The rich linguistic, ethnic and cultural diversity of Ethiopia provides an unprecedented opportunity to understand the level to which cultural factors correlate with–and shape–genetic structure in human populations. Using primarily new genetic variation data covering 1,214 Ethiopians representing 68 different ethnic groups, together with information on individuals’ birthplaces, linguistic/religious practices and 31 cultural practices, we disentangle the effects of geographic distance, elevation, and social factors on the genetic structure of Ethiopians today. We provide evidence of associations between social behaviours and genetic differences among present-day peoples. We show that genetic similarity is broadly associated with linguistic affiliation, but also identify pronounced genetic similarity among groups from disparate language classifications that may in part be attributable to recent intermixing. We also illustrate how groups reporting the same culture traits are more genetically similar on average and show evidence of recent intermixing, suggesting that shared cultural traits may promote admixture. In addition to providing insights into the genetic structure and history of Ethiopia, we identify the most important cultural and geographic predictors of genetic differentiation and provide a resource for designing sampling protocols for future genetic studies involving Ethiopians. Ethiopia is one of the most ethnically and culturally diverse countries. Here, the authors look at genetic and cultural variation in 1,214 Ethiopians to unravel the relationship between genetic admixture and cultural factors. Its geographic position and history  despite the considerable variation known to exist in cultural practices, particularly in the southern part of the country . For example, Ethiopian ethnic groups have a diverse range of religions, social structures and marriage customs, which may impact which groups intermix, and hence provide an on-going case study of socio-cultural selection40, The Council of Nationalities, Southern Nations and Peoples Region, (2017)38 that can be explored using DNA.While the relationships of Beta Israel with other Jewish communities have been the subject of focused research following their migration to Israel41, Gurdasani et al.42, and Mallick et al.42. Our study includes people from 68 distinct self-reported ethnicities  that comprise representatives of most of the major language groups spoken in Ethiopia, including Nilo-Saharan (NS) speakers and three branches  of Afroasiatic (AA) speakers, as well as languages of currently uncertain classification  (www.ethnologue.com) , first language  and/or second language  . These practices include self-declared cultural practices such as male and female circumcision, and 29 different pre-marital and marriage customs, including arranged marriages, polygamy, gifts of beads or belts, and covering the bride in butter.Here we analyse autosomal genetic variation data at 534,915 single nucleotide polymorphisms (SNPs) in 1214 Ethiopian individuals that include 1082 previously unpublished samples and 132 samples from Lazaridis et al.4. We also compared them to a further 16 labelled groups comprised of 39 ancient individuals53 , including 106 unpublished samples from nine groups  than to other African populations 62 among individuals of the same ethnicity vary substantially across Ethiopian groups  decrease of homozygosity with increasing population census size across ethnic groups in the SNNPR .Principal components analysis (PCA)43 between their haplotype-sharing patterns inferred by CHROMOPAINTER63. Mimicking van Dorp et al.37, we performed two CHROMOPAINTER analyses in order to infer the broad time periods over which lines of ancestry between individuals diverged . However, TVD estimates have been shown to be more powerful at distinguishing subtle genetic differences among e.g., African groups65. The second, which we call “Ethiopia-external,” instead compares patterns in each Ethiopian only to those among individuals in non-Ethiopian groups. As the “Ethiopia-internal” analysis compares haplotype patterns in each Ethiopian to those in other Ethiopians, including other members of the same ethnic group, it is more sensitive for detecting endogamy effects and admixture among Ethiopian groups59. In contrast, the “Ethiopia-external” analysis mitigates signals related to both of these factors, while remaining sensitive for inferring whether Ethiopians having varying proportions of ancestry related to non-Ethiopian sources due to e.g., different admixture histories43. We illustrate this in simulations mimicking our real data p val < 0.05) between genetic distance and each of geographic distance, elevation difference, ethnicity and first language, after controlling each factor for the others where possible  between genetic distance and each of religion and second language  of genetic isolation between people reporting as Christians versus those reporting as Muslims or those reporting as practicing traditional religions , which remained after accounting for geographic or elevation distance  or language group  , with averages summarised in Supplementary Fig. 67. Despite this, under the “Ethiopia-external” analysis, Ari and Wolayta are more genetically similar to members of their own ethnicities on average, regardless of occupation  from all other ethnic groups under the “Ethiopia-internal” analysis , and linguistics69. However, we clarify this further by showing the Chabu to be significantly more genetically similar to the Mezhenger sample than other samples examined here  also often categorising genomes according to ethnic group  of which had estimated dates <30 generations ago (<900 years ago)  between genetic similarity and shared cultural practices only under the “Ethiopia-internal” analysis that is sensitive to intermixing among Ethiopian groups  genetic similarity among ethnic groups participating in the practice relative to those who did not participate or whose participation in the practice was unknown  have arisen through their relatively recent isolation. Consistent with this isolation, these groups also exhibit signatures of recent endogamy as reflected by higher degrees of genetic homogeneity .Our simulations demonstrate how two different types of analyses, which we term “Ethiopia-internal” and “Ethiopia-external”, can disentangle relatively recent from ancient shared ancestry to better understand the origins of different ethnic groups . This corresponds to the time period during which iron working is thought to have first appeared in Ethiopia79 and supports the marginalisation theory of their origins80 consistent with previous genetic studies37.In the Ari, we infer very similar sources and dates of admixture in independent analyses of distinct clusters that correspond to occupational groups  and ancestry proportions that are similar to those inferred in the Mezhenger Fig. . These itry Fig. , which iA caveat to the interpretation that groups with similar inferred admixture sources and proportions under the “Ethiopian-external” analysis share similar recent ancestry is that this analysis will have reduced (or no) power to discriminate between Ethiopian groups that indeed have separate ancestral sources if we have not included relevant non-Ethiopian groups to represent these sources. The large number of non-Ethiopian groups included in this sample, particularly those geographically proximal to Ethiopia, diminishes this possibility, but more samples from other sources, in particular from ancient individuals in Ethiopia, may increase our ability to identify older ancestral differences between Ethiopians using these techniques.82, African83 and worldwide countries84. We also identify a correlation between genetic similarity and elevation difference, even after correcting for genetic similarity over geographic distances. Strikingly, we also see a correlation between spatial distance and the degree of genetic ancestry related to Mota, an ancient individual 4whose remains were found in the Gamo Highlands of present-day Ethiopia 4500 years ago , share more recent ancestry with non-Ethiopian Bantu and NS Nilotic speakers. In contrast, Ethiopian AA speakers in the northeast share more recent ancestry with Egyptians and West Eurasians Fig. . The inf36 (see Supplementary Note 36), have relatively more Mota-like ancestry, and the Ethiopian Mursi (cluster 2) have relatively more Dinka-like ancestry  may be masked by more recent admixture  to oral histories recorded for various ethnic groups. For example, some Mezhenger report that their ancestors originally migrated from Sudan to the present-day Gambella Regional State where Anuak lived, after which they migrated with the AA Omotic-speaking Sheko for a period before settling in their present-day homeland . Consistent with this, in the “Ethiopia-external” analysis the Mezhenger have high inferred ancestry matching to the Sudanese Dinka  while adding further details and/or novel insights and directions for future investigation. Our interactive map is designed to facilitate evaluation of genetic evidence for such claims, providing results from both the “Ethiopia-internal” and “Ethiopia-external” analysis to enable comparisons analogous to the examples above. Future work can compare these and other published genetic results e.g.,6 to oralnka Fig. .83. Finally, our analyses illustrate how cultural practices, e.g., participation in certain cultural and marriage customs, can operate as both a barrier and a facilitator of gene flow among groups, and consequently act as an important factor shaping human diversity and evolution.Our study also highlights the importance of considering topographical and cultural factors, in particular language, ethnicity and in some cases occupation, when designing sampling strategies for future Ethiopian genetic studies, e.g. genome-wide association studies (GWAS), which our interactive map can also assist with. Similar sampling strategies may be necessary to capture the genetic structure of peoples in some other African countries that also exhibit relatively high levels of genetic diversity and structureDNA samples from the 1082 Ethiopians whose autosomal genetic variation data are newly reported in this study  were collected in several field trips from 2000 to 2010, through a long-standing collaboration including researchers at University College London and Addis Ababa University. All study participants, including non-Ethiopians whose genetic variation data are newly reported in this study, gave their informed consent. Local permissions were obtained in all cases where applicable local ethical approval and regulations existed, e.g., Cameroon, Ministry of Higher Education and Scientific Research, Permits 0188/MINREST/B00/D00/D10/ D12 and 317/MINREST/B00/D00/D10 and University of Yaounde I; Ethiopia, Ethiopian Science and Technology Commission and National Ethics Review Committee. Sample collection/usage for all unpublished data included in this study were approved by the UK ethics committee London Bentham REC . The analyses reported here were approved by UCL REC (Project ID: 5188/001).43, where possible we genotyped those individuals whose grandparents’ birthplaces and ethnicity were coincident43. However, for a few ethnic groups , we did not find any individuals fulfilling this birthplace condition; in such cases we randomly selected individuals whose grandparents had the same ethnicity. In these cases, the geographical location was calculated as the average of the grandparents’ birthplaces 86. To these data we added present-day Indians and Iranians published by Broushaki et al.52, and Lopez et al.55, and genomes from present-day Africans published by Skoglund et al.54, Gurdasani et al.42 and Mallick et al.28  from Africa87, including GB20 ‘Mota’ from Ethiopia4, and 19 high coverage (>5X) published ancient non-African samples53  from 264 labelled present-day populations, and 40 high coverage aDNA genomes (including Mota), as described in this paragraph. Among these, non-Ethiopian samples newly released in this study include 23 Arabs from Israel, 13 Arabs from Palestine, 8 Bedouins from Saudi Arabia, 18 Berbers from Morocco, 7 Kotoko from Cameroon, 6 Muganda/Baganda from Uganda, 6 Mussese from Uganda, 13 Senegalese and 12 Syrians. All newly reported DNA samples in this study were genotyped using the Affymetrix Human Origins SNP array, which targets 627,421 SNPs , and merged with the Human Origin datasets published by Lazaridis et al.https://www.ebi.ac.uk/ena), with each file checked for correct format and metadata using PicardTools. We estimated post-mortem damage using ATLAS88 with “pmd”, recalibrating each BAM file using ultra-conserved positions from UCNE (https://ccg.epfl.ch/UCNEbase/) and running ATLAS with “recal”, and then generated maximum likelihood genotype calls and phred-scaled genotype likelihood (PL) scores for each position using ATLAS with “call”. We used Conform-GT (https://faculty.washington.edu/browning/conform-gt.html) to ensure that strand was consistent with 1000 Genomes89 across present-day and ancient datasets, merging the data and running Beagle 4.190 with “modelscale = 2” and the genetic maps at http://bochet.gcc.biostat.washington.edu/beagle/genetic_maps/plink.GRCh37.map.zip to re-estimate genotypes and impute missingness. We used vcf2gprobs, gprobsmetrics and filterlines (https://faculty.washington.edu/browning/beagle_utilities/utilities.html) to filter SNPs with an imputation accuracy of less than 0.98, and then we phased all samples using shapeit491 with “–pbwt-depth 16” and using their provided genetic maps.BAM files for ancient samples were downloaded from the ENA website  that had PI_HAT > 0.15 and PI_HAT > min, where {X_i, Y_i, S_i, D_i} are the {mean, median, standard deviation, median-absolute-deviation}, respectively, of pairwise PI_HAT values among individuals from population i. For populations with <=2 sampled individuals, the standard deviation and median-absolute-deviations are undefined or 0; therefore in such cases we added to the list any pairings with PI_HAT > 0.15 that contained >=1 person from that population. Using a stepwise greedy approach, we then selected individuals from this list that were in the most pairs to be excluded from further analysis, continuing until at least one individual had been removed from every pair. This resulted in a total of 234 individuals removed, including 62 Ethiopians. All remaining Ethiopian pairs after this procedure had PI_HAT < 0.2.To identify putatively related individuals, we used PLINK v1.960 from EIGENSOFT version 7.2.0, with standard parameters and the lsqproject option. For the PCA of all individuals  on the SNP data using smartpca61, taking the median value within each group. Second, we pruned SNP data based on linkage disequilibrium (–indep-pairwise 50 5 0.5), which left us with 359,281 SNPs, and used PLINK v1.9 to detect runs of homozygosity (ROH). This ROH procedure find runs of consecutive homozygous SNPs within groups that are identical-by-descent; here we report the total length of these runs per individual  of identity by descent (IBD) between pairs of individuals. For each population and chromosome, fastIBD was run for ten independent runs using an IBD threshold of 10−10, as recommended by Browning and Browning62, for every pairwise comparison of individuals62. For each population, we report the fraction of the genome that each pair of individuals shares IBD  with the census population size using standard linear regression  between a phased target haploid and a set of phased reference haploids. By modelling correlations among neighboring SNPs , CHROMOPAINTER has been shown to increase power to identify genetic relatedness over other commonly-used techniques such as ADMIXTURE and PCA63. In brief, at each position of a target individual’s genome, CHROMOPAINTER infers the probability that a particular reference haploid is the one which the target shares a most recent common ancestor (MRCA) relative to all other reference haploids. These probabilities are then tabulated across all positions to infer the total proportion of DNA for which each target haploid shares an MRCA with each reference haploid. We can then sum these total proportions across the reference haploids assigned to each of K pre-defined groups.To quantify relatedness among individuals, we employed a “chromosome painting” technique, implemented in CHROMOPAINTER37, we used two separate CHROMOPAINTER analyses that differed in the K pre-defined groups used:Following van Dorp et al.K = 264 groups only  DNA patterns of each sampled individual to that of non-Ethiopians from 28, leading to K = 346 groups total28.2. “Ethiopia-internal”, which matches DNA patterns of each sampled individual to that of all sampled groups, comprising 264 non-Ethiopian groups plus the 78 Ethiopian clusters defined in Supplementary Fig. Relative to our genetic similarity score  under the “Ethiopia-internal” analysis, our score under the “Ethiopia-external” analysis mitigates the effects of any recent genetic isolation  that may differentiate a pair of Ethiopians. This is because individuals from groups subjected to such isolation typically will match relatively long segments of DNA to only a subset of Ethiopians  under analysis (1). However, this isolation will not affect how the same individuals match to each non-Ethiopian under analysis (2), for which they typically share more temporally distant ancestors. Consistent with this, in our sample the average size of DNA segments that an Ethiopian individual matches to another Ethiopian is 0.68 cM in the “Ethiopia-internal” analysis, while the average size that an Ethiopian matches to a non-Ethiopian is only 0.23 cM in the “Ethiopia-external” analysis, despite the latter analysis matching to substantially fewer individuals overall and hence having a higher a priori expected average matching length per individual.55, van Dorp et al.59, and Broushaki et al.52, for each analysis (1) and (2) we estimated the CHROMOPAINTER algorithm’s mutation/emission  and switch rate  parameters using ten steps of the Expectation-Maximisation (E-M) algorithm in CHROMOPAINTER applied to chromosomes 1, 8, 15 and 22 separately, analysing only every ten of 4081 individuals as targets for computational efficiency59. This gave values of {321.844, 0.0008304} and {178.8922, 0.0006667} for {Ne, Mut} in CHROMOPAINTER analyses (1) and (2), respectively, after which these values were fixed in a subsequent CHROMOPAINTER run applied to all chromosomes and target individuals. The final output of CHROMOPAINTER includes two matrices giving the inferred genome-wide total expected counts (the CHROMOPAINTER “.chunkcounts.out” output file) and expected lengths (the “.chunklengths.out” output file) of haplotype segments for which each target individual shares an MRCA with every other individual.Following López et al.i,j we used total variation distance (TVD)43 to measure the genetic differentiation  between their K-element vectors of CHROMOPAINTER-inferred proportions , i.e:ividual i is inferred to match to individuals from group k  where the two individuals are from different groups  from C is greater than or equal to that among all Y, where Y ∈ {A,B} (tested separately). We report the proportion of 100 K such permutations where this is true as our one-sided p value testing the null hypothesis that an individual from group Y has the same average genetic similarity with someone from their own group versus someone from the other group  is enclosed with a white rectangle in Supplementary Fig. For each Ethiopian group A contains ten Ethiopians, each of those Ethiopians are allowed to match to nine people from cluster A under the “Ethiopia-internal” analysis, while Ethiopians outside of cluster A are matched to all ten. This may create a slight discrepancy in the k representing the Ethiopian clusters, which in turn may affect differences in TVD among Ethiopian group labels. To test this, we repeated the above using an alternative “Ethiopia-internal” painting where each Ethiopian is matched to all other Ethiopians from their cluster and k after randomly removing one individual, while matching to all individuals from every non-Ethiopian group as before. This gives a K = 346 length vector of i as before, but where each Ethiopian now has been painted against the same numbers of individuals from the K groups. We found that results change very little, e.g., with the TVD values among all pairwise combinations of Ethiopian groups  between individuals i and j as Gij= 1 − TVDij, geographic distance (dij) using the haversine formula applied to the individuals’ location information, and elevation distance (hij) as the absolute difference in elevation between the individuals’ locations. We assessed the significance of associations between Gij and dij and between Gij and hij using 1000 permutations of individuals’ locations.To test for a significant association between genetic similarity and spatial distance, we used statistical tests that are analogous to the commonly-used Mantel testWhen using distance bins of 25 km, we noted that the mean genetic similarity across pairs of individuals showed an exponential decay versus geographic distance in the “Ethiopia-internal” analysis Fig. . Therefoα,β,λ), we first used the “Nelder-Mead” algorithm in optim in R to infer the value of λ that minimizes the sum of eij2 across all pairings of individuals i,j when α = 0 and β = 1, and then found the MLE for α and β under simple linear regression using this fixed value of λ. As the main observed signal of association between genetic and spatial distance is the increased Gij at small values of dij, , our reported p values are the proportion of permutations for which the mean Gij among all  with permuted dij < 25 km is greater than or equal to that of the (unpermuted) real data (Supplementary Table To infer maximum likelihood estimates (MLEs) for  using lm in R. When testing for an association with elevation, we only included individual pairs  whose elevation distance was less than 2500 km, which occurred in 730,880 (99.6%) of 733,866 total comparisons, to avoid undue influence from outliers. As we expect (and observe) the change in genetic similarity δ to be negative as spatial distance increases, our reported p values provide the proportion of permutations for which the MLE of δ in the 1000 permutations is less than or equal to that of the real data . Then to test for an association between genetic similarity and geographic distance after accounting for elevation, we used Eq.  from Eq. , excluding the Negede-Woyto and Chabu as they have not been classified into any language family. For (E), we compared genetic similarity across three religious affiliations , excluding religious affiliations recorded as “Traditional” as practices within these affiliations may vary substantially across groups.We then tested whether sharing the same (A) self-reported group label, (B) language category of reported ethnicity, (C) self-reported first language, (D) self-reported second language, or (E) self-reported religious affiliation were significantly associated with increased genetic similarity after accounting for geographic distance or elevation difference. We used 75 group labels for (A)  data. For the “Ethiopia-internal” analysis when testing genetic similarity against geographic distance, this is the same p value procedure as above, i.e., the proportion of permutations for which the mean Gij among all  with permuted dij < 25 km is greater than or equal to that of the (unpermuted) real data  whil whil4) wGij. Applying this permutation procedure for each of (A)-(E), we repeated all tests described above, reporting p values in Supplementary Table As group label, language and religion can also be correlated with spatial distance and with each other -(E) while permuting -(E), our final 63 to classify 1268 Ethiopians  into clusters of relative genetic homogeneity28. To do so, we first used SHAPEIT93 to jointly phase individuals using default parameters and the linkage disequilibrium-based genetic map build 37 (available at https://github.com/johnbowes/CRAFT-GP/find/master). We then employed CHROMOPAINTER to paint each individual against all others, i.e., in a manner analogous to the “Ethiopian-internal” analysis, though using a slightly different set of reference populations  and hence slightly different {Ne, Mut} values of {192.966, 0.000801}. We used default parameters, with the fineSTRUCTURE normalisation parameter “c” estimated as 0.20245. To focus on the fine-scale clustering of Ethiopians, we fixed all non-Ethiopian samples in the dataset as seven super-individual populations  that were not merged with the rest of the tree. We performed 2,000,000 sample iterations of Markov-Chain-Monte-Carlo (MCMC), sampling an inferred clustering every 10,000 iterations. Following Lawson et al.63, we next used fineSTRUCTURE to find the single MCMC sampled clustering with highest overall posterior probability63. Starting from this clustering, we then performed 100,000 additional hill-climbing steps to find a nearby state with even higher posterior probability. This gave a final inferred number of 180 clusters containing Ethiopians. Results were then merged into a tree using fineSTRUCTURE’s greedy algorithm. We used a visual inspection of this tree to merge clusters, starting at the bottom level of 180 clusters, that had small numbers of individuals of the same ethnicity, as shown in Supplementary Fig. We used fineSTRUCTURE43 to generate a measure of cluster certainty using the last 100 fineSTRUCTURE MCMC samples43. In particular for each of these 100 MCMC samples, we assigned a certainty score for each individual i being assigned to each final cluster j (out of 78) as the percentage of individuals assigned to the same cluster as individual i in that MCMC sample that are found in final cluster j.  For each combination of individual and final cluster, we averaged these certainty scores across all 100 MCMC samples. For each of our 78 final clusters, in Supplementary Data We followed Leslie et al.56 to cluster Ethiopians. To do so, we first pruned the dataset for SNPs in linkage disequilibrium using PLINK v.261, removing SNPS with an r2 > 0.1 within a 50-SNP window, which left 139,032 SNPs. We then applied ADMIXTURE to the Ethiopians using these SNPs and a varying number of clusters K = 2 − 15 and default parameters.We also used ADMIXTURE v.1.3.072 to each of the 78 clusters to infer the proportion of ancestry that each clusters’ individuals share most recently with 275 ancestry surrogate populations, consisting of 264 present-day non-Ethiopian populations and aDNA samples from 11 populations including Mota  over one or more narrow time periods  is the same as the one used in (1). For the “Ethiopia-internal” analysis, we had to repaint each individual in the target cluster for step (2); to do so we used the previously estimated CHROMOPAINTER {Ne, Mut} parameters of {180.5629, 0.000610556}.GLOBETROTTER requires two paintings of individuals in the target population being tested for admixture: (1) one that is primarily used to identify the genetic make-up of the admixing source groups (used as “input.file.copyvectors” in GLOBETROTTER), and (2) one that is primarily used to date the admixture event (used as the “painting_samples_filelist_infile” in GLOBETROTTER). For both the “Ethiopia-external” and “Ethiopia-internal” analyses, we used the respective paintings described in “Using chromosome painting to evaluate whether genetic differences among ethnic groups are attributable to recent or ancient isolation” above to define the genetic make-up of each group for painting (1). For (2), following Hellenthal et al.58. All GLOBETROTTER results, including the inferred sources, proportions and dates of admixture, are provided in Supplementary Data 78 and uses an average birthdate of 1975 for sampled individuals that matches our recorded information.In all cases, we ran GLOBETROTTER for five mixing iterations  and performed 100 bootstrap re-samples of individuals to generate confidence intervals around inferred dates. We report results for null.ind = 1, which attempts to disregard any signals of linkage disequilibrium decay in the target population that is not attributable to genuine admixture when making inferenceA are more genetically similar to each other than an individual from classification A is to an individual from classification B, we followed an analogous procedure to that detailed above to test for genetic differences between group labels A and B. Again let A and B, respectively, with A and B and put them into a new group C. If C that was randomly chosen from A with probability 0.5 or otherwise randomly chosen from B, so that C had i,j) from C is greater than or equal to that among all Y, where Y ∈ {A,B} (tested separately).To test whether individuals from language classification i,j) that were from different ethnic/occupation labels . We report the proportion of 100 K such permutations where this is true as our one-sided p value testing the null hypothesis that an individual from language classification Y has the same average genetic similarity with someone from their own language group versus someone from the other language group  and described in Supplementary Note H ethnic groups in total reported participating in a practice, any pair of ethnicities that both reported participating in this practice added a contribution of 1.0/H to that pair’s cultural similarity score, rather than a contribution of 1 as in the original cultural similarity score. Similarly, if Z ethnic groups in total reported not participating in a practice, any pair of ethnicities that both reported not participating in this practice added a contribution of 1.0/Z to that pair’s cultural similarity score.We also calculated a second cultural similarity score whereby practices shared by many groups contributed less to a pair’s score than practices shared by few groups. To do so, if A, B were each calculated as the average such measure between all pairings of individuals where i is from A and j from B. We then applied a mantel test using the mantel package in the vegan library in R with 100,000 permutations to assess the significance of association between genetic and cultural similarity across all pairings of ethnic groups  versus Y=(iii). For one cultural practice, we tested X=(ii) versus Y=(iii). For three cultural practices, we tested {X=(i) versus Y=(ii)}, {X=(i) versus Y=(iii)}, and {X=(ii) versus Y=(iii)}.For each of the 31 cultural practices, all 46 ethnic groups were classified as either (i) reporting participation in the practice, (ii) reporting not participating in the practice or (iii) not reporting whether they participated in the practice. For cultural practices where at least two of (i)-(iii) contained > =2 groups, we tested the null hypothesis that the average genetic similarity among groups assigned to category X was equal to that of groups assigned to Y, versus the alternative that groups in X had a higher average genetic similarity to each other. To do so, we calculated the difference in mean genetic similarity among all pairs of groups assigned to X versus that among all pairs assigned to Y. We then randomly permuted ethnic groups across the two categories 10,000 times, calculating p value < 0.05 for one of the above permutation tests . In each of the above regressions, we fit our models using all pairs of Ethiopians that were not from the same language classification at the branch level , in order to account for only spatial distance effects that are not confounded with any shared linguistic classification. We calculate the average spatial-distance-adjusted genetic similarity between each ethnic group A,B as the average G*ij between all pairings of individuals where i is from A and j from B. Then to adjust for language classification, we calculated the expected spatial-distance-adjusted genetic similarity for each pairing of language branches C,D as the average adjusted genetic similarity across all pairings of ethnic groups A, B where A is from C and B is from D. For each pair of ethnic groups that share a reported cultural trait shown in Fig. Six practices gave a sed Eqs. –4) abov abovp van groups  where a greater or equal number of group pairings showed this trend, also considering various different values of excess average MRCA segment size (Supplementary Table For each of these six cultural practices shown in Fig. Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-13Reporting Summary


B, n = 27) and SS of Mateur . High levels of genetic diversity in SS were revealed, with a total of 212 alleles, a high mean number of alleles (12.47 ± 4.17) and a high average polymorphism information content (PIC) (0.81 ± 0.10). The observed heterozygosity was considerable in SSB and SSM . The inbreeding level measured by the population inbreeding coefficient ISF is higher in the SSM population (0.121) than in the SSB population (0.090). The higher genetic diversity level detected in SSB reflected the effect of new Italian Sarda genes introduced by intra-uterine artificial insemination recently practiced in this population. The Wilcoxon test and the mode-shift distribution indicated that the SS breed is a non-bottlenecked population. The structural analysis reflected the historical miscegenation practiced during the breed creation and highlighted further ancient miscegenation, which could date back to the first waves of sheep introduction to the western Mediterranean region. Microsatellite markers were successfully applied in the assessment of the genetic variability of SS and should be used in monitoring this variability during the application of conservation strategies.This study analyzed the genetic variability, inbreeding and population structure of the Tunisian–North African dairy sheep breed, the Sicilo-Sarde (SS), created by crossing the Sarda and Comisana dairy breeds. The level of variability in the SS, considered as an endangered breed after a dramatic decrease, was assessed using 17 microsatellite markers by analyzing the two breed populations sampled from their respective cradles: SS of Beja (SS Milk became available for human consumption, since the onset of the Neolithic, between 12,000 and 9000 years ago in the Fertile Crescent identified as a domestication center ,4. SinceThe ability of the SS to produce milk makes it the only dairy breed in the North African region despite its quite limited production potential obtained after a long suckling period practiced by breeders . This spThe sustainability of the sheep-milk cheese production in Tunisia has been threatened in recent decades after a dramatic decrease in the number of dairy breed . Its decline from 200,000 female units in 1991 to 26,500 units females in 1995 and to 25,000 in 2000 indicate that this breed faces a threat of extinction and that the situation has reached a critical level ,10. The Since the climate change is expected to make overall climates warmer and dryer, the hardy indigenous sheep breeds that have proven their adaptability to increasing temperatures are considered a main way to meet not only international food demands but also environmental sustainability . These sB from Beja region (n = 27) and SSM from Mateur region (n = 25). Unrelated animals were sampled from each farm or small flock based on the information provided by farmers and breeders when pedigree data were not available. The out-group sheep breeds, from Tunisia , Queue Fine de l’Ouest , Noire de Thibar ); from Morocco ); and from Italy  and Lamon ), were used to explore the population structure and the genetic uniqueness level of the SS. Genomic DNA extraction was carried out from 200 µL of whole blood using the Wizard Genomic DNA Extraction Kit  following the manufacturer’s protocol. The total of samples were genotyped at 17 microsatellites loci panel established from the ISAG/FAO recommended microsatellites markers [A total of 52 individuals were sampled from two populations of the dairy Sicilo-Sarde native breed: SS markers  and from markers ,19,20,21o) and expected (He) heterozygosity, and gene flow (Nm) were calculated using GENETIX version 4.05.2 [PAR) in the different populations using the rarefaction method was counted using ADZE software [FIS, FIT and FST; [A) and FST pairwise distances among populations was estimated using GENETIX version 4.03 [FST distances among breeds were represented by a neighbor-network graph constructed using Splits Tree4 software [The MICRO-CHECKER version 2.2.3  softwaren 4.05.2 . The numsoftware . The MSAsoftware  was usedand FST; ). The Poand FST; . Nei’s [and FST;  genetic ion 4.03 . This soion 4.03  was applion 4.03 , to asseion 4.03 . FST dissoftware . The gensoftware . To choosoftware . The outsoftware  and CLUMsoftware .FIS, FIT and FST for each locus and across all loci are given in FIS = 0.100 ± 0.110, FIT = 0.110 ± 0.100, and the FST index was equal to 0.010 ± 0.020 (p < 0.001). The result of the genetic variability parameters revealed that the microsatellites panel used in this study was highly informative and appropriate for characterizing the unique Tunisian and North African dairy breed. The usefulness of this panel was endorsed by previous sheep genetic diversity studies [All seventeen screened loci were included in the data, since no evidence of null alleles, stuttering bands or large allele dropout was found. A summary of the polymorphism of the genotyped markers is given in  studies ,19,20,21 studies  and in M studies  and Alge studies  sheep brB and SSM was studied in terms of MNA, AR and PAR, as summarized in B and SSM were similar to the values reported by [M population  in highly selected Italian Holstein Friesian cattle, but small values (0.07 to 0.19) were recorded in the native Italian Burlina cattle breed.During the first investigation of Tunisian sheep by microsatellite markers, Ben Sassi-Zaidy et al.  found a  similar  as well y et al.  and by Ky et al.  in the Sh Africa . In dairh Africa  revealedo across loci was considerable and slightly lower than He (p < 0.05) was quite limited . The Ho values within the SS breed were similar to that observed in its ancestral breed the Italian Sarda  despite the limited deviation from the HWE. These results can be explained by the management strategy that is followed by the OTD office, which is based on traditional farming with a simple paternity control and without selection pressure reflecting the absence of selective forces acting on the studied loci or on the closely linked genes. Furthermore, this inbreeding level could have resulted from the dramatic reduction in sample size for the SS in the nineties. This fact seems to be slightly corrected in the population SSB of Beja (FIS = 0.090 ± 0.038) after the introduction of new Italian Sarda genes by intra-uterine artificial insemination, made available in 2004/2005 under the supervision of the Sicilo-Sarde Breed Association created in the Beja region. Tolone et al. [FST index (0.0096) indicated a very low differentiation between the SSB and SSM. In fact, only 0.96% of the genetic variability is explained by difference between the two sub-populations, and a large part of the total variability (99.04%) is explained by the difference among individuals. Thus, the heterozygosity deficiency revealed in the SS breed is explained by inbreeding rather than the Wahllund effect. Ben Sassi-Zaidy et al. [For the two dairy populations, the average H than He . The dev± 0.108; ) but hig± 0.108;  in the Te et al.  revealedy et al. .B and SSM, under the three mutation models IAM, TPM and SMM were conducted using the Wilcoxon test  and the Nei’s (DA) genetic distances between SSquencies . The totThe genetic structure of SS dairy breed was investigated with K number of expected clusters ranging from 1 to 9, using different out-group breeds from Tunisia—Queue Fine de l’Ouest (QFO), Noire de Thibar (NTH), Barbarine (BAR) and D’man (DM)—and from Italy—Appenninica (APP) from the Center of Italy and Lamon (LAM) from the Veneto region. The optimal number of ancestral populations according to the method of Evanno et al.  was K = At K = 3 , the VenB and SSM were split into two clusters  was detected between SSB and SSM, highlighting their genetic closeness. Limited genetic distance was also revealed between the BAR and QFO native Tunisian breeds. NTH breed presented a low differentiation from the group of the SS breed and the group formed by BAR and QFO. The DM originating from Morocco was the most genetically distant from the other North African breeds. The APP and then the LAM Italian breeds were the most genetically differentiated. These results were illustrated in the neighbor network built from the FST distances (The pair-wise distance  was compistances . The graIn this study, we investigated the genetic variability level, the demographic bottleneck genetic signature and the population structure of the Tunisian Sicilo-Sarde dairy sheep breed using microsatellites analysis. The results confirmed that this endangered breed presented high levels of genetic diversity, no significant inbreeding and a specific genetic structure that could be related to its dairy vocation. This outcome underlined the success of the applied conservation strategy started to save this breed. Further studies need to be carried out to set up an appropriate selection scheme based on the economic potential and the adaptability of this dairy breed.


Y-chromosome analysis provides valuable information regarding the migration patterns of male ancestors, ranging from the Paleolithic age to the modern humans. STR and SNP genotyping analysis provides data regarding the genetic and geographical ancestry of the populations studied. This study focused on the analysis of the Y-chromosome in Maronite Cypriots and Armenian Cypriots, who came to the island as a result of different historical events. The aim was to provide information on the paternal genetic ancestry of Maronites and Armenians of Cyprus and investigate any affinity with the Greek Cypriots and Turkish Cypriots of the island. Since there is limited information in the current literature, we proceeded and used 23 Y-chromosome STRs and 28 Y-chromosome SNPs to genotype 57 Maronite Cypriots and 56 Armenian Cypriots, which were then compared to data from 344 Greek Cypriots and 380 Turkish Cypriots. All samples were assigned to eight major Y-haplogroups but the most frequent haplogroup among all Cypriots is haplogroup J in the major subclade J2a-L559. The calculated pairwise genetic distances between the populations show that Armenian Cypriots are genetically closer to Greek and Turkish Cypriots compared to Maronite Cypriots. Median Joining Network analysis in 17 Y-STR haplotypes of all Cypriots assigned to J2a-L559, revealed that Cypriots share a common paternal ancestor, prior to the migration of the Armenians and Maronites to Cyprus, estimated in the Late Bronze Age and Early Iron Age. Cyprus is the third largest island in the Mediterranean Sea neighboring with Lebanon, Turkey, Syria, Israel, Egypt and Greece. Because of its geographical location, the country has a long history of governance and several cultures which have influenced, not only the ethnic composition of its population but also its culture and its lifestyle . This inth and 6th century, respectively [th century, when the King of Jerusalem, Guy de Lusignan, acquired Cyprus, during the Frankish Era in 1192 [Maronites are the largest of the three religious groups. The first Maronites and Armenians came to the island during the 7ectively . Maronit in 1192 .The first arrival of Armenians in Cyprus is estimated to have occurred during the Byzantine Era, but there was a continuous influx to the island since 578 AD [The human Y-chromosome is a great tool for the analysis of the migration pattern of paternal lineages through the years. Previous studies have been undertaken to determine the haplogroups of the Maronites (including Maronite Cypriots) and Armenians but not of Armenian Cypriots –8. So faet al (2010) analyzed the ancestry of 42 Maronite Cypriots, which were included in the Lebanese Maronite population. Between all populations, haplogroups J-M267, J-M172, and E-M35 were the most frequent, with the most common haplogroup in Maronite Cypriots being E-M35 [Maronite Cypriots originated mainly from Lebanon . A studyPrevious studies have been performed for the determination of the origin of Armenians , 8. A stThe present study aimed to determine whether paternal lineages of Maronite Cypriots and Armenian Cypriots had affinity with the Greek Cypriots and Turkish Cypriots. A comparative analysis with other available population data also investigated the level of admixture and influence from other neighboring as well as more distant populations in the context of historical events and concomitant population migrations. The genetic data which were used from other studies for the genetic calculations were Y-Short Tandem Repeats (Y-STRs) and Y-Single Nucleotide Polymorphisms (Y-SNPs) –12.The design and execution of this project fulfilled all the personal data protection requirements stipulated by the General Data Protection Regulation (GDPR 2016/679). The study was approved by the Cyprus National Bioethics Committee (CNBC). Informed consent was given by all interested participants. Inclusion criteria were as follows: unrelated males, who were 18 years old or above, born in Cyprus, with a Maronite or Armenian paternal ancestry.A total of 57 and 56 unrelated Maronite Cypriots and Armenian Cypriots, respectively, participated in the study. Unfortunately, due to the SARS-CoV-2 pandemic, a decision was taken to stop further recruitment of individuals.DNA extraction was carried out using a QIAGEN BioRobot Universal (BRU) with QIAamp 96 DNA Swab BioRobot Kit or by using QIAamp DNA investigator kits from QIAGEN . A reagehttps://yhrd.org/) with the accession numbers: YA004717, YA004718. Y-23-STR haplotypes of all individuals were imported individually, in the NEVGEN predictor tool (https://www.nevgen.org/) to predict their respective haplogroups [The extracted total nuclear DNA was quantified using an internally validated published method running on the AB-7500 RT PCR System and set up using the BRU. Automated Y-STR PCR set-up followed using 500pg DNA template. The Y-STR amplification kit used was PowerPlex Y23 (Promega). All relevant controls were included in the STR PCR set-up to verify the validity of the results generated. Following PCR, aliquots of amplified products were run on the AB-3130xl capillary electrophoresis genetic analyzer (Applied Biosystems). Once the run was completed, the data were analyzed via GeneMapper ID V3.2 using the analysis parameters previously imported for this Y-STR system. Data have been submitted to the online Y-STR Haplotype Reference Database (YHRD) (logroups . After shttps://www.ncbi.nlm.nih.gov/) and the International Society of Genetic Genealogy (ISoGG) (https://isogg.org/)  (gg.org/) . Two metGenetic data (Y-STRs) from Greek Cypriots and Turkish Cypriots obtained from previous studies , 16 werehttps://yhrd.org/). YHRD performs an assessment of male population stratification among worldwide populations using Y-STR and Y-SNP frequency distribution [https://yhrd.org/) [The Y-STR Haplotype Reference Database (YHRD) is a searchable worldwide database of Y-STR Haplotypes (ribution . Pairwisrd.org/) , 20. Rstrd.org/) . The stard.org/) . Pairwisrd.org/) . MultidiA dataset for the Y-STR analysis was created, using the data of all Cypriots and the available population data from countries where Maronites and Armenians may have originated before coming to Cyprus. For the Turkish Cypriot population, only 17 Y-STR loci were available from a previous study , therefoMedian Joining Network (MJN) diagrams were constructed using the software Network 4.6.1.6 Fluxus Engineering –25, reprEach sample in each population was assigned to a Y-haplogroup. Y-haplogroup frequencies were calculated for each population and then combined analysis was followed for the comparison between the haplogroups in all populations. Mappies were created using Υ-haplogroup frequencies and R programming, which represent the proportion of each haplogroup in a pie chart, located to the centroid of each country . Y-pathsThe aim of the study was to find the genetic differentiation between all Cypriots, Greek, Turkish Cypriots, Armenian Cypriots and Maronite Cypriots. In the current study, 57 and 56 Y-STR profiles from Maronite and Armenian Cypriots , respectBoth software analysis results were concordant with respect to the genetic correlation between Cypriots . MaronitPairwise genetic distances between Maronite Cypriots and Armenian Cypriots and populations from other countries were calculated, using two different software, SPAGeDi and YHRD. For the calculation, available datasets from Cyprus, Armenia, Egypt, Lebanon, Syria and Turkey were used –12, 15. Armenian Cypriots were very close genetically with all Cypriots, based on the results of both software . The lowThe genetic correlations between the populations are shown in the MDS plot in Using the data from the current study and other publications, the number and percentage of individuals who carry a number of Cypriot haplotypes  were calculated. The percentage of shared haplotypes represent the number of individuals, calculated also as a percentage, who have an exact match with the 17 Y-STR haplotype in each Cypriot group. As mentioned above, Maronite Cypriots share a number of haplotypes between them and Armenian Cypriots . The higAdditional pairwise genetic distances according to the Y-haplogroup paths were calculated using YHRD online tool , S1 Fig.Maronite Cypriots were very close genetically with Ethiopia (Tigray) and Egypt (Assiut), with genetic distances between 0–0.05. Moderate genetic differentiation was observed among Maronite Cypriots and populations from the following countries/regions: Albania, Azerbaijan, Bahrain, Lebanon, Northern Region of Egypt, Iran, Iraq, Israel and regions under the Palestinian Authority, Democratic Republic of Congo, Mediterranean Region of Turkey, Northern Israel, Russia and Yemen .Armenian Cypriots were shown to have low genetic differentiation with more populations in contrast to the Maronite Cypriots. Armenian Cypriots were demonstrated to be genetically closer to Central Anatolia, Mediterranean and Southeastern Anatolia Turkey, Iran and Iraq with Rst values lower than 0.01. Low genetic differentiation between Armenian Cypriots was observed with populations from countries/regions of: Albania, Azerbaijan, Bahrain, Bulgaria, Democratic Republic of Congo, Italy, Northern Iraq, Northern Israel, Lebanon, Romania and Yemen . ModeratIn order to visualize the genetic correlations of Maronite Cypriots and Armenian Cypriots with the aforementioned countries, contour maps were created based on additional calculations, using R programming and R studio .Eight major Y-haplogroups: E, G, I, J, R, L, Q and T with their subclades within the major haplogroups were identified. Most of the samples were assigned to their predicted haplogroup, in accordance with the Y-DNA Haplogroup Predictor NEVGEN tool, with the exception of 6 samples out of the 113, which were assigned to a different haplogroup. Predicted and assigned Y-haplogroup for each individual is shown in Y-haplogroup frequencies were calculated separately for each population, Maronite Cypriots and Armenian Cypriots. All 113 samples were assigned to major haplogroups and Y-haplogroup subclades. As shown in Differences were observed when comparing the Y-haplogroup frequencies between Maronite Cypriots and Armenian Cypriots. Y-haplogroup E, subclade E1b1b-M123 was found at a high frequency (21.05%) in Maronite Cypriots, while in Armenian Cypriots it was found at a very low frequency (1.79%). Similarly, in Armenian Cypriots Y-haplogroup R1b-M343/M415 was found at a high frequency (26.79%) whereas in Maronite Cypriots it was observed at a much lower frequency (7.02%). Haplogroups J1a and J2a appeared at similar frequencies in the Maronite and Armenian Cypriots (12.28 vs 8.93%) and (26.32 vs 25%) respectively. In the remaining haplogroups the number of observations were low (0–3%) to allow any accurate deductions on relative frequencies.Y-haplogroup major subclade J2a-L559 is the most frequent Y-haplogroup among all Cypriots with frequencies greater than 20%. As shown in Y-haplogroup frequencies from Armenia, Egypt and Turkey from which Armenian Cypriots originated as well as Lebanon and Syria from which Maronite Cypriots originated, were used for comparative analyses with all Cypriots –10, 15. R1b-M343/ M415, which was the most frequent Y-haplogroup subclade in Armenian Cypriots, was found at a high frequency in all Armenian and Turkish populations . Thus, tMJN analysis was performed on the Greek Cypriot, Turkish Cypriot, Maronite Cypriot and Armenian Cypriot 17 Y-STR data which were previously assigned to the Y-haplogroup J2a-L559 . It is kMJNs based on Y-haplogroups, were constructed before  and afteY haplogroup J2a was introduced to Cyprus during the Late Bronze Age and Early Iron Age, approximately around 1050 BC. Both first and second expansion events between Armenian Cypriots, Maronite Cypriots, Greek Cypriots and Turkish Cypriots were estimated to have occurred 3066 years ago +/- 407 SD or 3066 +/- 476 SD .An alternative method was used to estimate the time of expansion. From both MJNs and expansion events, the expansion time was estimated 3136 years ago. Confidence intervals were calculated for both expansion events to be, 3136 +/- 1196 SD and 3136 +/- 934 SD, respectively. The above data from the two methods used for the expansion time calculation, support the statements based on Voskarides et al. (2016) and Heraclides et al. (2017) namely, that the Y haplogroup J2a was spread during the Late Bronze Age , 27.Y-STR haplotypes from Armenian Cypriots, Greek Cypriots, Maronite Cypriots and Turkish Cypriots were compared to find the genetic differentiation or any similarities between them. The common paternal ancestry between Greek Cypriots and Turkish Cypriots has been reported in the study by Heraclides et al. (2017) . The preBased on the inter-population analysis, Maronite Cypriots are genetically closer to the Lebanese population; this was an expected finding since in the history of Cyprus, it is documented that during the different waves of Maronites coming to Cyprus, most of them were from Lebanon. Moderate genetic differentiation is shown between Maronites and Egyptians, Israelis and Palestinians. Another reason could be that samples from different regions of Syria were used. No significant correlation was noted between Maronite Cypriots and Armenian and Turkish populations since the Rst values were greater than 0.15. Armenian Cypriots show very low genetic differentiation between Turks and Syrians. The genetic correlation between Armenian Cypriots and Turks is explained by the origin of many Armenian Cypriot samples in this study, which were from different regions of present-day Turkey . Additionally, Armenian Cypriots are genetically close with Lebanese and Israeli populations with Rst values lower than 0.05. The correlations between Turks, Lebanese and Israelis were expected because a number of Armenians came to Cyprus from these three countries. There is no noted history with regards to the relationship of Armenian Cypriots and Syrians.The most frequent haplogroup in Maronite Cypriots was Y-haplogroup J in major subclade J2a-L559 (23.80%). This subclade was common also among all the other Cypriots  and was observed at high frequencies (20–27%). J2a-L559 was found with a high frequency in all Armenian and Turkish populations, except in those populations of North Anatolia in Turkey. In Lebanon, Syria and Palestine the subclade was absent, but in Egypt it was found at a very low frequency (~2%).The subclade E1b1b-M123 was found in Maronite Cypriots at a high frequency (21.05%) . The Y hPrevious studies , 36 on tY haplogroup J1-M267 was found in all Cypriots with frequencies between the range 9–15%. Previous studies –39 foundComparing the Y-STR haplotypes with the data of the countries: Armenia, Egypt, Israel, Lebanon, Syria, Turkey and the calculated Rst, it was observed that Maronite Cypriots showed low genetic differentiation with a value between 0–0.05 with Lebanese and moderate differentiation with Egypt, Syria, Israel and Israel and Palestinian Authority Area populations. The genetic distances between Maronite Cypriots and countries of the Levant showed a low genetic differentiation between Maronites and Egyptians. Populations of Iraq, Israel and Palestinian Authorities and Turkey belong to the Levant and have a moderate genetic differentiation with Maronite Cypriots. Furthermore, subclade E1b1b-M35 was distributed mainly in Ethiopia and was confirmed with the genetic distance calculated for Maronite Cypriots (~0.05).Caucasian haplogroup J was also frequent in Armenian Cypriots and more specifically, subclade J2a-L559. The lowest genetic differentiation between Armenian Cypriots was observed with Turkey (Mediterranean and South-eastern regions), Iran and Iraq, with values ~0.01. Furthermore, Armenian Cypriots are also genetically closer with populations from Armenia, Azerbaijan, Bahrain, Beirut, Bulgaria, Croatia, Democratic Republic of Congo, Iraq, Iran, Israel, Romania, Yemen and Italy. There is also genetic correlation among Armenians and populations from Afghanistan, Central African Republic, Bosnia and Herzegovina, Cameroon, China, Croatia, Israeli and Palestinian Authority, Jordan, Slovenia, Russia, Yemen, Serbia, Libya, Mongolia, Northern Egypt and the Aegean Region of Turkey. Some genetic correlations are explained by the origin of J2a as well as subclade G2 that originated from Caucasus. However, haplogroup G was expanded to European populations such as Croatia, Romania, Hungary and Serbia . This exThe MJN analysis revealed two different expansion events among Cypriots. The first event consisted of Armenian Cypriots, Greek Cypriots and Turkish Cypriots and the second event consisted of all four Cypriot groups . According to previous studies, J2a-L559 was spread from the Caucasus to Anatolia, Greece and then to Cyprus, during the Late Bronze Age , 42–44. The sample size of Maronite Cypriots and Armenian Cypriots in this study was small (57 Maronite Cypriots and 56 Armenian Cypriots). This is a limitation of this study, but it should be noted that these samples, assuming that half of these populations are males, are estimated to constitute 1.63% and 3.73% of the Maronite Cypriot and Armenian Cypriot male population, respectively. More importantly, despite the small sample size of the participants, the Y haplogroup frequencies observed in this study are consistent with previous studies on non-Cypriot Maronites and Armenians , 45. LasThe only common haplogroup identified between all Cypriots in this study, is haplogroup J2a-L559. The subclade is characterized as mainly Caucasian  althoughS1 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableFor each haplotype, predicted and assigned haplogroups are shown and the methodology used for the Y-SNP determination.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Sistrurus catenatus) on a relatively undisturbed island in northern Michigan, USA. This species often persists in habitat islands throughout much of its distribution due to extensive habitat loss and distance‐limited dispersal. We found that the entire island population exhibited weak genetic structuring with spatially segregated variation in effective migration and genetic diversity. The low level of genetic structuring contrasts with previous studies in the southern part of the species’ range at comparable fine scales (~7 km), in which much higher levels of structuring were documented. The island population's genetic structuring more closely resembles that of populations from Ontario, Canada, that occupy similarly intact habitats. Intrapopulation variation in effective migration and genetic diversity likely corresponds to the presence of large inland lakes acting as barriers and more human activity in the southern portion of the island. The observed genetic structuring in this intact landscape suggests that the Eastern Massasauga is capable of sufficient interpatch movements to reduce overall genetic structuring and colonize new habitats. Landscape mosaics with multiple habitat patches and localized barriers  will promote gene flow and natural colonization for this declining species.Genetic structuring of wild populations is dependent on environmental, ecological, and life‐history factors. The specific role environmental context plays in genetic structuring is important to conservation practitioners working with rare species across areas with varying degrees of fragmentation. We investigated fine‐scale genetic patterns of the federally threatened Eastern Massasauga Rattlesnake ( Sistrurus catenatus) on a relatively undisturbed island in northern Michigan, USA. We detected an isolation‐by‐distance pattern and weak genetic structuring with spatially segregated variation in effective migration and genetic diversity. The lack of strong genetic structuring in this intact landscape suggests that the Eastern Massasauga is capable of sufficient interpatch movements to colonize new habitats and maintain gene flow at this scale.We investigated fine‐scale genetic patterns of the federally threatened Eastern Massasauga Rattlesnake ( BBI resembles the part of Ontario with the highest abundance of Eastern Massasaugas, with numerous occupied discrete habitat patches, minimal development aside from roads, and is situated at the species’ northern distribution limit  quantify available habitat and landscape connectivity across this comparatively unmodified landscape and (b) assess spatial genetic relationships using approaches not employed in previous Eastern Massasauga studies, yet at a spatial scale where strong genetic structuring has been found in Illinois, Ohio, and Pennsylvania  in the Great Lakes, situated in Lake Huron between the Upper and Lower Peninsulas of Michigan, USA.  is one of the larger islands  in 1978, 1979, 1990, and 2010 and by identifying open‐canopy areas using aerial imagery . We distributed survey sites to ensure that the heterogeneity of the landscape and genetic variation of individuals was adequately sampled , 1 U Taq DNA polymerase, 0.6 µl primers , and 5 µl double‐distilled H2O. We amplified microsatellite DNA loci with an Eppendorf Mastercycler nexus gradient thermal cycler and followed the protocol established by Anderson et al. (Scu215 (50°C), Scu210 (56°C), Scu211 (56°C), Scu212 (56°C), Scu213 (56°C), Scu214 (56°C), Scu216 (56°C), Scu202 (60°C), Scu203 (60°C), Scu205 (60°C), Scu200 (62°C), Scu201 (62°C), Scu204 (62°C), Scu206 (62°C), Scu208 (62°C), and Scu209 (62°C). To detect any potential contamination of our PCR runs, we used a negative control for each reaction set. Following PCR amplification, fragments were analyzed on an ABI3130xl Genetic Analyzer , at Annis Water Resources Institute, Grand Valley State University. We manually scored fragments using PeakScanner (vers. 2.0). Of the 102 samples, we reamplified and genotyped ~10%  of our total sample to verify our genotyping results and to calculate a PCR and allele scoring error rate.We extracted DNA from blood samples of 102 unique Eastern Massasaugas using QIAGEN DNeasy Tissue kits following the manufacturer's protocol. We genotyped individuals at 16 microsatellite loci developed by Anderson et al. . Each 10n et al. , except 2.4We tested all samples for departures from Hardy–Weinberg equilibrium and linkage disequilibrium using exact tests in the program GENEPOP (vers. 4.2) Rousset, . Using tI to identify patterns of spatial autocorrelation and is effective at detecting cryptic genetic structure  implemented in ADEGENET Jombart, . e choseJombart, . The sPCJombart,  using 9,We used two methods to identify the most likely number of genetic clusters among samples on BBI, first with the Bayesian clustering program STRUCTURE (vers. 3.4.1) .We also evaluated spatial genetic patterns on BBI with EEMS (estimated effective migration surfaces). EEMS is a visualization tool, developed for use in systems where IBD is present but deviations due to barriers or corridors have also contributed to rates of historical gene flow. Effective migration rate is based on a stepping‐stone model under idealized settings that would generate the same genetic dissimilarities between demes as seen in the data  for each distance bin. Distance bins were set at 0.5 km and extended from 0 to 5 km. Significant spatial autocorrelation occurred when r was greater or less than the 95% confidence intervals  by spatially thinning all BBI Eastern Massasauga records obtained from the Michigan Natural Heritage Database  maintained by MNFI and our surveys using a minimum distance of 200 m to avoid overemphasizing environmental data from any individual site. Background point selection was limited to a 10 km buffered region around all occurrence data (clipped to the extent of BBI). We screened a range of candidate environmental predictor variables representing land cover, elevation, and hydrology features potentially important for Eastern Massasauga presence (Supplement 1). First, we used the “corSelect” function from the fuzzySim r package  approach implemented in the R package ecospat   to 11  (Table Scu208) to 0.79 (Scu215) . The IBD results are more characteristic of a clinal pattern than defined clusters  than the western (n = 19) and eastern (n = 11) high diversity zones.EEMS showed that effective migration and diversity exhibited similar geographic patterns of high and low areas relative to the mean within our study area on BBI Figure . EffectiWe identified significant positive spatial autocorrelation between individuals at approximately 1 km on BBI, suggesting Eastern Massasaugas show restricted dispersal within 1 km Figure .3.2SD 183).Maxent models performed slightly better than ANN and the ANN/Maxent ensemble across the 10 replicate runs Table a–c. Howe4Our results confirm that gene flow is possible across Eastern Massasauga populations, even at relatively broad scales (~7 km). Our well‐connected landscape with abundant suitable habitat facilitated sufficient gene flow to result in very limited genetic structure, which is not typical for this species elsewhere , a marsh and prairie specialist that is sympatric with the Eastern Massasauga's historical distribution through southern Ontario, northern Ohio, and southeastern Michigan, has shown similar genetic structuring patterns to those reported by Chiucchi and Gibbs (Coluber constrictor flaviventris) and a sister taxon, Western Massasauga (S. tergeminus) in Kansas  compared to the Eastern Massasauga's southern distribution  likely has more to do with the intensity of anthropogenic landscape modification that transpired across the southern portion of the Eastern Massasauga's range than geographically stratified population structuring , no migrants from the mainland  revealed by our habitat analysis illustrates the importance of minimizing spatial separation of patches, particularly in forested landscapes. Interpatch connectivity and suitable habitat area have not been formally assessed at other Eastern Massasauga sites. However, we expect similar habitat attributes (aggregated and in close proximity) are present in the Ontario landscapes sampled by DiLeo et al. . This inDeveloping and employing management strategies to facilitate gene flow may aid Eastern Massasauga recovery. To prevent the further loss of genetic diversity and adaptive potential of Eastern Massasauga populations, increased gene flow among populations or genetic rescue via introductions or landscape management is likely required : Conceptualization; investigation; methodology; and writing—original draft preparation. Eric McCluskey (cofirst author): Conceptualization; funding acquisition; methodology; and writing—original draft preparation. Vijay Lulla: Software; visualization; and writing—review and editing. Ralph Grundel: Conceptualization; funding acquisition; writing—review and editing. Jennifer Moore: Conceptualization; funding acquisition; methodology; supervision; and writing—review and editing.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.Supplementary MaterialClick here for additional data file.


Procambarus clarkii was surveyed twenty years after its introduction into Lake Trasimeno , investigating both ecological and genetic aspects. Our results confirmed that P. clarkii is well acclimatized with a stable population structure, mainly characterized by a unique mitochondrial lineage, suggesting that a single introduction event may have occurred in the lake, complemented by secondary events.The population of Procambarus clarkii and to assess the genetic diversity by analyzing the relationships among mitochondrial DNA diversity. Our results confirmed that P. clarkii is well acclimatized in the lake, revealing a stable population structure favored by the resources and conditions typical of this ecosystem, which seem to be optimal for the maintenance of the species. Four distinct mitochondrial haplotypes were detected, but one of them was clearly overrepresented (76%), suggesting that a single predominant introduction event may have occurred in this area, likely followed by secondary events. The identification of the typical genetic variants provides a better understanding of the evolutionary scenarios of P. clarkii in this biotope and it can be helpful in management plans concerning the expanding populations of this invasive alien species.The deliberate or accidental introduction of invasive alien species (IAS) causes negative ecological and economic impacts altering ecosystem processes, imperiling native species and causing damage to human endeavors. A monthly monitoring program was performed in Lake Trasimeno  from July 2018 to July 2019 in order to provide an upgrade of the population ecology of  Procambarus clarkii  is widely spread across all continents, except for of Australia and Antarctica [P. clarkii has a great ecological adaptability that allows it to colonize most fresh and brackish water environments [P. clarkii is able to survive in waters with low oxygen concentration and can even survive out of water for a period of more than two days with a very low relative humidity level (30% RH) and for more than a month at 100% RH [P. clarkii is an asymptomatic potential carrier of the crayfish plague (Aphanomyces astaci) and an importer of pathogenic microfungal taxa such as Phoma glomerata (Coelomycetes) potentially harmful to human health, flora and fauna [P. clarkii is reported in the Delivering Alien Invasive Species Inventory for Europe (DAISIE) list as one of the 100 worst invasive alien species and it is on the list of species of European Union concern linked to the EU Regulation 1143/2014 on invasive alien species. Red swamp crayfish exerts undesirable impacts on natural water resources producing a loss of ecosystem services [P. clarkii population has rapidly increased in Lake Trasimeno [Invasive alien crayfish have moved outside their natural ranges leading to ecological threats and adverse economic impacts. The red swamp crayfish  Girard, 852 is witarctica . P. clarronments ,5. Its b 100% RH . In dry  100% RH , salinit 100% RH  and envi 100% RH ,12. It i 100% RH ,14 actinnd fauna ,16. P. cservices ,18. The services . Before P. clarkii can be modulated by changes in water temperature and Lake Trasimeno level [P. clarkii can reach 5 years of age [P. clarkii in this shallow and mesotrophic lake, increasing the economy of the primary  and tertiary Lake Trasimeno sector. Nowadays the species is well acclimatized in Lake Trasimeno and has also colonized the lakes of Piediluco, Corbara, Alviano and Pietrafitta and at least six other watercourses in the Umbria region.Trasimeno is a shallow lake subjected to severe hydrological level fluctuations through the seasons ,22. It ino level ,20. In Ts of age ,20, whers of age . Both ses of age . This crs of age ,26 and tP. clarkii, including the intentional or unintentional transport, combined with its natural expansion influenced its population structure and genetic diversity. To date, there are no investigations focusing on the genetic variation of this species in Lake Trasimeno. There are only few population genetic studies dealing with the red swap crayfish invasion in the European continent based on different molecular markers [It is well known that the human-mediated jump dispersal of  markers ,29,30,31 markers ,33,34. T markers . UnderstP. clarkii twenty years after its introduction into Lake Trasimeno. In particular, its main objectives were: (i) to assess whether changes in life-history traits of red swamp crayfish in the lake may have occurred through time; (ii) to describe the growth of P. clarkii as well as estimate the condition indexes of both sexes, such as abdominal index (TwB), hepatosomatic index (HIw) and gonadosomatic index (GSI); (iii) to investigate the influence of the lake water levels on the life cycle of the species; (iv) to assess the genetic diversity of P. clarkii populations in Lake Trasimeno, analyzing the relationships among their mitochondrial haplotypes. The present study is the first investigation of P. clarkii genetic variability in this basin, based on the most polymorphic mitochondrial marker (mtDNA control region). It points to reconstruct the invasion history of the red swamp crayfish in Lake Trasimeno and to understand how the natural and anthropogenic factors could have influenced this species. Indeed, identifying the typical genetic variants inhabiting this shallow lake can be helpful to test single versus multiple introduction events and potential bottleneck effects, in order to shed light on their origin and demographic trends. This genetic characterization based on the mitochondrial marker represents a new molecular approach, more informative for the detection of the genetic variability of P. clarkii, thus useful to detect and manage the species.This study presents a comprehensive overview of the red swamp crayfish status in Lake Trasimeno, with the aim to revisit the population ecology of 2 and an average depth of 4.5 m. This laminar and mesotrophic lake is characterized by a Mediterranean climate, with maximum rainfall in spring and minimum in autumn, causing significant hydrologic fluctuations [Lake Trasimeno is located in the province of Perugia  and is the largest Italian peninsular lake, with a surface of 126 kmtuations . It is aP. clarkii by multiparametric probes for dissolved oxygen, pH, temperature and conductivity  and Secchi disk  for transparency.The hydrological level (±0.1 cm) was recorded daily . Temperature, transparency, pH, dissolved oxygen, percentage of dissolved oxygen saturation and conductivity were detected monthly in parallel with sampling of P. clarkii  were sampled from July 2018 to July 2019 in a protected wildlife area in the south-east part of Lake Trasimeno. A mean number of 92 males and 47 females were monthly collected from the lake by a professional fisherman within 24 h using fyke nets. Monthly samples were immediately transported to the laboratory and chill-killed using an ice-water bath. Morphometric parameters were recorded for both sexes in order to evaluate growth in length and weight and the crayfish condition indexes. Each crayfish was sexed and the following morphometric and biological parameters were measured:total body length (from the tip of the rostrum to the posterior margin of the abdomen (TL)length of the cephalothorax or carapace (CL)total body weight (W)abdomen weight (AW)hepatopancreas weight (HIw)mature gonad weight A total of 1820 specimens of rostrum) were measured by a digital caliper to the nearest 1 mm; wet weights of total body, carapace, abdomen, hepatopancreas and gonad were measured using an electronic balance within 0.001 g accuracy. The sexual activity in males was assessed by observing the presence or absence of the spines on the third and fourth walking legs. Sexual maturity of females was assessed by observing the internal egg stages [Length of total body and cephalothorax  and hepatosomatic index (HIw) in both sexes, as well as and gonadosomatic index in females (GSI) were estimated using the following formulas [formulas ,20. HepaSex ratio: Hepatosomatic Index:Abdomen Index Tw/B (Tail Muscle to wet body weight ratio):Gonadosomatic index: Data were processed using the statistical programming environment R  and dply® Genomic DNA Purification Kit . A good quality of DNA was obtained from both kind of tissue with different concentrations expressed in ng/μL. The primers used for PCR amplification were those published by Li et al. [www.genecodes.com accessed on 12 February 2021) together with those retrieved from GenBank [A total of 29 specimens were analyzed in order to determine DNA content and to sequence the mitochondrial control region. Genomic DNA was extracted both from the crayfish tail muscle and hepatopancreas using the Wizardi et al.  and the  GenBank  and comp GenBank ). In ordwww.ub.edu/dnasp/index_v5.html accessed on 13 June 2021) [Mitochondrial DNA sequence variation parameters were estimated by using DnaSP 5.1 software (ne 2021) . Haplotyhttp://www.megasoftware.net accessed on 17 June 2021) by using the Neighbor-Joining algorithm, also including the mtDNA sequences available from GenBank.The evolutionary relationships among haplotypes were visualized through Molecular Evolutionary Genetics Analysis Integrated software  .Seasonal chemical and physical parameters of Lake Trasimeno, such as water temperature, transparency, pH, dissolved oxygen, oxygen saturation and conductivity, are reported in 2 = 22.64; p < 0.05), accounting for 60–80% for most every monthly sample, excepted May, June and July 2019 showing a sex ratio close to 1:1   .TL = 8.7, df = 1792, p < 0.001; tW = 0.49, df = 1817, p = 0.2) (Size (TL) and weight (W) of males were generally smaller than for females . Mean top = 0.2) .p < 0.001). For both sexes a statistically significant linear relationship between total length and total weight emerged .The total mean of carapace length (CL) in females was 48.59 mm , and in males 46.76 mm . Higher carapace length was recorded during the warm months in females, with a maximum mean of 54.33 mm in July 2019, and lower in the colder months, with a minimum mean of 44.59 mm in November 2018. In males the carapace length slightly increased during the monitoring period, from a minimum mean value of 43.31 mm in September 2018 to a maximum value of 53.02 mm in June 2019 . Frequency of molts . In females, the maximum percentage of molting was recorded in November 2018 (48.6%) and April 2019 (48.8%). In males, a higher molting frequency (34.9%) was observed in January 2019, corresponding to the lowest percentage of sexual activity . The frequency of sexually active males was generally high throughout the sampling months, except January, April and May 2019 . However, abdomen weight showed similar seasonal variations for both sexes, being higher from May to July and lower (up to 60%) in late summer and in colder months. A similar trend has also been observed for the Tw/B index. Lower HIw values were measured for males (10–35%) compared to females  compared to those of females in each sampling month A. Simila females B. HW = 11, df = 1818, p < 0.001). Generally, hepatopancreas weight of most females was between 0.8 and 1.5 g, while for males it ranged between 0.8 and 1.2 g. Hepatopancreas weight, and consequently HIw, showed seasonal fluctuations, but in the opposite way to Tw/B (p = 0.89)  D.A total of 718 bp of the mtDNA control region, spanning from nucleotide position (np) 4717 to np 5434, for each sample were analysed. The overall sequence alignment revealed the presence of 12 polymorphic sites (S), represented by any singleton variable sites of two variants, and 12 parsimony informative sites. The average number of nucleotide differences was 3.14. Nucleotide diversity (π) across all individuals was estimated at 0.00438, and the haplotype diversity (Hd) was determined at 0.414.p > 0.10); therefore, variation pattern of the mitochondrial control region may be a reliable reflection of the population history of P. clarkii. The negative Fs value (−0.01845) derived from sequence analyses was 0.773 (p = 0.393 > 0.05), thus suggesting that P. clarkii experienced a significant population expansion.Both Tajima’s D nor Fu and Li’s D* values were negative and statistically significant: neither of neutrality test rejected the null hypothesis of neutral evolution , eel (Anguilla anguilla), pike (Exos lucius) and perch (Perca fluviatilis) [P. clarkii by the professional fishermen may not be neglected in this basin.Whereas in Dörr et al.  an inveriatilis) . At the P. clarkii from Lake Trasimeno showed dimensions ranging from 6.2 cm to 14.0 cm of total length , mainly in a range between 8.5 to 10.5 cm. This outcome suggests that the red swamp crayfish is able to grow more in this shallow lake than in its original habitats [P. clarkii population recorded in this lake seems to be slightly shortened since the beginning of the millennium [The population of habitats ,54,55. Hllennium .The growth of crayfish is a process that involves a series of molting and inter-molting periods. Previous studies showed that molting period can be influenced by changes in hydrological levels and temperature of lake ,20,26. IFor both sexes, molting activity significantly decreased in summer. Several published studies analyzed how temperature affects molting, some suggesting that it is favored at temperatures below 10 °C , others P. clarkii with mature ovarian eggs was observed when the water temperature was about 20 °C and hydrological levels were around −0.8 m. However, in this study, with a hydrological level of −0.8 m but a lower temperature, the number of sexually mature females decreased. Indeed, for female crayfish pre-spawning phase (onset of egg maturation) takes place in June, and then the spawning phase (egg maturity) occurs in August-September, when the temperature was higher, suggesting that in Lake Trasimeno the reproductive cycle of P. clarkii females can be more affected by temperature rather than the hydrological levels.Molting is inversely related in both sexes to sexual maturity ,57, whicIn crayfish, the condition indexes based on weight are a valuable support to investigate the health state of a population ,60 Tw/B P. clarkii populations, the survey was extended to all available red swamp crayfish mitochondrial control region sequences. Unfortunately, to date, the only data concerning non-coding mtDNA are those published by Li et al. [Population genetic analyses can be helpful to assess origin, pathways of introduction and demographic changes associated with the dispersal of invasive species . In ordei et al. , who, byP. clarkii population genetics and invasion routes in Europe. Previous investigations were undertaken in the European continent, Italy included, using the same population genetic approach, but focusing only on coding mtDNA markers, mainly 16S [P. clarkii is recent [P. clarkii into Lake Trasimeno, but the divergence of HT03 can strengthen the hypothesis of multiple secondary introductions as reported in the whole European continent [Indeed, the present study implemented the historical scenario based on mtDNA control region reported by Li et al. , by infeinly 16S  and CO1 inly 16S ,31. Thess recent . It is es recent , suggestP. clarkii has a stable population structure with decent condition indexes for both sexes and is skilled to deal with fluctuating water temperatures and levels and is favored by the resources and conditions typical of the Trasimeno ecosystem, which seems to be optimal for the maintenance of the species. As a consequence, almost 20 years after its first report, P. clarkii is still well acclimatized in the Trasimeno area. It is noteworthy that all mitochondrial haplotypes were unique when compared to those already known. In addition, the prevailing of a mitochondrial haplotype (HT01) suggests that a single predominant introduction event may have occurred in this area, likely followed by secondary events. Overall, the biology and genetic diversity investigation of P. clarkii provided relevant information which could be valuable for the management and control of this invasive species.Morphometric and condition indexes indicate a good health state of both sexes of crayfish. The breeding period in Lake Trasimeno seems to be strongly related to water temperature and fluctuates with the hydrological level of the lake. In females, the pre-spawning phase is in June, and the spawning phase is from August to October. The frequency of sexually active males is generally high during all months of sampling and reaches its maximum value in the warmer summer months. Despite the imbalanced sex ratio showing a higher number of males than females, our results confirmed that