Acacia s.l. farnesiana, which originates from Mesoamerica, is the most widely distributed Acacia s.l. species across the tropics. It is assumed that the plant was transferred across the Atlantic to southern Europe by Spanish explorers, and then spread across the Old World tropics through a combination of chance long-distance and human-mediated dispersal. Our study uses genetic analysis and information from historical sources to test the relative roles of chance and human-mediated dispersal in its distribution. The results confirm the Mesoamerican origins of the plant and show three patterns of human-mediated dispersal. Samples from Spain showed greater genetic diversity than those from other Old World tropics, suggesting more instances of transatlantic introductions from the Americas to that country than to other parts of Africa and Asia. Individuals from the Philippines matched a population from South Central Mexico and were likely to have been direct, trans-Pacific introductions. Australian samples were genetically unique, indicating that the arrival of the species in the continent was independent of these European colonial activities. This suggests the possibility of pre-European human-mediated dispersal across the Pacific Ocean. These significant findings raise new questions for biogeographic studies that assume chance or transoceanic dispersal for disjunct plant distributions. It is identified as the most widely distributed of all Acacia s.l. species in the world [A. farnesiana is currently regarded as invasive in 12 out of 15 geographical regions globally. Its seeds have been shown to maintain viability after more than a century [Acacia classifies A. farnesiana in the genus Vachellia, in this study we use the name Acacia farnesiana to maintain consistency with historical references that compared it with the original type species known from Egypt (Acacia nilotica).This study investigates the relative roles of chance long-distance and human-mediated dispersal in the pan-tropical distribution of he world , and is he world ,8. Accorhe world , A. farn century . AlthougA. farnesiana in the Americas, extending from Brazil and Peru to Mexico and the semi-arid regions of southern USA. Its status in the Caribbean is cryptic, and it is considered alien in Argentina [A. farnesiana may have dispersed from the Americas and attained its current pan-tropical distribution.The biogeographic literature identifies the native range of rgentina –13. ThisA. farnesiana from the Americas to other parts of the world. We hypothesized that: (i) the introductions to southern Europe were via colonial interactions with the Americas and (ii) introductions to Africa, South Asia, Southeast Asia and Fiji were from southern European sources. In the case of Australia, we tested three alternative hypotheses to explain the pre-British presence of the species in the continent: (i) arrival via Southeast Asia through colonial Portuguese or Spanish interactions; (ii) direct arrival from the Americas through European voyagers or colonial interactions or (iii) pre-European arrival either through oceanic or human-assisted dispersal.In this study, we used genetic analysis and historical accounts to investigate the dispersal of 1.1.Acacia s.l. have been for utilitarian purposes like land stabilization [Acacia nilotica (L.) Delile was depicted in tomb paintings of Ancient Egypt of the second millennium BCE [shittim [Enquiry into Plants by Theophrastus (ca 350 BCE) and De Materia Medica by Dioscorides (50–70 CE) referred to the tree as akakía, a name alluding to its characteristic spiky thorns, and mentioned it as growing in Egypt [While recent movements of lization , acaciaslization . Acacia nium BCE –17, idennium BCE , and ref[shittim . Early Gin Egypt ,21.Acacia nilotica, they also classified the New World species as acacias [Acacia Indica Farnesiana, followed by an illustration and description of Acacia aegyptiae (Acacia nilotica) to show the features that distinguished the two from each other. He noted that the seeds of this tree had been obtained from the island of Santo Domingo and germinated in 1611 in Farnese's garden [Acacia farnesiana alongside Acacia nilotica for purposes of comparison [Acacia farnesiana into Europe.When early Spanish and Portuguese explorers in the Americas encountered plants that looked similar to previously described and well-known Old World species such as  acacias ,23. One s garden . Subsequmparison ,26. The Acacia farnesiana is a thorny shrub, between 2 and 3 m in height. Its yellow, ball-like inflorescences bear a delicate scent which is highly valued in the perfume industry [huisache or aromo in North America, mimosa bush in Australia or cassier in France [industry . The spen France . Its flon France ,30. The n France .A. farnesiana and A. nilotica and indicate the role of human-assisted dispersal, biogeographic studies generally feature chance dispersal and other ecological factors for species with pan-tropical distributions. This is particularly so for species such as A. farnesiana that have dispersed to Australia. Although early British explorers identified the plant as already present in the continent prior to European colonization, they offered no speculation or explanation of how it may have arrived from the Americas [A. farnesiana in Australia have typically attributed its pre-British presence in the continent to transoceanic dispersal and not considered the possibility of precolonial human-assisted dispersal from the Americas. However, there is growing linguistic and genetic evidence for some plant species being introduced from the Americas into Oceania by Austronesian sailors in pre-Columbian times . The. TheA. fona spp.  and Adanustralia . Hence o2.2.1.A. farnesiana , and analysed the microsatellite loci Af18, Af24, Af05, Af38, Af19, Af32, Af25, Af03, Af10, Af26, Af47, Af14 and Af46 . We foll2.3.genalex v. 6.41 [F: inbreeding coefficient).We used  v. 6.41  to calcu2.4.tructure v. 2.3.3 [A. farnesiana, and to associate Old World and Australian samples with their American source populations. The likelihood of different values for the number of clusters (K) was calculated under an admixture model, with loci assumed to be unlinked. Multiple runs at the appropriate value(s) of K were combined using Clumpp v. 1.1.2 [istruct v. 1.1 [We used a Bayesian approach, implemented in Sv. 2.3.3  to deterv. 1.1.2  and plott v. 1.1 .genalex v. 6.41 [RST measure of genetic diversity, with 9999 permutations.We used Analysis of Molecular Variance (AMOVA)  to deter v. 6.41  to condu2.5.migrate-n [a2 [We used coalescent modelling to assess migration rates between geographically defined populations, effective population sizes and divergence times. Different programs model the coalescent process to estimate a subset of these parameters. We used igrate-n  to estimte-n [a2  to estim2.5.1.m = m/μ, where m is the rate of migration for each gene copy and μ is the mutation rate per gene copy per generation), and population sizes for extant and ancestral populations  [We used genotypes of individuals at each locus in a coalescent model to estimate migration rates between populations (species) ,47.Populations were pooled to reduce the number of parameters being estimated. Three pooled populations were considered in the Americas based on regional biogeography, rather than national boundaries: Arizona and Northwest Mexico (north of the Trans-Mexican Volcanic Belt); South Central Mexico (from the Trans-Mexican Volcanic Belt to the Isthmus of Tehuantepec); and Central and South America plus the Caribbean Islands . The other two pooled populations considered were the Old World, as defined above, and Australia. Estimated migration rates were considered to be significant if zero was not within the 95% highest posterior density (HPD) interval.FST. Bayesian uniform priors for θ and m were bound between 0 and 50, and between 0 and 100, respectively. A burn-in of 50 000 steps was followed by a further 50 000 recorded steps with sampling every 100 steps for each locus. Convergence on stationary distributions of parameters was assessed based on the similarity of posterior distributions of independent runs, and the effective sample size.A simple stepwise mutation (SSM) model of molecular evolution was used for each of the microsatellite loci, and mutation rates were allowed to vary among loci. For each hypothesized divergence, we ran two parallel Bayesian MCMC analyses of four heated chains , with independent random starting points. Starting parameters were based on a UPGMA tree and 2.5.2.a2 [Divergence time between genetically distinct regions , along with gene flow rates and effective population sizes, were estimated using IMa2 . We usedχ2-distribution. Specifically, we tested for differences in effective population size of extant and ancestral populations, non-zero migration rates and asymmetrical migration rates. Relative divergence times, population sizes and rates of gene flow were converted into absolute values assuming a generation time of 3 years [−4 mutations/marker/generation  or 5.0  species ,51), as 3.3.1.A. farnesiana to be highest in the Americas, and lowest in the Old World . At K = 2, both clusters were broadly distributed in the Americas , so , so truc. [K = 5 a,b. At KAmericas . OutsideK = 5, Clusters 1, 3 and 4 were all found in the Americas (b). One individual from the Philippines had ambiguous ancestry; the other was assigned to the same cluster as the population from South Central Mexico (Cluster 3). Australian populations were assigned a unique cluster (Cluster 2). No individuals from any population across the species distribution were assigned to Cluster 5 with a probability higher than 0.75.At Americas . ClusterAmericas b. One inRTR = −0.026; p = 1.0000). The variation between populations across the entire range, and between populations within regions was significant . This means that although the populations we defined are genetically distinct, populations in different regions are no more genetically distinct than populations within the same regions.A two-level AMOVA, with populations grouped into regions , partitioned 0% of total genetic variation among regions, 19% among populations, 75% among individuals and 6% within individuals. The variation between regions was not significant . Based on the estimates of gene flow, we can infer the source populations for the introduced samples. The migration rate into the Old World from South Central Mexico was significantly higher than all other inferred migration rates. Rates of migration from each American region to Australia were moderate, but within this the migration rates from South Central Mexico, and from northwest Mexico and Baja California were marginally higher than the migration rates from Central and South America.Using Americas . Signifi3.3.2.a2, we estimated the divergence time between these regions as 795  or 1695 (360–8115) years ago, depending on the mutation rate used for scaling. The broad confidence intervals for divergence time make these results difficult to interpret , the only regions that had diverged sufficiently to allow calculation of divergence time were Australia and the Americas. Using IMnterpret . We testnterpret  and 5. A3.3.3.θ  from migrate-n analysis varied from 2.32 in Arizona and northwest Mexico to 0.42 in the Old World, but these values have large confidence intervals and most of these are overlapping .The genetic data support our hypothesis that the introductions of  century ,14. The 4.2.K = 5 in Spain would be consistent with the latter scenario.The genetic data support the hypothesis that the plant underwent secondary introductions to other parts of the Old World from southern Europe. Samples from Spain show the highest genetic diversity. Other populations from the Eastern Atlantic Islands, Madagascar and Mascarene Islands, and India are genetically similar to those from Spain and contain a subset of this diversity. This could be the result of an initial introduction to Spain, with an associated population bottleneck followed by subsequent spread to other populations and further bottlenecks. Alternatively, the higher genetic diversity in Spain could be the result of multiple introductions, with only one of these introductions involving other parts of the Old World. The high level of admixture of genetic clusters at A. farnesiana to Asia from Spain may have followed sixteenth century Mediterranean trade routes connecting southern Europe with North Africa, Arabia and India [A. farnesiana populations in the Eastern Atlantic islands, Madagascar and Mascarene islands in the Indian Ocean, and Fiji in the Pacific Ocean are all part of the same genetic cluster that includes southern Europe and the Indian subcontinent. This corresponds with the expansion of Portuguese trade along Africa's Atlantic coast and in the Indian Ocean region during the sixteenth and seventeenth centuries, and followed subsequently by Dutch, French and British trade and colonization in Africa, Asia and the Pacific during the eighteenth and nineteenth centuries. The plant's introduction to Fiji is ascribed to the gardening efforts of foreign traders and European missionaries during the 1860s [The dispersal of nd India . The A. he 1860s , who mayA. farnesiana to the Philippines were independent of the introduction via southern Europe. The Structure analysis at K = 5 assigned samples from the Philippines to the same genetic cluster as plants from South Central Mexico (Puebla−Morelos). This genetic connection with South Central Mexico appears to be limited to the Philippines, with no further dispersal of this genotype to other Pacific islands or Old World locations. The Spanish galleon trade across the Pacific Ocean from Mexico from the sixteenth to the nineteenth centuries involved the introduction of numerous plants to the Philippines and the Mariana Islands (Guam) [A. farnesiana may have been introduced during this period by people travelling between these two places.For Southeast Asia, the genetic data demonstrate that introductions of s (Guam) –61, and 4.2.1.A. farnesiana to Australia. Some of the earliest botanical explorations of northern Australia [There are no historical records for the introduction of ustralia ,63 note ustralia –66. Hencustralia ,67).A. farnesiana in the continent: (i) arrival via southeast Asia through colonial Portuguese or Spanish interactions; (ii) direct arrival from the Americas through European colonial voyages or (iii) pre-European arrival either through oceanic or human-assisted dispersal.We tested three alternative hypotheses to explain the pre-British presence of 4.2.2.A. farnesiana introductions from Old World colonial networks in Southeast Asia. If the Australian populations had arrived through Spanish or Portuguese colonial trade networks, they would most probably share a genetic cluster with populations from the Old World or Southeast Asia. However, the genetic clustering in the Structure analysis at both K = 2 and K = 5 shows that the populations from Australia form a different genetic cluster from the Old World or the Philippines populations, hence suggesting a separate introduction.The combined genetic and historical evidence does not support the hypothesis of 4.2.3.A. farnesiana to Australia from the Americas through European colonial voyages or subsequent interactions. The divergence times based on the IMa2 coalescent analysis are difficult to interpret due to the large confidence intervals, but suggest introduction prior to European voyages and colonial interactions across the Pacific Ocean. The results of the Structure analyses also do not support direct arrival in Australia from the Americas through European colonial trade. The Australian population is not in the same genetic cluster as any of the other populations that are associated with European introduction.The combined genetic and historical evidence does not support the hypothesis of a direct introduction of 4.2.4.migrate-n and IMa2, are low or non-significant. At K = 5, Australian populations are assigned to Cluster 2. A small number of samples from northwest Mexico and Baja California have Q-values above 0.5 for assignment to this cluster, and these may reflect the source populations for earlier introduction to Australia. These results could imply that following an early dispersal event from northwest Mexico to Australia, the populations subsequently diverged due to isolation or due to no further introductions from the Americas.The genetic data offer strong evidence for a pre-European arrival from the Americas. This could have been through chance via oceanic dispersal, or through human agency. Genetic matching does not correspond with Spanish and Portuguese colonial activity in the Americas or in Southeast Asia, as discussed above. Estimates of migration rates between Australia and the Americas, using 4.3.A. farnesiana arrived in Australia remains a historical enigma. It may be that a single chance event of oceanic dispersal [A. farnesiana may have arrived through pre-European human-mediated introduction [Ipomoea batatas) was transferred by Austronesian sailors from the Americas into Oceania in pre-Columbian times [A. farnesiana samples from Fiji showed they were relatively recent introductions from southern Europe or India [A. farnesiana from intermediate islands in the north and south Pacific between the Americas and Australia may offer some clues.How ispersal  brought ispersal . Alternaoduction . Our resan times ,71. As nor India  and therA. farnesiana in Australia is whether the plant has long-standing recognition or use by indigenous groups. Our fieldwork in northwest Australia indicates that some Aboriginal languages in the Kimberley region such as Miriwoong have an indigenous name for A. farnesiana (moorloomboo), and that it is identified as a native plant typically growing on black soil country [A. farnesiana in other Indigenous languages of northern Australia combined with genetic analyses may provide insights into how the plant may have arrived and spread inland.One question that arises in relation to pre-European human-mediated introduction of  country . Further5.A. farnesiana from the Americas across the Pacific Ocean well before the arrival of Europeans to Australia. There are other plant species associated by their names and Aboriginal stories with pre-British colonial introductions to northern Australia from Southeast Asia and possibly further away from other parts of the Indian Ocean region. These include moringa (Moringa olifera), commonly referred to as Koepanger's tree  [Tamarindus indica) [Our study is significant in providing the first genetic analysis of a plant introduction into continental Australia within a historical time frame of probably more than 750 years. While not conclusive, it also demonstrates the remarkable possibility of human-mediated dispersal of t Timor)  and tama indica) , which i indica) . With in indica) ,36, our Acacia s.l. with disjunct intercontinental distributions, such as Acacia heterophylla and Acacia koa [The importance of recognizing the role of pre-European human-assisted plant dispersal goes beyond the Australia–Pacific region, and has broader implications for biogeographic studies of disjunct plant distributions around the world. Debates regarding disjunct plant distributions usually assume this is due to chance or transoceanic dispersal (e.g. ). The roacia koa , which aA. farnesiana to Australia, but also demand fundamental reconsideration of the pre-European history of indigenous interactions throughout the world.There is a small, but growing, body of the literature using this interdisciplinary approach to investigate the ancient human history behind the current biogeographic distributions of various plant species –84. Furt


Eucalyptus nitens with only partially tracked pedigree. The sib-ship reconstruction allowed the identification of selfs (4% of the sample) and the exploration of their potential effect on inbreeding depression in the traits studied. We detected signs of inbreeding depression in diameter at breast height and growth strain while no indications were observed in wood density, wood stiffness and tangential air-dry shrinkage. After the application of a corrected sib-ship relationship matrix, additive genetic variance and heritability were observed to increase where signs of inbreeding depression were initially detected. Conversely, the same genetic parameters for traits that appeared to be free of inbreeding depression decreased in size. It therefore appeared that greater genetic variance may be due, at least in part, to contributions from inbreeding in these studied populations rather than a removal of inbreeding as is traditionally thought.Accurate inference of relatedness between individuals in breeding population contributes to the precision of genetic parameter estimates, effectiveness of inbreeding management and the amount of genetic progress delivered from breeding programs. Pedigree reconstruction has been proven to be an efficient tool to correct pedigree errors and recover hidden relatedness in open pollinated progeny tests but the method can be limited by the lack of parental genotypes and the high proportion of alien pollen from outside the breeding population. Our study investigates the efficiency of sib-ship reconstruction in an advanced breeding population of  Eucalyptus, where degrees of relatedness can vary widely due to mixed mating system ) in winter of 2014. Wood quality was measured on two different log lengths—log 1 from 1.4-3m and log 2 from 3-6m during winter of 2015. Wood traits assessed include density (WD [kg/m3]), wood stiffness  and growth strain  measured as log split width. Average tangential air-dry shrinkage was assessed only for log 2 (TS [%]). Wood density was measured as basic wood density through the maximum moisture content method . Wood stThe sib-ship inference was obtained by implementing the algorithm described in Wang (2004). This algorithm assumes markers to be selectively neutral, unlinked between loci and in linkage equilibrium. Additionally, they should have two or more codominant alleles and meet the assumption of Mendelian segregation. The sib-ship reconstruction was performed following the above described algorithm implemented in the COLONY package  using 50y is vector of measurements, β is vector of fixed effects representing intercept and seed orchard effects, a is the vector of random breeding values following A is the average numerator relationship matrix [r is the vector of random replication effect following I is the identity matrix and r(s) is the vector of random set nested within replication effects following e is the vector of random residuals following X and Z are the incidence matrices assigning fixed and random effects to measurements in y.The efficiency of relatedness recovery through sib-ship reconstruction on accuracy of genetic parameters such as additive genetic variance, heritability and genetic correlation was investigated with respect to their standard errors by implementing a linear mixed model within the ASReml-R statistical package  as follop matrix  developed is the vector of random dominance effects following D is the dominance relationship matrix created by setting the relationship coefficient of 0.25 between individuals belonging to the same full-sib family, diagonal elements were set to 1 for outbred individuals and 0.5 for selfs [Since the sib-ship reconstruction produced full-sib relationships see  section,Y is matrix of phenotypic measurements, a is vector of random additive genetic effects following var(a)∼N, where G1 is variance-covariance structure of additive genetic effects following st and 2nd trait, st and 2nd trait, r is vector of random replication effects following var(r)∼N, where G2 is variance-covariance structure for replication effects following st and 2nd trait, r(s) is vector of random effect of set nested within replication following var(r(s))∼N, where G3 is variance-covariance structure of set nested within replication effects following st and 2nd trait, e is a vector of random residual effects following var(e)∼N, where R is a variance-covariance structure for residual effects following st and 2nd trait, st and 2nd trait.A bivariate mixed linear model was used to estimate genetic correlations and test its benefit on accuracy of breeding values as follows:Two heritability estimates were obtained. Narrow sense heritability estimates were obtained using the original pedigree information or information from sib-ship reconstruction, and was estimated as follows:A second estimate was modified to include the estimated dominance variance using the dominance relationship matrix, and based on the reconstructed sib-ship structure as follows:th trait and th trait. Accuracies of breeding values were estimated as follows:i is inbreeding coefficient of ith individual [A is relationship matrix inferred from either the documented pedigree or the sib-ship reconstruction, c is vector of contribution where contributions were set equally for each individual as 1/n.Genetic correlations were estimated as the Pearson-product moment correlation as follows:dividual . The effdividual . Effectidividual  as folloE. nitens population. From within these, 500 random SNPs were selected for sib-ship analysis. The sib-ship reconstruction was performed in the COLONY package [The EUChip60k  provided package  and reco package  was estiWe compared the phenotypic performance of selfs and outbred individuals. Since there is the assumption that the selfing reduces genetic variance , the staEstimated heritabilities were low to moderate, ranging from 0.08 (DBH) to 0.45 (WD) and from 0.09 (DBH) to 0.34 (WD) in pedigree and sib-ship reconstruction based analyses. The heritability after sib-ship reconstruction decreased in most of the traits with the largest change from 0.41 to 0.28, observed in TS. By contrast, the heritability of DBH and GS2 increased after sib-ship reconstruction from 0.08 to 0.09 and from 0.18 to 0.22, respectively . Across When a dominance relationship matrix was included in the model, dominance variance was uncovered in ST1, ST2, and TS but was only found statistically significant in the last trait . HoweverMultivariate analysis based on pedigree information resulted in only a slight improvement in the accuracy of breeding values compared with results from the univariate analysis  and mostThe implementation of information from sib-ship reconstruction in multivariate analysis resulted in mixed results for estimation of genetic correlations, reflecting trends in additive genetic variance. Traits that showed a decrease in additive genetic variances resulted in reduced pairwise genetic correlations and vice versa. The highest improvement in genetic correlation estimated using sib-ship information was reached for traits suffering from inbreeding depression (DBH and GS2) . SimilarThe effect of sib-ship reconstruction in breeding values ranking was investigated with Pearson product moment correlations as well as with comparison of estimated genetic gains. The correlation between breeding values reached from 0.74 to 0.85 and corresponded to the changes in estimated genetic gain. The highest impact of sib-ship reconstruction was observed in DBH where the response to selection increased for 50% compared to pedigree based analysis . This isThe size, amount and precision of genetic parameter estimates dictates the level of genetic progress in any breeding activity. Heritability, an essential genetic parameter, is heavily influenced by the principle resemblance between relatives  and reliPedigree reconstruction  has beenE. nitens breeding program, where only the maternal side of the pedigree was tracked across all generations. Sib-ship reconstruction recovered the main portion of the pedigree-based half-sib relationships and also identified pedigree errors. Nevertheless, the methodology can only recognize major classes of relatedness such as unrelated, half-sib and full-sib pairs [Our study investigates a third generation of breeding population within the New Zealand ib pairs , which iib pairs  found atib pairs , 48. Capib pairs  tested dib pairs  proposedOpen pollination lacks inbreeding control and our sib-ship reconstruction recovered 27 individuals (4%) derived from selfing events, which largely contributed to a decrease in effective population size from 196.46 to 74.46. Selection of such individuals for the next generation of the breeding population would result in an undesirable increase in inbreeding level when no control over the paternal contribution is performed. This could potentially result in a considerable decrease of effective population size, needed for the long-term sustainability in genetic progress. Additionally, higher inbreeding levels may cause inbreeding depression, which in some populations/traits causes inferior phenotypic expression –53.E. nitens [Eucalyptus species [We investigated the phenotypic performance of individuals derived from selfing compared with individuals derived from outcrossing . The ave. nitens  which fo species . However species  which maMost of the traits showed a reduction or no change in heritability estimates and no change in accuracy of breeding values when sib-ship was used, which corresponds with findings in Vidal et al. . Other sThe recovery of full-sib families allowed us to construct a dominance relationship matrix, which was implemented in the mixed model to investigate non-additive genetic variance. Our analysis identified dominance variance in ST1, ST2, and TS but it was significant only in the last trait. The implementation of non-additive relationship matrices, especially dominance, improved the accuracy of genetic parameters in previous studies –59. HoweMultivariate analyses can further improve the precision of genetic parameters through genetic correlations , 64. OurThe precision of genetic correlations and the accuracy of breeding value estimates showed large improvement when information from sib-ship reconstruction was implemented. However, many genetic correlation estimates decreased and thus remained non-significant. Generally, about 100 families should be tested to obtain reliable and significant genetic correlations . The genOverall, the correspondence between breeding values produced in pedigree based and sib-ship reconstruction based model were between 0.74 and 0.85  which isIn addition, the estimated response to selection rapidly changed with change in heritability between investigated strategies. For traits suffering from inbreeding depression such as DBH, GS1 and GS2, the estimated genetic gain based on the original pedigree was largely underestimated. This was especially the case for DBH where the genetic gain when using sib-ship reconstruction increased for 50% compared with the scenario based on the original pedigree . In contOur analyses showed benefits from sib-ship reconstruction by identification of selfs to investigate the possible detrimental effect of inbreeding depression found in three studied traits. This allowed us to understand different patterns in genetic parameter changes when information from sib-ship reconstruction was implemented. Despite only rough inference about relatedness in advanced breeding populations due to an inability to recover the low classes of relatedness, sib-ship reconstruction provides a useful tool to recover major categories of relatedness where parental genomic information is missing. The technique will enable more effective management of genetic diversity and provide more precise estimates of genetic parameters compared with the previously incomplete documented pedigree.We found the implementation of sib-ship reconstruction resulted in higher heritability and genetic correlation estimates and improved accuracy of breeding values in traits showing signs of inbreeding depression. In traits free of inbreeding depression, the impact of sib-ship reconstruction was a decrease in heritability and only a negligible improvement in breeding values accuracy. We found lower correspondence between breeding values estimated from pedigree and sib-ship reconstruction, which can potentially bias the predictive ability in a genomic evaluation and prior pedigree reconstruction is recommended. Sib-ship reconstruction will be very useful implementation tool for breeders to ensure sufficient genetic diversity is present through inbreeding identification and eliminate selfs from selection in open-pollinated programs.


SSR markers. Genetic diversity was analyzed using both individual‐ and population‐based approaches. Surveys of seed lot sources allowed characterizing seed‐mediated gene flow. Past sorghum diffusion dynamics were explored by comparing Mount Kenya sorghum diversity with that of the African continent. The absence of structure in pearl millet genetic diversity indicated common ancestry and/or important pollen‐ and seed‐mediated gene flow. On the contrary, sorghum varietal and genetic diversity showed geographic patterns, pointing to different ancestry of varieties, limited pollen‐mediated gene flow, and geographic patterns in seed‐mediated gene flow. Social and ecological processes involved in shaping seed‐mediated gene flow are further discussed.Crop populations in smallholder farming systems are shaped by the interaction of biological, ecological, and social processes, occurring on different spatiotemporal scales. Understanding these dynamics is fundamental for the conservation of crop genetic resources. In this study, we investigated the processes involved in sorghum and pearl millet diversity dynamics on Mount Kenya. Surveys were conducted in ten sites distributed along two elevation transects and occupied by six ethnolinguistic groups. Varieties of both species grown in each site were inventoried and characterized using  IncreasPennisetum glaucum (L.) R. Br.; Busso et al., Sorghum bicolor (L.) Moench), restricted cross‐pollination among varieties preserves their genetic distinctiveness on a local scale , which has the national mandate for collecting and conserving all plant genetic resources and documenting all accompanying information. Based on this mandate and given that KALRO was a partner of the present study, no specific permission was required to undertake it. Although KALRO has not designated any ethical review board, it has equivalent committees and administrative organs that review proposed research activities before granting approval. Local government administrative as well as agricultural extension officers were informed of the study and kept updated on its activities. During the survey, the mandate given to KALRO as well as the importance of the study, both nationally and globally, was explained to farmers. According to KALRO's procedures, prior informed consent was obtained verbally and not recorded. Farmers were told that the process would only involve anonymous information.2.2This study was conducted on the eastern slope of Mount Kenya, in ten sites located in the Meru, Tharaka‐Nithi, and Embu counties Fig. , and alokhat). In midlands and lowlands, rural populations base their livelihood on subsistence economy, relying on small‐scale and low‐input traditional crop–livestock farming systems , beans (Phaseolus vulgaris L.), cowpeas (Vigna unguiculata (L.) Walp.), or mung bean (Vigna radiata (L.) R. Wilczek) depending on the agroecological zone. Different varieties of these crops are usually grown within the same plot. Pearl millet and single‐season sorghum varieties can be grown during both cropping seasons (either from October to January or from March to June), but farmers also grow ratoon sorghum varieties that are sown in October, cut down in January to stimulate regrowth, and finally harvested in July. Ratoon sorghum is frequently grown around field borders or terraces . According to farmers, they improve soil conservation and stability, increase soil water availability, and enable them to save time and labor  taken at random in each site. Among farmers, 42.3% were men. The large majority of interviewed farmers were from the dominant ethnolinguistic group in the area, and husbands and wives belonged to the same group.During the interviews, the local names of all sorghum and pearl millet varieties sown in October 2006 were recorded. For each named variety, we asked farmers whether it was local (landrace) or exogenous . For sorghum, we also asked whether it was a single‐season or a ratoon variety. Harmonization of variety names was later performed by a native Central Kenya Bantu languages speaker to limit errors due to spelling, transcription, and pronunciation differences among languages. A “named variety” hence corresponds to a unit identified using the same name by farmers, within or among sites.Farmers were also asked whether the seed lot of each variety sown in October 2006 was of self‐produced origin, provided by a relative, a friend, or a neighbor, bought at the market, or obtained from another exogenous origin . We also collected for each variety the source from which farmers got the variety for the first time .2.3.2The strategy for on‐farm germplasm collection was based on varietal frequency at each site of study. For each site, a farm subsample  was defined to represent the diversity and frequencies of each variety  using a modified CTAB protocol , 0.1 μl of forward primer labeled with a fluorochrome (10 μmol L−1), 0.15 μl of unlabeled forward primer (10 μmol L−1), 0.25 μl of unlabeled reverse primer (10 μmol L−1), 0.5 μl of each dNTP (2.5 mmol L−1 each), and 0.1 μl of Taq polymerase 5 U μl−1 ; and H2O qsp 10 μl. Amplifications were performed in 96‐well thermocyclers using the following program: 5 min at 95°C for initial DNA denaturation followed by 35 cycles of 30 s denaturation at 94°C, 90 s for primers annealing (55–58°C), 30‐s extension at 72°C, and a final extension of 10 min at 72°C. PCR products were pooled by groups of 4 loci and loaded on a capillary sequencer . Alleles were scored with GeneMapper v4.0 (Applied Biosystems). Genotypes were manually checked for every individual. Marker data for two highly polymorphic SSR markers (PSMP 2214 and PSMP 2227) were excluded from the final analysis due to scoring ambiguities.For pearl millet, 12 SSRs that are fairly well distributed on its genetic map were selected for this study , as well as for remembered initial seed sources . We also computed these fractions separately for seed lots identified as local varieties by farmers . Global and pairwise Fisher's exact tests were conducted to compare seed source proportions  between local and exogenous varieties, and among sites.The relative importance of seed sources was computed for the seed lots sown in October 2006 at each site (sorghum seed lots: 2.5.2stats). The overall and pairwise genetic differentiations among the ten sites were measured for sorghum and pearl millet using the global fixation index  was assessed by computing the observed number of alleles and heterozygosity, allelic richness, corrected for sample size, and unbiased gene diversity (expected heterozygosity). These indices were compared among sites using paired pairwise Wilcoxon tests with a false discovery rate correction  was estimated for K ranging from 1 to 10, with 20 runs for each K value. The burn‐in period was set at 500,000 and 1,000,000 iterations were performed. Outputs were summarized using Structure Harvester , we selected the K value presenting the highest rate of change in the log probability of data, following Evanno, Regnaut, and Goudet  was below a 0.8 threshold were considered as resulting from admixture between populations. Individuals with a q value equal or superior to 0.8 for a given population were assigned to that population. We also used the TESS v2.3.1 software . Isolation by distance among sites was tested with a Mantel test assessing the correlation between pairwise Nei's genetic distances , using those plants that could be assigned to a population  were present in most sites and dominant in their portfolio , except in site M1. Furthermore, significant differences of composition among some pairs of sites reflected the uneven distribution of the main local variety, Ciakaungi, which was absent from the southernmost (M1) and northernmost sites .A total of 20 pearl millet named varieties were inventoried across the ten sites, out of which 5 were exogenous varieties, 14 of local origin, and 1 of unknown origin. The main pattern arising from pairwise comparisons of variety‐based FST among sites , where ratoon varieties (mostly Mugana) were grown. In the other sites, farmers grew mostly single‐season varieties, but differences existed among sites, L1, L3, L4, and M2 showing similar patterns in cultivating Mugeta while farmers in site L2 grew mainly Mucarama and those of site M1 grew Gatururu. Furthermore, exogenous varieties (mainly Kaguru) were present in most sites, but their proportion was lower for sorghum than for pearl millet .For sorghum, a total of 30 named varieties were inventoried, 10 of them were exogenous, mostly released by the formal improvement system  and 18 were local (16 single‐season and 2 ratoon), the other 2 being of unknown origin. Differences of varietal composition were observed among sites for this species. Two major groups emerged from the pairwise comparisons of variety‐based The analysis of the sources of October 2006 seed lots showed that they were mainly self‐produced for both species, with averages of 56 and 54% for pearl millet and sorghum over the 10 sites (Table p < .05), but no such differences were observed for pearl millet (p = .143). For both species, seed sources did not differ significantly among sites (Fisher's exact test: p > .05).Significant differences in the proportion of the different seed sources  were observed for sorghum between exogenous and local varieties as local varieties were more self‐produced and obtained from relatives (Fisher's exact test: p < .05).The initial sources of seed lots were often forgotten by farmers. Otherwise, seeds had mainly been obtained initially from exogenous institutions, such as extension services, NGOs, church, or government administration (Table 3.2FST) value of 0.007 (p < .001) and only one significant pairwise FST value at the 5% threshold . Results of STRUCTURE and TESS clustering algorithms converged to show an absence of structure in pearl millet genetic diversity on Mount Kenya .For pearl millet, there were no significant differences among sites for gene diversity  and for unbiased allelic richness . The mean unbiased allelic richness was 3.21 (range from 2.2 to 3.9). It was significantly lower in lowland sites L3 and L4 as compared to other sites and significantly higher in sites M4 and M5 as compared to lowland sites (p < .05). Observed heterozygosity (Ho) was very low, in contrast to pearl millet, and it did not vary among sites .For sorghum, gene diversity and allelic richness were lower than for pearl millet. The mean gene diversity was of 0.44 (range from 0.34 to 0.53), but there were no significant differences among sites . The groups inferred by STRUCTURE corresponded to those identified using TESS, and they matched with the information on the local or exogenous status of the samples, their growth cycle length, as well as with variety names. Three distinct genetic groups clustered exogenous varieties, corresponding, respectively, to Kaguru (A), Seredo, and Serena (B), and to varieties bearing various names (C). The two other groups clustered local varieties corresponding, respectively, to single‐season varieties (D), mainly Mugeta and Mucarama, and to ratoon varieties (E), mainly Mugana  in the sPCA. These patterns were not determined by mechanisms of isolation by distance, as the Mantel test showed no relationship between pairwise log‐transformed geographic distances and genetic distances among the 10 sites . Instead, the geographic patterns displayed by mapping the three first scores of the sPCA were congruent with DAPC scatter plots (axis 1–3). Both analyses revealed the genetic differentiation of midland sites M3, M4, M5, and M6, where ratoon varieties were grown, from the other sites  enabled us to discern geographic patterns in the distribution of indigenous sorghum genetic diversity as shown by the significant Monte Carlo test for global structure . Thus, genetic cluster 1 corresponded to durra, bicolor, and intermediates from northeastern Africa, group 2 to kafir and intermediates (mainly kafir–caudatum and guinea–caudatum) from southern Africa, group 3 to caudatum and intermediates (mainly guinea–caudatum) from Central and East Africa, group 4 to guinea and intermediates (durra–caudatum and guinea–caudatum) from West Africa, and group 5 to caudatum, guinea, and intermediates between them from southeastern and Central Africa (extended Great Lakes region). Ratoon and single‐season varieties from Mount Kenya were assigned to two different groups according to DAPC outputs. The ratoon landraces were assigned to the group 5 while both local and exogenous single‐season varieties were assigned to the group 3 . Among the sites growing only single‐season local varieties, site M1 presented significantly different varietal and genetic compositions. These patterns seem to result from the combination of processes at different spatiotemporal scales. First, the coexistence of distinct genetic groups in the area suggests the contribution of gene pools from different origins, which was confirmed by comparing Mount Kenya genetic diversity with that of the African continent. Second, our results show that barriers to gene flow exist among sites (restricted seed‐mediated gene flow) and within sites (restricted pollen‐mediated gene flow), which maintain the diversity structure and geographic patterns observed.To improve our understanding of the diversity patterns observed, we investigated the seed‐mediated gene flows through the survey of local seed systems. Seed sources were found to be similar for sorghum and pearl millet, with a major importance of local sources as seeds were mostly self‐produced and bought on local markets, usually located at less than 10 km. Interestingly, the geographic structure of seed systems did not limit pearl millet genetic homogenization on Mount Kenya, while it limited that of sorghum. This situation shows that the combination of wide diffusion of planting material and intense pollen‐mediated gene flow plays a major role in blurring genetic differences among cultivated populations at a regional scale. Our results thus illustrate the complex interplay between crop breeding systems, which determine pollen‐mediated gene flow, and the multiple processes involved in shaping seed‐mediated gene flow on different timescales, which are further discussed hereafter.4.1At the Mount Kenya scale, patterns of pearl millet varietal and genetic diversity suggest that the combination of both high pollen‐mediated gene flow among varieties and seed‐mediated gene flow by the mean of a large diffusion and adoption of exogenous varieties drove genetic homogenization. Indeed, pearl millet genetic diversity showed no structure, neither at the region nor at the site scales, and no genetic differences were detected between indigenous and exogenous or between different named varieties grown at a same site.Kiraka and Ikira Sati were largely grown in most sites. Kiraka was introduced in the area in the 1930s from West Africa by a missionary according to elder farmers , while Ikira Sati was released under the name ICMV 221 by ICRISAT India and was bred from West African material  were genetically distinct from sorghum grown in the rest of Kenya, while local single‐season varieties (Mugeta and Mucarama) clustered with accessions collected in different other regions of Kenya  and the rest of the region, while the reverse is observed for the diffusion of local single‐season varieties. In addition, site M1 also appeared isolated from the rest of the sites as it presented different varietal and genetic compositions. Such connectivity patterns can result from the combination of multiple factors, which are discussed hereafter. On a region or country scales, most studies on sorghum genetic diversity also reported the existence of a strong geographic structure, for instance in Niger . Such a limited extent of local seed systems was frequently observed , where they were not sold . Secondly, farmers in the southern midlands , growing only single‐season varieties, and those in the northern midlands , growing mainly ratoon local varieties, purchase seeds in markets located in their respective areas. Last, farmers in central midlands (M3 and M4), who grow both local single‐season and ratoon varieties, purchase seeds in both lowlands (where single‐season varieties are sold) and highland markets (where ratoon varieties are sold). However, this relationship between the varieties grown by farmers in the different sites and those sold on the markets is bidirectional: Farmers buy seeds that are available and accessible in the closer markets, but conversely markets have to respond to farmers needs in terms of use and adaptation.4.3.2Varieties obtained by farmers either directly from other farmers or on the markets are expected to be adapted to their needs in terms of agronomic, organoleptic, and other characteristics Bellon, . CriteriFurthermore, adaptation of varieties to different agroecosystems is also potentially a major factor influencing seed diffusion networks, as discussed by Bellon et al.  for maiz4.3.3in situ conservation of ratoon varieties should thus be designed.Our study enabled to highlight clear geographic patterns of genetic diversity for sorghum. Ratoon varieties are of special interest for conservation, as they are genetically different from the rest of the Kenyan populations, being related to Great Lakes populations from which they evolved separately. Their uneven distribution suggests that they present particular characteristics, either related to agronomic and climatic adaptation to high‐altitude agroecosystems, or related to cultural preferences of the ethnolinguistic groups that grow them. These varieties are potentially at risk of extinction because of their agronomic and processing properties , and the overall preferences of consumers for maize  for each site.Table S3. Summary of information and genetic diversity estimates per locus.Table S4. Pairwise FST values between sites for pearl milletFigure S1. Distribution of the major pearl millet (N = 238 variety‐farm) and sorghum (N = 367 variety‐farm) named varieties among sites.Figure S2. Results of STRUCTURE assignment for pearl millet , and sorghum .Figure S3. DAPC scatterplot (DA 1 and 2) of the five African genetic groups inferred using STRUCTURE (N = 1799).Click here for additional data file.


Solanum tuberosum) can be expanded by the introgression of many related species with large genetic diversity. The analysis of the genetic structure of a potato population is important to broaden the genetic base of breeding programs by the identification of different genetic pools. A panel composed by 231 diverse genotypes was characterized using single nucleotide polymorphism (SNP) markers of the Illumina Infinium Potato SNP Array V2 to identify population structure and assess genetic diversity using discriminant analysis of principal components (DAPC) and pedigree analysis. Results revealed the presence of five clusters within the populations differentiated principally by ploidy, taxonomy, origin and breeding program. The information obtained in this work could be readily used as a guide for parental introduction in new breeding programs that want to maximize variability by combination of contrasting variability sources such as those presented here.The reported narrow genetic base of cultivated potato ( Modern potato cultivars were developed from a few clones brought from the American continent to Europe in the XVI century. After the Irish potato famine in XIX, its genetic base was drastically narrowed . To recoThe genetic characterization of available potato populations allows us to assess their diversity and structure and to identify the genotypes that could work as a source of new alleles in potato breeding programs. The aim of these programs has been to incorporate resistance to biotic and abiotic stresses, as well as to develop better processing qualities and combine these characters with high yield and commercial quality , using cThe genetic structure of a population can be assessed by several methods using molecular markers information, such as STRUCTURE , EIGENSTInformation generated from genealogy in breeding programs is generally not used for the assessment of genetic diversity and characterization of populations. However, it is very valuable as it complements the data provided by the molecular markers and helps to understand the genetic base and history of a population.The objective of this work was to provide a methodology based on a molecular characterization using DAPC and a pedigree characterization to assess the genetic structure and diversity of a potato population made of genotypes of diverse origins.Solanum tuberosum Gp. Andigena), wild species (Solanum chacoense Bitt. and Solanum tarijense Hawkes (syn Solanum berthaultii)), and a Solanum tuberosum var. Calén x Solanum gourlayii hybrid and their reciprocal  and of the National Institute of Agricultural Technology . It also included some varieties from Group Andigena  was used for the extraction. DNA quality and quantity of each sample was determined with electrophoresis in 1% (w/v) agarose gels and spectrophotometry. Final DNA concentration was determined using PicoGreen quantitation .adegenet package (1.4–2 version) [The population was genotyped with the Illumina Infinium Potato SNP Array V2  –23. Illuversion)  for R soversion) , and onladegenet package [find.clusters function was used to detect the number of clusters in the population. It uses K-means clustering which decomposes the total variance of a variable into between-group and within-group components. The best number of subpopulations has the lowest associated Bayesian Information Criterion (BIC). A cross validation function  was used to confirm the correct number of PC to be retained. In this analysis, the data is divided into two sets: a training set (90% of the data) and a validation set (10% of the data) The member of each group are selected by stratified random sampling, which ensures that at least one member of each group or population in the original data is represented in both training and validation sets. DAPC is carried out on the training set with variable numbers of PCs retained, and the degree to which the analysis is able to accurately predict the group membership of excluded individuals  is used to identify the optimal number of PCs to retain. At each level of PC retention, the sampling and DAPC procedures are repeated many times [adegenet) uses DAPC analysis to identify alleles with the largest contributions to form the linear discriminants and assign the genotypes to the clusters, in order to know the population structure. To confirm the allocation of individuals to clusters by DAPC analysis, a Nei genetic distance matrix [The population structure was analyzed by a DAPC  using th package  for R so package . The finny times . The bese matrix  was calce matrix .st) and a subpopulation inbreeding coefficient (Fis) were calculated using GenAlEx software (6.5 version) [e, Ho) and Percentage of polymorphic loci were also calculated using GenAlEx software.The analysis of molecular variance (AMOVA), the coefficient of genetic differentiation among populations (Fversion)  with 999Peditree ® software [Potato pedigree database (Wageningen) public database [Peditree ® software and were used to calculate the Coefficient of Relationship (CR) [A database of pedigree information of the potato lines and varieties was compiled and presented in a tree using software . Informadatabase . The numhip (CR)  among thMaximum average contribution (MAC) was calculated as the frequency of the contribution of each parent to each offspring according to the level in the genealogy, in relation to the frequency of occurrence of each parent in the genotypes that make up each subpopulation. A maximum of five generations of ancestors was used for this analysis.find.clusters function , and Linear Discriminant 2 (LD 2) separated among S. tuberosum groups . Groups 2, 3 and 5 were roughly at the same level with respect to LD 2, and groups 1 and 4 were above and below them, respectively. DAPC analysis of subpopulations 1 and 2, separated each of them into three groups  genotype, were placed in subpopulation 2 which is a S. tuberosum Gp. Tuberosum  and identified 23 and 18 SNPs, respectively , and homozygotes and heterozygotes were balanced, (Fis = -0.022). The percentage of polymorphic loci was high across all populations. The genetic diversity of the whole population was high , but was higher in tetraploid subpopulations  than in the diploid subpopulation (5) (o) were higher than expected (He) for all the tetraploid subpopulations . They were similar in S. tuberosum Gp. Tuberosum subpopulations  but was a slightly lower in S. tuberosum Gp. Andigena subpopulation (Ho = 0.439). On the other hand, Subpopulation 5 (diploid) values were the lowest (Ho = 0.063). The groups detected within subpopulations 1 and 2 (three groups in both cases) were not well differentiated, which is supported by the low Fst values . Both subpopulations had Fis values close to zero, which means that there was no excess of homozygotes or heterozygotes .Results in tion (5) . Observest)  and one group of S. tuberosum Gp. Andigena (3), the lowest value was found between subpopulations 2 and 3. Fst values between S. tuberosum Gp. Tuberosum subpopulations were low, suggesting low genetic differentiation among them.The coefficient of genetic differentiation among populations (Fst)  was highSubpopulation 1 had 83% of INTA Balcarce breeding program clones and varieties. Serrana INTA had the highest value of MAC with 26.7% and appeared in the 17% of the individuals of the maternal path . MPI 59.S. demissum, S. stoloniferum, S. verneii, S. phureja) and S. tuberosum Gp. Andigena in their genetic background, which are INTA and CIP breeding clones and varieties from Argentina, United States, Netherlands and Chile. Also, subpopulation 2 has 34% of the CIP germplasm, originated from different breeding populations of recurrent selection which achieved resistance or tolerance to different stresses : 11% belongs to LTVR population, 42% belongs to B population and 0.06% belongs to LBHT population. The maternal and paternal ancestors that made the greatest contribution to the genotypes of the subpopulation are not so prominent. Conversely, there is a greater number of genotypes with small contributions. Katahdin is again the most frequent ancestor both in the maternal and paternal paths, but with very low values of MAC in both cases.Subpopulation 2 has 45% of individuals with wild species  x Furore and Saskia. Their frequencies of appearance are very similar but the MAC values varied between 7.3 and 4%, Gabriela is the genotype with greater contribution. The paternal path is strongly influenced by Innovator who appeared in the 41% of the genotypes and its contribution to the group reaches the value of 50%. Katahdin is the most frequent genotype of the paternal path  but again its contribution to the genetic background of the group is very low (8.75%). Saskia x (CPC 1673–20 (adg) x Furore) and CPC 1673–11 (adg) x Furore are present also in the paternal background of this group in 53% of the individuals, with low contribution (7.03%).The similarity matrix with the coefficients of relationship (CR) of each subpopulation presented low values of similarity between individuals of each group analyzed. The subpopulation A had only the 0.4% of the CR values above 0.5. Nevertheless, in the subpopulations B and D none of the CR values were greater than 0.5.andigena cultivars.Genetic variability is essential to breeders for the generation of improved varieties resistant to diseases and adapted to adverse environmental conditions, without neglecting yield and industrial quality. Thus, it constitutes a source of new alleles for diverse complex traits of breeding interest contributing to broad potato genetic base. In the present study, population structure and genetic diversity was assessed in a diverse panel composed by genotypes that belong to two potato breeding programs with different strategies and final objectives, potato varieties from different countries around the world, wild species and DAPC analysis divided the population into well-defined clusters related to their genetic structure, which was associated with provenance, ploidy, taxonomy and breeding program of the genotypes. The groups generated are according to the different taxonomic backgrounds. This result was confirmed by a dendrogram of Nei distance of the whole population. The differences in the assignment of the genotypes to the groups between the two methods are based on the fact that dendrograms were constructed with Ward method, which is an agglomerative hierarchical method of clustering, and subpopulations were identified using DAPC which employs k-means with increasing number of clusters, a relocation non-hierarchical clustering method. Non-hierarchical methods begin partitioning samples into groups and iterate by moving them among groups to optimize measurements of within-group homogeneity and between-group heterogeneity. Those groups do not have hierarchical relationships between them . The aggS. tuberosum Gp. Tuberosum individuals had low differentiation among them. This result is in accordance with Hirsch et al., [Subpopulations composed by of  et al.,  who alsoS. tuberosum Gp. Tuberosum subpopulations were quite similar. According to this result, genetic background of subpopulations 1 and 2 could be quite homogeneous because they were generated from selection and crosses within breeding programs with a specific objective, consequently there could be no differences in allele frequencies within each subpopulation.In addition, the subgroups into the most numerous The SNPs that mostly contributed to the division of the population in clusters were those which mostly differ among the subpopulations. They were related to regions of the genome driving genetic divergence among groups . Some ofS. tuberosum Gp. Tuberosum. Also, the germplasm belonging to each of these subpopulations has a low coefficient of similarity between individuals. Ho value corresponded to Andigena subpopulation was lower than Tuberosum, probably because the Andigena subpopulation was formed by a small number of genotypes from the same place, which could represent a small sample of the total variability available from the Andigena Group. However, the lowest Ho value was that of diploid group, which could be a consequence of the small number of individuals that made up the subpopulation. This result is in agreement with Hirsch et al. [o in Andigena and Phureja populations using the Illumina Infinium Potato SNP Array but composed by a significant number of individuals.Heterozygosity values were higher in Group Tuberosum subpopulations than in the others. This may be related to the fact that the largest number of individuals in the total population are h et al.  who suggh et al. , therefoh et al.  found grS. tuberosum Gp. Tuberosum subpopulations detected the most frequent parental genotypes and those which did the most contribution to the subpopulations. In subpopulation 1, the maternal parent with the largest contribution was Serrana INTA which is an Argentinian variety known for its virus resistance and storage quality. It was widely used as progenitor in breeding programs for virus resistance at INTA Balcarce [Solanum species to introduce them into new potato varieties. In group 4, the main contributor and frequent paternal ancestor was Innovator. The genotype CPC 1673–20 (adg) was present in both paternal and maternal paths. This could be the reason why this subpopulation is the one that differs the most from the other two subpopulations of S. tuberosum, since there are not repeated progenitors with high frequency or contribution in the other two groups. Consequently, these might be the ones that were yielding the difference in the allelic frequencies with respect to the other groups of S. tuberosum Gp. Tuberosum. The genotype CPC 1673–20 was found among the first sources of resistance to Globodera rostochiensis, used in breeding programs [The pedigree analysis into the Balcarce ; 39 and Balcarce , 41. On Balcarce . Accordiprograms , 44. In S. tuberosum Gp. Tuberosum subpopulations analyzed with very low contributions to the genetic differentiation but contributing to the genetic background of all the groups. This variety was released in 1935 by the Beltsville USDA breeding program. It has been widely used in breeding programs in the United States and is found as a parent of most American cultivars of the 20th century. Also, it is a recent ancestor and appears numerous times in the American cultivars pedigree [Nevertheless, Katahdin was present as the most frequent parental in both parental paths of all the pedigree , 45. Accpedigree ,46.DAPC and other statistical methods were used to assess plant population structure by different authors ,47–48. AS1 FigA) Graph of number of clusters vs. BIC. B): Graph of cross-validation of DAPC.(PDF)Click here for additional data file.S2 FigA) Dendrogram from Nei genetic distance matrix for subpopulation 1. B) Dendrogram from Nei genetic distance matrix for subpopulation 2.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.


Scutellaria playfairii and S. tashiroi, were spatially and ecologically partitioned with partial overlap. Multilocus genome-scan analyses and in silico evaluation revealed ancestral interspecific gene flow but distinct genetic compositions, implying that adaptive divergence contributed to their speciation. Ecological niche modeling and principal component analysis suggested incomplete divergent niches between the two species; the species distribution is therefore consistent with Hutchinson’s metaphor of multidimensional hypervolume niches rather than attributable to a single factor. Constraint ordination analysis supported this inference of a combination of variables explaining the genetic structure. The rare occurrence of hybrids in the sympatric population suggested hybrid breakdown, providing further evidence of divergent selection blocking gene flow. The correlation of environmental variables with integrated genetic components demonstrated that environmental heterogeneity maintains the species and population differentiation. This study highlights the importance of environmental heterogeneity and divergent selection for the rapid speciation and recent diversification of island plants.Divergent selection plays a critical role not only as a speciation driver but also in maintaining post-speciation divergence. In the absence of direct evidence, ancestral interspecific gene flow between incipient species can reflect ancient selective pressure for ecological speciation. In the present study, two late-Pleistocene diverged species endemic to Taiwan,  Via4 proposed two stages of selective pressure on ecological speciation and divergence. Stage 1 involves selection against effective migration, and stage 2 involves independent responses to selection within the new lineages. Stage 1 was suggested to clearly define the difference between allopatric and sympatric speciation5, whereas directional selection could further independently act on the diverged lineages to reduce the potential for interspecific mating upon physical contact after speciation7. However, because past environmental conditions are difficult to record, present environmental heterogeneity can usually explain post-speciation divergence but not the actual driving force for ecological speciation, i.e., only stage 2 of Via can be verified4.In addition to accelerating speciation, divergent selection is also responsible for maintaining differences between species. Studies have increasingly shown that environmental variables, rather than geographic distance, are responsible for genetic divergence8, detecting the degrees of interspecific gene flow through time can support or reject ecological speciation. A selective process leads to species (or population) divergence more rapidly than a stochastic drift process10. Hence, gene flow will rapidly be blocked if the selection continues to maintain or enhance the divergence. The development of modeling-based simulation analyses such as the approximate Bayesian computation (ABC) has enabled the evaluation of the best-fit evolutionary scenario of isolation vs. gene flow to resolve the speciation hypothesis. In combination with correlation analysis between genetic variation and environmental variables, the effect of environmental heterogeneity on species differentiation and local adaption can be clarified.Since ecological speciation is a time-dependent process, i.e., genomic barriers for gene flow gradually increase between adaptively divergent speciesScutellaria playfairii Kudo and S. tashiroi Hayata, which both have a relatively limited spatial distribution. Scutellaria playfairii and S. tashiroi are endemic to Taiwan and are sparsely distributed among low-elevation hills. The S. playfairii are mainly distributed in the southern Taiwan, while S. tashiroi are common in the stream valleys of eastern Taiwan. Despite most populations of these two species were allopatrically distributed, there are still some sympatric populations being found in the southeastern Taiwan  can be used for exploring the ecological association and discriminating environmental requirements of each species, which are widely used in testing hypotheses of ecological divergence and speciation19. The hypothetico-deductive evolutionary analyses, such as the ABC approach, provide powerful statistical verification method to evaluate the best speciation model by using summary statistics and simulation without exact likelihood calculations21. The ABC is widely used in unravelling complex evolutionary history such as the relict cycad species22, phylogenetic closely related skullcap flowers23, Dysosma versipellis-pleiantha complex24, etc. Through integration of these frameworks, we can test the ecological divergence of S. tashiroi and S. playfairii, and evaluate the relative importance of each environmental factor on their genetic divergence. These integrated approaches has successfully applied in another two Taiwanese Scutellaria species to confirm their taxonomic relationship23.Because geo-environmental heterogeneity can act as a selective agent for species and population differentiation, ecologically divergent selection and local adaptation may be valid explanations of species differentiation and population structure. Quantifying the effects of environmental factors on geographic and genetic distributions can aid the clarification of the mechanisms driving and maintaining species/population differentiation. For example, multivariate analyses that using linear dependent similarity matrix can assess how does the constraint factors  affect the response (e.g. genetic distance)Scutellaria species through in silico speciation-processing modeling and correlations between climatic variables and genetics. Here, we extract environmental factors that cause or sustain genetic differences and highlight the important role of integrated environmental elements in plant genetic make-up.In this paper, we assessed the ecological speciation and post-speciation adaptive divergence of these two recently diverged S. playfairii and S. tashiroi showed similar genetic diversity with respect to expected heterozygosity (ranges: 0.231 ± 0.061~0.340 ± 0.067 in S. playfairii and 0.253 ± 0.069~0.371 ± 0.069 in S. tashiroi) and the inbreeding coefficient (fixation index: 0.431 ± 0.085~0.727 ± 0.053 in S. playfairii and 0.387 ± 0.061~0.663 ± 0.043 in S. tashiroi)  based on the remaining 15 neutral loci revealed a non-significant difference in genetic variation between species but a significant divergence between populations , according to the highest ΔK (lnP(K) = −2465.31, ln’(K) = 1393.59, |ln”(K)| = 1005.47, ΔK = 3633.97, Fig. K = 3 was also optimal, separating the Wulu population of S. tashiroi from the other two S. tashiroi populations  between any populations of two species, means that the distinguished genetic cluster of the Wulu population of S. tashiroi is not attributed to hybridization between two species.We then performed Bayesian clustering analysis (BCA) and discriminant analysis of principal components (DAPC) to illustrate the grouping patterns of these five populations. BCA suggested that the best clustering number was two  and adaptive loci  suggested that drift  processes and selective pressures affected population genetic structure and species divergence simultaneously but not synchronously.All three positive outlier loci were homozygous (Supplementary Table S. playfairii and S. tashiroi. Among four speciation scenarios (see Methods), the ancestral migration (AM) model  describing early interspecific gene flow after divergence best explained the current genetic divergence of S. tashiroi and S. playfairii relative to the complete isolation (CI), secondary contact (SC), and continuous migration (CM) models  was similar to the divergence time . Scutellaria species are annual herbs. Consequently, the divergence and early interspecific gene flow can be traced back to the early Pleistocene. This estimate of the divergence time is thus consistent with the estimate of <0.2 Mya based on nuclear and chloroplast genes15. The detailed parameter estimates of the AM model are listed in Supplementary Table An ABC framework permitted an evaluation of speciation scenarios for els Fig. . We deteχ2 = 7.839, df = 1, P = 0.0051, Supplementary Fig. χ2 = 0.833, df = 1, P = 0.3614; PC3: KW test: χ2 = 0.364, df = 1, P = 0.5463, Supplementary Fig. Z = 2.626, P = 0.009, Table After removing variables of multicollinearity, the remaining five environmental variables with the smallest variance inflation factor  revealed partial overlap of niches between the two species in principal component analysis (PCA) space  or five remaining variables without multicollinearity (ENM2). The average test of the cross-validated area under the curve (AUC) for predicting the distribution of S. playfairii was 0.830 (±0.171) and 0.841 (±0.093) for replicate runs of ENM1 and ENM2, respectively; the AUC for predicting the distribution of S. tashiroi was 0.921 (±0.025) and 0.888 (±0.047) in ENM1 and ENM2, respectively. The high AUC value indicates good performance in modeling the distributions of both Scutellaria species. The range of species distribution predicted by ENM1 is closer to the actual recorded sites of S. playfairii, with a likely distribution in low-altitude mountains of eastern Taiwan and lowlands of southwestern Taiwan  of all variables along axis 1 and axis 2, except a marginal significance in bio3 along axis 1 .The distributions of the sampled populations in the space of constraint ordination analysis by dbRDA were similar to the DAPC clustering patterns Fig. . The scaS. playfairii and S. tashiroi. Interspecific gene flow was detected at the beginning of speciation  processes are not mutually exclusive for explaining the speciation of these two Scutellaria species. Although S. playfairii and S. tashiroi recently diverged   between ion Fig. , implyinlow Fig. , could b33. Drastic climatic change in the late Pleistocene may have resulted in distributional divergence between S. playfairii and S. tashiroi, which diverged roughly 0.2 Mya (as estimated by nuclear and chloroplast DNA sequences15) or <0.15 Mya (as estimated by microsatellite markers in this study). Larger-scale studies have demonstrated that both climate and geo-historical factors can explain the plant diversity of eastern Asian archipelagos, reflecting as well the interaction of recent ecological and evolutionary processes36. The inference of late Pleistocene divergence with initial gene flow , with slight differences in micro-environments , no chimera individuals, and lower marginal density of recent contact model  and three populations of S. tashiroi  were sampled for genetic accession. These populations all differed in terms of habitat characteristics . Neutral loci and candidate adaptive loci (positive outliers) were determined based on a multinomial-Dirichlet distribution under simulations of reversible-jumped Markov chain Monte Carlo (rjMCMC) using BayeScan 2.154. Outlier loci were determined after running ×105 iterations with a thinning interval of 1000, posterior odds (PO) >10 (strong selection P = 0.90), and a q-value < 0.05 for the false-discovery rate as the threshold settings. We conducted DAPC and BCA using the function dapc in the R55 package adegenet56, and we used STRUCTURE 2.3.457 to discriminate genetic clusters. DAPC is a “without a priori” method using partial synthetic variables to minimize variation within groups59, whereas BCA is a population-model-based approach based on the Hardy-Weinberg and linkage equilibria60. For DAPC, groups of “species” and “populations” were set to conduct the DAPC separately, and the first two principal components (PCs) were selected to discriminate groups. For BCA, the individual assignment was conducted under admixture model without incorporating locality information (i.e. without LOC prior). The correlated allele frequency model was assumed for BCA. Total 10 independent runs were conducted for BCA. Each run contains 1 million MCMC simulation followed by 100 thousand steps burn-in. These 10 runs were aligned and summarized using CLUMPP61. Analysis of molecular variance (AMOVA) by hierarchical groupings of species and populations permitted an assessment of population structure.The evaluation of the genetic diversity of plants was based on 19 selected microsatellite loci, adapted from Chiang 62 and could exhibit different evolutionary scenarios to neutral loci64. One million simulations were generated using the package simcoal2 implemented in ABCtoolbox65. The summary statistics were reduced to five partial least-squares components following the instructions in ABCtoolbox. Using the R package ABCestimator, we performed a general linear model post-sampling regression adjustment on the best 5,000 simulations to obtain the marginal density of each model. We selected the best model scenario using the Bayes factor and ratio of marginal densities for each model.We tested four scenarios by ABC: (1) CI model: gene flow was not permitted during divergence Fig. ; (2) AM S. playfairii and S. tashiroi, a total of 71 climatic variables were retrieved from WorldClim  and Consultative Group for International Agricultural Research - Consortium for Spatial Information (CGIAR-CSI)  (Supplementary Data). The occurrence data of both species were obtained from our sampling sites, herbarium records, and Global Biodiversity Information Facility . Total 75 (S. playfairii) and 78 (S. tashiroi) records have been obtained. Species occurrence data were checked and cleaned. We followed instruction in dismo packages implemented in R66. Repeated records (multiple records of the same species in the same grid) of occurrence sites were removed to leave one record only in order to avoid unnecessary weight. Furthermore, each specimen was checked if available, and occurrence records of misidentified species were discarded. Occurrence data that did not project onto land were removed. Finally, twenty-three and sixteen records were preserved for S. playfairii and S. tashiroi, respectively. Variables with a high VIF were removed to reduce multicollinearity. We repeated this procedure until the VIF values of all remaining variables were <10, and finally, five environmental variables, alt, AET, bio3, bio13, and bio14, were retained for further analyses.To infer the niche distributions of S. playfairii and S. tashiroi. Next, we performed the Kruskal-Wallis rank-sum test to detect differences in environmental variables between species. Then, we analyzed the distribution of environmental variables along the PCs of significant species differences by GLM to confirm the significance of the correlation of variables on the PCs. Multivariate logistic regression was further conducted to test whether the environmental variables significantly predicted species differentiation.First, PCA was conducted to reduce the dimensionality of these five variables defining the niche space, thus enabling comparison of the integrity of Grinnellian niches between 67 and the R package raster68. For ENM, 71 environmental variables (ENM1) and five retaining variables (ENM2) were used to predict the potential species distributions. All variables were re-scaled to a resolution of 30 arc-sec (approximately 1 km × 1 km). A maximum of 2000 iterations was conducted for each species, and the species occurrence data were randomly divided (10%) to train the model. One regularization multiplier and 10,000 background points were set to create models for each species set. The logistic output consisting of a grid map with a probability value ranging from 0 to 1 was generated and visualized with the R package maptools69. The predicted models were evaluated by the AUC value, which is a ranked approach for evaluating model fit and determines the probability that a presence location is ranked higher than a random simulation.To demonstrate the spatial distributions of the environmental niches of the two species, ecological niche modeling  was performed under the maximum entropy model implemented in MaxEntS. tashiroi and S. playfairii, dbRDA was conducted with the first two principal components (PC1 and PC2) of the microsatellite data as the genetic response and the remaining five environmental variables  after VIF analysis as the environmental predictors. However, since only five populations were sampled for genotyping, which limits the number of variables applying to the genetic response, we repeated the dbRDA several times by discarding one of the environmental predictors. Finally, we found that bio13 could not obtain a significant explanation in any analysis. Thus, bio13 was excluded, leaving only the four other environmental variables as predictors for the dbRDA. These four constrained environmental variables were then transformed to the Euclidean distance to perform the RDA . Type II ANOVA was then used to test the significance of each environmental variable for explaining the genetic composition under 9999 permutations. We further analyzed the distribution of environmental variables along the ordination axes using the GLM. GLM analysis facilitates the elucidation of the importance of variables in the explanation of genetic composition under the dbRDA. We fit counters of current climate variables onto the ordination space of dbRDA plots to show the association of environmental variables with genetic divergence.To test environmental effects on the genetic variation of the sampled populations of Supplementary files


Lupinus genus and is an important grain legume in the Ethiopian farming system. However, there has been limited research effort to characterize the Ethiopian white lupin landraces. Fifteen polymorphic simple sequence repeat (SSR) markers were used to assess the genetic diversity and population structure of 212 Ethiopian white lupin  landraces and two genotypes from different species (Lupinus angustifolius and Lupinus mutabilis) were used as out-group. The SSR markers revealed 108 different alleles, 98 of them from 212 landraces and 10 from out-group genotypes, with an average of 6.5 alleles per locus. The average gene diversity was 0.31. Twenty eight landraces harbored one or more private alleles from the total of 28 private alleles identified in the 212 white lupin accessions. Seventy-seven rare alleles with a frequency of less than 5% were identified and accounted for 78.6% of the total alleles detected. Analysis of molecular variance (AMOVA) showed that 92% of allelic diversity was attributed to individual accessions within populations while only 8% was distributed among populations. At 70% similarity level, the UPGMA dendrogram resulted in the formation of 13 clusters comprised of 2 to 136 landraces, with the out-group genotypes and five landraces remaining distinct and ungrouped. Population differentiation and genetic distance were relatively high between Gondar and Ethiopian white lupin populations collected by Australians. A model-based population structure analysis divided the white lupin landraces into two populations. All Ethiopian white lupin landrace populations, except most of the landraces collected by Australians (77%) and about 44% from Awi, were grouped together with significant admixtures. The study also suggested that 34 accessions, as core collections, were sufficient to retain 100% of SSR diversity. These accessions (core G-34) represent 16% of the whole 212 Ethiopian white lupin accessions and populations from West Gojam, Awi and Australian collections contributed more accessions to the core collection.White lupin is one of the four economically important species of the  Lupinus is a large and diverse genus in family Fabaceae. The number of species belonging to this genus is not well defined and different published works mention a range from 200 to over 1700 [L. albus, L. angustifolius, L. luteus and L. mutabilis. White lupin  has been cultivated for several thousand years in the Mediterranean region where it originated, and along the Nile valley (including Ethiopia)[ver 1700 . HoweverEthiopia). It is wEthiopia).In Ethiopia, white lupin exclusively grown by smallholder subsistence farmers and is valued for its grain and soil fertility maintenance properties. Traditii.e., a group of accessions from an existing germplasm collection that is chosen to represent the genetic spectrum of the entire collection, could facilitate efficient use and management of large collections [For any improvement and conservation management strategy, understanding the genetic diversity between and within populations is an important step . Major clections .The genetic diversity of white lupin and other species of lupin conserved in different parts of the world has been characterized using morphological and agronomical attributes , isozymeet al. [The Ethiopian Biodiversity Institute (EBI) conserved more than 275 white lupin landrace accessions. A study by Raman et al. claimed tet al. , sourceset al. .However,et al.  and limiet al.  characteThus, the objectives of this study were: to characterize the genetic diversity of Ethiopian white lupin accessions using SSR markers, to determine the intra-and inter-population genetic diversity and to establish a core collection of Ethiopian white lupin.Lupinus mutabilis genotype used in this study were obtained from the Ethiopian Biodiversity Institute (EBI), whereas one Lupinus angustifolius genotype was kindly provided by Dr. Alemayehu Assefa . The landraces were mainly collected in northwestern Ethiopia  as revealed from the passport data. In addition, 20 white lupin accessions which are without passport data  and the two genotypes each from different species used as out-group, were included in this study. We considered all the accessible (those that are in active collections) landrace accessions conserved in the gene bank of EBI and these number of sampled landraces represents 75% of the Ethiopian white lupin landrace accessions collected from all lupin producing zones of the country . Full descriptions of these accessions and a map of their collection areas are provided in The 212 white lupin landrace accessions and one A total of 178 accessions were grown in Ethiopia and sampled leaves were dried using silica gel and transported to the Biosciences eastern and central Africa-International Livestock Research Institute (BecA-ILRI)Hub in Nairobi, Kenya. The remaining 36 accessions were planted at the BecA-ILRI hub screen house, for DNA extraction. Approximately equal amounts of leaves from five different plants per accession were bulked to represent the accession . Genomic2O.PCR amplification was carried out with the following conditions: initial denaturation at 94°C for 3 min, followed by 30 cycles of denaturation at 94°C for 30s, annealing at 55°C—65°C (depending on the particular primer) for 30s to 1min and extension at 72°C for 2min; and a final extension at 72°C for 15 minutes. Annealing temperatures of each primer were determined by primer digital software . PCR products of contrasting florescent labels were pooled based on dye color and band intensities to have uniform signal strength. The pooled PCR amplicons were denatured with Hi-Di formimide at 95°C for 3min and then separated by capillary electrophoresis using an ABI3730 DNA genetic analyzer  using GeneScan- 500 Internal LIZ and 1200 Internal LIZ Size Standards based on size of amplicons. The fragment analysis data from ABI3730 system were analyzed and allele sizes scored with GENEMAPPER V 4.1software (Applied Biosystems).For SSR assays, a total of 16 polymorphic SSR markers based on reports by Raman et al. and PhanThe genotypic data were subjected to various within and among groups genetic diversity measures. Basic statistics including: major allele frequency, total number of alleles, gene diversity and polymorphism information content (PIC) using PowerMarker genetic analysis package (version 3.25) ; and numGenetic distances between each pair of accessions and between pre-grouped populations were measured based on both shared allele frequencies and Nei's genetic distance using PowCore collections are a group of accessions from an existing germplasm collection that is chosen to represent the genetic spectrum of the entire collection . Core coad hoc quantity related to the second-order change of the log probability of data with respect to the number of clusters inferred by Structure[CLUMPAK server) was used[To study population differentiation, pair-wise Fst among all pairs of populations were computed . The modStructure. Once th was used.Two hundred and twelve Ethiopian white lupin landrace accessions and the two out-group genotypes were amplified using 16 SSR markers, which successfully amplified across all accessions. However, one marker (Lup146) produced very large fragment size, which could not be sized with the available largest GeneScan- 1200 LIZ Size Standard at the BecA-ILRI Hub and hence was eliminated from genotyping. Moreover, the remaining 15 SSR markers were checked for medium to high polymorphism before embarking on further analysis. The 15 SSR markers revealed 108 alleles, of which 98 alleles were from the 212 white lupin landrace accessions and 10 alleles from out-group genotypes.SSR locus diversity data are summarized and provided in Twenty eight landraces harbored one or more private alleles  from theThe genetic diversity indices of populations are summarized and provided in The analysis of molecular variance showed that 92% of allelic diversity was attributed to individual accessions within populations while only 8% was distributed among populations . No signFrequency based genetic distances between all pair-wise combinations among the 212 white lupin accessions and the out-group genotypes were calculated as per Nei et al.  distanceThe resulting distance matrix was used to construct an UPGMA tree. The generated dendrogram revealed a complex accession distribution pattern with no clear grouping based on geographic origin. At 70% similarity level, the UPGMA dendrogram resulted in the formation of 13 clusters comprised of 2 to 136 landraces, with the out-group genotypes and five landraces remaining distinct and ungrouped ; Table 5For the different areas of collection, genetic distances between populations from different parts of Ethiopia and the out-group were greater than any other combinations of paired population. Gondar population is the most similar to population of West Gojam (0.027), whereas population collected by Australians showed the greatest genetic distance with population of Gondar (0.147). An UPGMA tree of the six collection areas including the out-group was constructed based on Nei’s genetic distances and resulted in the formation of three groups with regard to areas of collection or origin . The firGenetic core collections were constructed among Ethiopian white lupin landrace accessions based on 15 polymorphic SSR markers. The study revealed that thirty four accessions (core G-34) were sufficient to retain 100% of SSR diversity, i.e. captured all the 98 alleles which were detected from the 212 Ethiopian white lupin landraces. These accessions (core G-34) represent 16% of the whole 212Ethiopian white lupin accessions considered in this study. Detailed descriptions of the thirty four white lupin landraces which is proposed to constituent national core collection for Ethiopian white lupin landrace accessions are provided in Fst values among pairs of populations ranged from 0.008 (between West Gojam and Gondar) to 0.472 (between Gondar and the out-group) with an overall average of 0.179 (Different population genetics parameters including genetic differentiation (Fst), Nei's unbiased genetic distance and Nei's genetic identity were analyzed using GenAlEx 6.5 . Fst valof 0.179 . PopulatA total of 15 SSR markers were used to analyse the population structure of the panel of 212white lupin landrace accessions and out-group genotypes using a model-based approach in Structure, giving 1 to 20 possible clusters (K). The results were then permuted for each K value using the Structure software; and the results were collected through structure harvester . The LnPIn this study, SSR polymorphism was high as revealed by the high allelic richness per locus, which varied widely among the markers from 3 to 12 alleles .Relatively moderate to high PIC, Ho and He values were also observed in most markers as well as high number of private alleles and high proportion of rare alleles, indicating a high level of genetic diversity in the Ethiopian lupin landraces studied. Our results are supported by previous studies based on morphological and agronomic traits, ISSR, AFLP, SSR and DArT marker systems analyses, which reported high genetic diversity in white lupin landraces in Ethiopia, 31,32anThe dendrogram based on UPGMA revealed two major clades with a complex accession distribution pattern. Nevertheless, at about 70% similarity, the UPGMA dendrogram revealed 13 clusters comprised of 2 to 136 landraces while the out-group genotypes and five landraces remained distinct and ungrouped. Similar relationship and clustering pattern were reported for Ethiopian white lupin landraces based on agronomical and phenological traits . Raman eet al. [et al. [Nevertheless, the dendrogram did not indicate any clear divisions among the white lupin accessions based on their geographical locations. A supportive result is documented by Sbabou et al. who foundet al. ; and it et al.  who show [et al. . One posCluster analysis of populations based on areas of collection showed that population grouping did not follow strictly geographical proximity. Therefore, we evaluated them to understand whether alternative clustering methods such as model based clustering would resolve similar patterning. From the dendrogram analysis, it was observed that there were two major populations corresponding to the major collection areas on one hand and populations collected by Australians on the other. By the Structure analysis, these two major populations were resolved but with significant admixtures. The dendrogram analysis also provided some evidence for gene flow between the subpopulations. Generally, there was good correspondence between the population pattern observed in the dendrogram and the population structure identified using Structure.The sampling percentage of a core collection has long been under debate. Brown  suggesteFinally, although molecular marker technologies including SSRs have been widely applied for germplasm characterization and diversity studies, there are limitations on their application mainly because of their limited abundance in the genome. Next-generation sequencing (NGS) approaches which enable genome-wide marker analysis coupled with high-throughput genotyping platforms holds promise to significantly increase our understanding of the genomic information that could be considered in designing strategies to crop improvement. Furthermore, it has been possible to develop diverse next-generation-based reduced representation protocols, which could be optimized to any crop species with or without a reference genome sequence . Such prThe study showed that genotyping combined with clustering and population structure analysis is a powerful method for characterizing germplasm. It was also able to access and evaluate some polymorphic SSR loci which can be effectively used in future genetic analysis studies and molecular breeding programs. Further, this study confirmed that genetic diversity exists in the Ethiopian white lupin landrace accessions with several rare alleles. Nevertheless, the result also claims much redundancy in the landrace collections as more than 64% of them clustered together and only 34 accessions are sufficient to retain 100% SSR diversity i.e. captured all the 98 alleles which appeared to be present in the whole accessions considered for this study. The extent and pattern of the existing genetic diversity does not strictly follow the geographic origin or areas of collection. Generally, the results suggest that future breeding programs could exploit the available genetic diversity harbored in the Ethiopian white lupin landraces and to design and implement appropriate conservation strategies including core collection establishment and future national collection missions.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.