Rucervus eldii) with three recognised subspecies  represents one of the most threatened cervids found in Southeast Asia. The species has experienced considerable range contractions and local extinctions owing to habitat loss and fragmentation, hunting, and illegal trade across its distribution range over the last century. Understanding the patterns of genetic variation is crucial for planning effective conservation strategies. This study investigated the phylogeography, divergence events and systematics of Eld's deer subspecies using the largest mtDNA dataset compiled to date. We also analysed the genetic structure and demographic history of R. e. eldii using 19 microsatellite markers. Our results showed that R. e. siamensis exhibits two divergent mtDNA lineages (mainland and Hainan Island), which diverged around 0.2 Mya (95% HPD 0.1–0.2), possibly driven by the fluctuating sea levels of the Early Holocene period. The divergence between R. e. eldii and R. e. siamensis occurred around 0.4 Mya (95% HPD 0.3–0.5), potentially associated with the adaptations to warm and humid climate with open grassland vegetation that predominated the region. Furthermore, R. e. eldii exhibits low levels of genetic diversity and small contemporary effective population size  with widespread historical genetic bottlenecks which accentuates its vulnerability to inbreeding and extinction. Based on the observed significant evolutionary and systematic distance between Eld’s deer and other species of the genus Rucervus, we propose to classify Eld's deer (Cervus eldii) in the genus Cervus, which is in congruent with previous phylogenetic studies. This study provides important conservation implications required to direct the ongoing population recovery programs and planning future conservation strategies.Eld's deer ( Species characterised by fragmented and isolated populations are increasingly prone to factors that can lead to reduced levels of genetic diversity and loss of evolutionary potential which, in turn, limit the populations' ability to adapt to the changing environment3 and as a consequence, elevate the risk of local extinctions5. Therefore, preservation and maintenance of adequate amount of genetic diversity in natural populations of threatened species is crucial for their long-term survival in the wild6.Small populations are susceptible to loss of genetic diversity as a consequence of founder events, genetic bottlenecks, and inbreeding, accelerated by the cumulative impacts of natural and anthropogenic factorsRucervus eldii) is a localised and endangered tropical Southeast Asian cervid7. It was once widely distributed across South and Southeast Asia, extending from Manipur in Northeast India to Myanmar, Thailand, Cambodia, Vietnam up to Hainan Island in China9. Over the past 200 years of known history, the species has witnessed severe range contraction across its historical distribution range. Overexploitation of natural resources, habitat loss and fragmentation, hunting, and illegal trade are the major cause of its population decline10. The species at present is listed as endangered by the IUCN Red List7. It is classified into three subspecies; Manipur's brow-antlered deer (R. e. eldii), Burmese brow-antlered deer (R. e. thamin), and Siamese Eld's deer (R. e. siamensis)7. However, the Hainan Island (HI) population of R. e. siamensis is, at times, considered to be the fourth subspecies of Eld's deer12. The current distribution of Eld's deer subspecies is highly fragmented  was declared extinct in 1951, and the current wild population in Keibul Lamjao National Park (KLNP) in Manipur, India has recovered from 14 individuals rediscovered in 197513. It represents the most threatened subspecies of Eld’s deer which is highly susceptible to local extinction compared to R. e. thamin and R. e. siamensis, due to its small population size and restricted habitat in KLNP14. Despite its vulnerability to inbreeding and extinction due to stochastic and demographic factors, limited information is available on the genetic status of this subspecies to assist its population management programmes. R. e. siamensis underwent a significant decline across its distribution range during the last century and was declared extinct from Thailand and Vietnam. Presently, the subspecies occurs in scattered populations in Lao PDR and Cambodia and Hainan Island in China7. R. e. thamin has also witnessed severe range contractions and is considered extinct from Thailand. Presently, this subspecies represents the largest population of Eld’s deer in the wild with localised populations in Myanmar and introduced populations in Thailand15.Eld's deer or brow-antlered deer 18 indicating the historical distribution of Eld’s deer in this region which is presently isolated due to the rise in eustatic sea levels. Evidence of faunal compressions and local extinctions due to the increased sea levels has been reported in the Isthmus of Kra region, which divides the Indochinese and Sundaic sub-regions19. These changes together with dynamic climatological and vegetational conditions facilitated the diversification of many ruminant species23 and would have influenced the patterns of genetic diversity, divergence and evolutionary history of Eld’s deer subspecies.Thailand is considered as the historical centre of the geographical distribution range of Eld’s deerCervus or Rucervus still persists. Eld's deer share morphological similarities with swamp deer (Rucervus duvaucelii) and the extinct Schomburgk's deer (R. schomburgki)24. Based on these affinities, all three species are currently classified together in the genus Rucervus25. However, previous molecular studies assigned Eld’s deer to the genus Cervus29 but Grubb8 revived Thomas’s30 classification and assigned the species to genus Rucervus which is currently adopted by the IUCN Species Survival Commission. Despite the species being globally endangered, few studies have attempted to assess its genetic status31. Studies on microsatellite genetic variation are limited to the HI population of R. e. siamensis11. Lack of adequate information on the spatial genetic structure and levels of genetic variability among different populations of Eld’s deer subspecies is an impediment for planning effective conservation breeding and population management strategies.A taxonomic conundrum of assigning Eld's deer to either genus R. e. eldii and R. e. siamensis to estimate the age of these subspecies and the major divergence events within the Eld's deer group. To resolve the taxonomic dilemma of assigning Eld's deer to either genus Rucevus or Cervus, we reconstructed the phylogenetic association of species of tribe Cervini using Bayesian analysis of complete mitochondrial genomes. We also estimated the microsatellite genetic diversity, contemporary and ancestral effective population size (Ne), and demographic history of R. e. eldii using microsatellite markers. Due to logistical and legal constraints of range-wide biological sampling of Eld’s deer subspecies, we confined the microsatellite analyses to R. e. eldii. The findings of this study will provide insights into the patterns of genetic association among the subspecies with a deeper understanding of the evolutionary history of Eld's deer, which is essential for planning conservation and management strategies.In this study, we integrated the available mitochondrial DNA (mtDNA) control region dataset to assess the patterns of genetic diversity, phylogeography and phylogenetic relationships among the Eld's deer subspecies. We analysed the complete mitochondrial genomes of hd = 0.805 ± 0.020 and low nucleotide diversity; Pi = 0.023 ± 0.0005. All the three subspecies of Eld’s deer contain unique control region haplotypes localized within the populations of the respective subspecies  out of the total 23 haplotypes observed in all the three subspecies. R. e. siamensis exhibited four control region haplotypes (n = 9) with hd = 0.639 ± 0.126. Interestingly, we observed no haplotype sharing among the populations of R. e. siamensis. All the identified haplotypes in R. e. siamensis were unique to their respective populations with one haplotype (H2) observed in the Hainan Island, two haplotypes (H3 and H5) in Dusit Zoo, Thailand, and one haplotype (H4) in Paris Zoo, France. All the unique individuals of the captive (n = 23) and wild populations of R. e. eldii (n = 27) identified using 19 microsatellite loci shared a single control region haplotype.A total of 23 haplotypes were observed in the control region (447 bp) of three subspecies of Eld’s deer segregated by 126 polymorphic sites with an overall haplotype diversity; (Mean ± SD) R. e. eldii was separated from R. e. thamin and R. e. siamensis by a minimum of 9 and 16 mutational steps, respectively. Moreover, the haplotype network differentiated the mainland and HI populations of R. e. siamensis by ten mutational steps. In comparison, mainland populations of R. e. siamensis were separated by 13 mutational steps from R. e. thamin. Furthermore, the HI population showed a sequence variation of ~ 2.7% with both the mainland populations of R. e. siamensis and R. e. thamin at the control region , 22 tRNA genes, two rRNA genes, and a non-coding control region . Further, the HI and mainland populations of R. e. siamensis also exhibited unique control region haplotypes. Therefore, we assigned two mitogenomes to the HI population (NC014701 and HM138200) and one to the mainland population based on the similarity indices of the blast search and estimated the divergence between these populations of R. e. siamensis. The obtained value for the index of substitution saturation (Iss)32 for the analysed mitogenome dataset was 0.4093 and the critical Iss.c values for the symmetrical and asymmetrical trees were 0.8424 (P < 0.001) and 0.6743 (P < 0.001), respectively. Both the values of Iss.c were significantly higher than the observed Iss values. Therefore, the complete mitogenome dataset was found suitable for phylogenetic and divergence analysis.Sequencing of the complete mitochondrial genome of 27  with 95% Highest Posterior Densities (HPD) is provided in Supplementary Table Cervini. We recovered an early divergence of common ancestors of the genera Axis and Rucervus ~ 4.5 million years ago (Mya) (95% HPD 3.6–5.4) from the main phylogenetic stock of tribe Cervini. The split between Eld's deer and the Cervus-Rusa group occurred around 2.7 Mya (95% HPD 2.1–3.3) during the Late Pliocene. Subsequently, the diversification of Cervus and Rusa species took place around 1.8 Mya (95% HPD 1.4–2.2). However, early separation of Philippine spotted deer  from the common ancestors of the Cervus/Rusa group around 2.2 Mya (95% HPD 1.7–2.7) indicates that R. alfredi is the oldest form of living rusine deer.The divergence time estimation based on Bayesian analysis of 13 PCGs, two rRNA genes (12S and 16S), and control region sequences obtained a strongly supported time-calibrated tree, which is consistent with the previous phylogenetic studies27 Fig. . ApproxiElaphurus davidianus and R. eldii took place ~ 2.0 Mya (95% HPD 1.5–2.5) during the Early Pleistocene period. Within R. eldii, we obtained two diversification events. The divergence of R. e. eldii from R. e. siamensis which occurred ~ 0.4 Mya (95% HPD 0.3–0.5), followed by the divergence of HI and the mainland populations of R. e. siamensis ~ 0.2 Mya (95% HPD 0.1–0.2).Simultaneously, the divergence of Cervini based on 13 PCGs, two rRNA genes and control region sequences using the Bayesian approach yielded a statistically supported phylogenetic tree of tribe Cervini . Fallow deer (Dama dama) and Persian fallow deer (Dama mesopotamica), chital (Axis axis) and hog deer (Axis porcinus) were allied together as sister species in their respective clades. Within the genus Cervus, all three species; red deer (Cervus elaphus), sika deer (Cervus nippon) and white-lipped deer  were monophyletic (BPP = 1). However, the genus Rucervus and Rusa were observed to be paraphyletic. R. duvaucelii was nested with the genus Axis (BPP = 1) while R. eldii was associated with Pére David's deer (Elaphurus davidianus) as a sister group. The monophyly of genus Rusa was disrupted by the disjunct position of R. alfredi, which was allied to the clade comprised of species of the genus Cervus and Rusa (R unicolor/R. timorensis).Phylogenetic reconstruction of tribe Ho = 0.13 ± 0.04; He = 0.31 ± 0.05) compared to the wild population of R. e. eldii  in KLNP ; Gst = 0.15 (P < 0.001) and allelic differentiation . The estimates of the inbreeding coefficient (F) were 0.342 ± 0.100 and 0.566 ± 0.098 in the wild and captive populations, respectively.We successfully genotyped 66 out of total 79 faecal pellet samples, with 48 samples from KLNP and 18 samples from captive populations including Manipur Zoological Garden (n = 6), Assam State Zoo (n = 8) and National Zoological Garden (n = 4). The remaining samples from Manipur Zoological Garden (n = 4) and Alipore Zoological Garden (n = 9) failed to amplify, therefore excluded from the microsatellite analysis (see Supplementary Table P < 0.001), viz. Infinite Allele Model (IAM); Two-phase Model (TPM) and Stepwise Mutation Model (SMM)  with a stable population trend until ~ 3000 generations  supported the demographic estimates with the first peak observed at ~ 50 generations and a second peak at ~ 3000 generations, indicating the time at which the population underwent genetic bottlenecks  method obtained low R. e. eldii showed that the population underwent a steady decline during the past ~ 100–200 generations (median 4281–4717)  than those obtained using the MCMC approximation method (median = 20) (see Supplementary Table Assessment of the contemporary effective population size of wild population of 17) Fig. b around 33. Genetic diversity has potential impacts on the persistence and adaptation of populations to environmental changes34. Small populations generally exhibit low genetic variation due to population bottleneck and founder effects induced by natural as well as anthropogenic factors1. Eld's deer occur in small and fragmented populations, and its management largely depends on human-assisted maintenance of genetic diversity through population reintroductions. Hence, information on the genetic variability of Eld's deer populations across its distribution range is of prime importance for planning conservation strategies. This study examines the most extensive mtDNA control region dataset available to date, to provide information on the levels of genetic diversity, phylogeographic and phylogenetic patterns of Eld's deer subspecies.Demographic, environmental, and genetic factors profoundly affect the viability of populationsR. e. eldii and delivered a comprehensive insight into the demographic history and past effective population size of this subspecies. The results of this study, combined with low population densities and increasing anthropogenic threats, raise serious conservation concerns and indicate increased susceptibility of Eld's deer to genetic, demographic, and stochastic factors.Additionally, this is the first attempt to estimate the divergence events among the Eld's deer subspecies using complete mitochondrial genomes. Due to the unavailability of biological samples from other subspecies, we restricted the microsatellite analyses to R. e. thamin exhibited the highest number of haplotypes (18) followed by R. e. siamensis (4) and R. e. eldii (1) which occurs in small and isolated populations  indicated the presence of genetic structure among the Eld's deer subspecies. Consistent with the wide geographical distribution, persistence in multiple populations, and comparatively stable demographic history, ons Fig. b. We obsR. e. siamensis. This subspecies exhibits two divergent mitochondrial lineages corresponding to the HI and mainland populations, which diverged during the Late Pleistocene ~ 0.2 Mya (95% HPD 0.1–0.2)  Fig. . The lowsis Fig. . During R. e. eldii and R. e. siamensis took place around 0.4 Mya (95% HPD 0.3–0.5). During the Late Pleistocene and Early Holocene period, Northeast India was dominated by open grassland habitats39, which would have facilitated the dispersal of grassland dependent species during the period. The dynamic influences of climatic and environmental factors during the Pleistocene have significantly driven the divergence and phylogeographic structure of several species40. We hypothesise that different ecological niches occupied by the Eld’s deer subspecies might have facilitated their diversification. Overall, the mitochondrial genomes investigated in this study revealed two major divergence events among the Eld's deer subspecies during Late Pleistocene. However, one limitation of our study is the unavailability of the complete mitochondrial genome of R. e. thamin, which would have provided more profound insights into the evolutionary history of Eld's deer.The divergence between Cervini which occurred around 4.5 Mya (95% HPD 3.6–5.4) during Pliocene. The first lineage gave rise to species of the genus Rucervus and Axis, and the latter radiated into the genera Rusa, Cervus, Dama, and Elaphurus including the Eld’s deer   and assigned the captive and wild populations to separate genetic clusters  of A. porcinus, which co-occurs with R. e. eldii in KLNP sympatrically. Both A. porcinus and R. e. eldii exhibit low to moderate levels of microsatellite genetic variability, which can be attributed to their single, small and isolated populations, inhabiting the space-constraint habitat in KLNP. Most of the selected microsatellite loci significantly deviated from the HWE, possibly due to the small population size, non-random mating, and impacts of inbreeding44 in R. e. eldii. In summary, the existing wild and captive populations of R. e. eldii exhibit low levels of microsatellite genetic diversity. The wild population in KLNP is highly susceptible to inbreeding because of the increased probability of interbreeding of related individuals due to its small and geographically isolated population.The populations of R. e. eldii has gone through genetic bottleneck (see Supplementary Table MRCA) indicated two genetic bottleneck events  around 15,000–25,000 Ybp are therefore consistent with the biogeographical history of the region. Subsequently, the appearance of open grasslands with aquatic herbaceous associates in the Loktak Lake region during Late Holocene39 indicates the emergence of extensive habitat conditions favourable for grassland dependent species, such as R. e. eldii. As shown in our results, an increase in the ancestral population size during the past ~ 2500–300 generations (median 540–4876) , which caused a steady decline in the population size till past ~ 100 generations (median = 4281) , far below the minimum threshold required to prevent inbreeding depression (Ne > 100) and to maintain long-term evolutionary potential (Ne > 1000) as proposed by Frankham et al.51. The HI populations of R. e. siamensis also contain low genetic diversity11, but large population size, which will undoubtedly benefit its population recovery in the wild. Unlike the HI populations, R. e. eldii exists as a single isolated wild population in a space-constrained habitat in KLNP with low population abundance in the wild14. Therefore, R. e. eldii is exceedingly susceptible to extinction as any further reduction in the number of breeding individuals will have detrimental effects on the viability of the population. Given these factors, it is crucial that improved conservation efforts with overarching strategies are implemented with immediate objectives to maintain population abundance in the wild as well as restoration of genetic diversity. Based on the observed patterns of genetic diversity and population genetic structure of R. e. eldii, we recommend initiating the conservation breeding using individuals from both captive and wild populations. Subsequently, the captive-bred founder stock can be used to ameliorate the levels of genetic diversity of the wild population by restocking the existing population in KLNP and establishing alternative populations to ensure its long-term persistence in the wild.Low levels of genetic diversity, past effective population size, and widespread genetic bottleneck events in 14. Therefore, planning immediate conservation strategies to mitigate the prevalent anthropogenic pressures, and arrest any further change in the existing land-use regime will benefit in protecting the ecological integrity of the Loktak Lake including KLNP, thereby ensuring the long-term survival of R. e. eldii in the wild. Any further delay in mitigating these conservation challenges may lead to an irreversible damage to the genetic diversity of the species.Furthermore, high human population density and increasing resource dependence, together with encroachment and changing land-use patterns in and around KLNP and Loktak Lake are leading to overexploitation of natural resources, thereby affecting the ecological integrity of the ParkThe long-term survival of Eld's deer in the wild largely relies on human-assisted maintenance of genetic diversity through population reintroductions, restocking, and conservation breeding. However, the lack of range-wide information on genetic variation raises apprehensions on the effectiveness of these strategies. The need for mapping the spatial genetic variation using an extensive set of mitochondrial and nuclear markers cannot be overstated, especially given the critical status of Eld's deer across its distribution range. The information generated in this study will aid in evaluating the efficacy of the ongoing population recovery programs adopted for Eld's deer subspecies. Moreover, a collaborative approach among various institutions and policymakers across Eld's deer range countries is crucial to support the conservation efforts for ensuring the long-term survival of this species in the wild.R. e. eldii including freshly defecated faecal pellets from Keibul Lamjao National Park (KLNP), Manipur (n = 48) and captive populations (n = 31) of Alipore Zoological Garden, West Bengal (n = 9), Manipur Zoological Garden, Manipur (n = 10), Assam State Zoo, Assam (n = 8) and National Zoological Park, New Delhi (n = 4) during 2006–2009  for both control region and mitogenome amplifications were carried out in 20 µL volumes containing 10–20 ng of the total genomic DNA, 10 µL of 2× PCR buffer (Applied Biosystems), 0.2 mg/mL Bovine Serum Albumin (BSA), 0.3 µM dNTPs, 0.3 µM of forward and reverse primers with 0.1 µL (0.5 units) of DreamTaq DNA Polymerase (ThermoFisher Scientific). PCR conditions followed an initial denaturation at 94 °C for 5 min, followed by 35 cycles at 95 °C for 35 s, 55 °C for 45 s, and 72 °C for 1 min 30 s. Reactions were terminated with a final extension of 10 min at 72 °C. We used positive and negative controls to monitor the efficacy and reliability of the reactions. PCR products were visualised for positive amplicons using 2% agarose gel electrophoresis using ethidium bromide and were examined under UV illuminator. Positive amplicons of control region and 23 fragments of the mitochondrial genomes were sequenced using Sanger sequencing in ABI 3500XL Genetic Analyser using BigDye v.3.1 sequencing kit (Applied Biosystems).We sequenced the control region (447 bp) using 63 (see Supplementary Table R. e. eldii (n = 31) using non-invasively collected fresh faecal pellet samples. PCR was performed in 10 µL reaction volumes with 2.0–3.0 µL of total genomic DNA, 5 µL of QIAGEN Multiplex PCR Buffer Mix (QIAGEN) and 0.2 µM of each labelled forward and 0.2 µM of the non-labelled reverse primers. PCR cycles of the microsatellite loci were performed at an initial denaturation at 94 °C for 15 min followed by 40 cycles of 94 °C for 30 s with annealing temperatures ranging from 48 to 60 °C for 45 s, 72 °C for 40 s, and final elongation at 72 °C for 30 min. The products were then genotyped in ABI 3130 Genetic Analyser using LIZ 500 Size Standard (Applied Biosystems). Positive and negative controls were used to monitor the efficacy and reliability of the reactions.We used 19 microsatellite lociR. e. eldii out of total 48 faecal pellet samples collected from KLNP and captive populations (n = 23) from Alipore Zoological Garden (n = 9) Manipur Zoological Garden (n = 10) and National Zoological Park (n = 4) using 19 microsatellite markers. We also included the control region sequences (n = 3) obtained from the complete mitochondrial genomes sequenced in this study. The faecal pellet samples obtained from Assam State Zoo (n = 8) were excluded from phylogeographic analysis because they failed to amplify at the control region. The control region sequences were aligned using the CLUSTAL W algorithm64 in program MEGA X65. Haplotype (hd) and nucleotide diversities (Pi) were calculated using program DnaSP v.5.1066. Haplotypes were derived by removing non-informative sites, and gaps were considered. The spatial relationship among the haplotypes was reconstructed using the median-joining network in PopART v.1.767. Bayesian Analysis of Population Structure (BAPS) v.6.069 was used to identify the number of genetic clusters. We performed the non-spatial clustering with admixture analysis using an assumed number of clusters (K) ranging from 1 to 30 with ten replicates for each value of K and 500 iterations. Pairwise genetic differentiation (Fst) of the identified clusters were calculated using program DnaSP v.5.1066. Phylogenetic relationships among the Eld's deer subspecies were reconstructed using the partial fragment of the control region based on the Bayesian approach as implemented in program MrBayes v.3.270. The most appropriate model for nucleotide substitution was selected based on the Akaike Information Criterion (AIC)71 values using program jModelTest v.2.1.373. We performed two MCMC chains of 30 million simulations sampling at every 10,000 generations and discarded the initial 3 million runs as burn-in. The output tree topologies were edited using FigTree v.1.4.2 (http://tree.bio.ed.ac.uk/sofware/fgtree/).We obtained a global alignment of 200 control region sequences (447 bp) representing all the three subspecies of Eld's deer . The alignments were visualised manually and Phred Quality Score of each nucleotide variation (Q ≥ 20) was selected as a threshold for considering the variable sites (https://apps.thermofisher.com/apps/spa/#/apps). The mitochondrial genome annotation was performed in CGView Server74. The complete mitochondrial sequences were submitted to GenBank (Accession No: MT555112–MT555114). Further, we included the published complete mitogenomes of 10 species of tribe Cervini, Bos javanicus, and Muntiacus reevesi obtained from GenBank to estimate the divergence events and reconstruct the accurate phylogenetic position of Eld's deer  Cervidae (18.4 Mya)78 for the Bovidae/Cervidae split and tribe Muntiacini (8 Mya)79 as calibration points. We used the lognormal uncorrelated relaxed-clock model and Yule speciation process with normally distributed prior parameters. We performed two independent divergence analyses with 50 million generations, sampling at every 1000 generations with a 10% burn-ins.The complete mitochondrial genome of 16, 357 bp sequenced from three individuals of Cervini based on 13 PCGs, two rRNA and control region sequences was executed in MrBayes v.3.270. The best suitable model for nucleotide substitution was obtained based on the lowest AIC values using jModelTest v.2.1.373. We ran two MCMC chains of 50 million simulations sampling every 10,000 generations. We discarded the initial 5 million runs as burn-ins. Parameter convergence and effective sample size (> 400) for divergence and phylogenetic analyses were analysed in program TRACER v.1.6. TREEANNOTATOR v.1.8.2 (BEAST package) was used for annotating the output tree with a burn-in of 10%. The output tree topologies were visualised and edited in FigTree v.1.4.2 (http://tree.bio.ed.ac.uk/sofware/fgtree/).Phylogenetic analysis to reconstruct the molecular phylogeny of tribe 80. The likelihood of two unrelated individuals and siblings sharing the same genotype  was executed in Gimlet v.1.3.381. The overall summary statistics comprising of the total number of alleles per locus (Na), observed (Ho) and expected (He) heterozygosity, and deviations from the Hardy–Weinberg equilibrium (HWE) were calculated in GenAlEx v.6.082. The Polymorphic Information Content (PIC) values were estimated in program CERVUS v.3.0.783. We used model-based hierarchical Bayesian clustering approach implemented in program STRUCTURE v.2.3.484 to identify the presence of genetic structure in R. e. eldii populations. We used an assumed number of clusters (K) ranging from 1 to 10. For each K, we executed ten iterations with a burn-in of 50,000 followed by 5,00,000 MCMC simulations under the admixture model with correlated allele frequencies. The optimal number of clusters in the sampled populations were determined following Evanno et al.85 approach executed in the web version of STRUCTURE HARVESTER v.0.6.9486. The assignment probability plot using individual membership coefficient was produced using program DISTRUCT v.1.187. Further, the genetic differentiation estimates Fst, Gst and Jost’s D among the identified clusters were estimated using program strataG88 package in R with 1000 bootstrap iterations.We performed the identification of alleles using GeneMarker v.2.7.489 assuming that microsatellite mutations followed the Infinite Allele Model (IAM), Stepwise Mutation Model (SMM) or Two-phase Model (TPM). In TPM we used 95% single-step mutational events at 12% variance89. We also performed the one-tailed Wilcoxon test to determine the significance of the heterozygosity excess. The allelic distribution curve was analysed to determine whether the population is under mutation-drift equilibrium (L shaped curve) or not (mode shift)91.To detect the signatures of bottleneck events, we performed the transient heterozygosity excess and mode-shift tests in program BOTTLENECK v.1.2.0294 to estimate the contemporary effective population size using the bias-corrected version in program NeEstimator v.295. The linkage disequilibrium approach is the most suited method for microsatellite data which has been proven to be relatively robust in assessing the effective population size94. We also implemented the approximate likelihood MCMC approach using VarEff v.1.2 package in R (https://qgsp.jouy.inra.fr/)96 to estimate the past changes in effective population size. This approach implements coalescence theory to generate posterior distributions of effective population size using an approximation of likelihood based on different prior demographic parameters. It implements the distribution of motif distance frequencies between alleles at the tested microsatellite loci97. This method allows recovering the posterior distribution of the time to the most recent common ancestor (TMRCA) between two alleles and provides the time of the bottleneck events in the number of generations in the past. We implemented a Two-phase Mutation Model (TPM) to estimate the past changes in the population size with proportion for multi-step mutations of c = 0.2 and an average microsatellite mutation rate (μ) of 2.0 × 10–3 per generation103. We estimated the effective population size from present to 5000 generations in the past, assuming the generation time (G) of 5 years for Eld's deer7. Priors for past effective population size were derived from global estimates of Theta (θ = 4Neμ) executing the Theta function in VarEff96. MCMC runs were performed using 10,000 batches with sampling at every ten batches and a burn-in of 10,000. We performed trial runs by changing the analysis parameters and finally selected the parameters recommended by Nikolic and Chevalet96 (see Supplementary Table We implemented the linkage disequilibrium methodSupplementary Information 1.Supplementary Information 2.


Anopheles funestus are important steps for designing effective resistance management strategies. Here, we investigated the association between patterns of genetic structure and expression profiles of genes involved in the pyrethroid resistance in An. funestus across Uganda and neighboring Kenya.Insecticide resistance is challenging the effectiveness of insecticide-based control interventions to reduce malaria burden in Africa. Understanding the molecular basis of insecticides resistance and patterns of gene flow in major malaria vectors such as An. funestus were collected across the four localities in Uganda and neighboring Kenya. A Microarray-based genome-wide transcription analysis was performed to identify the set of genes associated with permethrin resistance. 17 microsatellites markers were genotyped and used to establish patterns of genetic differentiation.Blood-fed mosquitoes CYP9K1, CYP6M7, CYP4H18, CYP4H17, CYP4C36). However, expression levels of key genes vary geographically such as the P450 CYP6M7 [Fold-change (FC) = 115.8 (Arua) vs 24.05 (Tororo) and 16.9 (Kisumu)]. In addition, several genes from other families were also over-expressed including Glutathione S-transferases (GSTs), carboxylesterases, trypsin, glycogenin, and nucleotide binding protein which probably contribute to insecticide resistance across Uganda and Kenya. Genotyping of 17 microsatellite loci in the five locations provided evidence that a geographical shift in the resistance mechanisms could be associated with patterns of population structure throughout East Africa. Genetic and population structure analyses indicated significant genetic differentiation between Arua and other localities (FST>0.03) and revealed a barrier to gene flow between Arua and other areas, possibly associated with Rift Valley.Microarray-based genome-wide transcription profiling of pyrethroid resistance in four locations across Uganda  and Kenya (Kisumu) revealed that resistance was mainly driven by metabolic resistance. The most commonly up-regulated genes in pyrethroid resistance mosquitoes include cytochrome P450s (The correlation between patterns of genetic structure and variation in gene expression could be used to inform future interventions especially as new insecticides are gradually introduced. Anopheles mosquitoes of which four species  have been identified as the major malaria vectors in Africa. In Uganda, the main malaria vectors are Anopheles funestus, An. gambiae sensu strict and An. arabiensis , and Susceptible . In other locations, the resistant (R) sample was included. The extraction was performed using the Picopure RNA isolation Kit . The RNA extraction was performed as previously described by Riveron et al., 2017 [Genes associated with pyrethroid resistance in the five locations were detected using the 8 X 60K Agilent microarray chip customarily designed for escribed . This chescribed , is madeescribed , 8,540 Eescribed . It alsosequence , 27; andl., 2017 . The qual., 2017 . Resista−ΔΔCT method after normalization with the housekeeping genes, the ribosomal protein S7  and actin5C (AFUN006819) [Agilent GeneSpring GX 13.0 software was used to analyze the microarray data. The differentially expressed genes were identified using a threshold of twofold-change (FC) and a statistical significance of P<0.05 with Benjamin-Hochberg correction for multiple testing. The microarray data from this study is deposted in the Array Express under accession no. E-MTAB-9529. Finally, quantitative reverse transcription PCR (qRT-PCR) assays were performed to validate microarray results for the key candidate genes; 1 μg of RNA from key resistance genes, comparing the permethrin-resistant mosquitoes to FANG susceptible (R-S) mosquitoes, was used as a template for complementary (cDNA) synthesis using the superscript III  following the manufacturer’s guide. The qRT-PCR was carried out as previously described , 28 with0 females confirmed as An. funestus s.s were genotypes at seventeen microsatellites loci genome-widely distributed [Randomly field-collected Ftributed , 31.2 and 1μl of genomic DNA (15ng/ul). The mosquitoes from Uganda and Kenya samples  were genotyped as previously described by [FIS). Linkage disequilibrium between pairs of microsatellite loci was assessed using FSTAT v.2.9.3.2 [P-values. Genetic differentiation between populations was assessed by estimating Wright's F-statistics [FST was assessed using G-based exact tests for genotypic differentiation [For each locus and each population sample, heterozygosity was computed using GENETIX v.4.05  and the .2.9.3.2 . Signifiatistics , calculaatistics . Statistntiation , availabWe applied a Bayesian model-based clustering algorithm to infer population structure and to assign individuals  to clusters without a priori knowledge of population units and limits. This approach, implemented in STRUCTURE v 2.3.4 , uses inFST / (1-FST) on the logarithm (ln) of geographical distance [The correlation between genetic and geographical distances was assessed by the regression of distance , and tesKruskal-Wallis test was used to compare the average number of alleles and the average proportions of heterozygosity between populations using GraphPad Prism 5. The Bonferroni correction procedure  was applThe triangular hybridization performed in Lira between mosquitoes resistant to permethrin (R), unexposed to insecticide (C), and the FANG susceptible laboratory strain (S) allows a comparative analysis of transcription profile in this location. A high number of the probes were significantly differentially expressed (p<0.05) for the R-S (9263), R-C (4132 with fold-change of 1.5), and the C-S (4128) . HoweverAn. gambiae with an unknown function. The nucleotide-binding protein 2 (nbp2) (Afun008887) gene was also consistently overexpressed in all three comparisons although with the highest fold change seen in R-S (FC: 17.15)  and other included a hypothetical protein ortholog of the AGAP000603 in : 17.15) . This ge: 17.15) . The sg2: 17.15) .Several detoxification genes or resistance-related genes were commonly and significantly overexpressed in R-S and C-S strains. Among the most overexpressed genes commonly observed in R-S and C-S were proteases such as a trypsin-related protease (Afun008293), which was the top upregulated with FC 133.61 in R-S and 86.53 in C-S. Several detoxification genes were commonly upregulated in both strains, with cytochrome P450s being the most; notably, CYP9K1 (three probes) overexpressed with FC 13.68 in R-S and 31.80 in C-S. Two other genes CYP9J11 and CYP9J3 were also up-regulated with FC 5.44 in R-S and 4.38 in C-S for CYP9J11 and FC 3.72 in R-S and 2.72 in C-S for CYP9J3. CYP6M7 was also overexpressed with FC 3.90 in R-S and 2.94 in C-S . GlutathA set of five transcripts belonging to cytochrome P450 genes were commonly upregulated in R-S and R-C, with CYP4H17 , CYP4H19 , CYP4K2 , CYP4D15 , and CYP6M1b  . The traCYP6AH1 (two probes), CYP6M4, CYP6P5, CYP9J11 (two probes), CYP4H16, and CYP4C36. Three other genes, argininosuccinate lyase , chymotrypsin 1 , glycine dehydrogenase  (CYP302A1 (two probes) CYP6M2, CYP4C25 (three probes), and three glutathione-S-transferases (GSTs) GSTe2 GSTe3, GSTe4, were upregulated only in the R-C strain (Probes from other detoxification genes were uniquely upregulated in a comparison. Those found only in R-S included the cytochrome P450s: CYP6M7 (five probes), C, 8.53)  and fourC, 8.53) . Other dC strain .P < 0.01). Overall, 3553 probes were differentially expressed in the Arua population, 5903 in the Bulambuli population, 9263 in the Lira population, 7346 in the Tororo population and 5598 in Kisumu. When comparing the four Uganda populations, a total of 1420 probes were commonly differentially expressed . The number of probes that were differentially expressed  between R and S mosquitoes for each locality and between them is indicated in xpressed  whereas xpressed .CYP6Z1 (two probes), CYP9K1, and CYP6AG1. However, the overexpression levels of some candidate genes show significant geographical variation between locations. In Arua, CYP6M7 (FC 115.88) has the highest overexpression levels than other locations. Another P450, CYP4H18, although commonly expressed in all five localities, was significantly present in Arua than other localities, suggesting a bigger role for this gene in Arua and supporting a differential expression in Arua compared to other Uganda locations.Among the detoxification genes, several cytochromes P450s were most commonly overexpressed in all the five locations. Most of these P450 genes showed a similar level of expression in all the five localities and included This shift in expression pattern was also observed for other genes commonly overexpressed in all five locations. These include Afun000500 (glycogen) and Afun007894 (Trypsin) with the highest overexpression in Arua (FC 39.71 and FC 4.96) .The unique glutathione-s-transferase GST gene (GSTe1) commonly up-regulated in the five locations had high FC values in Arua and Kisumu (FC 7.45 and FC 8.87 respectively) . Beside CYP4H17 was the most over-expressed P450 gene with the highest FC value in Arua (FC 21.68) followed by Bulambuli, Tororo, and Lira with FC values of 11.55, 7.51 and 7.45, respectively. Another over-expressed P450 was CYP9K1 (Afun007549) with the highest expression level in Arua  followed by Lira, Bulambuli, and Tororo with FC values of 13.68, 12.62 and 6.64, respectively. CYP4C36 was also overexpressed in Arua (FC 10.55) and had a similar expression level in Bulambuli and Lira (FC 4.44 and 4.07 respectively), whereas a lower fold change (FC 2.64) was observed in Tororo. CYP9J11 (Ortholog of CYP9J5 in An. gambiae) (Combined_c6791) had a similar expression level in the four localities  gene was commonly over-expressed in four localities with a similar expression level  followed by Tororo, Bulambuli, and Kisumu with FC values of 5.34, 3.04 and 2.74, respectively .CYP6M4 was the most overexpressed gene with the highest FC value in Tororo (FC 9.17) followed by Lira, Kisumu, and Bulambuli with FC values of 5.25, 4.99 and 3.73 respectively. Most of these P450 genes showed a similar level of expression in all the four localities and included CYP304B1, CYP6M1, CYP4K2, CYP4H16, CYP9J3, and CYP6M3  was over-expressed. Of the P450s, d CYP6M3 . The unid CYP6M3 .CYP325D1 was observed and showed high expression in Arua. For those overexpressed only in Arua and Lira, the P450 CYP6P1 and CYP6P9b were detected, although with higher expression in Arua  than in Lira . The CYP306A1 was also upregulated and showed a similar level of expression in both localities. The list of genes overexpressed only in Arua and Tororo was dominated by the CYP4C36 and CYP307A1 with higher overexpression in Arua for both genes , suggesting that both genes are mainly driving resistance in Arua. The GSTe2 was common to both localities with FC, 3.06 in Arua vs. FC, 2.15 in Tororo.Analysis of the list of genes commonly overexpressed in only two localities revealed that, for Arua and Bulambuli, only CYP6M4, CYP9K1, CYP6P9b, CYP304b1, CYP6M7), one GST (GSTe2), one protease (Trypsin1) and one carboxylesterase Afun009227 (c_9227) were selected for validation with microarray data using qRT-PCR. A positive and significant correlation  was recorded between qRT-PCR and microarray fold change measurements (Eight transcripts overexpressed in resistant samples including five cytochrome P450s (urements .An. funestus in Uganda (notably between Arua and others) and Kenya could suggest the presence of barriers to gene flow that are affecting the spread of resistant genes. However, knowledge of the population genetic structure of An. funestus in Uganda and Kenya is limited.The significant differences in the gene expression profiles observed between populations of An. funestus females were scored at 17 microsatellites loci. These microsatellite loci were highly polymorphic with a number of distinct alleles per locus ranging from 8 (AFUB12) to 20 (FUNO) for the combined five populations (P = 0.92). However, Kisumu showed the lowest number of alleles for many loci with the minimum of 3 alleles observed for AFUB12 in this location. Mean observed heterozygosity across all loci ranged from 0.689 (Tororo) to 0.717 (Arua) and was not significantly different among populations (P = 0.85).Genotypes of ulations . The aveFIS estimates (A significant heterozygosity deficit was observed in 29 out of 85 tests across the markers after Bonferroni correction at (P< 0.01) across the markers as shown by stimates . Some mastimates .FIS = 0.135; P<0.01) was observed within each population studied due to significant heterozygote deficiency  to 0.037 (Arua- Kisumu). The highest significant FST estimates were obtained between Arua and other localities  suggesting the presence of barriers to gene flow between these Arua and these locations  (FST estimates between Arua and other populations at some loci such as FUNO (0.27–0.32) and AFND6 (0.04–0.091). These two loci are located on the 2R chromosome at the vicinity of a major QTL  previously associated with resistance to pyrethroids and close to a cluster of cytochromes P450s. This high FST estimates could suggest a difference in selection pressure between Arua and other locations in this genomic region in association with a difference in resistance mechanism supported by the variation in gene expression patterns from the microarray study. In contrast, the four locations of Bulambuli, Lira, Tororo, and Kisumu showed a very low and nonsignificant FST pairwise estimate, suggesting a high level of gene flow between these populations (FST / (1-FST) against the natural logarithm of pairwise geographical distance , suggesting that the population genetic structure of An. funestus in Uganda and Kenya did not conform to isolation by distance model  . Analysiulations . The segce model  and suggo method . In agre cluster .An. funestus mosquitoes is spreading throughout Africa, threatening the success of malaria control methods. In this study, we characterized the mechanisms driving insecticide resistance in An. funestus population from Uganda and neighboring border town in Kenya. We further investigated the population genetic structure in the malaria vector An. funestus to help predict the pattern of spread of resistance and improve the design of insecticide resistance management strategies.Insecticide resistance in An. funestus populations in Uganda and neighboring Kenya are resistant to permethrin [An. funestus population had been reported in several countries in Africa as the main detoxification enzymes implicated in pyrethroids resistance [CYP6M7, previously shown to confer type I and II pyrethroid resistance in field populations of An. funestus in Africa [rmethrin . Microarrmethrin , 44, 45.sistance . Howeversistance , 12 suggsistance . A clearn Africa , 47. Then Africa , in a son Africa , supportCYP9K1 and CYP9J11, indicating a possible link to the increased pyrethroid resistance. CYP9K1 had already been implicated in pyrethroid resistance in Anopheles gambiae in Africa [CYP6P1, CYP6P9b, CYP6Y2) although with low fold change but showing high expression in Arua. Surprisingly, one of the duplicated P450 genes CYP6P9a, which has been shown to play a main role in pyrethroid resistance in southern populations of An. funestus [CYP6P9a indicates that the resistance mechanism in Uganda and neighboring Kenya is different from that observed in Malawi [Other P450 genes belonging to the CYP9 family were overexpressed in Arua including n Africa . In addifunestus , 14 was n Malawi .FST values have shown a greater genetic differentiation between Arua and other localities  suggesting the presence of barriers to gene flow. In addition, An. funestus populations from Uganda and neighboring Kenya are subdivided into two distinct genetic entities: Population around the Rift Valley (Arua) and populations from other side of the valley . The existence of these two genetic entities was confirmed by different genetic approaches (i.e. Structure and genetic differentiation). This result confirms the fact that another factor explains the population genetic structure of Anopheles funestus in these localities. This observation suggests the impact of the Rift valley as a barrier to gene flow between populations of An. funestus in this region. Our results corroborate the previous microsatellite study in An. gambiae in Uganda, where Rift valley shows a great barrier to gene flow [FST at two loci located at the vicinity of a known pyrethroid resistance genomic region (rp1) [The reasons for the potential shift in the expression levels of the genes remain unknown but could be due to the nature of the selection that gave rise to the resistance. This shift in gene expression further highlights the genetic plasticity of natural populations of malaria vectors and their ability to adapt to various selection pressures. The finding of geographical differences in the role of key resistance genes in Arua suggests the presence of barriers to gene flow. ene flow , 17. Howon (rp1) , suggeston (rp1) . It willAn. funestus [The genetic test of isolation by distance suggested no significant correlation between genetic diversity and geographical distance confirming that this difference could be due to ecological or geographical factors . The caufunestus .An. funestus populations , suggesting that overall there is a high level of gene flow between populations of this species across most of Uganda showing a genes of interest such as insecticide resistance genes or future gene drive constructs, could spread quickly among populations of this vector. A previous study on the genetic structure of An. funestus populations using microsatellites markers implemented in other parts of Africa have already demonstrated the high gene flow [An. funestus populations from Bulambuli, Lira, Tororo, and Kisumu, revealed by our analysis, indicates the existence of inter-connected continuous populations of this malaria mosquitoes. Such observations were already reported in other genetic studies in the populations of An. funestus and An. gambiae [When Arua was excluded, our results showed low levels of genetic differentiation between ene flow –54. The  gambiae , 56.The genetic structure and variation in gene expression could be used to make an informed decision in future interventions especially as new insecticides are needed to control malaria across these countries. In addition, the finding of differences in the molecular basis of resistance of permethrin within a given country means that national resistance management strategies without characterization of underlying resistance mechanisms from localities may be flawed. The similarity of resistance profiles in Bulambuli, Lira, Tororo, and Kisumu, suggests that the same resistance management strategy could be implemented across these localities but might need to be different in Arua. These results have added to our understanding of the dynamics of vector species and will be valuable for planning effective vector control activities based on population genetic structure and resistance genes in Uganda and neighboring Kenya.S1 Table(DOC)Click here for additional data file.


Teli (a Hindu caste) populations of Maharashtra, India using 20 autosomal Short Tandem Repeat (STR) genetic markers. We further investigated the genetic relatedness of the studied populations with other Indian populations.This study was planned to evaluate the genetic diversity in the admixed and –25 and 5.01 × 10–24, respectively. The results of Neighbor-Joining tree and Principal Component Analysis showed that the studied populations clustered with the general populations of Jharkhand, UttarPradesh, Rajasthan and Central Indian States, as well as with the specific populations of Maharashtra (Konkanastha Brahmins) and Tamil Nadu (Kurmans). Overall, the obtained data showed a high degree of forensic efficacy and would be useful for forensic applications as well as genealogical studies.The studied populations showed a wide range of observed heterozygosity viz. 0.690 to 0.918 for the admixed population and 0.696 to 0.942 for the Teli population. This might be due to the multi-directional gene flow. The admixed and Teli populations also showed a high degree polymorphism which ranged from 0.652 to 0.903 and 0.644 to 0.902, respectively. Their combined value of matching probability for all the studied loci was 4.29 × 10 Marathi' is the native and official language of the state, several regional languages and their dialects are also spoken across Maharashtra, because people from different regions such as Biharis, Gujaratis, Sindhis, Punjabis, Parsis, Marwaris, Kannadas and Tamilians are settled across the state [Teli caste. The 'Teli' community derives its name from Sanskrit word 'talika' or 'taila' which means oil and it indicates towards the traditional occupation of the Teli community which was to extract oil from sesame and mustard seeds. One of the Hindu mythological references of the Teli caste indicates that the first Teli individual was created by 'Lord Shiva' to rub him with oil [The state of Maharashtra is located in the western peninsular region of India. It is the third-largest state by area and the second-most populous state in the country. It shares its geographical boundaries with the states of Karnataka and Goa in the South, Telangana in the South-east, Chhattisgarh in the East, Gujarat and Madhya Pradesh in the North, Dadra-Nagar Haveli in the North-west, and the Arabian Sea in the West Fig. . As per he state .The popuhe state , 4, and he state , 5–7. Nowith oil .Fig. 1a Teli populations of this state. The genomic data of the selected populations was evaluated using in-silico or computational techniques through various population data software and servers such as GeneMapper™ ID-X, Arlequin v3.5, POPTREE2, PAST 3.02a, etc. The in-silico techniques have served as an efficient approach for the evaluation of very large genomic data sets such as STRs, SNPs, large sequence and NGS data [The overall social, cultural, and lingual diversity of the state of Maharashtra led us to evaluate the genomic diversity of the admixed and NGS data –12 becauTeli population of Maharashtra, we randomly selected 158 and 69 unrelated healthy adults, respectively.The subjects in the admixed group belonged to almost all the population groups residing in the state of Maharashtra and hence represented the diverse population of Maharashtra. On the contrary, the subjects in the Teli group were recruited only from theTeli community. An online randomization tool-the randomizer (www.random.org) was used to randomly allocate subjects to each group, prior to the sample collection.To investigate the genetic diversity of the admixed and TMID-X v1.5 software . Positive and negative controls were used in the experiment to assure the quality control. Additionally, the authors conducting this study have passed the proficiency test conducted by GITAD, Spain .First, an interview was conducted to confirm that each participant’s ancestors have been residing within the geographical boundaries of Maharashtra for more than three generations. Next, blood samples were collected from each participant following the ethical guidelines and the declaration of Helsinki . The colThe obtained genetic data was analyzed using statistical software.The GenAlex 6.5 software  was usedTeli population group. The locus D3S1358 showed minimum allele number of 5, and loci Penta E and D21S11 showed maximum allele number of 19 in the admixed population group. On the other hand, in the Teli population group, the loci D3S1358 and TPOX showed minimum allele number of 5, and locus Penta E showed maximum allele number of 17. The range of allele frequencies for the admixed and Teli population group were 0.003 to 0.427 and 0.007 to 0.435, respectively. Allele 11 of locus TPOX was observed to be the most frequent allele in both admixed  value in the admixed (0.690 to 0.918) group as well as the Teli (0.696 to 0.942) group might have resulted from the inflow of genes in the studied populations from various directions.The obtained forensic efficacy parameters for the admixed and Teli population group ranged from 0.849 (TPOX) to 0.974 (Penta E), with the combined value for all the studied loci as 1, for both the groups. In the admixed group, the power of exclusion (PE) ranged from 0.413 (CSF1PO) to 0.832 (D1S1656) with the combined value for all the studied loci as 0.999999998666, whereas in the Teli group,the PE range was 0.422 (D5S818) to 0.882 (D1S1656) with the combined value for all the studied loci as 0.999999999652. The combined value of matching probability for all the studied 20 autosomal STR loci was found to be 4.29 × 10–25  for the admixed population group ranged from 0.857 (TPOX) to 0.980 (Penta E) and the PD for the Teli) populations and the reported Indian populations namely, the Konkanastha Brahmins (Maharashtra) [Mahadev Kolis (Maharashtra) [Iyengars (Tamil Nadu) [Kurumans (Tamil Nadu) [Yerukulas (Andhra Pradesh) [Koras  [Baniyas (Punjab) [Teli populations of Maharashtra pooled into one cluster with the Konkanastha Brahmins of Maharashtra and the Kurumans of Tamil Nadu. The populations of Rajasthan, Uttar Pradesh, Madhya Pradesh and Jharkhand also pooled with the studied populations, which might be the result of ancestral relatedness [Koras of West Bengal, the Baniyas of Punjab and the pooled populations of Indian geographical region were observed to be the outliers in the NJ tree, which could be attributed to the isolation on the account of distance [Mahadev Koli population of Maharashtra, despite being geographically close to the studied populations, showed genetic distinction, which might be the result of small effective sample size, the founder effect and drift [A neighbour joining (NJ) tree Fig. a based orashtra) ; the Mahrashtra) ; the Iyeil Nadu) ; the Kuril Nadu) ; the YerPradesh) ; the Kor Bengal) ; the Ban(Punjab) ; the pop(Punjab) ; the pop(Punjab) ; the pop(Punjab) ; the pop(Punjab) ; and the(Punjab) . The NJ atedness , 4. The distance . The Mahnd drift . The maxst distance calculation between the studied and compared populations were undertaken. In the PCA plot , at nine loci with the Koras , at seven loci with the Mahadev Kolis (Maharashtra), at four loci with the Konkanastha Brahmins (Maharashtra) and the Baniyas (Punjab), at three loci with the pooled Indian populations and the population of Rajasthan, at two loci with the Kurmans (Tamil Nadu), the Central Indian population and the population of Jharkhand, and at one locus with the Iyengars (Tamil Nadu) and the population of Uttar Pradesh. Similarly, the results of pair-wise Fst distance analyses in the Teli population group also showed significant variations at the ten loci with the Koras , at six loci with the Mahadev Kolis (Maharashtra), at four loci with the Konkanastha Brahmins (Maharashtra) and the Yerukulas (Andhra Pradesh), at two loci with the Baniyas (Punjab), the population of Jharkhand, the population of Uttar Pradesh and the pooled Indian populations, and at one locus with the Iyengars (Tamil Nadu), the Kurmans (Tamil Nadu), the population of Rajasthan and the Central Indian population. No significant variations were observed in the studied populations among all the compared 15 loci  and locus-wise Flot Fig. c, both tst distance study were found to be consistent with each other, and support the genetic relatedness observed in the neighbourjoining tree and the maximum likelihood phylogram.Overall, the results of the Principal Component Analysis and the FSince the obtained genetic data showed a high degree of polymorphism and forensic efficacy,it might be useful for forensic DNA application, genetic and genealogical studies, and may enrich the national autosomal STR database.The small sample size was the main limitation of this study. However, the analyzed samples well explain the polymorphic nature of the studied genetic markers and the genetic affinity of the studied population with the previously reported populations. We further propose the use of larger sample size and Next Generation Sequencing (NGS) studies.Additional file 1: Table S1. Allele frequencies and forensic parameters for the 20 autosomal STR loci in the Teli population of Maharashtra, India (n=69).Additional file 2: Table S2. Fst pairwise genetic distances between the admixed population of Maharashtra and the compared populations with their corresponding p-valueFst pairwise genetic distances between the admixed population of Maharashtra and the compared populations with their corresponding p-value.Additional file 3: Table S3. Fst pairwise genetic distances between the Teli population of Maharashtra and the compared populations with their corresponding p-value.


Bubo bubo) is distributed throughout Asia and Europe and contains approximately twelve subspecies. Three subspecies, B. b. kiautschensis, B. b. ussuriensis, and B. b. tibetanus, are separately distributed in the refugia and plateau habitats of China. However, the genetics of these subspecies and populations have not been studied. Genetic differences were investigated among 32 individuals from six populations of these three B. bubo subspecies based on the mitochondrial genome. Low genetic diversity but high haplotype diversity was observed in these subspecies. The phylogenetic relationship of three B. bubo subspecies distributed in China was proven to be coordinated with geographic and environmental gradients. This study provides the first detailed insights into the mitochondrial genetic diversity of three Eurasian eagle-owl subspecies distributed in China and demonstrates the utility of the mitochondrial genome in intraspecific genetic population analyses of these eagle-owls.The Eurasian eagle-owl ( Bubo bubo) is a large owl that occurs in a wide range of habitats in Asia and Europe , which is one of the country’s species richness hotspots. It has been proposed that the topographical complexity of this region would provide stable habitats during ice ages (Hewitt B. b. ussuriensis and to the Qinghai-Yunan Plateau of China (QYC) regions with a distribution of B. b. tibetanus  and Hubei (HB) populations of B. b. kiautschensis; Qinghai (QH) and Yunnan (YN) populations of B. b. tibetanus; and Beijing (BJ) and Heilongjiang (HLJ) populations of B. b. ussuriensis . Haplotype diversity (ĥ) and nucleotide diversity (π) were calculated with DnaSP v.5.0  from a single feather calamus and amplified the mitochondrial DNA genome using a set of seven primer pairs . The subspecies B. b. kiautschensis contained an ancestral haplotype  with a cumulative frequency of 28%.The mitogenome sizes of these individuals of B. bubo contained the typical 13 PCGs, 22 tRNA genes, two rRNA genes, and two control regions (CRs). Duplicate CR copies were observed in all of the tested individuals, and there was a moderate sequence similarity (75–94%) between the two copies. Interestingly, we did observe 1- and 3-bp deletions/inserts in three loci in CR1 and 1-, 2- and 4-bp deletions/insertions in three loci in CR2, all of which were not located in CR-specific conserved blocks. In general, the structure of the mitochondrial genome was found to be conserved among the three subspecies by sequencing the whole mitogenomes of 32 individuals.The mitogenome of Table S2).Protein-coding genes of the mitogenome separately produced 1–22 polymorphic sites in all of the tested individuals, among which 20 sites were synonymous and 30 sites were non-synonymous. The ATP8 gene and most of the tRNA-coding genes were not polymorphic within the studied samples, and only tRNA-Phe, tRNA-Val, and tRNA-Glu were variable in these individuals  and a low level of nucleotide diversity (0.0009–0.0018) estimated based on the mitogenome sequences  and the most significant genetic differences among groups (CTF = 0.20) (Table S3). The results illustrated genetic differences between the HLJ and BJ populations of B. b. ussuriensis and suggested that the two populations were significantly isolated.AMOVA was performed to further examine the genetic structures of Bubo bubo kiautschensis, B. b. tibetanus, and B. b. ussuriensis showed a well-differentiated relationship with a recent divergence. The median-joining network analysis showed a line-like topology, clustered according to geographical origin (PP = 1.0), but Clade II was poor-supported (PP = 0.78) (l origin . This re = 0.78) . Above a


Ruppia is one of these aquatic genera with a complex taxonomy due to the absence of clear distinguishable traits and several hybridization events. Two species co-exist throughout Europe, R. maritima and R. spiralis (previously known as R. cirrhosa), but recent molecular studies also found several indications of hybridization, introgression and chloroplast capture between these species. However, the full extent and frequency of hybridization and introgression in this genus has not been studied so far, nor is it clear how these hybrid lineages can co-exist locally with their parental species. In this paper, we wanted to detect whether a single coastal wetland where both species co-exist can act as a Ruppia hybrid zone. As a case study, we chose the Camargue, a Mediterranean coastal wetland that harbors a wide diversity in aquatic habitats, especially in terms of salinity and hydro-regime. We sampled several Ruppia populations within this wetland. To identify each sample and reconstruct the local genetic structure of the two parental species and their hybrids, we used both chloroplast and nuclear microsatellite markers. Afterward, we tested whether different species had different habitat preferences. Our results confirmed that R. maritima and R. spiralis are two strongly divergent species with different reproductive ecologies and different habitat preferences. This prevents frequent hybridization and consequently we could not detect any trace of a recent hybridization event. However, we found several populations of later-generation hybrids, including a population of R. maritima x hybrid backcrosses. The hybrid populations occupy a different habitat and are genetically distinct from their parental species, although they tend to be morphological similar to parental R. maritima. Although local hybridization and introgression in Ruppia is less frequent than we expected, the taxonomy of Ruppia is complicated due to ancient hybridizations and several back-crossings.In many aquatic plant taxa, classification based on morphology has always been difficult. Molecular markers revealed that the complexity in several of these aquatic taxa could be addressed to recurrent hybridization events and cryptic species diversity. The submerged macrophyte genus  Helianthus species found in extreme habitats such as the desert floor or salt marshes have hybrid origins  Grande  which ensure the flowers to reach the water surface. This allows so-called epihydrophilous pollination: male pollen float over the water surface to the female flowers for fertilization. R. maritima has short peduncles (<5 cm) and the flowers remain submerged  that end in an acute leaf tip whereas R. spiralis leaves are broader (4–11 mm) and end obtusely  Grande . Howeverm hereon . The twoubmerged . Their fubmerged . Other dobtusely . HoweverRuppia and to provide a barcoding tool to unambiguously identify Ruppia species. They confirmed that R. maritima and R. spiralis are two separate genetic entities that can be placed in different chloroplast haplogroups , R. spiralis has long spiraling peduncles (>5 cm) (R. maritima has narrow leaves (2–5 mm) with an acute leaf tip, although these tips can become more obtuse-like in older leaves. R. spiralis has broader leaves (4–11 mm), and obtuse leaf tips  . If floweaf tips . Some pomaritima .Ruppia populations could be categorized in three major habitat types: semi-permanent ponds, shallow temporary ponds and temporary ditches, which we, respectively, abbreviated in the population names as P, T, and D .Plant DNA was extracted from dried leaf tissue using the E.Z.N.A (R) HP Plant DNA Mini Kit Protocols . To genotype each specimen, we used a set of species-specific nuclear microsatellite markers that were combined in a multiplex reaction as well as a set of four chloroplast DNA markers. We used a QIAGEN multiplex PCR Plus kit to generate DNA-fragments with a PCR reaction in a thermal cycler (MJ research PTC-200 and Bio-Rad My Cycler). These fragments were run on an ABI3730XL sequencer . We manually scored the results with GeneMarker V2.20  that are known to amplify well in several but not all R. spiralis populations , that could be placed in a R. spiralis species complex. Although we considered amplicon length from a fragment analysis of these four cpDNA markers instead of their sequences, we should be able to distinguish at least both major species complexes . Although these markers are known to amplify in both species, the amplification success in  species . TherefoR. maritima marker set , the second PCA comprises all samples that are visualized with the R. spiralis markers  . We used R-package Adegenet for this analysis . The firlations) . To checlations) . DAPC islations) . GeneticR. maritima dataset, a pure R. spiralis dataset and a Ruppia hybrid dataset. For population genetic analyses, we used the software Genodive, which can work with both diploid and polyploid data /(N−1) in which G is the number of different genets and N the total number of individuals  whereas tetraploids have partial and full heterozygotes . Secondly, there is the problem of unknown allelic dosages in heterozygotes: if a sample has two different alleles for a single locus, e.g., A and B, it is difficult to deduct whether the true genotype is either AAAB, AABB or ABBB. This uncertainty strongly affects all allele-frequency based calculations . The sofient GST . Howeverient GST . Parametient GST . NeverthGST  and presence or absence of the genetic entity as a categorical variable with two levels. We counted the number of populations for each pond type where this group was, respectively, present or absent  had a chloroplast that could be placed in a R. spiralis species complex haplogroup. Using amplicon length of the chloroplast markers, we could clearly distinguish both species complexes, however this approach did not visualize all minor variants with certainty such as obtained from sequences.Based on the morphological identifications, we considered eight populations as rphology . When thR. maritima specific microsatellite markers had acceptable amplification success in these three populations, the observed amplification patterns strongly differ from pure R. maritima. While pure R. maritima had a very low genetic diversity (NA = 1.3) and no heterozygotes , populations T1, T2,‘ and T3 had a much higher allelic diversity (NA = 5.8) and at least some levels of observed heterozygosity (HOBS = 0.22). Besides, markers that had overall lower amplification success in the pure R. maritima populations , had even poorer amplification success in these three populations  were amplified with both sets of microsatellite markers. However, the ulations . Marker  results : populatR. maritima and all samples have a R. maritima chloroplast. However, these fourteen samples completely failed to amplify two microsatellite markers (RMV56 and RMB12), had several unique alleles for the remaining seven markers (NA = 3.1) and were the only R. maritima samples with some levels of observed heterozygosity . The assumed hybrid populations T1 and T2 were characterized exclusively by R. maritima diagnostic alleles . Within population T3, marker RM3 did not amplify and marker RUMR4 was characterized by a unique allele of 131 bp which was not observed before in any other Ruppia population . Marker RM3 also detected four new alleles R. spiralis  but none of them was encountered in T1, T2, and T3.To detect hybrids with the pulation . In popuR. spiralis chloroplast, except for populations T1, T2, and T3 . In population D1b, 167 bp was also the most dominant allele  out of four detected alleles . The assumed hybrid populations T1, T2, and T3  are almost completely monomorphic for allele 173 (97%) and entirely homozygous for this marker.Next, we considered the three cross-amplifying markers that were included in both marker sets. Markers RMB5 and RMB53 had very bad amplifications in populations with a , and T3 . Furthertion D1b . Marker R. spiralis microsatellites. Within the R. spiralis specific set of markers, a group of four microsatellite markers  did not amplify in populations T1 and T2, another set of three markers  failed to amplify in population T3 . The first hybrid cluster contains populations T1, T2, and D1b, the second only population T3. This clustering pattern was confirmed with the DAPC . The T1-T2 hybrid cluster is spread over a larger area, comprising more variation. However, some samples of population T2 can be found in the T3-cluster. This is confirmed in the DAPC based on the R. maritima samples  and no observed heterozygosity (HOBS = 0.00). This results in a low clonal diversity (R = 0.04), with only six different MLGs for all pure R. maritima samples (p > 0.05) and all samples were kept in the dataset for further analyses. Based on other parameters, these MLGs are rather the result of strongly inbred lineages: seven loci were monomorphic  and hence removed for further species-specific analyses (GIS = 1). As shown in ST and ρ were very high . These parameters are based both on observed and expected heterozygosity, hence the absence of observed heterozygosity in all samples will give a distorted image. Allelic differentiation (DEST = 0.41), based on the number of effective alleles, was less biased than GST and ρ but is affected by the low number of polymorphic loci. The PCA and DAPC based on the R. maritima marker set confirmed that there is no strong population structure in this pure R. maritima dataset . Testing for clonal structure revealed that these MLGs are truly the result of clonal growth (p < 0.05) and hence only one sample of each MLG is kept. Three populations had one or two MLGs that occurred twice . We could not confirm that these MLGs were true clones (p > 0.05) and all samples are kept. The R. spiralis populations had 11.8 alleles per locus on average, but the mean number of effective alleles is 2.5, thereby indicating low evenness (HOBS = 0.66 and HEXP = 0.59) was comparable but there were large differences among loci, possibly partly as a result of tetraploidy. Overall GIS for R. spiralis was negative (−0.18). We found little population differentiation , which is concordant with the observed pattern in both the PCA and DAPC , without a clear population structure. Changing the value of K did not affect the pattern of hybrid clustering.The twelve pure richness . Only twevenness . The oveand DAPC . Within R. spiralis multiplex detected 40 alleles in the hybrid samples, of which ten were unique for the hybrids. Six of them were found exclusively in T3, sometimes strongly differing in allele lengths from the pure R. spiralis populations. Besides, there was also little overlap in alleles between the T1-T2 cluster and the T3 cluster. The R. maritima multiplex detected 64 different alleles in these hybrid samples, whereas only 17 alleles were found in pure R. maritima. Except for one unique allele, all R. maritima alleles were re-encountered in the hybrid populations. Contrary to the R. spiralis marker set, population T3 has the lowest number of alleles per locus of these hybrid populations and only five unique alleles. Population T1 has the largest number of R. maritima alleles  and T3 (R = 0.5) but clonal structure could not be confirmed for T3 (p > 0.05), probably due to the low number of alleles. Adding the R. spiralis markers, no clones could be detected anymore in T2. Overall, clonal richness was estimated at 0.89 with both sets of microsatellites. Considering gene diversity, we found especially large differences among the R. spiralis markers: three loci  were fixed heterozygotes for one or more hybrid populations, other loci were completely monomorphic   . The obsRuppia maritima was found exclusively in temporary ditches, the hybrids exclusively in the shallow temporary ponds and R. spiralis was most often encountered in large semi-permanent ponds (p < 0.05 for each contingency table).nt ponds . The FisRuppia that co-occur within a single Mediterranean wetland. We particularly focused on the presence of hybrid lineages. In addition, we combined both genetic and ecological data to detect differences in ecology and habitat use between these different Ruppia lineages. Our results show that both R. spiralis and R. maritima can co-occur within the same wetland area but have different reproductive ecologies and habitat preferences. We also detected several populations of later-generation hybrids. These hybrid populations occupied a distinct habitat, different from the habitat of the parental species. We could not detect any signs of recent hybridization events (F1 hybrids) or frequent introgression between R. spiralis and R. maritima, although we detected one recently backcrossed lineage, presumably between a local hybrid population and R. maritima.In this paper, we used genetic tools to identify different lineages of the genus R. spiralis, R. maritima and the hybrids. R. spiralis was mainly found in large semi-permanent ponds but also occasionally detected in the two temporary habitat types. Previous molecular and detailed ecological studies already revealed that R. spiralis has a strong preference for more permanent waterbodies, including some marine environments . Subsequently, we considered these hybrid populations as R. maritima during sampling. Previous reports of Ruppia hybrids also noticed that they wrongly identified Ruppia hybrids as Ruppia maritima in the field .The microsatellite data for both species are available at Dryad (doi: LB has performed the fieldwork and laboratory work, analyzed the data, and wrote the manuscript. LT helped during the field work and contributed laboratory equipment. LT and BV helped during data-analysis and manuscript writing. All authors contributed to the manuscript 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.


Epidemiology seeks to determine the causal effects of exposures on outcomes related to the health and wellbeing of populations. Observational studies, one of the most commonly used designs in epidemiology, can be biased due to confounding and reverse causation, which makes it difficult to establish causal relationships. In recent times, genetically informed methods, like Mendelian randomization (MR), have been developed in an attempt to overcome these disadvantages. MR relies on the association of genetic variants with outcomes of interest, where the genetic variants are proxies or instruments for modifiable exposures. Because genotypes are sorted independently and at random at the time of conception, they are less prone to confounding and reverse causation. Implementation of MR depends on, among other things, a strong association of the genetic variants with the exposure, which has usually been defined via genome-wide association studies (GWAS). Because GWAS have been most often carried out in European populations, the limited identification of strong instruments in other populations poses a major problem for the application of MR in Latin America. We suggest potential solutions that can be realized with the resources at hand and others that will have to wait for increased funding and access to technology. Mendelian randomization (MR) is a relatively new epidemiological method that aims to establish causal relationships between complex diseases and their risk factors. Since being described ~17 years ago ,3,4. NowThe majority of MR studies to date have been carried out in European populations, whereas their implementation in Latin American populations has been particularly lacking, although recently, a couple of pioneering undertakings were reported ,7. Part Here, we examine the routes that could lead researchers working on Latin American populations to the identification of strong IVs for modifiable exposure underlying the regional risk for complex diseases. A combination of the routes explored is likely to be the best approach to achieve the goal of setting the stage for a systematic implementation of MR in Latin American populations.These are analyses that combine data from local studies and are not restricted to one continental population. For instance, the 1982 Pelotas cohort from Brazil has been part of a number of so-called multi-ethnic, multi-ancestry, or trans-ethnic GWAS , wheThe studies included in multi-ethnic meta-analyses must satisfy certain requirements in terms of sample size, extent of genotyping, and phenotyping. Therefore, this is a route that not every Latin American study may be able to follow. For the most part, multi-ethnic meta-analyses at the moment consist of individuals of European descent, with individuals of other ancestries accounting for a much smaller proportion. https://www.pewresearch.org/hispanic/fact-sheet/latinos-in-the-u-s-fact-sheet/). Nevertheless, it should be mentioned that there are several ongoing studies in the US that have prioritized the collection of data on Hispanic populations and have recruited considerable numbers of Latinos, including (but not limited to) the Women’s Health Initiative , the Study of Latinos , the Multi-ethnic Study of Atherosclerosis , and the Multiethnic Cohort . The Population Architecture using Genomics and Epidemiology (PAGE) study (https://www.pagestudy.org/), which comprises two of these cohorts plus the Icahn School of Medicine at Mount Sinai BioMe biobank, is dedicated to increasing representation of ancestrally diverse populations living in the US in genetic epidemiology research ).http://www.elsa.org.br/oelsabrasil.html [DNA do Brasil. It is particularly interesting because there are already a lot of epidemiological data on ELSA participants, and the addition of genetic information will generate a coveted resource for genetic epidemiologists. A public–private initiative to sequence the genome of 15,000 Brazilians from the Estudo Longitudinal de Saúde do Adulto  study iWhen running large-scale studies in a local population is not possible, there is the option of trying to validate genetic variants associated with the risk factor of interest in Europeans. Most contemporary Latin American populations originated from the admixture between Native Americans, Europeans, and Africans; thus, they may have inherited genetic risk factors from these parental populations. Examples of this pathway can be found in Mexico, where SNPs associated with BMI in Europeans were tested in Mexican children and adults ,26. EuroThere has been a lot of candidate gene research carried out in Latin America  polymorphism rs9939609 with body mass index (BMI) and obesity in the Brazilian population, by performing a PubMed and LILACS search , and searches for FTO and rs9939609 in the Biblioteca Digital Brasileira de Teses e Dissertações (http://bdtd.ibict.br/vufind/). Additionally, we considered papers that investigated the association of rs9939609 with other traits in order to extract the allele frequency of this SNP in the particular Brazilian population under study. FTO (and SNP rs9939609) was one of the first genes to be detected as strongly associated with BMI in GWAS performed in populations of European descent [https://www.ebi.ac.uk/gwas/). The A allele is associated with an increase in BMI or the risk of obesity. The average frequency of the A allele in Europe is 0.414, in populations of the Americas it is 0.262, and in African populations it is 0.494, according to the 1000 Genomes database .As an illustration, and with the caveat of not being a formal systematic review and meta-analysis, we have compiled all data we could find on the association of the fat mass and obesity-associated gene , while 12 were done on special groups, such as individuals who underwent bariatric surgery, or diabetes patients (https://www.who.int/en/news-room/fact-sheets/detail/obesity-and-overweight), which included 1873 individuals . We found that individuals who were homozygotes AA had 1.42 times  greater odds of being overweight or obese than individuals who were homozygotes for the T allele (2 by genotype (N = 1420). Results in this case showed no evidence of association with rs9939609 . The weighted average frequency of the A allele for different subgroups is shown in FTO and BMI or obesity, there were quite a few that investigated other outcomes, interactions, and patient populations, in certain cases assuming an underlying FTO–BMI association. Second, regression analysis of this relationship was infrequent; therefore, so was adjustment for covariates that may confound it. Third, there was limited mention of confounding by population stratification, despite the well-known admixed background of Brazilians; nonetheless, some studies attempted to take this into account, adjusting for skin color, race, or ethnicity. Only a couple of studies ran models adjusted for principal components [Some conclusions can be readily extracted from this succinct analysis. First, while several studies examined the relationship between mponents  or genetmponents . Fourth,Unfortunately, because of the disparities between studies, including more of them in meta-analyses was not possible. As a consequence, we would like to propose that authors report findings more thoroughly, by submitting as much detail as possible on the information listed in We have offered a brief assessment of the potential of using genetic data from Latin American populations in future studies employing MR or other similar genetically informed methods of causal inference. For MR to work, strong instruments for the exposures of interest are required; hence, we believe this should be our initial focus. Latin America is a large, diverse region, which includes 20 countries and 14 dependent territories, and is home to almost 645 million people. In addition, there are ~60 million Hispanic/Latinos presently living in the US. Although Latin American populations share a complex genetic background emerging from past conquest and colonization that brought together indigenous peoples, European settlers, and enslaved Africans, each has also experienced a distinct history of admixture and adaptation.https://www.ukbiobank.ac.uk/) or the China Kadoorie Biobank (https://www.ckbiobank.org/site/), could potentially be replicated in Latin America’s largest countries, of which Mexico is leading the way (https://mxbiobankproject.org/). Combining these efforts and subsequently adding the smaller countries would generate a major resource for the region’s public health system, which would be worth investing in.No study will be able to capture this diversity in its entirety, so unravelling the genetic basis of Latin American disease risk factors will be like putting a puzzle together, and each piece helps. Studies that can run GWAS or do whole-genome or exome sequencing will be highly valuable, as well as those that can become part of a multi-ethnic meta-analysis or a regional consortium, but the contribution of smaller studies within countries, in particular those where the former are not an option, is important too. Besides supplying evidence for the association of the genetic instrument with the risk factor, they are likely to contribute evidence of pleiotropic associations and associations with confounders, which may be sources of bias in forthcoming MR studies. In order to extract the maximum benefit, we must ensure the data are adequately reported and can be safely and rapidly shared, harmonized, and analyzed by collaborating researchers across the continent. While difficult to put together for practical and economic reasons, even larger initiatives, like the UK Biobank (