Whole-genome re-sequencing data from 10 geographically isolated Tibetan pig populations were collected and analyzed in this study. Population genetic analyses, including Principal Component Analysis (PCA), phylogenic tree, genetic differentiation, deleterious variant, contribution to meta-population genetic diversity and selective sweep were performed. Limited genetic differentiation was identified among these Tibetan pig populations. Most deleterious variants were low-frequency mutations and population specific. Contribution to the meta-population was largest in the TT population, based on gene and allelic diversity. Genes under selection were involved in hypoxia adaptation, hard palate development, facial appearance, and perception of smell. Several geographically isolated populations of Tibetan pigs inhabit the high-altitude environment of the Tibetan Plateau. Their genetic relationships, contribution to the pool of genetic diversity, and their origin of domestication are unclear. In this study, whole-genome re-sequencing data from 10 geographically isolated Tibetan pig populations were collected and analyzed. Population genetic analyses revealed limited genetic differentiation among the Tibetan pig populations. Evidence from deleterious variant analysis indicated that population-specific deleterious variants were the major component of all mutational loci. Contribution to the meta-population was largest in the TT (Qinghai-Tibet Plateau) population, based on gene diversity or allelic diversity. Selective sweep analysis revealed numerous genes, including RXFP1, FZD1, OR1F1, TBX19, MSTN, ESR1, MC1R, HIF3A, and EGLN2 which are involved in lung development, hard palate development, coat color, hormone metabolism, facial appearance, and perception of smell. These findings increase our understanding of the origins and domestication of the Tibetan pig, and help optimize the strategy for their conservation.  Tibetan pigs originated on the Tibetan Plateau, and the record of their breeding by humans can be traced back to the 7th century . TibetanIn this study, using whole-genome resequencing data, we analyzed the genetic relationship among the 10 geographically isolated Tibetan pig populations as well as between Tibetan pigs and wild pigs. Gene and allelic diversity of each Tibetan pig population was calculated to identify their contributions to the population pool. Finally, selective signatures in the genomes of Tibetan pigs were scanned to identify the genes involved in adaptation to the harsh environment of the Tibetan plateau. A total of fifty Tibetan pigs from 10 geographically isolated populations and 15 wild pigs were used for this study. As reported in a previous study, all Tibetan pigs were collected from the Qinghai-Tibet Plateau, including Chinese Tibet, Sichuan, Yunnan, and Gansu province ,8. As deSscrofa11.1). SAMtools (v1.9) was used to merge and sort the BAM files [http://broadinstitute.github.io/picard/). Subsequently, SNP calling was performed for each individual pig using GATK4 HaplotypeCaller then joined using the GenomicsDBImport and GenotypeGVCFs programs from GATK4 (v4.1.2.0) [QD < 2.0 || FS > 200.0 || SOR > 10.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0”. Libraries with inserts of 500 bp were constructed for each pig. 100-bp paired-end reads were generated using the HiSeq2000 platform ,8. Raw r4.1.2.0) . To get http://vcftools.github.io/) [http://www.cog-genomics.org/plink2/). Before constructing the phylogenetic tree, high-density SNPs were pruned using PLINK (v1.9) with the parameters “--indep-pairwise 50 5 0.2”. The phylogenetic tree was constructed using the IQ-TREE software (multicore version 1.6.12) with 1000 bootstrap iterations based on the PHYLIP format data [Using VCFtools 0.1.16), biallelic SNPs located on autosomes were identified and kept (hub.io/) . Using Phub.io/) , variantmat data . The phy, biallelmat data .E), observed heterozygosity (HO), and the inbreeding coefficient for each Tibetan population were estimated using PLINK (v1.9). Nei’s minimum genetic distance and the gene frequency differentiation index (FST) between pairs of subpopulations were computed using Metapop v2 [Expected heterozygosity (Htapop v2 . Ensembltapop v2 . Using ttapop v2  their diS) and average Nei’s minimum genetic distance between subpopulations (DG) [T) was partitioned within and between the subpopulations. Average allelic diversity within subpopulations is calculated as the average number of alleles segregating in the subpopulations minus one (AS), while average allelic diversity between subpopulations is calculated as the average number of unique alleles in a subpopulation compared to the other subpopulations averaged over all possible subpopulation pairs (DA) [To estimate the maximal contribution to heterozygosity of each population, total heterozygosity was partitioned into the average expected heterozygosity within subpopulations (Hons (DG) . Similarirs (DA) . Using tirs (DA) . θπ [ST,F using a 10 kb no-overlapping window. To identify genomic regions under selection in Tibetan pigs, genome-wide θπ and STF were collected. Z-transformed STF and θπ ratios were analyzed using the scale function included with the R package. Genomic signatures were defined by a high Z-STF (Top 1%) and Z-θπ ratio . Genes located in the selective signature were obtained using R ‘biomaRt’ package [http://www.pantherdb.org).Selective sweeps were scanned with sequence diversity statistic θπ  and popu package , and theIn this study, sequencing data from 50 Tibetan pigs, from 10 geographically isolated populations of the Qinghai-Tibet Plateau, and 15 wild pigs were collected and analyzed . ApproxiTo investigate the genetic structure of 10 Tibetan pig populations and their relationship to wild pigs, we utilized principal component and phylogenetic analysis. For the first principal component (8.78%), no obvious segregation was observed among Tibetan pig populations, while significant differentiation was shown between Tibetan pig and wild pig populations A. EvidenFST) and pair-wise Nei’s minimum genetic distance (neiD) were examined among the Tibetan pig populations  and neiD . The greatest differentiation was observed between the GST and T3 populations based on FST (0.1904) and neiD (0.3261), while the least differentiation was between T2 and TT, based on FST (0.0775) and between TT and YNT based on neiD (0.1473). The smallest averages of differentiation and genetic distance were between TT and the other populations . This result is consistent with the population structure, as the TT population is located in a relatively central position of the PCA and phylogenic tree . The second-largest averages of FST and neiD were between T1 and the other populations . Collectively, these results are consistent with the population structure. Pair-wise genetic differentiation index  was calculated for each population based on whole-genome biallelic SNPs (Ho), 0.3678–0.4178 (EH) and 1.4769–1.6028 (RA). The maximum Ho was in the GST population (0.4385), followed by T3 (0.4036), and the minimum was in the T5 population (0.2379). The maximum EH was in the T3 population (0.4682), followed by AbaT (0.4178), and the minimum was in the TT population (0.3670). Some of the differences observed between EH and OH are due to different levels of inbreeding in each population. To assess the level of inbreeding within a population, average inbreeding coefficients for each were calculated. The range of inbreeding coefficients was 0.0083 (YNT) to 0.3537 (T5) (Genetic diversity (lic SNPs . The ran537 (T5) . The greSus cebifrons and Pietrain pig populations respectively [Conservation management aims to maintain genetic diversity to maximize survival potential and fitness . Often dectively . SimilarT, (0.7528%), followed by YNT (0.7056%) , followed by YNT (0.8696%). A gain of HT was the largest in the T5 population (−0.2737%), while the gain of AT was the largest in the T3 population (−0.6132%). T3 had the greatest negative contribution to both HS (−0.9713%) and AS (−0.9923%), implying this population had low within-population diversity (S (1.3501%) and AS (0.9260%), suggesting this population had the highest within-population diversity. This also could be explained by its high allelic richness (1.6028) and low average co-ancestry (0.7409) (G (−0.5973%) as its average Nei’s distance against others (0.1881) was the smallest among the populations   A. Similaiversity A, B. Thiiversity . The TT (0.7409) . In consulations . The TT (1.6028) . This ex(1.6028) C. ExpectFST (Top 1%) and, Z-θπ ratio   A. After  Top 1%) . Results Top 1%) .FZD1, DLG,1 and MMP25, were involved in hard palate development and craniofacial suture morphogenesis. FZD1 and its receptor are expressed ventral to the telencephalon and in periocular mesenchyme during the development of the face [DLG1 are associated with non-syndromic discontinuous cleft lip and palate [MMP25 expression appears to have a crucial role throughout all stages of palatal development [STF 7.69 and Z-θπ ratio 318.74) was identified in the region Chr 6:180001:190000. The MC1R gene is located in this selective region, which is reported to contribute to the black color of Chinese domestic pig [TBX19 is involved in the selection effect of domestication on the pig genome [TBX19 plays a crucial role in activating pituitary cell differentiation and induces proopiomelanocortin expression, which leads to generating the adrenocorticotropic hormone working in the hypothalamic–pituitary–adrenal axis [CYP2F1, CYP2A19, CYP2B22, CYP2B22, and CYP2S1, and ENSSSCG00000028627, and a lncRNA ENSSSCG00000041224 are located in a 120 kb selective region. ENSSSCG00000028627 is located in the peak of selective signal (Z-STF 8.15and Z-θπ ratio 4.23). These genes function in the synthesis of steroids, cholesterol and other lipids [MSTN and ESR1 were selected in the Tibetan pig in relative to wild pig. MSTN, a interesting gene, negatively regulates skeletal muscle cell proliferation and differentiation. Mutation in MSTN is associated with muscle hypertrophy [MSTN was selected in lowland domestic pigs relative to wild pig [ESR1 was also selected in Chinese domestic pigs relative to wild pigs [MSTN and ESR1 genes might be the result of gene flow from lowland pigs to Tibetan pigs. There were also three genes participating in sensory perception of smell, OR1F1, ENSSSCG00000037040, and ENSSSCG00000034150. OR1F1, an olfactory receptor, was confirmed to function in odor perception activation [In this study, we focused on some of the difference between wild and Tibetan pigs. These included facial appearance, coat color, hormone metabolism, growth, and hypoxia adaptation-related processes. Three genes, the face . Loss ofd palate . MMP25 eelopment . These gstic pig . TBX19 ig genome . TBX19 pnal axis ,36. Fiver lipids . It suggertrophy . MSTN wawild pig . ESR1 waild pigs , particiild pigs . Long-sttivation . RXFP1, CIC, MAPK8IP3, CCDC39, and SPRY1 (RXFP1 is directly associated with pulmonary function; low expression of this gene is found in diseased lungs [RXFP1, a relaxin family peptide receptor, protects astrocytes from hypoxia [HIF3A and EGLN2 were selected in Tibetan pigs. These genes are involved in the hypoxia pathway in Tibetan cattle [EPAS1 is considered a crucial gene in hypoxia adaptation of Tibetan pig [EPAS1 gene in this study. These previous studies aimed to find the candidate genes involved in hypoxia adaptation of Tibetan pig and used Chinese lowland native pigs as the control [Some interesting genes involved in lung connective tissue development and lung development included nd SPRY1 . RXFP1 ied lungs . RXFP1,  hypoxia . HIF3A an cattle . Unexpecetan pig ,7. Howev control ,7, This DUOX2 is a transmembrane protein located at the surface of thyroid follicular cells. Mutation in this gene is associated with congenital hypothyroidism [SULT1B1 is a protein gene involved in cytochrome P450 metabolism pathway [SCAPER mutation causes autosomal recessive retinitis pigmentosa with intellectual disability [Additionally, the integrated analysis identified three potential deleterious genes selected in Tibetan pigs relative to wild pigs. yroidism . SULT1B1 pathway . SCAPER sability . These dRXFP1, FZD1, OR1F1, TBX19, MSTN, ESR1, MC1R, HIF3A, and EGLN2 are involved in hypoxia adaptation, coat color, hormone metabolism, facial appearance, and perception of smell. These findings help us understand the origin and domestication of the Tibetan pigs, as well as optimizing conservation programs.Based on population genetic analysis, the Tibetan pigs living in various regions of the Tibetan Plateau have undergone slight genetic differentiation. Analysis of the distribution of deleterious variants indicated that population-specific deleterious variants accounted for the major component of loci with negative effects. Results from contribution to genetic diversity analysis demonstrated that the TT population made the highest contribution to gene diversity and allele diversity to the meta-population. Selective sweep analysis revealed that numerous genes, including


Nothoceros aenigmaticus J. C. Villarreal & K. D. McFarland. This hornwort was described from North Carolina and is widespread in the SA, growing on rocks near or submerged in streams in six and one watersheds of the Tennessee (TR) and Alabama (AR) Rivers, respectively. Males and female populations occur in different watersheds, except in the Little Tennessee (TN) River where an isolated male population exists ca. 48 km upstream from the female populations. The sex ratio of 1:0 seems extreme in each population. In this study, we use nuclear and organellar microsatellites from 250 individuals from six watersheds (seven populations) in the SA region and two populations from Mexico . We, then, selected 86 individuals from seven populations and used genotyping by sequencing to sample over 600 bi-allelic markers. Our results suggest that the SA N. aenigmaticus and Mexican plants are a nested within a clade of sexual tropical populations. In the US populations, we confirm an extreme sex ratio and only contiguous US watersheds share genotypes. The phylogenetic analysis of SNP data resolves four clusters: Mexican populations, male plants (Little Pigeon and Pigeon river watersheds) and two clusters of female plants; one from the Little Tennessee and Hiwassee Rivers (TR) and the other from the Ocoee (TR) and Coosa (AR) Rivers. All clusters are highly differentiated . In addition, our individual assignment analyses and PCAs reflect the phylogenetic results grouping the SA samples in three clades and recovering males and female plants with high genetic differentiation . Our results point to Pleistocene events shaping the biogeographical pattern seen in US populations. The extreme sex ratio reflects isolation and highlights the high vulnerability of the populations in the SA.The southern Appalachian (SA) is one of the most biodiversity−rich areas in North America and has been considered a refugium for many disjunct plant species, from the last glacial period to the present. Our study focuses on the SA clonal hornwort,  Syntrichia caninervis Mitt. In the Mojave Desert only 11 males were found among 700 individuals examined  is rare , most brPlagiochila corniculata Dumort. is known only from female plants in North America and all European individuals bear only male sex organs , suggesting a fine scale trade-off between sexual reproduction and clonality  hornwort quasi permanent aquatic habitat of N. aenigmaticus. These factors might contribute to the males’ inability to develop and release functional sperm cells  to estimate the divergence time of the split between US and Mexican populations, (ii) to verify that US populations of N. aenigmaticus are strictly clonal and (iii) to assess and confirm the extreme sex ratio and clonal structure of the species in the SA.The isolation of sexes in SA may account for the lack of known sexual reproduction, which would further be compromised by the premature abortion of motile male gametes . In addirm cells . In the N. aenigmaticus. Divergence time between Mexican and US populations will show when the apparent geographic isolation of the female and male gametophytes in the US started and potentially explain the causes of the extreme sex allopatry in Appalachian plants. We analyzed 10 microsatellite loci distributed across all genomic compartments  for 273 collections of the species from seven populations in the SA and two in Mexico. Additionally, we selected 86 individuals (80 from the US and six from Mexico) and used genotyping by sequencing (GBS) methods to sample SNP markers to document species’ genetic diversity and contrast it to the estimates based on microsatellites.We used a published dataset  to date N. aenigmaticus. Dating relied on a Bayesian divergence time estimation as implemented in BEAST 1.8.3 . We restricted the sampling to the genus Nothoceros (R. M. Schust.) J. Haseg, and three outgroup genera  based on Nothoceros , N. endiviifolius (Mont.) J. Haseg. (2), N. fuegiensis (Steph.) J. C. Villarreal (2), N. minarum (Nees.) J. C. Villarreal (2), N. renzagliensis J. C. Villarreal, L. V. Campos & Uribe (1), N. schizophyllus (2), N. superbus (1), N. vincentianus (4) and, N. aenigmaticus (7). For the latter, we sampled accessions from high tropical elevations  as well as samples from US and Mexico. One individual of N. giganteus (Lehm. & Lindenb.) J. Haseg. from New Zealand was also included. We used a single calibration on the most recent common ancestor (MRCA) of the genus Nothoceros. We implemented a uniform distribution with a minimum age of 16 Mya and a maximum age of 42 Mya, following the dates estimated by Nothoceros.We used a dataset derived from ST 1.8.3 . Our infthoceros , such as1 The substitution model GTR+ CAT was used with the unlinked partitions and the statistical support was evaluated using 500 ML bootstrap replicates using the same substitution model.Two tree priors were used (birth-death and Yule priors). Runs were performed using a relaxed clock model; the latter with uncorrelated and log-normally distributed rate variation across branches. The Markov Chain Monte Carlo (MCMC) was run for 30 million generations using the relaxed clock model, with parameters sampled every 1,000 generations. Tracer v1.5  was usedN. aenigmaticus comprised 273 individuals, of which 250 were from the SA and 23 from two localities in Mexico , and these were predominantly aquatic forms. In these cases, samples were cultured in the lab to verify the presence of sex organs. chegonia .N. aenigmaticus had rarely been collected and seems restricted to high elevation  in the Neovolcanic Belt , three plastid  and four nuclear  microsatellite loci were selected for use in the population study . All samBecause of the non-recombining nature of the plastid and mitochondrial genomes, each allelic combination at the three plastids (hereafter chlorotypes) and two mitochondrial (mitotypes) loci were treated as an allele at a single locus. The locus mit4 proved to be difficult to score in the Mexico and United States (US) individuals and was excluded from downstream analyses.P); haploid genetic diversity (h) and unbiased haplotype diversity (uh) were calculated.For the chlorotype and mitotype microsatellites, haplotype diversity was estimated as the number of haplotypes per geographical region divided by the total number of samples (P). In addition, the number of private haplotypes  was estimated for chlorotypes and mitotypes  and number of effective alleles (Ne) were calculated using GenAlex 6.4b5 .Given the clonal nature of the species, all microsatellites were analyzed together to determine the number of unique haplotypes among the clones. Clonal assignments were made using genetic distance under the stepwise model with different thresholds between individuals  were estimated. Additionally, we also calculated the ratio between genets (G) and the total number of ramets sampled (N). This ratio (G/N) is another measure of clonality where G is identified as unique multilocus genotypes by genetic markers. As G/N approaches 1/N, the signal of clonality is strong. The indexes were calculated in GenoDive version 2.0b2 .p-values estimated based on 1000 permutations using a stepwise mutation model. A K-Means clustering were done by calculating the allele frequencies removing the source of the individual genotypes, the K-means clustering does not assume Hardy–Weinberg equilibrium. The Bayesian information criterion and Calinksi and Harabasz pseudo –F were used to assess the optimal value of K using 50,000 simulated annealings and 20 random repeats. Additionally, an assignment analysis was conducted to assign an individual to a population based on the allele frequencies and determine potential migrants.Fst values were computed in GenoDive version 2.0b2 , and p-vWe included 86 collections from seven populations in the GBS approach . A doublprocess_radtags option from the pipeline software Stacks version 2.3 (–inline_null –renz_1 pstI –renz_2 mspI -c -r -t 125 -q -s 10. Sequences reads were mapped to the nuclear transcriptome of N. aenigmaticus (SRA: ERS368224) (gstacks option (Stacks pipeline) on the full dataset. We concluded the SNPs calling filtering with populations (Stacks pipeline) applied to two different datasets, one including all GBS individuals (86) and a second one excluding those from Mexico . We set up the maximum accepted heterozygosity at 0 (–max-obs-het) because all samples were haploid gametophytes. Minimum minor allele frequency was 0.05 (–min-maf). The setting populations provides filtering options to only include loci that occur at certain frequencies in each population. We considered all individual a single population and eliminated loci that were missing from more than 50% of individuals (–R).Raw data were assessed for quality with the fastQC version 0.11.3 . Reads dsion 2.3 . To unifS368224)  using thS368224)  and the S368224)  to conveS368224) . We creaN. aenigmaticus was performed for both dataset  including only unlinked SNPs (–write-single-snp). The matrices were used for a RAxML v8.2.9 . The maximum accepted heterozygosity was set up at 0 (–max-obs-het) and the minimum minor allele frequency was 0.05 (–min-maf).The genetic diversity was estimated for the SNP matrix including Mexico and US individuals (86). Statistics were calculated with the Stacks program genind object from the R adegenet version 2.0.2 (dudi.pca function in the R package ade4 version 1.7-13 (n = 41), Little TN and Hiwassee Rivers (n = 31), and Ocoee and Coosa Rivers (n = 8) .To assess genetic structure and sex-ratio in SA, we excluded samples from Zempoala (Mexico). The SNP matrix for 80 US individuals (410 SNPs) was used hereafter. The SNP matrix was converted into a on 2.0.2  and usedn 1.7-13 . Based o (n = 8) . Fst valpopulations (Stacks pipeline) to a fineRADpainter input file, reducing the maximum number of SNPs allowed at a locus to 10. We re-ordered the loci with the script provided (sampleLD.R), as suggested by authors. Afterward, the co-ancestry matrix (RADpainter) was calculated, the individuals were assigned to populations and the coalescence tree was built (finestructure). The outputs were loaded into the program Finestructure GUI for visualization.We inferred population structure based on nearest neighbor haplotype co-ancestry using the fineRADstructure package , which iclones specific to each population.” Higher thresholds were evaluated but all resulted in a drastic reduction of the number of clones. Clonal diversity was quantified from the multilocus nuclear genotypes and values of several indexes, such as Nei’s genetic diversity and evenness (eve) were estimated. Additionally, we also calculated the ratio between genets (G) and the total number of ramets sampled (N). The indexes were calculated in GenoDive version 2.0b2  was computed according to ij were regressed on the natural logarithm of distance ln(dij) in order to obtain the regression slope, b, which quantifies the SGS. To test for SGS under the null hypothesis of no correlation of Fij and ln(dij), the spatial positions of individuals were permuted 10,000 times.Based on the genetic groups derived from the phylogeny and the PCA analysis, we conducted a SGS test using microsatellites. Spatial autocorrelation analysis of kinship among ramets was carried out using SPAGeDi 1.5. . ContiguN. aenigmaticus populations dates to 3.89 (1.03–7.76) Mya using a birth-death prior or 4.35 (1.16–8.4) Mya with the Yule prior. The diversification of the US/Mexican N. aenigmaticus crown group dates to 0.79 (0.061–1.93) Mya using a birth-death tree prior, or 0.88 (0.06–2.13) Mya with a Yule prior , followed by SA populations in the Little Pigeon (h = 0.496), Pigeon (h = 0.328), and Little TN (h = 0.211) Rivers (h = 0.86), followed by the Little TN (n = 0.589), Little Pigeon (h = 0.517) and Ocoee (h = 0.502) rivers  Rivers . Zempoal) rivers . Eleven ) rivers . Similar) rivers . Regardi) rivers . The mis) rivers .ST values for chlorotypes are high  with low levels of pairwise ΦPT between Little TN and Hiwassee Rivers (0.00) and between Zempoala and Cascada de Los Diamantes (0.099) and moderate values between Pigeon and Little Pigeon Rivers (0.272). Average ΦPT values for mitotypes are high  with low levels of pairwise ΦPT between Little TN and Hiwassee Rivers (0.013), between Zempoala and Cascada de Los Diamantes (0.035) and between Pigeon and Little Pigeon Rivers (0.004)  . In gene is high .Seventy-two, 43 and 26 clones  were detected using GenoDive . With nok = 7), namely Little Pigeon, Pigeon Balsam Mountain Preserve, Little TN/Hiwassee, Ocoee/Coosa, and Zempoala/Diamantes (The K-Means clustering recovered six groups (iamantes . Populatgstacks (Stacks pipeline) to create 6,157 loci (at least 5X mean coverage), of which 99% had BLAST hits on the transcriptome of N. aenigmaticus. Approximately 88 million sequencing reads were generated for the 86 samples. The total number of reads retained after demultiplexing, filtering and trimming was 64,836,726, varying between 18,885 for sample MA42 to 4,029,405 for sample MA237. Retaining only reads that mapped to the transcriptome reference, reduced the number of reads drastically, e.g., to 2,621 for MA42 and from 4,021,346 to 109,298 for the best mapped sample (MA135). An average of 43% of the reads mapped to the reference transcriptome was retained for N. aenigmaticus  were inferred from 491 SNPs  with 20.gmaticus . The US gmaticus : the firgmaticus  and 19.9r 0.5). Broadly, levels of nucleotide diversity (π) were low, ranging from 0 (Ocoee River) to 0,05436    . The numempoala) . In Zempempoala) . Pairwisempoala) .N. aenigmaticus from the US clustered in three main groups reflecting the contiguity of watersheds: one comprising samples from Pigeon and Little Pigeon Rivers , the second one samples from Little TN-Hiwassee Rivers  and the third samples from Ocoee/Coosa Rivers . Pairwise Fst values among these three groups were high and agreed with phylogenetic and PCA data. Fst value between Little TN-Hiwassee and Ocoee/Coosa Rivers was 0.98 with p-values < 0.005 for 234 of the 239 scaffolds compared; Fst value for Little TN-Hiwassee and Pigeon-Little Pigeon Rivers was 0.97 with p-values < 0.005 for 299 of the 304 scaffolds compared; and the Fst between Ocoee/Coosa and Pigeon-Little Rivers was 0.97 with p-values < 0.005 for 266 of the 274 scaffolds compared. The Fst value between sexes was also high (0.65) . In terms of genetic diversity considering three populations (contiguous watersheds), 2,571 loci  were shared by at least 50% of the individuals within each population. The loci were composed of 320,884 sites; 412 variants. Levels of nucleotide diversity (π) were low (<0.006 for variant positions and < 0.00001 for variant and fixed positions) (The PCA plot  based onsitions) . Followisitions) . In thisN. aenigmaticus from Ocoee plus Coosa Rivers  share more co-ancestry with individuals from Little TN plus Hiwassee Rivers  than with those from Pigeon and Little Pigeon Rivers   .k = 5), namely Little TN/Hiwassee Rivers, Ocoee River, Coosa River, Little Pigeon/Pigeon Rivers and Zempoala  was 31 (when the clones were made specific to each of population) or nine (clones not specific to each population). A single clone occurs in each US river, except Hiwassee and Coosa River (two clones each), which suggests prominent clonality in the species in SA. In Mexico, the population of Zempoala had five clones. The G/N ratio was low (G/N < 0.1) in Pigeon, Little Pigeon, Little TN and Hiwassee Rivers. The collections from Ocoee and Coosa Rivers show higher G/N ratio  (ij in genets were found in the two first distance classes (up to 1 km) in Pigeon and Little Pigeon Rivers; in km 1 and 10 in Little TN and Hiwassee Rivers; while in Ocoee and Coosa Rivers, it was significant in the third (5 km) and the fifth class (20 km)  . At the ctively) . The ave (20 km) .The evolution of separate sexes (dioicy) has intrigued biologists for centuries. The genetic advantages of separate sexes are evident , however, the costs of dioicy outnumber the theoretical benefits e.g., . This isN. aenigmaticus dates to 3–4 Mya , suggesting that the US plants experienced the last Ice age. The high differentiation between Mexican  and US  plants points to a long and on-going isolation of US populations with highly restricted contemporary migration between these two regions.The crown group of alpine neotropical  3–4 Mya . SA popuNothoceros aenigmaticus is dioicous and hence the spores are “unisexual.” Several scenarios are possible. (1) The occurrence of both sexes in the SA thus requires at least two independent dispersal events: one for male plants (Pigeon/Little Pigeon River) and the other for female plants  and a third potentially to Balsam Mountain Gap . (2) It is possible that the current species distribution is product of a co-dispersal of male and female spores and the species was widespread in the SA region after a dispersal from Mexico in the Plio-Pleistocene. (3) Or, local unknown disturbances fragmented and isolated the populations in the US. In this case, we envision a scenario of dispersal from Paramos to Mexico/US, following local vicariance of US watersheds from Mexico.The Mexican/SA populations were probably founded following dispersal from sexually reproducing, and hence spore producing, alpine tropical populations. Although the spores do not exhibit the syndrome necessary for effective wind-mediated long distance dispersal  shown in yophytes . Male spyophytes . Using mAustrocedrus chilensis (D. Don) Pic. Serm. & Bizzarri (Distichlis spicata (L.) Green  Green . In the ronments . No studly 30 km , 2. The ly 30 km .N. aenigmaticus are genetically closer between nearby individuals than between distant individuals. Gene flow is limited in the species, and it seems to occur mostly between contiguous watersheds. Usually, SGS is the result of limited gene dispersal and can be influenced by a broad array of life-history traits including clonality and selfing. In our case, clonality is a crucial determining factor because clonality increases kinship at a small spatial scale. Therefore, clonality, SGS and SSS may have an impact on the complete absence of sexual reproduction in US N. aenigmaticus.Our expanded sampling confirmed the SSS, especially among distant watersheds. The observed fine-scale SGS indicate that within contiguous watersheds, alleles of N. aenigmaticus and it is more evident in male gametophytes. In Mexican N. aenigmaticus, sexually functional male and female plants grow intermixed. Mexican male plants are small and inconspicuous (less than 4 mm wide) with abundant (>20) antheridial chambers per gametophyte, typical of dioicous hornworts  and lack of suitable habitats.Estimates of divergence times suggests that clones have persisted since the Pleistocene  and hencPT and Fst values  Lindb. has genetically highly structured populations  suggesting a highly limited dispersal capability of N. aenigmaticus. Almost every single river is separated by a mountain divide, suggesting that dispersal may be highly limited without having spores. Intercatchment dispersal mainly via water birds has repeatedly been reported in plants, particularly angiosperms (Platyhypnidium riparioides (Hedw.) Dixon, episodic dispersal of vegetative diaspores and occasional from spores contribute to the structured population dynamic of the species with very little downstream dispersal . Based on microsatellite and SNP data, some genotypes are shared between contiguous watersheds, and hence the current genetic diversity of this species is not partitioned by watersheds. The data show sex-specific SGS with watersheds displaying a single sex. The presence of only clonal N. aenigmaticus in the SA suggests that the species is “locked” in the SA. Given the low extant genetic diversity, which can only be increased by somatic mutations in the absence of sexual reproduction, any slight alteration of the habitat may cause the local extinction of the species. Nothoceros aenigmaticus may surge as a model to study the impact of clonality on the deterioration and lack of sexual function , the proliferation of somatic mutations (in organellar and nuclear genomes) and mutational meltdown in haploid plants.The SA PRJNA562471.The sequence data are already published and found in MA-G performed and analyzed GBS data, performed population genomic analyses. JV conceived the study, conducted fieldwork, developed and analyzed microsatellite data, performed phylogenetic analyses. KM planned and conducted fieldwork, provided research context, natural history and geographic data on the species. BG conceived and planned the study, provided funding. All authors read and approved the manuscript.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.


Umbri, whose origins are still debated. Here, we investigated the mitochondrial DNA (mtDNA) variation of 545 present-day Umbrians (with 198 entire mitogenomes) and 28 pre-Roman individuals (obtaining 19 ancient mtDNAs) excavated from the necropolis of Plestia. We found a rather homogeneous distribution of western Eurasian lineages across the region, with few notable exceptions. Contemporary inhabitants of the eastern part, delimited by the Tiber River and the Apennine Mountains, manifest a peculiar mitochondrial proximity to central-eastern Europeans, mainly due to haplogroups U4 and U5a, and an overrepresentation of J (30%) similar to the pre-Roman remains, also excavated in East Umbria. Local genetic continuities are further attested to by six terminal branches  shared between ancient and modern mitogenomes. Eventually, we identified multiple inputs from various population sources that likely shaped the mitochondrial gene pool of ancient Umbri over time, since early Neolithic, including gene flows with central-eastern Europe. This diachronic mtDNA portrait of Umbria fits well with the genome-wide population structure identified on the entire peninsula and with historical sources that list the Umbri among the most ancient Italic populations.Umbria is located in Central Italy and took the name from its ancient inhabitants, the  Markedly, advances in high-throughput genotyping technology have highlighted how the present-day genetic variation of human populations is the outcome of past population movements. In prehistoric times, the Mediterranean area experienced three significant migration waves whose legacy is retrieved in the mitochondrial pool of modern and ancient populations: the Paleolithic hunter-gatherers who survived and re-expanded from glacial refuges, the Neolithic farming societies that moved from the East, and the herders from the Pontic-Caspian steppes inaugurating the Bronze Age19. This complexity is the result of multifaceted inputs that shaped its gene pool since the Upper Paleolithic. Inferring the contributions of each process is further complicated by similar  dispersal patterns from, to and even within the Italian Peninsula, often separated by short time frames. It is generally agreed that the ancestral contribution came from the ancient Italic peoples, among which Latins  achieved a dominant position establishing Roman civilization; whereas the invasions after the fall of the Roman Empire did not significantly alter the peninsular gene pool19.In this scenario, the Italian Peninsula played a pivotal role in human migrations around the Mediterranean Sea, as testified by the higher degree of its current genomic variability compared with other European populations24. Southern populations were mostly influenced by Greek and Arab colonizations, Northern Italians might reflect admixture with French and German-speaking populations, while Central Italy occupies its own intermediate position creating a continuous cline of variation across the peninsula (with Sardinians as outliers)29. Most of these studies were performed on a large geographic scale producing low-definition results and mainly focusing on modern populations. As for microgeographic studies on Central Italy, only Etruscans (in Tuscany) and Picentes (in Marche) were the target of specific analyses that highlighted their genetic affinity with the current inhabitants37. However, Umbria, another crucial region in Central Italy, is still unexplored. The name derives from the ancient Umbrians (or Umbri), traditionally considered an indigenous and very old population38. In the first century Common Era (CE) Pliny the Elder stated: “The population of Umbria is considered the oldest of Italy, and it is believed that the Umbrians had been called Ombrikòi by the Greeks because they survived the rains when the earth was flooded” . Nevertheless, the origin and ethnic affinities of the Umbrians are still in some degree a matter of dispute.Concerning the phylogeography of Italy, it is difficult to identify a clear genetic pattern able to discriminate southern, northern and central populations in spite of several attempts based on autosomal and uniparental markers39. The degree of interaction between these ancient populations is still unclear. The Romans came into contact for the first time with the Umbrians during the fourth century BCE and established Latin colonies in the area at the beginning of the third century BCE. After 260 BCE, Umbria was already under the full control of Rome40, while the Etruscan culture (and language) disappeared only at the time of the “Social War” (90-88 BCE) with the attribution of Roman citizenship to all Italic people41. Nowadays Umbria is somewhat smaller than ancient Umbria, but its inhabitants still preserve significant difference in the dialects spoken on the two banks of the Tiber42.Archaeological and historical data suggest that during the Early Iron Age , Umbrians were among the first communities with strong and well-defined cultural identities in Central Italy, together with Etruscans (to the west), Picentes (to the east) and Samnites (to the south). They originally occupied the eastern part of the today’s Umbria region, placed on the left bank of the Tiber River, soon extending their territories in western Umbria and Tuscany. Around the sixth century BCE, the Etruscans, who had already begun to influence the Umbrian culture, took control over the western territories and the Tiber became the natural border between Umbrians and EtruscansPlestinam Paludem . The Plestini plateaus represented an obligatory way in the trans-Apennine routes, but stable settlements have not been attested before the beginning of the Iron Age44. The geographical position, the wealth of water, the possibilities offered by the exercise of hunting and fishing, the goodness of the pastures and the abundance of timber have undoubtedly encouraged the stabilization and growth of the population during the Iron Age.An important necropolis in East Umbria is placed in the so-called Umbri Plestini, who were buried in Plestinam Paludem , selected from a larger dataset of 545 samples covering the entire region, as well as the mitogenomes of 19 Iron Age dem Fig. . This di21, confirms the goodness of the sampling and testifies for an extensive maternal admixture  values in six sub-areas, considering geographic and historical criteria .Phylogenetic analyses were then performed. The mutational motifs of the 545 Umbrians clustered into 369 haplotypes belonging to numerous haplogroups and sub-haplogroups when using Haplogrep 2.0 and SAM 2 on EMPOP  from each of the six regional divisions for complete mtDNA sequencing. With this approach we obtained 191 novel mitogenomes  analysis, using the two methodologies as “artificial populations” and comparing the distributions of variant occurrences in the two datasets. As shown in Supplementary Figure p value 0.57), thus confirming that even the 198 complete mitogenomes can be accounted as a population dataset representative of modern Umbrians. Moreover, also the macro-haplogroup distributions in the six sub-regions showed the same pattern of the control-region data, confirming significant differences only for haplogroups J and K in East and South Umbria, respectively , were classified into different sub-haplogroups (147 with Haplogrep and 137 with EMPOP). The frequencies of major haplogroups widely overlap with those obtained from the control-region dataset, without any significant differences , it has been proposed that most of its subgroups diversified in the Near East during the Last Glacial Maximum (LGM) and spread into Europe in the Late Glacial49. Some J1c sub-lineages have been also proposed as Early Neolithic founder lineages50. As for super-haplogroup U, four sub-haplogroups were detected, including U4, the same lineage that pushes modern eastern Umbrians close to central-eastern Europeans in the PCA.The 19 mtDNA sequences were classified into 17 mitochondrial haplogroups and eight super-haplogroups. They are all typical of present-day West-Eurasian populations with the most represented lineage being J (32%), followed by H (26%) and U (16%) Figs. . A similp value 0.33). However, the high frequency of haplogroup J in ancient Umbrians (32%) can currently be observed only in the eastern part of the region (30%). Virtually all lineages (except for the paragroups J* and R*) identified in pre-Roman remains are still recognizable nowadays in Umbria, thus suggesting a possible genetic continuity since pre-Roman times  with modern Umbrians, all dated back to the Holocene , one associated to the early Bronze Age and the other to a Bell Beaker Complex53. The latter sample is at the root of the reconstructed J1c3g tree, which has been dated to 5.4 ± 0.3 kya. Four more recent J1c3g mtDNAs have been also identified in one individual from Spain dated to the sixth century CE and archaeologically interpreted as a Visigoth54, one Hungarian conqueror55 and a pre-Christian Icelander56, both from the early tenth century CE, and a medieval sample from Denmark57.The incidence of each major haplogroup identified in our ancient sample is comparable with the one observed in present-day Umbrians  drawn by geographic criteria and historical/cultural information. A wide range of haplotypes, mostly belonging to western Eurasian haplogroups (97%), testify for the high mtDNA diversity in Umbria. The incidence of these lineages across the region is quite homogeneous with the notable exception of haplogroup K, reaching the highest frequency (17%) in South Umbria, and haplogroup J, which encompasses 30% of current inhabitants of the eastern area. In the western Eurasian PCA plot, the latter sub-region is pushed close to populations from central-eastern Europe by haplogroups U4 and U5a that show high frequencies in those areas.Umbri Plestini. These pre-Roman samples were classified into the same haplogroups identified in contemporary inhabitants. Moreover, the six terminal branches  shared between ancient and modern mitogenomes suggest a genetic continuity in the region during the Holocene. These specific lineages were also identified in a wide range of available ancient samples outside the region, including Neolithic Mediterranean remains as well as Yamnaya, Bell Beaker and more recent samples from central-eastern Europe. These variegated connections are summarized by the lineage geographic/temporal patterns and are specifically shown by the J1c3g ancient mtDNA tree dated between the Late Neolithic and the Early Bronze Age.Then, we extended our analyses to complete mitogenomes (191 sequenced for the first time), randomly selecting the targeted samples to avoid phylogenetic biases and to maintain the population-wide characteristics of our dataset. This higher level of resolution allowed us to refine the haplogroup affiliation in more than 70% of the samples and to make a diachronic comparison with 19 ancient mitogenomes from Umbri Plestini and preserved in some, perhaps more isolated, sub-areas. These ancestors reached Umbria coming from various population sources at different times during the Holocene, from early Neolithic farmers spreading across the Mediterranean to Bronze Age and Medieval connections with central-eastern Europeans, possibly including few nomadic groups (Yamnaya) from the Pontic-Caspian steppes. This microgeographic and diachronic mtDNA portrait of Umbria fits well with recent genetic data on the entire peninsula. The Y-chromosome counterpart pointed to different male ancestries for the Italian populations24 and the autosomal data revealed several ancient signatures and the largest degree of population structure detected so far in Europe29. Notably, two of the three published genomic clusters  overlap in Central Italy and precisely in Umbria, the “Heart of Italy”. In a wider multidisciplinary context, this hypothesis is also supported by historical sources that list the Umbri among the most ancient Italic populations40 and by the assumed Indo-European origin of their language, distinct from the Etruscan one spoken by neighboring people during the Iron Age59.In brief, it is apparent that distinctive mtDNA variants have been brought into the region by the ancestors of 61, were also included.The modern collection consisted of 538 DNA samples from healthy and unrelated subjects with an Umbrian maternal grandmother as a terminal maternal ancestor. Swab or mouthwash rinsing samples were collected from volunteers, representing the entire Umbrian area. Written informed consents were obtained from all donors, who provided information about place of birth and geographical origins up to three generations of Umbrian maternal ancestry. Total DNA was extracted with the MagCore Automated Nucleic Acid Extractor following manufacturer’s protocols. Seven additional Umbrian samples, collected and sequenced in our labs for previous projectsAll analyses were carried out in accordance with relevant guidelines and regulations, and all experimental protocols were approved by the Ethics Committee for Clinical Experimentation of the University of Perugia (protocol no. 2017-01).Umbria was divided into six sub-areas 62 using Sequencher 5.10 (Gene Codes Corporation). These were analyzed together with the control-region sequences from the 191 complete genomes (see below) and seven previously published, for an overall number of 545 control regions  techniques: 82 by the Illumina MiSeq64 and 103 through the Ion PGM System65 68 and filtered using BCFtools to obtain the final SNP dataset. Three different in-house scripts  were used to obtain the final haplotypes (both with and without heteroplasmies). The final haplotypes were also double-checked through a manual visualization of the bam files with the Integrative Genomics Viewer (IGV) software. Common criteria used for calling mtDNA variants were adopted as reported by Olivieri and colleagues58. In addition, some problematic fragments were replicated by Sanger sequencing and the congruence with the initial control-region data was evaluated.FASTQ files were aligned to the reference sequence  using BWAPlestia in Colfiorito , as previously reported71.Molecular analysis of the archaeological specimens was performed under sterile conditions in a dedicated ancient DNA (aDNA) facility at the Laboratory of Molecular Anthropology and Paleogenetics , following strict guidelines and standard precautions to avoid contaminations. After a silica-based DNA extraction72 was used for initial sequencing quality control, adapter trimming and paired-end read merging. Merged reads were filtered for a minimum length of 30 base pairs and mapped to rCRS (NC_012920.1) using CirculaMapper , a tool integrated in EAGER and specifically designed for the analysis of circular reference genomes. After removing PCR duplicate, only reads with a map quality score ≥ 30 were retained and used for reconstructing mtDNA consensus sequences using schmutzi (parameters: − logindel 1 − uselength)46. Bases with individual likelihood < 20 were considered as unassigned positions (Ns). Present-day human contamination was evaluated by an iterative likelihood method implemented in schmutzi using a non-redundant database of 197 human mitochondrial genomes available in the software package. Damage patterns at the ends of the molecules were calculated using contDeam, a program provided with the schmutzi package.After demultiplexing, raw reads were analyzed using a specific pipeline developed for aDNA. The EAGER pipeline73. Intra- and inter-population comparisons based on the number of pairwise differences between sequences were performed using an Arlequin integrated R script74.Several mtDNA sequence variation parameters were estimated using DnaSP 5.1 software75, but the initial classification was revised and manually updated in agreement with PhyloTree build 1776 and SAM 277 on EMPOP78.Haplogroups were predicted using HaploGrep2 softwarehttps://empop.online/tools).All (modern and ancient) haplotypes underwent a posteriori mtDNA sequence data quality control using EMPcheck, a tool to perform plausibility checks on a rCRS-coded data table , as previously reported58.In order to graphically display (and summarize) the relationships among the analyzed mtDNAs, Principal Component Analyses (PCA) were also performed using Excel software implemented by XLSTAT, as previously describedSupplementary file1Supplementary file2Supplementary file3


Thunnus tonggol in Malaysian waters was investigated using mitochondrial DNA D-loop and NADH dehydrogenase subunit 5 (ND5). A total of 203 (D-loop) and 208 (ND5) individuals of T. tonggol were sampled from 11 localities around the Malaysian coastal waters. Low genetic differentiation between populations was found, possibly due to the past demographic history, dispersal potential during egg and larval stages, seasonal migration in adults, and lack of geographical barriers. The gene trees, constructed based on the maximum likelihood method, revealed a single panmictic population with unsupported internal clades, indicating an absence of structure among the populations studied. Analysis on population pairwise comparison ФST suggested the absence of limited gene flow among study sites. Taken all together, high haplotype diversity , coupled with a low level of nucleotide diversity , “star-like” haplotype network, and unimodal mismatch distribution, suggests a recent population expansion for populations of T. tonggol in Malaysia. Furthermore, neutrality and goodness of fit tests supported the signature of a relatively recent population expansion during the Pleistocene epoch. To provide additional insight into the phylogeographic pattern of the species within the Indo-Pacific Ocean, we included haplotypes from GenBank and a few samples from Taiwan. Preliminary analyses suggest a more complex genetic demarcation of the species than an explicit Indian Ocean versus Pacific Ocean delineation.The population genetic diversity and demographic history of the longtail tuna  Thunnus tonggol, locally known as aya/tongkol hitam or longtail tuna, is a pelagic-neritic marine fish classified in the subgenus Neothunnus from the tribe Thunini within family Scombridae along with blackfin (T. atlanticus) and yellowfin  tuna (Thunnus species (T. tonggol is distributed exclusively in the Indo-Pacific region between 47°N and 33°S (es) tuna . It is t species  but is a species . T. tongand 33°S  and is oand 33°S . It reprand 33°S . It is aand 33°S .T. tonggol has tripled in the last 20 years (T. tonggol), kawakawa (Euthynnus affinis), and frigate tuna (Auxis thazard and A. rochei) and contributed 5% of the total marine landings , and frigate tuna (A. thazard and A. rochei)  .T. tonggol; it has, in fact, received less attention than other pelagic species in Southeast Asian waters  marker, revealed low genetic differentiation between localities, which suggested a panmictic stock structure (T. tonggol within the Indo-Pacific region (T. obesus), populations from the South China Sea, the Philippines Sea, and western Pacific Ocean consist of a single intermixing identity  along the Indian coast, displayed multiple geographically distinct stocks  assay  that inhabits Setiu Wetland, Terengganu, Malaysia (The mtDNA NADH dehydrogenase subunit 5 (ND5) and the non-coding and highly polymorphic D-loop markers were adopted to conduct the first population genetics and phylogeographic studies among wild populations of  in tuna . HoweverP) assay ; meanwhi markers . HoweverMalaysia .T. tonggol in Malaysian waters based on mtDNA D-loop and ND5 markers, as well as further investigating the phylogeography of T. tonggol within the Indo-Pacific region, where samples from Taiwan  and India  were included in molecular analyses. The output from this study would be beneficial for the conservation and management of this species.The present study aimed at elucidating the population genetics of Only a small clipping of the pectoral fin from each individual fish was collected from the local wet markets. This species is not in the IUCN list of endangered or protected species. As only dead specimens were sampled, no permit was required and no ethical consideration was linked to the study.Thunnus species by having a moderate length pectoral fin, reaching the origin of the second dorsal fin and blackish caudal fin  with a final volume in each tube of 25 µl. The temperature profile for the D-loop was: initial denaturation at 94 °C for 5 min followed by 32 cycles of 94 °C for 30 s, 55 °C for 30 s, 72 °C for 1 min, final extension at 72 °C for 5 min, and final hold at 4 °C. Amplification conditions for the ND5 gene were: initial denaturation at 94 °C for 2 min followed by 35 cycles for 94 °C for 20 s, 59 °C for 20 s, 72 °C for 1 min 30 s, final extension at 72 °C for 5 min, and final hold at 10 °C. PCR products were visualized on 1.7% agarose gels stained with SYBR Safe to confirm their presence and estimate the size of DNA fragment amplified. PCR products were then sent for sequencing  in the forward direction only using an Applied Biosystem ABI3730x1 capillary-based DNA sequencer.Genomic DNA was isolated from fin tissue by using the salt extraction method . The isohttp://blast.ncbi.nlm.nih.gov/Blast.cgi) before further analyses. All haplotypes were deposited into GenBank under the accession numbers MK643829–MK644008 (D-loop) and MN252922–MN252981 (ND5).Multiple sequences were aligned and edited using ClustalW implemented in MEGA 6.0 . DNA seqThe complete aligned datasets were used to estimate the number of haplotypes, haplotype diversity (H), and nucleotide diversity (π) in DnaSP 6.0 . The polT. orientalis (AB933631) was included as an out-group taxon for D-loop sequences, while the Yellowfin tuna, T. albacares (KM588080) was included as out-group taxon for ND5 gene sequences. To infer the relationships among haplotypes from Malaysian waters, a minimum spanning network (MSN) was constructed by using the median-joining method implemented in NETWORK version 5.0.1.1 (The phylogenetic relationships among haplotypes were determined based on the maximum likelihood (ML) method implemented in MEGA 6.0. The best nucleotide substitution models with the lowest BIC score (Bayesian Information Criterion) for the D-loop and ND5 sequences were Tamura 3-parameter (T92)  and Hase 5.0.1.1 .ST for both datasets were determined using Arlequin 3.5 software (ST), sequence-based statistics (NST)  , and KSTplicates  in DnaSPST values while geographical distances between sampling locations were measured by using Google Earth. Geographic distance was ln transformed and the strength of the relationship was examined with reduced major axis regression  in IBD v1.52.Analysis of molecular variance (AMOVA) was performed to infer the population subdivision with three hierarchical levels, including genes within individuals, individuals within demes, and demes within groups of demes , by usinMF592988–MF593012, Veraval (22) MF593027–MF593048, Ratnagiri and Veraval (92) KC313300–KC313393 (MF593013–MF593026), and four samples from Taiwan (TW) were included in phylogenetic tree reconstruction, pairwise comparisons ФST, AMOVA, and genetic distance analyses. Altogether 309 haplotype sequences were realigned, and ambiguous GenBank sequences were eliminated. Only 152 Malaysian haplotypes  were used after trimming the sequence to a final length of 388 base pairs (bp).To understand its phylogeography within the Indo-Pacific region, GenBank sequences of the D-loop obtained from the west coast of the Indian Ocean (WCIO) (Kochi (25 haplotypes) KC313393 ), east cT. tonggol populations. Fu’s FS (D (0), after expansion (ϴ1), and relative time since population expansion (τ), were computed in Arlequin 3.5. The values of time (τ) were transformed to estimate the actual time (T) since population expansion, using the equation τ = 2μt, where t is the age of the population in generations and µ is the sequence mutation rate per generation. In the present study, a mutation rate of 3.6 × 10−8 mutation per site/year was applied for the D-loop  but low to moderate nucleotide diversity  ) revealed 113 polymorphic sites (42 singletons and 71 parsimony informative sites), defining 180 haplotypes, where 14 (7.78%) were found in two to five localities and the rest (92.2%) were either private to a single locality or singleton haplotype. The ND5 sequences (855 bp) revealed 60 variable sites , defining 60 haplotypes, where 17 (27.9%) were shared by two to 11 populations, five (8.2%) were private to a single locality and 38 (63.9%) were singleton haplotypes. The D-loop region was AT rich, while, ND5 gene sequences demonstrated higher percentages of CG (56%). All populations of –0.0039) .The phylogenetic reconstruction inferred from the mtDNA D-loop region and ND5 gene revealed a gene tree with mainly unsupported clades (<50%) and obscure patterns of geographical segregation associated with genetic distribution . This waST analysis corroborated the low and non-significant population structure of T. tonggol from Malaysian waters , NST  and KST*  produced low and not significant values, which corresponds with a high level of gene flow (Nm) among Malaysian T. tonggol populations . Correspondingly, the pairwise genetic distances among populations also exhibit relatively low values ranging from 0.0197 to 0.0251 (D-loop) and 0.0020 to 0.0038 (ND5). The hierarchical AMOVA indicated that more than 99% of the total genetic variation in Malaysian T. tonggol was contributed by genetic differences within populations. Attempts to identify if population subdivisions exist among the T. tonggol populations (BT vs. other populations) returned a non-significant FCT value with less than 1% contribution to the total genetic variation, while more than 99% of the total genetic variation was contributed within populations, based on both datasets. The Mantel Test also supported earlier findings, demonstrating no correlation between genetic differentiation  and geographical distance  among Malaysian populations.Pairwise comparison Фo 0.1191 ; ND5: −0o 0.1191 ), exceptT. tonggol from Taiwan (TW), the east coast of the Indian Ocean (ECIO), and some of the west coast of the Indian Ocean (WCIO) were clustered with the Malaysian haplotypes, while some other WCIO haplotypes were placed into another clade with high bootstrap support  , yet 90.01% of the genetic variation within the Indo-Pacific region was contributed by genetic differences within populations. Pairwise genetic distances between TW and Malaysian populations ranged from 0.0181 to 0.0219, while the genetic distances were relatively higher for pairwise comparisons involving ECIO and WCIO, that is, from 0.0238 to 0.0371 (D-loop sequences of  support . ML tree WCIO-KT . Meanwhi > 0.05) . A hierao 0.0371 .D and Fu’s FS  neutrality tests were detected in all populations inferred from both the mtDNA D-loop and ND5 gene (0) and after expansion (θ1) were detected, i.e (on average) 0.116 and 54,915.300 based on D-loop sequences, while 0.103 and 81,838.400 were based on the ND5 gene marker  analysis revealed two significant increases in effective population size that occurred 200,000 and 950,000 years ago based on the D-loop marker (P > 0.05)  . Populat samples .T. tonggol showed high haplotype diversity  but low to moderate nucleotide diversity   , skipjack tuna Katsuwonus pelamis   (According to –0.0039) . High ha–0.0039) . These r= 0.007) , yellowf= 0.084) , and lon 0.0016) . FurtherT. tonggol from Malaysian waters exhibited an absence of geographical structure associated with mtDNA sequences, as evidenced by the single-clade gene trees  and SB, inferred from the D-loop and ND5 sequences respectively, based on the population pairwise ФST analysis, however, all other analyses suggested genetic homogeneity with other T. tonggol populations in Malaysian waters. We believe that this could be due the different weightage of algorithms or characters used in the various analyses, perhaps a different emphasis on nucleotide versus haplotype diversity.Populations of ne trees , ambiguone trees  and 4, lrees  and after expansion (θ1) also suggested a rapid population expansion of T. tonggol in the past as also reported in a previous study of populations from India  and 100,000 years ago . Based on the D-loop marker, two expansion events were detected, where the first round occurred around 200,000 years ago  or 284,000 years ago    ) and thepresent) . In geneT. tonggol, especially in Malaysia, and from the management point of view, this is a critical issue. The present study provides the first baseline population genetic data on T. tonggol populations in Malaysian waters, which is important information for management planning by authorities.To date, there is limited information on the population structure of T. tonggol in Malaysia forms a panmictic population as observed by the wide distributional range of this species and non-significant low ФST values among the populations studied, thus suggesting a single evolutionary significant unit (ESU). However, this study was based on mitochondrial markers and therefore, restricted to only the pattern of maternal inheritance. For a holistic genetic perspective of bi-parental inheritance, co-dominant markers, such as microsatellites, should be included.Managing fishery resources takes a significant effort to protect and replenish the genetic pools for a sustainable harvest. Molecular analyses, in complement with other approaches, may serve as a reliable measurement for an efficient preservation strategy . The genT. tonggol populations in Malaysian waters revealed the absence of population structure as inferred by both mtDNA markers and therefore, could be regarded as a single stock unit for management purposes based on the current data. Their inferred demographic history suggests that T. tonggol populations expanded significantly during the middle and early Pleistocene. Overall, this study is a critical first baseline, providing insights for stock management of this neritic species in Malaysian coastal areas. Coupled with other related information, the assimilation of this genetic information could aid the development of effective management plans in the future, not only in Malaysia but also in neighboring countries sharing the same waters. Finally, this has contributed further insights into genetic locality, delineating the species within the Indo-Pacific biogeographical region.The 10.7717/peerj.9679/supp-1Supplemental Information 1Hap001-042 (SCS-1), 043-085 (SOM), 086-104 (CS), 108-152 (SCS-2), 153-166 (ECIO), 168-296 (WCIO), 297-300 (TW).Click here for additional data file.


Penaeus monodon) is one of the most commercially important aquaculture species, a systematic assessment of genetic divergence and structure of wild-caught and domesticated broodstock populations of the species is yet to be documented. Therefore, we used skim sequencing (SkimSeq) based genotyping approach to investigate the genetic structure of 50 broodstock individuals of P. monodon species, collected from five sampling sites (n = 10 in each site) across their distribution in Indo-Pacific regions. The wild-caught P. monodon broodstock population were collected from Malaysia (MS) and Japan (MJ), while domesticated broodstock populations were collected from Madagascar (MMD), Hawaii, HI, USA (MMO), and Thailand (MT). After various filtering process, a total of 194,259 single nucleotide polymorphism (SNP) loci were identified, in which 4983 SNP loci were identified as putatively adaptive by the pcadapt approach. In both datasets, pairwise FST estimates high genetic divergence between wild and domesticated broodstock populations. Consistently, different spatial clustering analyses in both datasets categorized divergent genetic structure into two clusters: (1) wild-caught populations (MS and MJ), and (2) domesticated populations . Among 4983 putatively adaptive SNP loci, only 50 loci were observed to be in the coding region. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that non-synonymous mutated genes might be associated with the energy production, metabolic functions, respiration regulation and developmental rates, which likely act to promote adaptation to the strong artificial selection during the domestication process. This study has demonstrated the applicability of SkimSeq in a highly duplicated genome of P. monodon specifically, across a range of genetic backgrounds and geographical distributions, and would be useful for future genetic improvement program of this species in aquaculture.The domestication of a wild-caught aquatic animal is an evolutionary process, which results in genetic discrimination at the genomic level in response to strong artificial selection. Although black tiger shrimp ( The world production of inland aquaculture reached 51.3 million tonnes in 2018, with their dominant production of 97.2% comprising of finfish, while mariculture produced 30.8 million tonnes, with 56.2% represented by mollusc ,5,6. To Population structure is the organization of genetic diversity, and it is influenced by multiple evolutionary process, such as genetic drift, mutation, gene flow, natural selection and demographic history ,9. It isPenaeus monodon, commonly known as black tiger shrimp is widely distributed across the Indo-Pacific region, and is one of the most commercially important aquaculture species [P. monodon have only been limited to several traditional markers such as mtDNA RFLP [ species ,30. To dDNA RFLP , allozymDNA RFLP , microsaDNA RFLP ,33 and SDNA RFLP ,35, mitoDNA RFLP  and elonDNA RFLP , while nDNA RFLP ,42,43,44Skim sequencing (SkimSeq) is one of the less complex NGS methods, which uses low coverage (1–10X) whole genome sequencing of multiple individuals for high resolution genotyping ,46,47,48P. monodon from different origins (wild vs. domesticated), we genetically assayed 50 individuals with the SkimSeq approach using the short read Illumina sequencing platform. This study aimed to: (i) investigate the genetic structure of wild and domesticated populations, (ii) quantify the genetic differentiation between populations and (iii) examine the presence of putative loci causing the genetic variation. The genomic analyses and genetic resource acquired from this study will be useful to support future genetic improvements in P. monodon culture and brood-stock selection activities.Given that the SkimSeq is known as low coverage genome sequencing approach , and so P. monodon individuals consisting of both domesticated and wild-caught broodstocks were collected from five sampling sites  across Indo-Pacific regions (P. monodon broodstock populations from MMD (Madagascar), MT (Thailand) and MMO  were obtained from domesticated shrimp farms, while MS  and MJ (Japan) were caught from the wild. The muscle tissues were extracted from broodstock individuals and preserved in 99.5% v/v ethanol and stored at −20 °C until DNA extraction. All samples were collected in accordance with the animal care and tissue collection protocol as approved by the Universiti Malaysia Terengganu’s Animal Care and Biosafety Committee.A total of 50  regions , and theP. monodon broodstocks were isolated from the muscle tissues using Wizard Genomic DNA Purification Kit following the manufacturer’s protocols . The concentration and purity of the extracted genomic DNA were quantified based on A260/280 nm ratio using BioDrop . DNA quantifications were conducted using real-time PCR fluorescence measurements of double stranded DNA [® DNA PCR-Free Library Prep Kit  and Covaris M220 , following the kits’ protocol at Monash University Malaysia Genomics Facility . Accurate quantification and quality checking of the DNA libraries were conducted using the KAPA Library Quantification Kit , while qualitative estimation of the libraries was performed by Agilent Technologies 2100 Bioanalyzer . Detailed QC parameters, including library cluster density, library complexity, percent duplication, GC bias, and index representation were generated on the MiSeq system  to ensure a uniform concentration of all samples prior to SkimSeq. The pooled library was denatured based on the Illumina NextSeq denaturation guideline and paired-end sequencing was carried out using NextSeq 500/550 High Output v2 300 cycles kit on a NextSeq 500 .Genomic DNA of nded DNA  and the http://bowtie.cbcb.umd.edu) [http://jhhung.github.io/PEAT) [http://www.usadellab.org/cms/index.php?page=trimmomatic) [P. monodon had about 30% low-quality reads, which were discarded before de novo genome assembly (ftp://public.genomics.org.cn/BGI/SOAPdenovo2) [ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST) [http://maq.sourceforge.net) [https://github.com/brentp/vcfanno) [From the Illumina platform, 151 base-pair paired-end (2 × 151) sequencing reads were obtained in FASTQ format. Sequencing reads matching to PhiX DNA sequences were first removed by aligning reads against the PhiX DNA sequence using Bowtie 2 software version 2.2.3 (umd.edu) ,56. The io/PEAT) , to ensumomatic) . The quaassembly . In the denovo2)  into a s/LATEST)  was used/LATEST)  to the arge.net) . The rearge.net)  to find vcfanno) . If a vaP. monodon, 5 individual sequences  were removed from the dataset due to the inconsistent SNP profiles and/or greater than 20% missing genotypes, while the remaining 45 samples were used for all downstream analyses. Furthermore, variants filtering was conducted using VCFtools software version 0.1.16 (https://vcftools.github.io/) [http://samtools.github.io/bcftools/bcftools.html). At first, the variants filtering steps included the removal of complex indels, SNPs with more than two alleles, and composite insertion and substitution events. Further filtering steps included the removal of sites with less than 5% overall minor allele frequency, missing genotypes in >90% of the samples in any population, and SNP sites with genotypes not in Hardy-Weinberg equilibrium in any population (PHWE < 0.001). After all filtering steps, a total of 194,259 individual SNP loci remained in the dataset. To detect putatively adaptive SNP loci among different wild and domesticated broodstock populations of P. monodon, we identified outlier SNPs from the 194,259 filtered individual SNP loci using pcadapt version 3.0.2 [p-values of each locus to detect adaptive loci. Default parameters were used for pcadapt analysis, and the “number_of_samples” parameter was set to 5 . The false discovery rate (FDR) threshold value set to 0.05, to control the false positive. Finally, an overall SNP loci dataset and putatively adaptive SNP loci dataset were created and used for all down-stream spatial clustering analyses.SNP profiles were analyzed and visualized using SNPRelate version 1.16.0 , to exclhub.io/)  and BCFton 3.0.2 ,68. The ST values (10 and 20 generations), as described previously [ST in POWSIM assumes the independence of the subpopulations, power was expressed as the proportion of significant outcomes for 1000 alterations per batch and a statistically significant test (p < 0.05).A power analysis using POWSIM v. 4.1  was carreviously . As FST http://cran.r-project.org/mirrors.html) [ST to determine the genetic differences between collection sites for the all SNP loci and outlier datasets using the default settings, with the samples grouped by collection sites. Clustering analysis, discriminant analysis of the principal component (DAPC), was conducted using the adegenet R package  on all SNP loci dataset and outlier datasets. Neighbor-joining trees were generated using all the SNP loci dataset and outlier datasets using Nei’s genetic distance method. The Bayesian clustering method, implemented in the STRUCTURE software v. 2.3.4 , was used to genetically assign individuals to clusters [The filtered data was imported as a genind object into R and down-stream spatial clustering analyses were largely conducted using the adegenet v2.0.1 R package (rs.html) . The Genrs.html) . The Genclusters . Simulatclusters . The calclusters . The cluclusters .L. vannamei in the NCBI database. For each detected 4983 SNP loci, flanking region of upstream and downstream 100 bp was extracted from the reference nucleotide sequence. Homology search between this extracted sequence set and CDS amino acid sequences of L. vannamei was performed under the homology threshold of e-value = 1 × 10−5. Homology search program of the SNP flanking sequences showed that the total of 50 coding regions containing nonsynonymous mutations were encoded by the putatively adaptive SNP loci. To know the functional distribution of the genes encoded within the adaptive SNP loci, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using KOBAS 3.0 software (http://kobas.cbi.pku.edu.cn/kobas3) and visualized with R [p < 0.05).To identify the genes encoded within the adaptive SNP loci, a homology search program was applied for each putatively adaptive SNP locus with the genome sequences available for the d with R . The hyphttps://ddbj.nig.ac.jp), with the DRA accession number: DRA010601. The data will be publicly available from 10 September 2020.Our raw data for 50 individuals were submitted to the DDBJ  broodstock populations of P. monodon for all SNP loci  and putatively adaptive SNP loci  datasets. Similar pattern of genetic differentiation was also observed between the three domesticated  broodstock populations of P. monodon, with pairwise FST values ranging from 0.003 (MMD vs. MMO) to 0.010 (MMO vs. MT) in all the SNP loci dataset, and 0.003 (MMD vs. MMO) to 0.042 (MMD vs. MT) in the putatively adaptive SNP loci dataset. In contrast, a high genetic divergence was observed between domesticated and wild populations, with pairwise FST values ranging from 0.145 (MMD vs. MS) to 0.153 (MJ vs. MT) in all the SNP loci dataset, and 0.836 (MJ vs. MMO) to 0.870 (MMD vs. MS) in the putatively adaptive SNP loci dataset. All of the pairwise FST comparison were significant (p ≤ 0.001), except for those between MMD and MMO for putatively adaptive SNP loci (p = 0.105)  .p = 0.000). A substantially low (9.8%) but significant divergence (p = 0.000) was observed among five broodstock populations of P. monodon . Interestingly, hierarchical AMOVA based on the putatively adaptive loci revealed a high genetic divergence among five broodstock populations of P. monodon , but no differentiation was observed among individuals , while the remaining variation within individuals was 19.2% , indicating a high level of genetic divergence of P. monodon broodstock populations. When all SNPs were used, genetic variation inferred by a hierarchical AMOVA reveals the largest component of genetic variability (74.1%) within the individual level . The por monodon . Overallas 19.2% . The hieP. monodon were observed. A discriminant analysis of principal components (DAPC) of all SNP loci datasets revealed that MT and MS populations were pronouncedly overlapped among each other, while others three populations were more or less discriminated from each other’s (P. monodon into two clusters: (1) wild-caught populations (MS and MJ), and (2) domesticated populations . However, NJ tree topology for all SNP loci dataset showed lower Nei’s genetic distance compared to the putatively adaptive SNP loci dataset, and only one wild-caught individual from Japan (MJ6) incongruently clustered with domesticated broodstock populations of P. monodon , and 50 of them yielded significant matches with CDS amino acid sequences  [AMOVA of putatively adaptive SNP loci showed a high total variance of 80.8% attributed to differences between the wild and domesticated groups. The low pairwise Fnbergii) ,87. The ST estimates, AMOVA, DAPC plot and STRUCTURE analysis agree with the genetic homogeneity of domesticated populations and their significant differentiation from the wild progenitor populations [Salmo salar) [Ctenopharyngodon idella) [Lates calcarifer) [Wild populations  co-locate on another branch in NJ tree, indicating they have a separate origin from the cultured populations . Besidesulations ,89, partulations , but in o salar) , grass c idella) ,92 and Acarifer) .In the present study, only one individual from MJ population was genetically identical to the cultured populations. Wild broodstocks, obtained as founders for shrimp domestication program, have been subjected to mass selection process over many generations, which may render lower genetic affinity between the cultured and wild populations. We also hypothesize that the founder broodstocks for the domestication program may have originated from more than one shrimp breeding company, and wild populations of different geographical localities. A lack of significant differentiation among domesticated populations may suggest the probability of a relatively short domestication history or genetically closely related populations .P. monodon [Lack of gene flow between domesticated and wild populations is not unexpected when the confined environment of shrimp hatcheries and farms were taken into consideration. Moreover, our experimental design have defiled the potential occurrence of escapees from farms to the natural environment, given that all individuals were only obtained from populations, which were isolated completely from each other with a minimum of 500 km apart (MT and MS). The genetic structuring pattern being impacted by farm escapees was not uncommon, and has been reported in  monodon ,95. Howe monodon .Haliotis midae) [The genetic homogeneity between the domesticated populations may be due to the artificial selection of favorable traits or adaptation to similar aquaculture practices. Aquaculture practices have been observed to reduce genetic variability in farmed reared stocks of other aquatic species ,98,99. Ds midae)  and scals midae) , inhabits midae) ,104,105.The effect of random genetic drift is more accentuated in smaller sample size . UnequalUnique genes also persists in differentiating the domesticated and wild populations, which can be explained by selection of differentially favored alleles, holding particular reference to genetic improvement program in captive breeding environment . The eviP. monodon. mtDNA has been widely used in population genetic studies to reconstruct phylogenetic relationships and analyze population structure [P. monodon broodstocks to adapt to strong artificial selection during domestication process. The putatively adaptive SNP loci identified by the pcadapt approach also identifies genomic regions associated with the strong artificial selection during the domestication process over temporal scales. Of the 4983 putatively adaptive SNPs, only 50 genes were encoded successfully annotated through BLAST analysis. We have an array of uncharacterized proteins, with their functions are not known. Moreover, it was also observed that five mitochondrial genes  were mutated among different populations of tructure , due to tructure ,116,117.tructure . In facttructure ,120. Vartructure , and is tructure , developtructure , biologitructure  and fitntructure , and puttructure . Naturaltructure ,128. Destructure , developtructure  and immutructure ,132. HowP. monodon across the Indo-Pacific region. We suggested that similar founder populations, artificial selection regimes of desired commercial traits and local adaptive process to similar aquaculture practices have dramatically reduced the genetic heterogeneity of the domesticated stocks. Despite being geographically isolated, we denote that the patterns of genetic homogeneity in wild populations maybe strongly influenced by hydrographic conditions and ecological niches which are expected to increase the adaptive processes of the populations towards their natural habitats. Genetic differences between wild and domesticated populations presented in this study can be likely explained by a number of unique genes within the putatively adaptive SNP loci. These genes were found to be significantly associated with the mitochondrial genome. Our results from KEGG pathway analysis further reinforce the potential selective force which have diverged both groups. We noted the presence of polymorphism in the mitochondrial regions, notably those that are related to the energy production, metabolic functions, respiration regulation and developmental rates. The combination of these functional properties of peptide encoded by mitogenome are linked to various environmental parameters, and this likely acting to promote the genetic isolation of domesticated populations from their wild. Separated by geographical distance and various selection breeding programs, these two groups may each develop local adaptation to ecological niches, biological traits and demographic histories found within their thriving habitats. Taken together, this study has demonstrated the applicability of SkimSeq in a highly duplicated genome of penaeid shrimp, P. monodon specifically, across a range of genetic backgrounds and geographical distributions. This ultra-low sequencing coverage has enabled the sequencing of low number of individuals to achieve either similar or perhaps more powerful coverage, but with lower cost than other traditional GBS methods. In future work, larger sample sizes from a larger number of populations collected from wider distribution range should be included within a similar comparative framework, to validate if this specific trend represents a general rule associated with the genetic divergence of P. monodon broodstocks.We observed a pronounced genetic divergence between the wild and domesticated broodstock populations of


Ethiopian sheep living in different climatic zones and having contrasting morphologies are a most promising subject of molecular-genetic research. Elucidating their genetic diversity and genetic structure is critical for designing appropriate breeding and conservation strategies.The study was aimed to investigate genome-wide genetic diversity and population structure of eight Ethiopian sheep populations.A total of 115 blood samples were collected from four Ethiopian sheep populations that include Washera, Farta and Wollo (short fat-tailed) and Horro (long fat-tailed). DNA was extracted using Quick-DNA™ Miniprep plus kit. All DNA samples were genotyped using Ovine 50 K SNP BeadChip. To infer genetic relationships of Ethiopian sheep at national, continental and global levels, genotype data on four Ethiopian sheep  and sheep from east, north, and south Africa, Middle East and Asia were included in the study as reference.Mean genetic diversity of Ethiopian sheep populations ranged from 0.352 ± 0.14 for Horro to 0.379 ± 0.14 for Arsi-Bale sheep. Population structure and principal component analyses of the eight Ethiopian indigenous sheep revealed four distinct genetic cluster groups according to their tail phenotype and geographical distribution. The short fat-tailed sheep did not represent one genetic cluster group. Ethiopian fat-rump sheep share a common genetic background with the Kenyan fat-tailed sheep.The results of the present study revealed the principal component and population structure follows a clear pattern of tail morphology and phylogeography. There is clear signature of admixture among the study Ethiopian sheep populationsThe online version of this article (10.1007/s13258-020-00984-y) contains supplementary material, which is available to authorized users. Given its proximity to the Arabian Peninsula, Ethiopia is considered as a corridor for the introduction of livestock species including sheep to the African continent  adapted to diverse agro-ecological environments. Detail description of the populations is summarized in Table 15 adaptehttps://WWW.zymoresearch.com/m/D4068). All 115 genomic DNA samples were genotyped using Ovine 50 K SNP BeadChip  by GeneSeek/Neogen .The samples were collected from different households in different villages using 5 ml of vacutainer tube with 1 ml EDTA as anti-coagulant. A maximum number of 1–3 distinct breeding adult animals were sampled per household based on flock owners’ information and typical phenotypic characteristics that allow to avoid sampling of related animals. Genomic DNA was extracted using Quick-DNA™ Miniprep plus kit following the procedures of Biological Fluid and Tissue protocol .Ovine 50 K SNP BeadChip genotype data of 105 animals representing six breeds from east (Kenya: Red Maasai), north  and south (South Africa: Namaqua Afrikaner) Africa, Middle East (Iran: Afshari) and south-west Asia (India: Garole) were included in the study to investigate the genetic relationships in greater detail and infer population structure of Ethiopian sheep populations at continental and global levels. The genotype data (50 K) were obtained from Sheep HapMap and Animal Resources dataset (https://www.sheephapmap.org/hapmap.php). Therefore, a total of 14 sheep populations, including extra four Ethiopian sheep, are included in the study as reference.To infer genetic relationships of Ethiopian sheep populations at national level, Ovine 600 K SNP BeadChip genotype data of 44 animals representing four extra Ethiopian sheep populations  were included in the study. Detail description of the extra four Ethiopian sheep populations is summarized in Table Genotypic data for 600 K and 50 K platforms were merged and common autosomal SNP markers  were obtained. These common autosomal SNPs with call rate less than 90% and minor allele frequency (MAF) less than 0.05 were filtered out using PLINK v1.9  and observed (Ho) heterozygosity and inbreeding coefficient (F) were estimated for each population using Arlequin v.3.5.2  and proportions of genome ancestry shared among the studied populations using ΔK method  was performed using PLINK v1.9  was observed in Moroccan and Namaqua Afrikaner sheep populations, respectively. Analysis of molecular variance for the 14 global populations grouped according to geographical regions revealed that 9.67% (P < 0.0001) of the variance was among groups  (Table P < 0.001) of the variance was among groups (Table P < 0.001) of the variation was among groups, 3.40% among populations within groups and 91.93% within populations  Table . When anps Table . We furtns Table .Fig. 1DiThe principal component analysis (PCA) plot incorporating the global populations indicated that the 14 sheep populations are differentiated based on regions  Fig. a. MoreovTo illustrate relationships within individuals and among Ethiopian sheep populations, PCA was performed Fig. b. PrinciA Neighbor-Net network constructed from pairwise comparison cluster the global dataset (14 sheep populations) according to their geographic region and tail phenotype as indicated in Fig. The close clustering of East African sheep populations and distinct separation from their Northern counterparts was well demonstrated by our phylogenetic and global PCA analysis results  ranging from 2 to 15 and cluster the studied populations according to their geographic region and tail phenotype  sheep populations separated from the rest of the sheep populations at K = 4.Genetic population structure analysis on the global dataset (14 sheep populations) was carried out using hypothetical ancestral clusters (K (K = 8), Ethiopian  and Kenyan (Red Maasai) sheep share up to 94% a single common genetic background (Cluster 3). The Asian thin-tailed sheep, Indian Garole, formed an independent cluster (Cluster 2) with high proportion (~ 95%). The South Africa fat-tailed sheep, Namaqua Afrikaner, formed a clear separate cluster (Cluster 5) with the highest proportion (~ 100%) from the rest of the populations. The two North African representatives shared one genetic background (Cluster 8), and both shared 22–26% common genetic background with Afshari of Iranian sheep (Cluster 1). The Asian thin-tailed sheep showed distinct clustering from east Africa and South African fat-tailed and from east Africa (Ethiopia) fat-rumped sheep.At optimal K) were tested with structure on the Ethiopian dataset . The highest ∆K value suggested K = 4 as most optimal number of ancestral clusters present in the national dataset  than the presumed ancestral populations in the Middle East  and North Africa (HE = 0.401). Populations found near to domestication center are expected to retain higher allelic diversity than those that migrated farther away  did not cluster together. Adaptation to different eco-climates that can impede gene flow, may have shaped this genetic sub-structuring  North African sheep populations could be due to gene flow between the two populations since they have close geographical proximity. Previous studies revealed that north Africa is mostly populated by fat-tailed sheep  sheep suggests its independent introduction into the continent. It is in line with the distinct histories and non-overlapping geographic distributions of the African thin-tailed with fat-tailed and fat-rumped sheep  and population structure analyses of Ethiopian sheep revealed four distinct genetic cluster groups according to their tail phenotype and geographical distribution. The short fat-tailed sheep populations did not represent one genetic cluster group which require further investigation. Our population structure analyses of Ethiopian sheep population demonstrated a clear pattern of the tail morphology and their phylogeography. Further investigation is required on morphometric basis of tail morphology variation in indigenous Ethiopian sheep populations to confirm the genetic basis of tail morphology variation investigated Below is the link to the electronic supplementary material.