to a fine powder in stainless steel grinding jars by using liquid nitrogen and Qiagen TissueLyser II Mill at 30 Hz for 2 min. Genomic DNA was purified according to the cetyl trimethylammonium bromide (CTAB) protocol , 1 μl of each primer (forward and reverse) with a working concentration of 10 μM from each respective multiplex primer set, 10 μl MyTaq HS DNA Polymerase 2x Mix (Bioline), and Type I ultrapure water to a final volume of 20 μl. The parameters of PCR amplification included: initial denaturation for 5 min at 94°C, followed by 35 cycles at 94°C for 30 s, 58°C for 30 s, and 72°C for 30 s, with a final elongation at 72°C for 10 min.PCR reactions for the analysis of SRAP markers were carried out as described earlier by Zagorcheva et al. . The resFragment analysis was performed on an automated capillary sequencer 3130 Genetic Analyzer  by using 36-cm long capillaries, POP-7 polymer , and GeneScan™ 500 LIZ™ as a size standard. GeneMapper Analysis Software v4.0  was used for fragment sizing. For SSR marker analysis, alleles were reported as base pairs. The SRAP marker analysis data were collected in the range of 100–950 base pairs, and only fragments whose fluorescent signal was higher than 300 relative fluorescence units were reported and transformed into a binary matrix of 1 and 0 for each allele corresponding to their presence or absence, respectively. The generated binary matrix was manually curated for the actual presence or absence of the respective allele peaks by a visual inspection of the electropherograms.The extraction of volatile compounds from the flower samples collected in GC headspace vials was assisted by vortexing with multi-tube vortexer (VWR) for 30 min. Then, 200-mg anhydrous sodium sulfate (Sigma) was added to each vial to remove the water contained in the samples. After vortexing for another 10 min, 1 ml of the resulting extract from each sample was transferred to a 2-ml vial for GC/MS analysis. GC/MS analyses were performed on an Agilent 7890A/5975C GC/MS system as described earlier . Microsoft Excel 2019 was used for building box-and-whisker plots. ArcMap 10.6.1  was used to build geographic maps of populations. Student's t-test (IBM SPSS statistics version 25) was used when comparing the mean of the selected parameters of a core collection with the whole data set. PowerCore 1.0  of each SSR primer pair was calculated by using PowerMaker   . The aveO. vulgare subsp. hirtum were analyzed at 11 loci generating a total of 72 alleles. The poor differentiation between the populations may be the result of an insufficient number of alleles. Additionally, the SSR primer pairs used in this study are derived from ESTs, which may result in the detection of a lower number of polymorphisms, compared to genomic SSRs in non-coding regions. The detection of lower polymorphism using EST-SSRs in comparison to nuclear SSRs has been reported in studies including diverse plant species like sugarcane  markers. The results revealed that genetic variability was distributed mainly within populations, however, there was also noteworthy genetic differentiation between different geographical localities. The results from the present study show that, in general, the genetic diversity of O. vulgare subsp. hirtum in Bulgaria is largely determined by the two separate regions characteristic of this species as the main part of the variation within each region is mainly due to a variation within the populations.Katsiotis et al.  studied O. vulgare subsp. hirtum plants in eight natural populations resulted in the identification of a total of 63 compounds, representing an average of 84% of the total area of the chromatograms. Despite the relatively large number of identified compounds, only 10, including α-thujene, α-pinene, myrcene, α-terpinene, p-cymene, γ-terpinene, thymoquinone, caryophyllene (E-), β-bisabolene, and carvacrol, of them had a mean value of more than 1%  CQ Yuan and RH Shan  data. Following SRAP analysis and identification of groups of plants with a similar genetic constitution, a second visit to the natural populations should be carried out during the period of active accumulation of metabolites of interest, which, for most of the plants, overlap the active flowering period. Then, the samples from organs and tissues accumulating the metabolites of interest should be collected and subjected to metabolite analysis. This second visit could also be used for an additional increase in the number of samples from particular populations showing higher genetic diversity and heterogeneity of the population structure. By following the above pointed data processing, the plants to be included in the core collection could be identified within the same vegetation period. Depending on the plant species and planned method for the establishment of a core collection, seeds or plant samples for in vitro or in vivo multiplication should be collected for subsequent growing in experimental fields and a more detailed characterization of the core collection. Considering the fact that SRAP markers could be universally applied to a wide range of plant species, and the methods for general metabolite analysis [GC/MS and/or liquid chromatography/MS (LC/MS)] are largely available for most of the medicinal and aromatic plants, we propose that the pointed strategy could also be routinely applied for the development of core collections from natural populations of a wide range of medicinal and aromatic plants, which are of interest for breeding and (semi-) industrial cultivation.Based on the obtained results, we propose the described here strategy for the development of a core collection for O. vulgare spp. hirtum (Link) Ietsw. The currently available EST-derived SSR markers do not provide enough resolution to allow for genetic differentiation of the populations, and further efforts will be necessary to identify SSR markers, which are suitable for population genetics studies in this species. The results from SRAP analysis showed low-range to mid-range genetic differentiation between the studied O. vulgare subsp. hirtum populations in Bulgaria and pointed out that the genetic diversity is determined by the two separate regions characteristic of the species as the main part of the variation within each region is mainly due to a variation within the populations. GC/MS analysis of flower volatiles from individual plants confirmed that the populations in Bulgaria belong to the carvacrol chemotype although single plants belonging to the thymol chemotype could occasionally be discovered. Large variations of the flower volatiles were observed between the two regions as well as among and within the populations. Based on the genetic diversity data generated with SRAP markers and the metabolic diversity evaluation via GC/MS analysis of flower volatiles, a simple and straightforward strategy for the development of a core collection representing both the genetic and metabolic diversity of the sampled plants was established, which prevents potential failure to identify and select plants with a unique metabolite composition. The strategy employs separate genetic and metabolic clustering of the analyzed plants and the combination of the results for the selection of plants representing all identified genetic and metabolite clusters. The efficiency of the proposed strategy for the selection of a core set was further demonstrated by a comparison with the application of the dedicated software PowerCore, which showed that the PowerCore elaborated set left unrepresented a number of minor metabolic clusters within the identified genetic clusters. Accordingly, we propose that the same strategy could be routinely applied for the development of core collections mirroring the genetic and metabolite diversity in natural populations of other medicinal and aromatic plant species, where the value of selected plants is often related to the abundancies of a small set of metabolites.The results from the present study show that SRAP markers proved to be a reliable and an affordable tool for the analysis of the genetic diversity and population structure in natural populations of The original contributions presented in the study are included in the article/MA was the main person involved in the study, performing all experimental work which later became the main part of her Ph.D. Thesis. TZ contributed to the analysis of SRAP marker data. MR was involved in SSR and SRAP fragment analysis by capillary electrophoresis and analysis of SSR data. KR was the supervisor of MA during her experimental work and contributed to all data analysis. IA was involved in the whole experiment and workflow design. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Anopheles minimus has been influenced by external stresses affecting the survival rate and vectorial capacity of the population. Since An. minimus habitats have continuously undergone ecological changes, this study aimed to determine the population genetic structure and the potential gene flow among the An. minimus populations in Thailand.The malaria vector Anopheles minimus was collected from five malaria transmission areas in Thailand using Centers for Disease Control and Prevention (CDC) light traps. Seventy-nine females from those populations were used as representative samples. The partial mitochondrial cytochrome c oxidase subunit I (COI), cytochrome c oxidase subunit II (COII) and cytochrome b (Cytb) gene sequences were amplified and analyzed to identify species and determine the current population genetic structure. For the past population, we determined the population genetic structure from the 60 deposited COII sequences in GenBank of An. minimus collected from Thailand 20 years ago.An. minimus were genetically divided into two lineages, A and B. Lineage A has high haplotype diversity under gene flow similar to the population in the past. Neutrality tests suggested population expansion of An. minimus, with the detection of abundant rare mutations in all populations, which tend to arise from negative selection.The current populations of An. minimus lineage A was similar between the past and present populations, indicating high adaptability of the species. There was substantial gene flow between the eastern and western An. minimus populations without detection of significant gene flow barriers.This study revealed that the population genetic structure of The online version contains supplementary material available at 10.1186/s13071-021-04998-7. Anopheles minimus is one of the principal malaria vectors in Southeast Asia, including Thailand [An. minimus lineage A, An. harrisoni and An. yaeyamaensis [Anopheles minimus has an anthropophilic preference and is an important vector of indoor malaria transmission [Thailand . The minamaensis . In Thaiamaensis . This spamaensis –4. Anophsmission , 5. Extesmission . These esmission , 7. Suchsmission .An. minimus in Vietnam appears to have been affected by genetic differentiation between populations, forced by ecotypic selection based on intraspecific behavioral differences and ecology in specific habitats [COI gene sequence has been used to assess historical and current gene flow among An. albimanus populations in the Caribbean and the Pacific regions of Colombia, which showed high genetic differentiation influenced by specific ecological conditions, human migration, and activity [An. minimus in Southeast Asia using an analysis of partial mitochondrial cytochrome c oxidase subunit II (COII) [An. minimus revealed that two different lineages, A and B, coexist in malaria transmission areas. These two lineages may split and expand to facilitate adaptation under different eco-climatic conditions. A previous study has shown that lineage A spread throughout Thailand, while lineage B was restricted to certain areas [The local adaptation of habitats . The paractivity . Chen etI (COII) . Reconstin areas .An. minimus [An. sinensis [An. baimaii [An. dirus [An. lesteri [An. darling [An. stephensi [Ae. aegypti [Ae. albopictus [The mitochondrial protein genes are appropriate molecular markers to determine the population genetic structures of species. Mitochondrial DNA (mtDNA) sequence data have been used to investigate the genetic variations and phylogenetic relationships of different species and to accurately assess gene flow and differences among populations –17. Seve minimus , An. sinsinensis , An. bai baimaii , An. dirn. dirus –23, An.  lesteri , An. dar darling , An. stetephensi , Ae. aeg aegypti , 28, andbopictus , 30.An. minimus in Thailand in the past 20 years. Therefore, the aims of this focused population genetic study were (i) to determine the current population genetic structure and the potential gene flow by investigating genetic differentiation among the populations of An. minimus and (ii) to compare the population genetic structure of between the current and past An. minimus population of 20 years ago. The understanding of the population genetic structure and the potential gene flow among the populations gained from this study can be used as molecular tools for monitoring mosquito populations and developing guidelines for malaria vector control strategies in Thailand. Such information can also be used to measure and monitor gene flow or spread of vector populations after applying a control measure.Populations experience different selective pressures due to changing environmental factors and human behavior. The mitochondrial protein-coding genes were used to investigate whether there has been any differentiation of the genetic structure of An. minimus were collected from five different populations in malaria transmission areas along the western and eastern borders of Thailand, including Tak, Surat Thani, Yala, Ubon Ratchathani, and Chanthaburi-Trat provinces. The collection sites were selected according to the malaria operational plan report FY2018 [An. minimus sensu lato were selected and further used for molecular identification to confirm the species identification.A total of 79 adult female t FY2018  t FY2018 . All speAn. minimus was extracted from the abdomens of the mosquitoes using the Genomic Mini Kits  following the manufacturer's instructions, except for the change of the lysis period to overnight at 60 °C. The partial mitochondrial genes COI, COII, and Cytb were used as genetic markers for molecular identification. The primers were designed based on the An. minimus complete mitochondrial genome (GenBank: KT895423) and gene-specific sequences with the most informative regions of each gene  and 54 °C (for primers of COI) for 40 s and extension at 72 °C for 50 s, and a final extension at 72 °C for 8 min. The PCR products were subsequently purified using Gel/PCR DNA fragments extraction kits . The purified PCR products were sequenced with the same primers as conventional PCR by Sanger sequencing using a 3730xl analyzer , serviced by Bio Basic, Inc., Singapore.The PCR amplifications were performed in a final volume of 20 µl containing genomic DNA, 1× OnePCR™ PCR reaction mixture , and 10 pmol of forward and reverse primers for each genetic marker  using the maximum likelihood (ML) method implemented in the MEGA 7 program [An. harrisoni were the results of previous studies retrieved from the GenBank database with accession numbers HQ877375–HQ877377 (COI), AF421307, AF421309, AY486111, AF417707, KT899887, KF687432 (COII), and EU071695–EU071697 (Cytb) [Anopheles dirus was used as an outgroup with accession numbers AF417707 (COI), KT899887 (COII), and KF687432 (Cytb) [COI), MT663561–MT663639 (COII), and MT663640–MT663718 (Cytb) were also included as representative sequences of lineage A and lineage B  –40. Anop2 (Cytb) –41. The An. minimus population, haplotype diversity (Hd) and nucleotide diversity (π) were calculated using the DnaSP program, version 6 [To determine the genetic diversity within the ersion 6 . The hapersion 6 .F-statistics (FST) were computed based on the variance in allele frequencies to detect genetic differentiation among the populations using the ARLEQUIN 3.5.1.2 program [Nm) using the FST variances. Analysis of molecular variance (AMOVA) was conducted among the different geographical populations using ARLEQUIN to calculate the proportion of genetic variation within and between populations.Pairwise  program . The levSSD) between the observed and expected mismatch distribution was performed as a test statistic to reflect a significant SSD (P < 0.05) value of historical demographic population expansion or contraction. Historical demographic expansions were also determined by neutrality tests conducted using two approaches, Tajima’s D [Fs [The frequency distribution of pairwise nucleotides was different between the observed and the expected distribution (mismatch distribution) under the expansion model of population demography implemented in ARLEQUIN  to examijima’s D  and Fu's’s D [Fs  tests, rAn. minimus between the present and 20 years ago, 60 sequences of An. minimus populations in Thailand were investigated using population genetic structure analysis, tests of neutrality, and demographic history events using ARLEQUIN, using the COII gene from the GenBank database accession numbers FN433526–FN433595 , Surat Thani (n = 20), Yala (n = 14), Chanthaburi-Trat (n = 24), and Ubon Ratchathani (n = 6)  than An. minimus lineage B  of 0.00639 and at least 0.00295, respectively. The haplotype diversity of each population ranged from Hd = 0.8 to 1  was lower than that of lineage A (π = 0.003 to 0.008)  to 14 (Chanthaburi-Trat) haplotypes of lineage A and four to five haplotypes of lineage B . The level of genetic differentiation of the Chanthaburi-Trat population was also divergent from other populations of lineage B  . The high level of migration (Nm) in the range of 1 to 2.5 was estimated from FST variances, indicating considerable gene flow among the populations  of both lineages A and B were determined for the populations in different sides of the country (the western and eastern parts) and by geographic regions . These populations were significantly different, with a low value of FST. A high level of migration (Nm > 1) of An. minimus between populations was also measured by the FST indices  indicated that the goodness of fit of the observed and expected mismatch distribution models is likely to be the same, leading to the acceptance of the population expansion model. Tajima’s D tests were not statistically significant for negative D values .As depicted in Fig. ns Table . A nonsies Table , whereasCOII gene, the An. minimus population genetically separated into lineages A and B in the course of the past 20 years. The comparison of the genetic distances between the populations of lineages A and B had similar patterns; the distance was 2.2% in the past population and 2.4% in the current population. Analysis of the genetic diversity of the entire population in previous studies compared to the current population indicated that the level of haplotype diversity remained the same, with high haplotype diversity in the range Hd = 0.6–1 in every population. However, the nucleotide diversity was low (π = 0 to 0.01).According to the analysis of the D and Fs neutrality test values, and the lack of significance between the observed and expected distribution of mismatch analysis  The population tends to undergo demographic expansion with no constraints from a geographic distance or geographical barriers. (3) These populations experienced exposure to negative selective pressure. The negative selection maintained their genetic structure by removing deleterious mutations [To reduce bias in marker selection, a combination of several mtDNA gene sequences was used to describe the overall population structure. Assessment of the population genetic structure using single-gene and multi-gene concatenated markers found that these populations present the same population structure, as follows. (1) The recent utations . Hence, utations , 55.An. subpictus, An. peditaneatus, and An. vagus from five different localities of Sri Lanka [An. sinensis in China [An. baimaii in northeast India [An. dirus in Southeast Asia and China [An. minimus across China, Thailand, and Vietnam [Mitochondrial protein-coding genes have been used to evaluate the selective pressure acting on mosquitoes’ mitogenomes . Severalri Lanka , An. sinin China , An. baist India , An. dirnd China –23, and  Vietnam , have beAn. minimus lineages A and B in the current population was observed in the same habitat of the active transmission area. However, it is not clear whether they differ in vectorial capacity for malaria transmission. The dominant lineage in the An. minimus populations was lineage A. Therefore, the abilities for genetic exchange between An. minimus lineages A and B were considered, with the noticeable genetic differences between the two lineages and the potential for gene flow among the populations evaluated separately. This study identified the distribution of both lineages in western and eastern Thailand. Earlier studies found lineage B only in western Thailand and suggested expansion through India [The coexistence of gh India , 56.A similar, low to moderate genetic differentiation level was observed in both lineages A and B, indicating possible gene flow between the populations in both lineages. No differences were observed in the pairwise comparison of the separated groups in the western and eastern regions, despite separation by land-use and urbanization changes. The low levels of genetic differentiation between the western and eastern populations suggest the lack of significant geographical barriers limiting gene flow. Alternatively, these populations might have exchanged genetic materials before being separated by urban areas as barriers, while gene flow between these populations could have been assisted by human activities. In previous studies, gene flow among malaria vectors was observed due to the absence of geographic distance and geographical barriers , 57–60. D and Fu’s Fs) [An. minimus experienced a population expansion. Population expansion was also suggested by the relatively high haplotype diversity and low nucleotide diversity observed, which indicated that the recent populations diverged from each other by rapid demographic expansion [An. dirus and An. aconitus populations in Southeast Asia experienced population expansion under significant negative selection [Demographic inference tests (Tajima’s Fu’s Fs)  revealedxpansion –64. All election , 65.An. minimus populations of 20 years ago had high genetic diversity, and we found evidence of population expansion. The mosquito populations in Southeast Asia are also affected by human activities such as deforestation and vector control, shaping the distribution and genetic variation within species [An. minimus in Thailand was similar to the structure detected 20 years ago, despite the changes in environmental factors over time [The  species . Hence,  species . Externaver time , 48. Forver time .An. minimus populations in Thailand might be impacted by environmental factors, enhancing gene flow among the populations studied, consistent with previous studies [Mosquito populations can rapidly adapt in response to changes in environmental conditions, such as climate change and human activities, which might influence the mosquito's survival rate, population density, and ecological distribution . Therefo studies –72. The  studies , 73. The studies . The inc studies –77. ThesAnopheles spp., potentially leading to increases in the vector density and the re-emergence of An. minimus [An. funestus populations have been shaped by various factors, not only geographical distance, but also the consequence of different breeding sites, mosquito migration, environmental changes, and human activities [Human activities and land-use change could have driven intraspecific divergence . Accordi minimus . Similartivities . Finallytivities –83.An. minimus. This study showed that lineage B had a population structure similar to lineage A. This study has limitations in collecting representative populations in eastern Thailand. Further studies are required to increase the sample sizes, and seasonal sampling design by generations may provide accurate results about the specific rate of population growth and dynamics. This study investigated the demographic history of population expansion, but cannot specify the direction and rate of population growth, a topic worthy of attention in future studies.The analysis of genetic differentiation between the populations in lineage B is still incomplete. There is little information on vector biology between the different genetic lineages of An. minimus in both eastern and western Thailand. Gene flow was apparent among the geographically distant An. minimus populations, with no evidence of impact by external selective pressure, environmental changes, and geographical barriers. In addition, the population genetic structure of An. minimus populations was persistent in the past 20 years. Such information may be useful for developing and implementing local malaria vector control strategies and monitoring population spread in the face of control interventions.This study identified the coexistence of two lineages of Additional file 1: Table S1. GenBank accession numbers as used for primer design.Additional file 2: Table S2. Primer list of mitochondrial genetic markers used in this study.Additional file 3: Table S3. The GenBank database accession numbers of cytochrome c oxidase subunit I (COI) gene.Additional file 4: Table S4. The GenBank database accession numbers of cytochrome c oxidase subunit II (COII) gene.Additional file 5: Table S5. The GenBank database accession numbers of cytochrome b (Cytb) gene.Additional file 6: Table S6. The GenBank database accession numbers of cytochrome c oxidase subunit II (COII) gene as the 20 years An. minimus population sequences.Additional file 7: Figure S1. The maximum likelihood (ML) tree of two different genetical lineages of Anopheles minimus, the An. harrisoni as in the red circle and the An. dirus used as outgroup. Bootstrap values less than 50% were excluded in the phylogenetic tree. a The ML tree of the COI gene. b The ML tree of the COII gene. c The ML tree of the Cytb gene. The labels in the tree include haplotype codes, and color indicates the different populations.Additional file 8: Table S7. Number of individuals, number of haplotypes, haplotype diversity (Hd), nucleotide diversity (π).Additional file 9: Table S8.FST comparisons for all the five populations.Additional file 10: Figure S2. Fluctuations in environmental factors enhance gene flow, including temperature change, land-use change, urbanization, deforestation, plantation, and insecticide use.Additional file 11: Figure S3. Fluctuation in environmental factors from the past to the present. a Fluctuation of environmental factors . b The average of mean daytime temperature from 2000 to 2019. c Average mean nighttime temperature from 2000 to 2019.


The corrected r2 between SNPs decreased with increasing physical distance from 100 Kb to 1 Mb. The LD values for IRI, ANA, EGY, and PAK populations were 0.16, 0.23, 0.24, and 0.21 for less than 100Kb, respectively, which reduced rapidly to 0.018, 0.042, 0.059, and 0.024, for a distance of 1 Mb. In all the populations, the decay rate was low for distances greater than 2Mb, up to the longest studied distance (15 Mb). The r2 values for adjacent SNPs in unrelated samples indicated that the Affymetrix Axiom 90 K SNP genomic array was suitable for GWAS and GS in these populations. The persistency of LD phase (PLDP) between populations was assessed, and results showed that PLPD values between the populations were more than 0.9 for distances of less than 100 Kb. The Ne in the recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results.Linkage disequilibrium (LD) across the genome provides information to identify the genes and variations related to quantitative traits in genome-wide association studies (GWAS) and for the implementation of genomic selection (GS). LD can also be used to evaluate genetic diversity and population structure and reveal genomic regions affected by selection. LD structure and Ne were assessed in a set of 83 water buffaloes, comprising Azeri (AZI), Khuzestani (KHU), and Mazandarani (MAZ) breeds from Iran, Kundi (KUN) and Nili-Ravi (NIL) from Pakistan, Anatolian (ANA) buffalo from Turkey, and buffalo from Egypt (EGY). The values of corrected  Recognizing and protecting the genetic diversity of domestic species is important in the development of breeding strategies . Recent The non-random association between alleles at different loci is referred to as LD or gametic phase disequilibrium. Knowledge of the pattern of LD in a population is an important prerequisite for GWAS, exploring population structures, and implementing genomic selection (GS) . The patr2 is less affected by allelic frequency and sample size than D'  by inferring the distribution of recombination events. LD can also be used to evaluate diversity and population structure and to identify genomic regions affected by selection . The patBuffaloes were introduced into Egypt from India, Iran, and Iraq during the seventh B.C. . The thrThe present study describes genetic diversity, LD between adjacent SNPs, the trend of LD with increasing distance, and the patterns of PLDP and Ne using genomic data from buffalo breeds of Turkey, Egypt, Pakistan, and Iran, which are genetically closer together than other water buffaloes across the world .The present study used data for 83 water buffaloes, including 14 Azeri (AZI), 11 Khuzestani (KHU), and eight Mazandarani (MAZ) from Iran, 12 Anatolian buffalo (ANA) from Turkey, nine Kundi (KUN), and 14 Nili-Ravi (NIL) from Pakistan, and 15 Egyptian buffalo (EGY) to assess LD structure and calculate Ne .IND and CRSNP), monomorphic genotypes, and genotypes with minor allele frequency (MAF) less than 5% were eliminated. MAF and missing genotypes of individuals and SNPs were filtered separately for each genotypic group. Then, the genomic data of all genetic groups were integrated, and the common genetic markers were identified. Finally, the SNPs that were not in the Hardy-Weinberg equilibrium were excluded, and the missing genotypes were imputed using BEAGLE software  that were mapped to the bovine sequence (UMD3.1 Bos Taurus) . Detailssoftware .ST), and population admixture were used to obtain a general overview of the structure of each population and identify animals falling outside their breed group. DAPC, PCA, and FST were performed using the adegenet package (http://www.rproject.org/), respectively. Additionally, the genetic structure of the populations was evaluated using ADMIXTURE software , principal component analysis (PCA), Weir and Cockerham unbiased fixation index  in population A and rij(B) in population B, SA and SB are the standard deviation of rij(A) and rij(B), respectively, and rA and rB are the average rij across all SNP i and j within the common set of markers.To determine LD decay, paired markers that were common to all populations were grouped at distances between 0 and 15 Mb at 100 Kb intervals, and the mean r2 indicates the mean of LD for a given distance, and c is the distance between markers in Morgan  , where NBefore frequency and genotyping pruning, there were 89,988 SNPs and 89 individuals. In the first step, six individuals were removed for low genotyping success (MIND >0.05), 637 markers were excluded based on HWE (p≤5.7e-007), and 7,618 SNPs for missing information (GENO >0.05). A total of 83 individuals with 82,043 SNPs passed the first step of QC; the total genotyping rate of these remaining individuals was 0.985. In the second step, MAF was assessed in each population separately, and SNPs with MAF>0.05 were removed . Then, tUnderstanding of population genetic structure is important to assess population stratification for GWAS, breeding program design, and developing strategies for genetic resources preservation. DAPC, PCA, and admixture analysis results were used to assess population structure. Both PCA and DAPC methods gave similar results. In both methods, genotype data formed three distinct clusters in the first two PCs. The ANA population from Turkey was partially separated from the Iranian cluster, which includes AZI, KHU, and MAZ . The firST among the studied populations (Supplementary Table 1); in most cases, the difference between pairs of populations was less than 0.05, indicating low genetic differentiation according to Wright’s classification. The reason for this is because there was high within, compared with between-population variance. However, the FST results confirmed the DPCA and PCA analyses by separating the populations into three genetic groups. The mean FST value across populations was 0.045 and varied from 0.011 for AZI from Iran and ANA from Turkey to 0.077 for MAZ from Iran and KUN from Pakistani. The smallest genetic distance was between the Iranian buffaloes and ANA from Turkey, while the largest distance was between the Iranian buffalo and KUN and NIL from Pakistani.There were small differences in FPopulation structures were investigated using ADMIXTURE software, assuming K as ancestral populations ranging from one to seven. Based on cross-validation error criteria, K = 2 and three had suitable resolution . The firr2 and D′ for adjacent SNPs in the populations for each chromosome (see S1 r2 for the pooled Iranian breeds (IRI), ANA, EGY, and PAK populations were 0.24, 0.28, 0.27, and 0.22, respectively  in the pooled IRI, which has 33 individuals, but from 0.35 to 0.28 (around 0.07 units) in ANA with 12 individuals, 0.34 to 0.27 (around 0.07 units) in EGY with 15 individuals, and 0.27 to 0.22 (around 0.05 units) in PAK with 23 individuals  were 46, 52, 51, and 47%  population had the highest D' (0.74), while the EGY had the lowest (0.64).The mean and standard deviation of D′, which represents the frequency of recombination events between adjacent SNPs, was 0.74, 0.67, 0.64, and 0.72 for IRI, ANA, EGY, and PAK, respectively see . A D′ var2. Factors such as sample size, the threshold for the frequency of rare alleles, the density of SNP, and the distances between markers will also affect the results. Further, the way that samples are selected may distort the diversity estimated for a population. A study on pig breeds using a 50 K SNP array and a large number of samples in each genetic group identified high selection pressure and low diversity in populations as the reasons for the high LD found  for distances <10 kb and increased to more than 50 percent for distances >1 Mb and to 70–80 percent for distances >10 Mb. This suggests that r2 values are more affected at longer distances by population size  decreased from 0.23 to 0.19, 0.16 to 0.12, and 0.15 to 0.11, respectively, for distances 30 to 100Kb, respectively (A similar situation is seen for cattle where the LD is higher in dairy cattle, which are under stronger selection than beef breeds . The pat50–75 Kb , whereaso 500 Kb . For beeectively .PLDP was calculated from the correlation between paired SNPs at distances of 0–15 Mb. An increase in the distance led to a decrease in PLDP between breeds see . At distPLPD among individual populations from Iran was above 0.95 for a distance less than 100Kb, which is similar results of These high PLPD values suggest that there may have been genetic exchange among these populations. The highest correlations previously reported among other pure and crossbred buffalo populations were 0.47 at the distance of 100 Kb , showingNe was estimated from the last 500 to recent generations in the present study. A trend of decreasing Ne was observed from more distant to recent generations: from 1,570 to 212, 1,049 to 59, 1,025 to 43, and 1,165 to 131 for IRI, ANA, EGY, and PAK breeds, respectively, from 500 generations ago to three last generations . SimilarThe conservation of genetic and biological diversity is dependent on Ne . AccordiThe present study showed that Ne of Iranian and Pakistani populations are greater than the population size threshold necessary to be genetically viable . The maiIn the present study, the LD structure, PLDP, and Ne were determined for seven buffalo populations and two populations pooled based on country or origin. The level of LD found indicated that it is appropriate to use the Affymetrix Axiom 90 K SNP genomic array for GWAS and GS in these populations. The correlation between the LD information and PLDP between geographically close populations was high, meaning that genomic information from one population can be used efficiently to predict genetic effects in another. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results. The Ne in recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost.


Odocoileus virginianus) and mule deer (Odocoileus hemionus) have broad and overlapping distributions in North America and a recognized capacity for interspecific hybridization. In response to contemporary environmental change to any of one or multiple still‐unknown factors, mule deer range is contracting westward accompanied by a westward expansion of white‐tailed deer, leading to increasing interactions, opportunities for gene flow, and associated conservation implications. To quantify genetic diversity, phylogenomic structure, and dynamics of hybridization in sympatric populations of white‐tailed and mule deer, we used mitochondrial cytochrome b data coupled with SNP loci discovered with double‐digest restriction site‐associated DNA sequencing. We recovered 25,018 SNPs across 92 deer samples from both species, collected from two regions of western Kansas. Eight individuals with unambiguous external morphology representing both species were of hybrid origin (8.7%), and represented the product of multi‐generational backcrossing. Mitochondrial data showed both ancient and recent directional discordance with morphological species assignments, reflecting a legacy of mule deer males mating with white‐tailed deer females. Mule deer had lower genetic diversity than white‐tailed deer, and both mitochondrial and nuclear data suggest contemporary mule deer effective population decline. Landscape genetic analyses show relative isolation between the two study regions for white‐tailed deer, but greater connectivity among mule deer, with predominant movement from north to south. Collectively, our results suggest a long history of gene flow between these species in the Great Plains and hint at evolutionary processes that purge incompatible functional genomic elements as a result of hybridization. Surviving hybrids evidently may be reproductive, but with unknown consequences for the future integrity of these species, population trajectories, or relative susceptibility to emerging pathogens.Hybridization is a natural process at species‐range boundaries that may variably promote the speciation process or break down species barriers but minimally will influence management outcomes of distinct populations. White‐tailed deer ( Given a relative lack of topography through this region, selected habitats between the deer species are not structured by elevation as in other areas, leading to increased syntopy. Improved understanding of how these species are interacting is an urgent priority within the state. Here, we provide an evolutionary perspective on interactions between two North American deer species of prominent management concern under contemporary global change.In addition to ecological interactions, MD and WTD have been shown to hybridize where populations overlap. Hybrids of both sexes may be viable and fertile, with hybrids analyzed from a genetic perspective found to consist of multi‐generational backcrossed individuals , where hybrid individuals backcross with one or both of the parental species through successive and continuing generations, is an important concept in wildlife conservation because such continued gene flow makes diagnosis of independently evolving units difficult , and a reduced dataset of non‐neutral SNPs to (1) investigate further the matrilineal evolutionary history of MD and WTD across North America  from a phylogeographic perspective, including the frequency and dynamics of hybridization considering multiple episodes of mitochondrial capture; (2) investigate population genomic structure, movement dynamics, and genetic diversity of monitored populations within Kansas to characterize their demographic trajectories from a conservation genomics perspective; and (3) assess the dynamics of nuclear gene flow, based on both neutral and non‐neutral loci, between MD and WTD that was detected from our population samples across a zone of sympatry. Given a growing realization that biodiversity interactions at the genomic level (through hybridization) are being exacerbated by global environmental trends, our work provides additional insights for development of adaptive management strategies for deer through the Great Plains, and whether the consequences of hybridization might benefit or impair future population integrity.22.12 centered at 39°36' N, 100°00' W) was in Graham, Norton, Sheridan, and Decatur counties  was in Scott, Logan, Gove, and Lane counties .Tissue samples (blood) from both MD and WTD were collected from live‐captured animals in late winter (Feb–Mar) across two study regions primarily composed of private land located ~130 km apart in the western third of Kansas, during 2018–2020  grassland, 18% native pasture, 5% urban, 2% water, and 15% woodland. Land cover in the south site was 71% cropland, 6% CRP, 15% native pasture, 6% urban, and 2% woodland.Zea mays), wheat (Triticum aestivum), and milo (Sorghum bicolor) dominated agricultural crops. Other less abundant crops included sunflowers (Helianthus annus), alfalfa (Medicago sativa), and soybeans (Glycine max). Pasture was typically composed of shorter, grazed, native mixed‐grass prairie. Prevalent grasses in the mixed‐grass prairie included little bluestem (Schizachyrium scoparium), sideoats grama (Bouteloua curtipendula) and blue grama (B. gracilis). Tall thistle , broom snakeweed (Gutierrezia sarothrae), Nuttall's sensitive briar , and Indian blanket (Gaillardia pulchella) were typical forbs, and widespread succulents included soapweed yucca (Yucca glauca) and plains prickly pear cactus (Opuntia macrorhiza). Tracts of CRP included tallgrass prairie species: big bluestem (Andropogon gerardi), Indiangrass (Sorghastrum nutans), and switchgrass (Panicum virgatum) intermixed with forbs such as white prairie clover , Maximillian sunflower (Helianthus maximiliani), purple prairie clover , and Illinois bundleflower (Desmanthus illinoensis). Woodlands were composed primarily of shelterbelts, small groups of clumped trees intermittently strewn throughout pastures and the large riparian area in the north site. Prevailing tree species included American elm (Ulmus americana), box elder (Acer negundo), green ash (Fraxinus pennsylvanica), hackberry , black cherry (Prunus serotina), eastern cottonwood (Populus deltoides), honey locust (Gleditsia triacanthos), black locust (Robinia pseudoacacia), mulberry (Morus rubra), black walnut (Juglans nigra), and eastern red cedar (Juniperus virginiana). Plum thickets (Prunus angustifolia) and smooth sumac (Rhus glabra) were common shrubs in both study sites.Corn  were captured during helicopter sessions in February 2018 and 2019 and March 2020 using a commercial helicopter crew . All deer were captured and handled according to guidelines approved by the American Society of Mammalogists Sikes, , under tA total of 92 deer yielded blood samples (~5 ml) taken via jugular or cephalic puncture for genetic analysis. For MD, this included 27 individuals from the northern population  and 21 from the southern population . For WTD, samples included 24 individuals from the northern population  and 18 from the southern population , WTD (n = 31), and most of the putative hybrids (n = 7) identified from ddRADseq Structure analyses, using primers Odh‐cytbF‐14153 (5′‐TCAATGACCAACATCCGAAA‐3') and Odh‐cytbR‐15399 (5′‐GGGTGTTGATAGTGGGGCTA‐3′), with PCR conditions similar to those of Latch et al. (n = 89), for both BTD (n = 17), and WTD (n = 153)  gene was amplified for a random subset of deer specimens, including MD  Table .2.2.2n = 328). We produced chronograms through Markov chain Monte Carlo (MCMC) searches in BEAST2 , haplotype diversity (Hd), nucleotide diversity (π), and pairwise sequence divergence in DnaSP v5  and incubated at 37°C for 2 h before heat inactivating at 80°C for 20 min. Samples were then ligated with 200 units of T4 ligase (NEB) and phased adaptors with CRYG and CG overhangs at 22°C for 1 h before heat killing. The ligated samples were purified with SPRI beads and then amplified for 18 cycles with 2× NEB Taq Master Mix to add unique barcodes to each sample. Libraries were purified, quantified, pooled, and size selected for the 300–744 bp library region and diluted to 2 nM prior to sequencing. UMGC sequenced 150‐bp single‐end reads across 0.25 lanes of a NextSeq 550 High‐Output FlowCell . The resulting fastq files were demultiplexed using Illumina bcl2fastq software and Trimmomatic  and visualized using gel electrophoresis (2% agarose). Samples all had sufficient yields of high molecular weight DNA (>100 ng) and were submitted to the University of Minnesota Genomics Center (UMGC), Minneapolis for ddRADseq amplification and sequencing. Following an in silico digest of the reference genome, it was determined which restriction enzymes were optimal using a sub‐sample of 8 individuals. UMGC prepared ddRADseq libraries and sequenced samples using the following protocols. For each sample, 100 ng of DNA was digested with 10 units each of 2.3.2http://www.bioinformatics.babraham.ac.uk/projects/fastqc). To process ddRADseq data and produce single nucleotide polymorphism (SNP) data sets, we used the process_radtags module of STACKS 2.5 , with a minor allele frequency of at least 5% (min_maf = 0.05), and heterozygosity upper bound of 0.8 (max_het = 0.8) that produced a variant call format (VCF) file. We also only retained one single nucleotide polymorphism (SNP) per locus (‐‐write_single_snp), to meet the assumptions of linkage equilibrium in subsequent analyses. The VCF file was filtered using VCFtools , effective numbers of alleles (EN), expected (EH), and observed (OH) heterozygosity, and inbreeding coefficients (FIS) were calculated for each sampled population in STACKS software using the populations module. Estimates of pairwise FST and DJost were calculated in R packages “hierfstat”  and then identifies markers under putative selection as those that are excessively correlated with population structure. A scree plot of the first 20 principal components (termed K in pcadapt) indicated that the optimal K from our data was 2 for computing correlations between loci and K principal components. We used Benjamini and Hochberg's . This uses VCFtools to create separate VCF files for neutral and non‐neutral locus sets. For non‐neutral loci, we conservatively included only loci found to be under selection from both outlier methods for further analyses and excluded any loci recovered from only a single method from further analyses. pcadapt analysis identified 1580 SNPs  is a multivariate approach that performs a principal components analysis (PCA) in a first step and then subjects the PC scores to a discriminant function analysis (DFA). Unlike PCA, DFA fits orthogonal discriminant functions that maximize variation between‐group relative to within‐group, making it well suited to differentiating genetic groups Jombart, . A K‐meaQ‐value. Higher Q‐values indicate a greater posterior probability that an individual belongs to that cluster. All other individuals were considered hybrids  was selected by evaluating mean likelihood scores and ∆K implemented in Structure Harvester  and the average interspecific heterozygosity across loci using the R package INTROGRESS version 1.2.3  was calculated. This method for calculating interspecific heterozygosity assumes that parental allele frequencies are known. Therefore, the same individuals used as parentals for HI estimation were also used to calculate interspecific heterozygosity. A triangle plot is used to represent the relationship between the HI and interspecific heterozygosity  or descendants of extended backcrossing  can be performed on any four‐taxon tree in the form , P3), outgroup) to identify introgression between P3 and either P1 or P2. Using our SNP data set, we used DSUITE, a tool that calculates D statistics and 4f ratios directly from VCF files created from STACKS analyses, and assessed significance using jackknifing  method implemented in TreeMix  changes through the evolutionary time frames of our study species. We set the mutation rate to 1.1 × 10−8 and generation time to 5 years as used previously in cervids  across 94 samples . After filtering for read quality and presence of correct barcodes, SbfI and TaqI recognition sites, a total of 78,496,017 read pairs were generated across all samples. After all quality filters that met our coverage and missing data criteria, and condensing our data to one SNP per loci, a total of 25,018 polymorphic SNP loci with high read depth coverage (33.45–90.35) were retained for further analysis of genetic diversity, population structure, and hybridization. No individuals were removed after filtering.We recovered 98,272,337 high‐quality reads  and WTD . Estimates of directional gene flow from northern to southern populations of MD were high, corresponding to 20  migrants per generation, but were low when considering dispersal from southern to northern populations, at 4  migrants per generation. For WTD estimates of dispersal in both directions were ~15  migrants per generation, indicating no barriers to gene flow between these populations.All measures of genetic diversity from nuclear data, including MD Table . MD fromns Table . All est3.2.2K = 2, separating MD and WTD by species for both DAPC (through BIC plot) and STRUCTURE (through Evanno method testing K = 1–10) analyses. There was no substructure based on location and extralimital WTD were grouped with Kansas WTD samples  and from both northern (n = 3) and southern (n = 2) WTD populations, but no hybrids were identified in the southern MD population. In the DAPC plot, hybrids are not spatially intermediate to WTD and MD but rather group on the periphery of each species cluster. An evaluation of all putative hybrids by INTROGRESS using a triangle plot yielded intermediate HI values, consistent with q‐values in STRUCTURE, and coupled with low interspecific heterozygosity. Together, all analyses of hybridization are consistent with backcrossing and historical bidirectional introgression  in all tests . From the Treemix analysis, the best fit to the data was obtained with three “migration” events (p < .001) indicating gene flow from MD  into both northern and southern WTD populations and also from WTD  into northern MD, providing further evidence of bidirectional gene flow and introgression . WTD show a significant population decline coincident with onset of the last glacial period (~100 kya) followed by a rapid recovery to contemporary eN estimates of ~500,000. Conversely, our results suggest that MD had sustained eN through much of the late Quaternary but have experienced a severe and recent population decline with low contemporary eN . Our data increase understanding of population genomic structure, quantify the dynamics of hybridization between MD and WTD in sympatry within Kansas, and relate these insights to a long history of genetic interactions among North American deer. Our primary findings include that (1) genetic diversity and demographic trends among deer species in western Kansas are congruent with ongoing ecological population trajectories; (2) given relatively high levels of interbreeding between MD and WTD in western Kansas we recognize yet another region of ongoing hybridization between these species, and this incomplete reproductive isolation reflects genomic introgression in both directions; and (3) mitochondrial capture of WTD haplotypes by MD reflects an ongoing process of interbreeding through the history of diversification of these species. We provide here a discussion of the importance of these findings for managing the future integrity of independent deer species within North America and considering important research priorities for ongoing studies.4.1Parelaphostrongylus tenuis nematodes), which generally cause silent infection in WTD but can lead to serious disease in MD , changing climate (long‐term precipitation and temperature regimes), and competitive dominance of WTD over MD  are highly divergent from all BTD, but are closely related to WTD samples, we can infer that MD, and most WTD, were isolated in a single region (separate from BTD) where MD experienced capture of WTD mitochondrial DNA, and suggesting that production of fertile hybrids at this point was between male MD and female WTD. Importantly, inclusion of samples from across the range of WTD indicate that mitochondrial capture by MD occurred subsequent to the divergence of WTD from Florida, Mexico, and South America, further supporting mitochondrial capture as opposed to lineage sorting as an explanation for well‐established mito‐nuclear discordance among these species  during the late Quaternary were the result of interbreeding between male MD and female WTD. However, evidence from other studies  of hybrid origin, but with clear external morphology of either MD or WTD. All have experienced multi‐generational backcrossing although the number of backcrossed generations is unknown. The consistency of direction of backcross across generations  cannot be determined from our data at present. But, given that the proportional assignment of loci to either species (hybrid index) or values for interspecific heterozygosity are very similar among all hybrid individuals as opposed to a more random spread of hybrids across genomic assignment space, functional segregation is likely. Further, patterns of genomic assignment of hybrids are repeated and generally consistent across three groups of hybrids, each with multiple individuals, including two WTD hybrids within MD‐N, three WTD hybrids within MD‐S, and three MD hybrids within WTD‐N. Evidence of balancing selection acting on ungulate species has been identified and may play a role in immunogenetic variation and how species respond to potential diseases and countering the effect of drift  and disease. Assessing relative connectivity of deer populations across broad landscapes through use of genomic data would identify land‐use practices that differentially influence deer movement and contribute to more effective adaptive management at the population level.One research avenue that greater sample coverage would enable is a range‐wide comparative population genomic analysis of all Genomic sequencing via ddRADseq methods provides much improved insight toward evolutionary processes compared with only one or a few loci, in relation to assessment of genetic diversity, demography, phylogenetic reconstruction, and estimation of gene flow. However, these data are still limited in their ability to address questions of gene function in response to changing environments or adaptive evolution. Targeted gene sequencing approaches, including exome capture, transcriptome sequencing, or whole‐genome sequencing, would provide lucrative opportunities for investigating the effects of hybridization between MD and WTD from a functional perspective in three key areas, including (1) a more thorough scan for genes under different forms of selection to relate critical life functions to environmental variables, and fitness consequences for deer in different regions; (2) identification of genomic regions or gene combinations that are consistently maintained or purged through the generation of hybrids and how the process of multi‐generational backcrossing can influence the integrity of species boundaries; and (3) assess the genomic underpinnings of susceptibility to diseases, in particular with respect to multi‐genic CWD susceptibility, and how the distribution of disease resistance is related to landscape features and biogeography, but also to hybrid zones and hybrid fitness.Hybridization of WTD and MD in Kansas is evidently not rare, at least within the regions studied. These regions were also explicitly the focus of our research due to concern for continued observed population trends, as well as increasing incidence of CWD. We have demonstrated that population trends (decline in MD and expansion in WTD) are reflected in estimates of population genetic diversity. These trends may also substantially promote the incidence of hybridization. Importantly, we have shown that genomic introgression in both directions between species exhibits potentially predictable associations of mitochondrial and nuclear assignment as well as consistent levels of genomic mixing. This suggests a mechanism for maintaining at least some viable hybrids that likely promotes distinction of species boundaries. Considering increasing focus on genomic interactions among these species, we expect that ongoing investigations will provide valuable insight toward management of these key game species across a continent‐wide zone of sympatry.The authors declare no conflict of interest.Fig S1‐S2Click here for additional data file.Table S1Click here for additional data file.


This study clarifies the role of refugia and landscape permeability in the formation of the current genetic structure of peoples of the Caucasus. We report novel genome-wide data for modern individuals from the Caucasus, and analyze them together with available Paleolithic and Mesolithic individuals from Eurasia and Africa in order (1) to link the current and ancient genetic structures via landscape permeability, and (2) thus to identify movement paths between the ancient refugial populations and the Caucasus. The ancient genetic ancestry is best explained by landscape permeability implying that human movement is impeded by terrain ruggedness, swamps, glaciers and desert. Major refugial source populations for the modern Caucasus are those of the Caucasus, Anatolia, the Balkans and Siberia. In Rugged areas new genetic signatures take a long time to form, but once they do so, they remain for a long time. These areas act as time capsules harboring genetic signatures of ancient source populations and making it possible to help reconstruct human history based on patterns of variation today. Thanks to this diversity the Caucasus is one of the global biodiversity hotspots that also contain considerable linguistic diversity, accounting for most of all languages on Earth3. The Caucasus provided some of the globe’s important refugia, where most of the terrestrial plants and animals (incl. humans) survived during a series of glacial maxima, and their current distribution largely reflects post-glacial expansion from these refugia1. These glacial refugia and the barriers to migration played an important role in human evolution, and generated much of the human genetic and ethno-linguistic patterns found in the world today7. The Caucasus contributed about half of the genetic ancestry of Yamnaya8—that is, pastoralists of the Pontic-Caspian steppe, who date from the Late Copper Age to the Early Bronze Age and made profound demographic and cultural impacts on much of Eurasia e.g. by spreading genomes, Indo-European languages and horse-riding14. It is hypothesized that Proto-Indo-European was generated from admixture between archaic languages from the Caucasus and Proto-Uralic languages in the Pontic-Caspian steppe16. Thus, the Caucasus has played an important role in the formation of past and present Eurasian genetic and cultural diversity.The Caucasus is a mountainous region located on the dividing line between Europe and Asia, between the Black and Caspian seas. In spite of relatively small geographic extent and mostly temperate climate, the diversity of natural landscapes, plant and animal species, and cultivars is unusually high in the Caucasus24. Autosomal genome and mtDNA variations in the Caucasus appear relatively homogenous, while the Y-chromosome diversity clearly shows geographic heterogeneity distinguishing the eastern from western Caucasians24. This east–west cline in the Caucasus was also shown for autosomal genome variation when Georgians were divided into sub-ethnic groups22. The studies of genome-wide autosomal profiles as well as the mitochondrial and Y chromosomal haplogroups support that there is genetic continuity in the present-day southern Caucasus stretching back at least 13,000 years to the Late Upper Palaeolithic25. In the North Caucasus this continuity was disrupted due to post-bronze age admixture with populations from the Eurasian Steppe26. At present genetic differentiation between ethnic/subethnic groups in the Caucasus correlates with landscape permeability to human movement—as determined by terrain ruggedness, forest cover, and snow cover—rather than ethnic or linguistic boundaries22.As research technologies advance and more samples are obtained, studies have been unveiling more of the genetic variation of Caucasian populations in relation to geography and various indicators of ethnicity22 hypothesized that the genetic makeup of the modern Caucasians was largely shaped up by human dispersal from few distinct glacial refugia in the last glacial period and the early Holocene, followed by less gene flow between the populations of the Greater Caucasus than between those of the rest of the Caucasus, where the populations have undergone substantial admixture in historical time. Understanding the nature of human movement from refugial populations was the primary motivation behind this study. Here we measure genome-wide genetic affinity between modern populations of the Caucasus and ancient populations of hunter-gatherers, test whether the genetic affinity between these populations is determined by geographic features, and infer major dispersal paths from refugial populations into the Caucasus. The results of this study allow us to reconstruct an integral picture of the peopling of the Caucasus from the last glacial period through the early Holocene.Tarkhnishvili et al.www.familytreedna.com).We collected hair and cheek swab samples from 77 men from geographically and linguistically distinct groups of the Caucasus: Kartvelian speakers from Georgia and Turkey, Northeast Caucasian speakers and Turkic speakers from the Russian Federation and Armenian speakers from Georgia’s southern province of Javakheti, descendants of the families displaced from Mush and Erzurum provinces of eastern Turkey in the early nineteenth century Table , Fig. 1.https://reich.hms.harvard.edu/downloadable-genotypes-present-day-and-ancient-dna-data-compiled-published-papers; see Supplementary Table 5. We did so in order to maximize the genetic signature of potential refugial populations in our analysis. We divided the ancient genotypes into 2000-year-long intervals, and then grouped each of these intervals into geographic units  written on the envelope or a piece of paper. In accordance with the preferences of the sample donors, the agreement was verbal and not written. The envelopes and papers are stored as evidence of voluntary provision of the samples and the related information. Analysis of data was done anonymously, using only location and ethnic information; only the first and third authors of the manuscript had access to names associated with the samples. Therefore, this study was based on noninvasive and nonintrusive sampling (volunteers provided hair and swab samples they collected themselves), and the information destined for open publication does not contain any personal information. The study methodology and the procedure of verbal consent was discussed in detail with and approved by the members of the Ilia State University Commission for Ethical Issues before the field survey started, and the commission decided that formal ethical approval was not needed for conducting this study. This is confirmed in a letter from the commission chairman, a copy of which has been provided to the journal editor as part of the submission process.28 in R version 3.5.229.First, we measured genetic affinity between the modern Caucasian populations, and between the modern populations and the ancient populations of hunter-gatherers, and then tested whether the genetic affinity between these populations was determined by geographic features. Data were mapped using QGIS Desktop 3.10.6-A Coruña, whereas graphs were created using the “ggplot2” package2 > 0.3, calculated in windows of 50 bp size and 10 bp steps (–maf 0.05 –indep-pairwise 50 10 0.3). Since all individuals in our dataset possess a single copy of the X-chromosome, we did not expect any differential ploidy bias, and SNPs on the X were treated similarly to those on the autosomes. Fst pairwise values were calculated using the smartpca program of EIGENSOFT30 with default parameters, inbreed: YES, and fstonly: YES. The relationship between the modern populations based on Fst values was visualized by constructing a neighbor-joining tree using the “ape” package31 in R version 3.5.2. The average and standard deviation of the inbreeding coefficient for each population was calculated using “fhat2” estimate of PLINK 1.9. The LD pruned genotypes were used in ADMIXTURE 1.3.032, performed in unsupervised mode in order to infer the population structure from the modern individuals. The number of clusters (k) was varied from 2 to 7 and the fivefold cross-validation error was calculated for each k33. We conducted principal components analysis in the smartpca program of EIGENSOFT30, using default parameters and the lsqproject: YES and numoutlieriter: 0 options. Eigenvectors of principal components were inferred with the modern populations from the Caucasus, while the ancient populations were then projected onto the PCA plots. We also assessed the relatedness between sampled individuals using kinship coefficients estimated by KING34.To evaluate genetic affinities and structure of the modern populations, we used Wright’s fixation index (Fst), inbreeding coefficient, admixture analysis and the principal component analysis (PCA). For these procedures we filtered the raw SNP genotypes in PLINK 1.9, first removing all SNPs with the minor allele frequency < 0.05, followed by LD pruning of markers that exceeded the pairwise correlation threshold of rhttps://github.com/DReichLab35 for f3- and f4-statistics, respectively. f3-statistics of the form f3 measure the amount of shared genetic drift of populations X and Y after their divergence from an outgroup. We used an ancient population and a modern Caucasian population for X, Y and Mbuti as an outgroup. f4-statistics of the form f4 show if population Test is equally related to X and Y or shares an excess of alleles with either of the two. In the f4-statistic calculation we used Mbuti for Outgroup, a modern population of the Caucasus for Test, and X and Y for contemporaneous ancient populations. This meant that f4 < 0 indicated higher genetic affinity between the test population and X, while f4 > 0 indicated higher genetic affinity between the test population and Y.To quantify genetic affinities between the modern and ancient populations, we used the programs qp3Pop and qpDstat in the ADMIXTOOLS suite  between the modern and ancient populations using the Least Cost Path Plugin for QGIS. The computation of LCD considers a friction grid that is a raster map where each cell indicates the relative difficulty (or cost) of moving through that cell. A least-cost path minimizes the sum of frictions of all cells along the path, and this sum is the least-cost distance (LCD). For impedance to human movement and expansion, we used 15 geographic features Table . All gri36 in R version 3.5.2. Time between the modern and ancient populations was measured in BP . We used GAMs because without any assumptions they are able to find nonlinear and non-monotonic relationships. GAMs were fitted using a Gamma family with a log link function. Penalized thin plate regression splines were used to represent all the smooth terms. The restricted maximum likelihood (REML) estimation method was implemented to estimate the smoothing parameter because it is the most robust of the available GAM methods36.Generalized additive models (GAMs) were used to fit the outgroup f3-statistic to time and variously calculated LCD between the modern and ancient populations using the “mgcv” package38. The best model was selected by the mean squared error of the cross-validation. Akaike’s Information Criterion (AIC) is generally used as a means for model selection. However, we preferred cross-validation for model selection because AIC a priori assumes that simpler models with the high goodness of fit are more likely to have the higher predictive power, while cross-validation without any a priori assumptions measures the predictive performance of a model by efficiently running model training and testing on the available data.Model and variable selection were performed by exploring LCD, time BP and the interaction term. The predictive power of the models was evaluated through a tenfold cross-validation. The cross-validation of many models was handled through R’s parallelization capabilities39 to measure the agreement.We additionally validated the effect of different subsets of geographic features by assessing the relationship between statistically significant values of f4-statistic (i.e. |Z|> 3) and each subset. The relationship between f4-statistic of the form of f4 and geographic features was determined by measuring the agreement between the negative/positive signs of f4-statistic and the difference in LCD (LCD.D) for each pair of contemporaneous ancient populations X and Y. LCD.D was calculated as (LCD1–LCD2), where LCD1 was least-cost distance between the test population and X, and LCD2 was least-cost distance between the test population and Y. LCD.D < 0 indicated less least-cost distance between Test and X, while LCD.D > 0 indicated less least-cost distance between Test and Y. So, the same sign of f4 and LCD.D values indicated agreement between geographic proximity and genetic affinity. We used Cohen’s kappa40 in R version 3.5.2. In addition, we checked whether contribution from ancient samples was related to today’s genetic differentiation. To do so, we calculated median of f3-statistic of ancient populations of each geographic grouping . Then we measured the manhattan distance of f3 median values of all combinations of the geographic groupings between the modern populations and compared the results to Fst and LCD using the Mantel test.In order to test if geographic features Table  accounte12 sampled individuals were inferred to be 3rd and 4th degree relatives, while the rest were unrelated: two 3rd degree relatives from CHCHN, four 4th degree relatives from KHEVS, two 4th degree relatives from LAZ, two 4th degree relatives from MSKH and two 4th degree relatives from TUSH. Our sample of modern populations showed near-zero levels of inbreeding Table . There wThe first and second PCA axes  to ancient populations dating from 13,950 to 15,950 BP and 37,950–39,950 BP Figs. . All modOur model generated least-cost paths from Eurasian and African ancient populations to the current Caucasian populations Fig. . All leaFst had the strongest correlation with LCD derived from the same geographic features that explained best f3 and f4 statistics , while ancient European or Balkan alleles are most common in modern populations of the North Caucasus, suggesting that European hunter-gatherers migrated to the Caucasus across the East European Plain rather than through Asia Minor. Remarkably, ancient genomes from Levant (Natufian culture) and Atlas Mountains (northwestern Africa), have the highest allele share with the same modern populations as ancient Anatolians, which suggests some admixture between these populations before Anatolian population started to expand to the Caucasus. The similarity of genetic gradients of the ancient and modern Caucasus in our PCA plot once again confirms genetic continuity spanning from the Late Upper Paleolithic until today25. Our study explicitly explains the reasons behind this phenomenon.Our results suggest that the current populations of the Caucasus bear detectable ancestry from Caucasian, Anatolian and Balkan hunter-gatherers. Caucasian hunter-gatherers (CHG) are the major gene contributors to the modern Caucasian populations. The proportion of CHG alleles is the highest in modern populations that live in close proximity to the archaeological sites in western Georgia, where the bones of CHG were discoveredTraces of different ancient populations (migration sources) in modern communities of the Caucasus decrease with (i) geographic distance weighted by physical barriers to dispersal, and (ii) the age of the ancient population. This signal is sufficiently strong to differentiate between the modern populations by their affinity to individual ancient populations. Our analyses suggest that the expansion of the refugial populations of Upper Paleolithic and Mesolithic hunter-gatherers was greatly impeded by rugged terrain, swamps, glaciers and desert, and the genetic legacy of these hunter-gatherers in the modern Caucasus is well explained by landscape permeability between the refugial human populations. The relationship between the ancient genetic signature and landscape permeability is stronger if the refugial populations are closer in time to the present-day Caucasus, suggesting that the genetic signature associated with a particular ancestral area fades away as a result of gene drift and later admixture events.42. These admixture events, coupled with our landscape permeability model, suggest that ancient Anatolians carrying the genetic signature of Caucasian hunter-gatherers would contribute more to Apennine hunter-gatherers through Bosporus-Dardanelles and the Balkans than to C. European ones, while C. European ones would get more genes through Eurasian Steppes from East Asian hunter-gatherers. These events of admixture from east to west, that may be facilitated by technological advances or advantages through natural selection , make our sample of modern populations more closely related to the ancient populations from Anatolia and the Apennine than to the ancient population from C. Europe.Although our model performed highly significantly, it failed to explain disagreement between genetic affinity and geographic proximity (as a function of landscape permeability) to ancient populations dating from 13,950–15,950 BP and 37,950–39,950 BP Figs. . All mod43 or between when anatomically modern humans arrived in Europe approximately 45,000 years ago44 and the LGM, climate was the mildest around 37,950–39,950 BP46 allowing more gene flow between western and eastern Eurasia. This gene flow is supported by the fact that this individual from the Balkans was more closely related to East Asians than Paleolithic and Mesolithic Europeans47, while a contemporaneous individual from western Russia was more closely related to later Europeans than to East Asians48. At glacial maxima human source populations were highly fragmented, had no or limited gene flow with each other and survived in glacial refugia, from where the populations would expand during benign climates and shrink back to during harsh climates5. Thus, our model better explains the genetic legacy of ancient populations in the modern Caucasus, if there is more genetic dissimilarity between these ancient populations than within them.Our sample of ancient populations dating from 37,950 to 39,950 BP were each represented by a single individual from the Balkans and E. Asia. More affinity of our sample of modern Caucasians with an ancient E. Asian individual than with a geographically closer Balkan individual 37,950–39,950 BP could be explained by two scenarios: either this Balkan individual was a vagrant from elsewhere, which was shown not to have contributed to any present-day populations22. The genetic affinity between the modern populations has the strongest response to the least-cost distance weighted by the same geographic features that account best for the genetic affinity between the ancient and the modern populations. However, without considering the past metapopulation picture, landscape permeability between present-day populations fails to explain the considerable fraction of variation in current genetic structure in the Caucasus.Even today genetic affinity (Fst) between modern populations of the Caucasus significantly correlates with least-cost distance between the hubs of these populations, which is in line with previous studies based on limited sample and genetic markers49. The genetic differentiation was the strongest between the populations from south and north of the Greater Caucasus main ridge, which once again indicates that the Greater Caucasus has been an important physical barrier for human dispersal since the last glacial period. However, there were also noticeable differences between populations from the same side of this mountain range, suggesting their different exposure to incoming migrants through heterogenous landscape. The western flank of the Caspian Sea appeared to be more permeable for human movement than the eastern flank of the Black Sea. None of the least-cost paths from Europe, Siberia and East Asia ran through the eastern flank of the Black Sea. The eastern flank of the Black Sea is walled by the Greater Caucasus to the north and the Lesser Caucasus and the Pontic Mountains to the south, while the western flank of the Caspian Sea is largely open. This difference between the eastern and western edges of the Caucasus explains the east–west cline in Y-chromosome and autosomal genome variations in the Caucasus24.The derived least-cost paths suggested that both the Greater Caucasus, the Lesser Caucasus and the Pontic Mountains considerably impeded the inferred human movements through the Caucasus Figs. . The GreWe were not able to sample other ethnic groups inhabiting the Caucasus. These groups would have provided more information about the genetic structure of the region. However, our sample was sufficient to test our hypotheses. Publications to date have measured the gnome-wide ancestry of ancient populations in many of the present-day communities on earth. However, to the best of our knowledge, this study is the first of its kind to estimate the relationship of genome-wide genetic affinity between modern and ancient populations to landscape permeability as a function of different geographic features, and infer major dispersal paths from refugial populations. Our model can help to time and map the dispersal and migration paths of prehistoric events not only in relation to the Caucasus but also elsewhere. Presence of significant negative correlation between the weighted geographic distance from the ancient source of a distinct genetic signature and genetic affinity with this source can be seen as evidence of migration via a particular path and at a particular time.Supplementary Information.


Plasmodium vivax is one of the five human malaria parasite species, which has a wide geographical distribution and can cause severe disease and fatal outcomes. It has the ability to relapse from dormant liver stages (hypnozoites), weeks to months after clearance of the acute blood-stage infection. An 8-aminoquinoline drug primaquine (PQ) can clear the hypnozoites, and thus can be used as an anti-relapse therapeutic agent. Recently, a number of studies have found that its efficacy is compromised by polymorphisms in the cytochrome P450 2D6 (CYP2D6) gene; decreased or absence of CYP2D6 activity contributes to PQ therapeutic failure. The present study sought to characterize CYP2D6 genetic variation in Madagascar, where populations originated from admixture between Asian and African populations, vivax malaria is endemic, and PQ can be deployed soon to achieve national malaria elimination. In a total of 211 samples collected from two health districts, CYP2D6 decreased function alleles CYP2D6*10, *17, *29, *36+*10, and *41 were observed at frequencies of 3.55–17.06%. In addition, nonfunctional alleles were observed, the most common of which were CYP2D6*4 (2.13%), *5 (1.66%), and the *4x2 gene duplication (1.42%). Given these frequencies, 34.6% of the individuals were predicted to be intermediate metabolizers (IM) with an enzyme activity score (AS) ≤ 1.0; both the IM phenotype and AS ≤ 1.0 have been found to be associated with PQ therapeutic failure. Furthermore, the allele and genotype frequency distributions add to the archaeological and genomic evidence of Malagasy populations constituting a unique, Asian-African admixed origin. The results from this exploratory study provide fresh insights about genomic characteristics that could affect the metabolism of PQ into its active state, and may enable optimization of PQ treatment across human genetic diversity, which is critical for achieving P. vivax elimination. Plasmodium species are known to cause malaria in humans; these are, Plasmodium falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi. Although P. vivax has often been regarded as causing a benign self-limiting infection, there is clear evidence that it can cause severe disease and fatality . The latter encompass deletions of the entire gene, gene duplications and multiplications, and structural rearrangements with the highly similar CYP2D7 pseudogene (CYP2D6 “star” (*) alleles listed by the Pharmacogene Variation Consortium (PharmVar) , normal metabolizers (NM), intermediate metabolizers (IM), and poor metabolizers (PM)], has been developed by the Clinical Pharmacogenetic Implementation Consortium (CPIC)  . Many ofharmVar) . A system (CPIC) .P. vivax involving 25 healthy malaria-naïve adults, who were given a combination of chloroquine and PQ as treatment, two participants had multiple relapses of malaria  suffered multiple attacks of vivax malaria at approximately two-month intervals despite taking PQ as prescribed  off the East African coast. Archaeological and genomic data provide evidence of sequential waves of human migration over the last two millennia to Madagascar from Indonesia and from East Africa by Bantu populations, together with more limited later arrivals of Arab, European, Indian, and Chinese peoples. These events have resulted in a genetically cosmopolitan Malagasy society . Plasmode region . Irrespereported . The Madreported . TherefoCYP2D6 genetic variation in a Malagasy population exposed to P. vivax transmission. Given that CYP2D6 emerges as a major pathway for PQ bioactivation, and decreased or absence of enzyme activity may contribute to PQ therapeutic failure, CYP2D6 genetic variation and its functional implications must be understood to develop strategies and expectations for P. vivax elimination in Madagascar as well as other P. vivax endemic settings.To reduce this knowledge gap, the aim of the present study was to characterize This investigation is a part of our ongoing malaria epidemiological studies being conducted in the western highlands fringe region of Madagascar . The stun = 167) and a migrant population in the rural Ampasimpotsy community of Tsiroanomandidy district (n = 44) relocated from the capital city, Antananarivo. These communities are located in the highlands fringe region and self-identified as being predominantly of the Merina highland ethnic group . The remaining blood sample and extracted DNA were stored at −20°C.Samples were collected between March and August 2015 from apparently healthy individuals of all ages during a study screening for asymptomatic malaria infections and G6PD status. Most individuals were male (92%) due to the parallel objective of qualitative phenotypic G6PD characterization. Details of the communities surveyed have been previously documented . Two hunCYP2D6*2, *3, *4, *6, *7, *8, *9, *10, *11, *12, *14, *15, *17, *29, *31, *35, *40, *41, *42, *44, *45, *49, *56, *59, *99, *100, and *101. CYP2D6*1 was assigned if no variants were identified. In addition, all samples were interrogated for the presence of CNVs including the CYP2D6*5 gene deletion, duplications, hybrid genes, and tandem arrangements utilizing a previously published quantitative multiplex method that interrogates four gene regions , as recommended by the manufacturer on a QuantStudio™ 12K Flex Real-Time PCR System . Data were analyzed with the TaqMan™ Genotyper Software and manually inspected. A total of 29 SNPs were tested, which allowed the identification of the presence of the following allelic variants using star allele nomenclature per PharmVar:  regions . Long-ra regions . AdditioStar alleles were called using nomenclature detailed by PharmVar. Subsequently, activity scores (AS) were determined for each diplotype and translated into phenotype as recommended by CPIC . Brieflyhttp://awarnach.mathstat.dal.ca/∼joeb/biol3046/PDFs/PopGen1_HWE.pdf. Expected genotype numbers were calculated, and chi-square (χ2) deviation between the observed and expected numbers was calculated for each genotype. A χ2 value greater than 3.841 at one degree of freedom (d.f.) and α = 0.05 is considered significant (p < 0.05). The online calculator https://www.socscistatistics.com/pvalues/chidistribution.aspx was used to generate a p-value from the cumulative χ2 score.95% Confidence interval (95% CI) lower and upper limit values for the allele frequencies were calculated as described in CYP2D6*2, *4, *10, *17, *29, *40, *41, *45, and *100). In addition, we detected CYP2D6*5 gene deletions, gene duplications , and alleles with tandem arrangements (CYP2D6*36+*10 and *36x2+*10). The most common nonfunctional allele was CYP2D6*4 (2.13%), followed by *5 (1.66%) and the *4x2 gene duplication (1.42%). Among the decreased function alleles, those with an AS of 0.25  were more common compared to those with an AS of 0.5 . Alleles found in the Malagasy population and their frequencies are summarized in All alleles detected were counted and calculated for their respective frequencies . Nine alCYP2D6*1/*1, *1/*2, *1/*10, *1/*17, *1/*29, and *1/*36+*10) were present at frequencies >5%, with CYP2D6*1/*10 being the most common (14.22%). Among the 41 observed genotypes, two (4.88%) were predicted to be UM , 18 (43.9%) NM , and 21 (51.22%) IM . No PM subjects were identified. Based on the low frequency of nonfunctional alleles observed in our population sample , about 0.32% of the population would be expected to be PMs.Forty-one genotypes were inferred, with frequencies ranging from 0.47% (1/211) to 14.22% (30/211) . Six genn = 2 each)]. Among the genotypes predicted to be NM, those with an AS of 1.25 were more common [six genotypes among 57 samples (27%)] compared to those with an AS of 1.5 [six genotypes among 44 samples (20.85%)] or 2.0 [five genotypes among 34 samples (16.11%)]. The distribution of the genotypes, predicted to be IM, according to their AS was as follows: AS 0.25, five genotypes among eight samples (3.79%); AS 0.5, five genotypes among 19 samples (9.01%); AS 0.75, five genotypes among 26 samples (12.32%); and AS 1.0, six genotypes among 20 samples (9.48%). Among those with an AS of 0.25 and 0.5, the nonfunctional CYP2D6*4, *4x2, and *5 alleles were present in a total of nine samples (33.33%). All genotypes, their frequencies, and translation via AS into phenotype are summarized in All six genotypes with frequencies >5% were predicted to be NM, and they were distributed equally among three AS groups . The observed and expected genotype numbers did not differ from each other for 36 genotypes including the six genotypes with frequencies >5%. Five genotypes, four observed in one subject each, contributed to the overall deficit in the expected genotype number. These five genotypes are highlighted in A χenotypes  showed tCYP2D6 is one of the Tier 1 Very Important Pharmacogenes with extensive variation (https://www.pharmgkb.org/vip/PA166170264/overview). CYP2D6 allele frequencies differ considerably among ethnically distinct populations . Based on the latter, CYP2D6*4 has the highest frequency of all nonfunctional alleles in the European group , whereas CYP2D6*5, observed at frequencies of 3–6% in most populations, occurs more frequently in South African populations. The CYP2D6*10 decreased function allele is the most common variant allele in the East Asian group , whereas CYP2D6*17 and *29 are more prominent in the Sub-Saharan African and African American/Afro-Caribbean groups, as compared to others . CYP2D6*41 allele frequency is higher in Middle Eastern populations than in other ethnic groups . Some alleles  are found at variable frequencies in almost every population studied, whereas others have only been found in a few populations to date . Finally, populations including South African, Caribbean, and others with diverse founding populations and admixture often reveal unique allele frequency patterns  is considerable higher compared to Europeans or Africans, although not quite as high as those observed across East Asia. In contrast, CYP2D6*17 and *29 allele frequencies  are more similar to those in African than in other populations, albeit not as high as in some African populations. Of note was the high frequency (12.09%) of the CYP2D6*36+*10 tandem rearrangement, which is typically only seen in East Asians (16–30%). These predominantly East Asian or African alleles were observed to come together as CYP2D6*10/*17 (2.37%), CYP2D6*10/*29 (2.84%), and CYP2D6*17/*36+*10 (3.32%) genotypes. Finding these allelic variants at notable frequencies is in full agreement with the archaeological and other genomic evidence that Malagasy populations originate from Asian and African populations and constitute a unique, heavily admixed population. Furthermore, the discovery of a subject carrying the rare CYP2D6*100 allele underscores the need to thoroughly characterize populations with a high level of admixture.In the Malagasy population, although some allele frequencies were comparable to those found in other populations, allele and genotype frequency distribution patterns were rather unique. This is particularly true for CYP2D6*10, *36, and *41, and nonfunctional alleles such as CYP2D6*4 and *5 in various combinations giving rise to IM and PM phenotypes , the CYP2D6*10 frequency is likely over-estimated, and the contribution of other allelic variants that have been reported in Malay populations . It is likely that some of the individuals in our study population may have undetected novel or rare alleles, including structural variants, which may or may not be functionally relevant. This limitation may influence the observed frequency of CYP2D6 alleles, especially the normal function CYP2D6*1 allele, which is assigned if no other SNPs are identified. Other alleles may default to CYP2D6*2, or other, however, depending whether they are on a CYP2D6*1-like or *2-like backbone. We refer to Nofziger et al. . There was no 5-hydroxylation activity detected for CYP2D6*36 and several other nonfunctional alleles, confirming their no function status . Interestingly, no 5-hydroxylation activity was detected for CYP2D6*10 and CYP2D6*29. The absence of detectable in vitro activity for these alleles is especially concerning, given that both are relatively common in many populations plagued by malaria, including the Malagasy. Thus, future studies need to address whether the CPIC recommended method of translating AS into phenotype is appropriate to predict CYP2D6 activity for PQ treatment.Being a unique population, Madagascar provides an opportunity to gain new insights into PQ metabolism and efficacy. First, in the present study, we tested for a discrete set of r et al.  for addiefficacy . An earlsm of PQ . Other rsm of PQ , CYP2C8 sm of PQ , CYP2C19sm of PQ , UDP-glusm of PQ , ATP-binsm of PQ , and solsm of PQ  may also malaria . Biologifunction . In otheo et al. . In thisCYP2D6 in a portion of the Malagasy population. Consistent with the historical peopling of Madagascar, the alleles associated with African and East Asian/Indonesian populations are among those most highly prevalent. Finally, current knowledge provides fresh insights about P. vivax elimination challenges for the global population at risk, and enables future studies to address those challenges with the goal to optimize PQ treatment across human genetic diversity. Such efforts would not only benefit successful P. vivax treatment, and eventual elimination, in Madagascar but all populations afflicted by this parasite.In conclusion, although allele and genotype frequencies need to be validated in a larger population sample, this snapshot study is highly informative as it revealed the presence of numerous decreased function alleles and predicts that a substantial proportion of this population has decreased CYP2D6 activity. In addition, this study provides new insight regarding the genetic admixture of