Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We analysed genome-wide 50 K single nucleotide polymorphism (SNP) data from 144 populations to describe the global patterns of molecular variation, compare them to those observed in other livestock species, and identify the drivers that led to the current distribution of goats.A high degree of genetic variability exists among the goat populations studied. Our results highlight a strong partitioning of molecular diversity between and within continents. Three major gene pools correspond to goats from Europe, Africa and West Asia. Dissection of sub-structures disclosed regional gene pools, which reflect the main post-domestication migration routes. We also identified several exchanges, mainly in African populations, and which often involve admixed and cosmopolitan breeds. Extensive gene flow has taken place within specific areas , whereas elsewhere isolation due to geographical barriers  or human management has decreased local gene flows.After domestication in the Fertile Crescent in the early Neolithic era , domestic goats that already carried differentiated gene pools spread to Europe, Africa and Asia. The spread of these populations determined the major genomic background of the continental populations, which currently have a more marked subdivision than that observed in other ruminant livestock species. Subsequently, further diversification occurred at the regional level due to geographical and reproductive isolation, which was accompanied by additional migrations and/or importations, the traces of which are still detectable today. The effects of breed formation were clearly detected, particularly in Central and North Europe. Overall, our results highlight a remarkable diversity that occurs at the global scale and is locally partitioned and often affected by introgression from cosmopolitan breeds. These findings support the importance of long-term preservation of goat diversity, and provide a useful framework for investigating adaptive introgression, directing genetic improvement and choosing breeding targets.The online version of this article (10.1186/s12711-018-0422-x) contains supplementary material, which is available to authorized users. Capra hircus) are the most important livestock species for poverty alleviation and rural development [Goats  as wild ancestor. Following domestication, goats accompanied humans in their migrations and dispersed throughout the world. As in other domestic species, goats migrated into Europe and arrived at the far north and west edges of the continent by about 5000 YBP [Goats are among the “big five” livestock species  recognized by the FAO  and are 5000 YBP . Expansi5000 YBP . Goats w5000 YBP , 7. The 5000 YBP , and tha5000 YBP , 10 to s5000 YBP . Goats r5000 YBP .Hence, goats have colonized a wide variety of different geographic and agro-ecological areas around the world. Following colonization, genetic drift, reproductive isolation, together with natural and human-mediated selection led to the development of nearly 600 breeds . These bBos taurus and Bos indicus–Bos javanicus (13% of variation) and, within taurine cattle, between European and African B. taurus (3.2%) [Previous genetic studies that explored the variation of sheep populations worldwide revealed a low degree of differentiation  between continents and high levels of haplotype sharing . In conts (3.2%) , 18.Although several goat breeds have already been characterized genetically , most stContrary to the weak population structuring found within mitochondrial DNA (mtDNA) lineages due to the high frequency (> 90%) and worldwide distribution of haplogroup A , a clearA recent study based on ancient DNA (aDNA) data showed that both mitochondrial and nuclear molecular variation of Neolithic goat herds was strongly structured . This evIn addition, according to nuclear aDNA evidence, early goat populations from the western, eastern and southern regions of the “Near East” contributed differently to the European, African and Asian modern goat populations. Subsequently, post-Neolithic migrations and trade led to an increase in animal movements and exchanges, with resulting admixture and reduction of genetic partitioning , which wTaken together, the available evidence points at a lack of geographical partitioning of modern maternal lineages, the existence of highly structured variation for the Y-chromosome, nuclear DNA and neolithic mtDNA data, and the occurrence of a number of local gene pools within different geographical regions. However, a comprehensive description of the worldwide distribution of present day goat diversity is still lacking.The commercial goat 52 K single nucleotide polymorphism (SNP) chip has been available since approximately 5 years , and hasThe aims of this study were to (i) investigate the distribution of goat genetic variation around the world, (ii) compare the present-day diversity patterns to those observed in ancient goat samples and in other ruminant livestock species, and (iii) reconstruct admixture and migration events that have shaped goat post-domestication history.The AdaptMap raw dataset includes 4653 animals representing 169 populations from 35 countries across six continents  by using Plink 1.9 , 36. IndTo identify individuals with high relatedness , we used an in-house script to calculate the number of Mendelian errors (ME) in pairwise comparisons between all individuals . Pairs oTwelve crossbred populations were omitted from the analyses  was used for subsequent analyses. Among the SNPs, 46,666 were mapped on autosomes, 1595 on chromosome X and 566 were unmapped. GenABEL  and BITEPhasing and imputing of missing data were carried out on 3197 animals and 46,654 autosomal SNPs with unique positions  by using Beagle v4.0 (r1399) .To better explore the geographical partitioning of diversity both between and within the most represented continents, separate datasets were created for Africa , Europe , and west Asia , and for the three continents together . Furthermore, for the 3CD and African datasets, individuals were grouped according to the results of the population structure analysis carried out on the working dataset in order to mirror gene pools rather than nominal breeds  and expected heterozygosity (HE), subsequently corrected over the number of usable SNPs, i.e., those having less than 5% missing data, by applying the formulas HO_corrected = HO_uncorrected (number of polymorphic SNPs/number of usable SNPs) and HE_corrected = HE_uncorrected /number of alleles, where number of alleles = 2 ; (ii) the inbreeding coefficient, FIS [ST fixation index [To estimate within-population diversity and between-population differentiation, we used the software Arlequin version 3.5.2.2  to calcuent, FIS , (iii) Won index , and theThe composite-likelihood method implemented in Jaatha version 2.7.0 , 46 was 2) values for small sample sizes. Since the large differences in population size within the WD set could negatively affect the estimations of SNeP, we fixed the number of analysed individuals per population to 22. Populations with fewer animals  were estimated with the SNeP software  with defMulti-dimensional scaling (MDS) plots based on kinship matrices were constructed with GenABEL  and BITEAnalysis of MOlecular VAriance (AMOVA)  was carr2 higher than 0.3 were selected. Then, we calculated the number of populations per continent for which a specific pair of SNPs was in LD.To assess possible differences in the LD structure between continents, we used the phased data to calculate the pairwise LD between SNPs with PLINK 1.9 , 36 by cR, between populations [To assess reticulate relationships between populations, we used Arlequin to calculate the Reynolds unweighted distances, Dulations , which wulations .Population structure was assessed by the maximum-likelihood based approach implemented in the Admixture software v1.3.0 . AnalyseTo investigate fine-scale genetic differentiation, we ran Chromopainter and FineStructure software version 2.0.4  on phasem0 to m11 were tested, with three replicates per m to assess consistency. To further assess the consistency of the migration edges found over the WD set, the Treemix software was also run on the 3CD set with the same settings as described above for a number of migrations between m0 and m15, and with three replicates per m.The occurrence of migration events was evaluated with the Treemix software  on the uO) and expected (HE) heterozygosity, were equal to 0.356 and 0.366, respectively, whereas within-population HO ranged from 0.135 (ICL_IS breed from Iceland) to 0.415 , and HE from 0.151 (ICL_IS) to 0.425 (CRP_RO)  was found for BEZ_IR, the Iranian Bezoars , three to Europe , two to West Asia (BEZ_IR and BRI_PK), one to South America (CRE_AR), while three were populations belonging to cosmopolitan breeds reared in Tanzania, USA and Argentina .Eighteen populations had statistically significant F FIS 0.25 was founST ranged from 0.000 (CRO_UG vs. NGD_UG) to 0.556 (ICL_IS vs. MAN_MZ) , ARR_IE from Ireland, MEN_MG and SOF_MG from Madagascar, PAL_ES from the Canary islands, except for BAB_PK, KAC_PK, KAM_PK and PAT_PK from Pakistan. Most low FST values were observed within specific geographical areas in Africa, e.g., Morocco and North Africa in general, Uganda–Burundi–Kenya, Tanzania–Ethiopia, or Mali–Burkina Faso–Nigeria–Cameroon, etc. , Central and South Italy were revealed Fig. . CRP_RO Populations that are highly isolated due to geographical or demographic barriers were also highlighted by particularly long branches in the Neighbour-network and well-defined clusters in Admixture barplots Fig. , such asIn the case of the wild goats from Iran, BEZ_IR, the long branch in the Neighbour-network probably reflects the combined effects of ascertainment bias, reproductive isolation, bottleneck and distinctiveness.Remarkable admixture was also highlighted, particularly a strong introgression of African ancestry into the breeds of South America, Spain and Southern Italy Fig. , and alsThe behaviour of transboundary breeds  and of those sampled in North America, South America and Oceania varied according to their ancestry Figs. , 5 and 6In the MDS plot of individual genotypes , and the resulting SNPs were tested on a large set of divergent breeds and populations [SNPs have many advantages compared to microsatellites (e.g. ), but arST . For thiulations .O were scored in breeds that are isolated by geographical barriers (e.g. islands) or by strict management procedures. In addition, moderate differences in heterozygosity values between breeds from different continents showed that any potential bias did not appear to have a strong impact on the major outcomes of our analyses. However, we cannot exclude an effect over the slightly lower HO that was scored for populations from specific geographical areas .In the case of our dataset, low Heas Fig. a, especiIn addition, the inclusion of the Boer breed in the discovery panel may have emphasized its distinctiveness, as suggested by the Admixture results K = 4; Fig. ; Fig. 4bEstimates of effective population size (Ne) globally indicated a gradual but constant reduction in size over time, as previously shown at a local scale , 32, 57.Likewise, the patterns of gene flow as inferred from the results obtained with the Jaatha software correlated inversely with genetic distances, as represented in Fig. O that characterizes the Malawi populations , whereas the high HO observed for populations in East Africa, such as Tanzania, Uganda and Sudan, most likely mirror admixture by cross-breeding.Observed heterozygosity showed a clear association with geography Fig. a, which O observed for GAR_IT and ION_IT [O.More generally, an increase in heterozygosity due to pastoralism and nomadism is frequently reported in sheep , 59 and d ION_IT . In CentWe performed different analyses that confirmed the occurrence of three main gene pools in goats corresponding to Europe, Africa and West Asia, which is similar to the between-continent partitioning of diversity previously observed in cattle , 18, 61 Frequently, continental subdivisions are interpreted as the legacy of gene pool divergence between ancestral populations that migrated out of the domestication centre along different routes . In the Thus, compared to sheep, the stronger subdivision observed in goats can be explained either (i) by the hypothesis that the level of population structure in Neolithic ancestors was higher in goats than in sheep, or (ii) that in spite of their similar size, these species have undergone quite different population dynamics at the global scale, with goats experiencing a lower level of post-domestication gene flow compared to the extensive exchanges documented in sheep .In the following section, the finer details of population structure and inferred migration events, obtained here, are combined with previous evidence from archaeological findings, written records and genetic data, to formulate hypotheses on the post-domestication worldwide spread of goat populations.Goat domestication took place ca. 10,500–9900 YBP in the region that covers present-day East Anatolia and Northwest Iran , 62. In Early domestic goats followed the spread of agriculture and farming by radiating from the Fertile Crescent to Asia, Europe and Africa. As shown by the diversity of aDNA, Neolithic goat populations from Southwest Asia possessed a remarkable genetic structure. Clear distinct gene pools characterized the populations in different areas surrounding the domestication centre, with early domestic goats from the west, east and southwest sides of the Fertile Crescent, respectively, showing genomic affinities with present-day populations from Europe, Asia and Africa . DivergeWithin Africa, breed clusters correspond to West, Northeast, East, Southeast Africa and Madagascar. Interestingly, the geographical distribution of the African gene pools overlaps well with that of groups of breeds that share similar morphological characters , i.e., sThe genetic makeup of the European breeds, representing the south, west and north regions of the continent, according to Admixture pattern includes a number of local gene pools  from Europe to Northwest Africa via Gibraltar and (ii) from Southwest Asia westwards to Northwest Africa along the Mediterranean coast and southwards to the eastern part of the continent. A contribution from the Iberian Peninsula into North African gene pools of livestock has already been identified in several species, including goats , 65.It is interesting to note  and to a lesser extent also in South European breeds from France and Italy, thus confirming previous evidence of introgression of African livestock gene pool into the Iberian peninsula and Southwest Europe [Treemix migration edges  and North Italy (VAL in particular) Fig. ; K = 25.Central and East Mediterranean goats from south Italy and Romania were simultaneously influenced by West Africa and Southwest Asia , via morphological standardization and systematic selection to improve production traits. At the genomic level, these practices caused a remarkably higher degree of LD in European and West Asian breeds than in the African populations, and the emergence of several single-breed clusters in the two high K reconstructions obtained with the Admixture software Fig. . They alSeveral recent exports of highly productive or specialized breeds outside their areas of origin are also identified, such as for the European Norwegian, Toggenburg and Saanen goats introduced to Tanzania and Kenya, Boer goats in Oceania, Europe and USA, Nubian and Saanen goats in Argentina, Angora populations bred in several countries worldwide, and Cashmere goats in Australia. This is often followed by intermixing with local gene pools, such as the introgression of the African gene pool into all the European goat populations reared in Tanzania, and the highly admixed nature of the Malya goats reared in the USA, or the Nubian and Saanen goats in Argentina. An exception to this trend is represented by the Cashmere and Angora goats, which still possess a Southwest Asian background although they were exported to Australia, Europe and South America.The case of the Boer breed represents a remarkable example of introgression combined with human-mediated selective pressures changing over time. In fact, this breed is recorded as having received an input from Indian and European breeds during the 18th or 19th century in order to improve milk production , but durOur study represents the widest assessment of goat diversity available to date. By investigating the patterns of population structure, gene flow and migration events, we highlighted a strong and ancient geographical partitioning of diversity between continents. In recent times, the introduction of cosmopolitan highly productive breeds to several countries across the world has led to genomic admixture and introgression into a number of local goat populations. In addition, we observed that geographical and reproductive isolation due to management practices account for gene flow reduction.We also outlined the major events that characterized the history of this livestock species: from their domestication centre in the Fertile Crescent, early domestic goats spread to Europe, Africa and Asia through divergent migration routes. This has determined the underlying genomic background and partitioning of the continental populations. During the subsequent centuries, geographical and reproductive isolation led to a regional sub-structuring of diversity. Additional and more recent migrations and/or importations spread domestic goats to the Americas and Oceania. At the global scale, our evidence reveals a remarkable level of diversity. Since introgression from cosmopolitan breeds and reduced gene flow may raise concerns about the long-term preservation of goat diversity, our results provide a useful framework for monitoring and protecting the farm animal resources represented by the goat world populations, and may help direct genetic improvement and breeding plans.Additional file 1: Table S1. Summary statistics calculated over the whole set of AdaptMap goat populations. Breed code including country code, breed name, continent and country of provenance, longitude, latitude, number of individuals pre-QC, post-QC and in the working dataset (WD), number of polymorphic loci, observed heterozygosity (HO), expected heterozygosity (HE) values corrected over the number of usable SNPs, inbreeding coefficient (FIS). Underlined figures indicate thinning of large population sizes. Statistical significance: *P < 0.05, **P < 0.01, ***P < 0.005.Additional file 2. Preparation of the continental datasets and list of populations excluded from SNeP software analyses.Additional file 3: Figure S1. Plot of FIS values. Red dots identify populations with statistically significant (P < 0.05) values.Additional file 4: Table S2. Pairwise estimates of number of migrants (Nm) and FST. Matrix of the number of migrants calculated between pairs of populations according to Jaatha software  and of FST  based on Arlequin software results. The colour-code assigned to each value follows the same scale adopted in Fig. ST values are statistically significant at P = 0.05, with the exception of those marked with §.Additional file 5: Figure S2. Heatmap of pairwise FST values. Variation of FST values calculated pairwise between breeds with Arlequin software. The corresponding numerical values and their statistical significance are given in Table S2  Africa; (b) Europe, and (c) West Asia. The first and second column from the left respectively indicate the number of populations in which a specific pair of SNPs was found in linkage and the corresponding percentage calculated over the total number of populations from that continent.Additional file 7: Figure S3. Effective population size (Ne). Trends in effective population size, Ne, estimated with SNeP software for a number of generations between 13 and 959. For further details  error values (upper panel) and number of iterations required to reach convergence (lower panel) calculated for Admixture software runs for K values from 2 to 100. The arrow indicates the K = 85 value with the lowest CV score.Additional file 11: Figure S5. MDS plots of Dimensions 1 versus 2 (panel a) and 1 versus 3 (panel b). Each point represents a single individual. The correspondence between breeds and symbols is given in the legend box in upper right corner.Additional file 12: Figure S6. Heatmap-like representation of Chromopainter coancestry matrix. The structure of the clusters on top of the heatmap is displayed in Figure S7 .Additional file 16: Figure S9. Additional Treemix software results. The panels represent (a) the fraction of variance explained by repeated runs of Treemix software on the 3-continents dataset for a number of postulated migration edges, m, increasing from m0 to m15 (3 replicates per m); and (b) Treemix software graph obtained from the 3-continents dataset and featuring 10 migration edges (m10).


Candidatus Liberibacter asiaticus impact the majority of commercial citrus species worldwide. These bacteria are transmitted by the Asian citrus psyllid (ACP), Diaphorina citri Kuwayama, which is now found in most citrus growing regions. With no known cure, ACP-vectored HLB is responsible for significant economic losses to the global citrus industry. A better understanding of the global genetic diversity of D. citri would improve current and future pest management and mitigation programs. To assess the genetic diversity of D. citri in worldwide collections, a total of 1,108 sequences belonging to ACP gathered from 27 countries in the Americas, the Caribbean, Southeast and Southwest Asia were examined for the study. 883 D. citri came from 98 locations in 18 different countries, and were sequenced using a 678bp fragment of the mitochondrial cytochrome oxidase I (COI) gene. Additionally, 225 previously-reported D. citri COI sequences, were also included in our analysis. Analyses revealed 28 haplotypes and a low genetic diversity. This is in accordance with previous reports on the little diversity of D. citri in worldwide populations. Our analyses reveal population structure with 21 haplotypes showing geographic association, increasing the resolution for the source estimation of ACP. This study reveals the distribution of haplotypes observed in different geographic regions and likely geographic sources for D. citri introductions.Citrus greening disease, or huanglongbing (HLB), is currently one of the most devastating diseases of citrus. The bacteria thought to be responsible for the disease,  Diaphorina citri Kuwayama [Hemiptera: Liviidae], is a phloem-feeding insect that in addition to causing physical damages to the plant, is the most effective vector of Candidatus Liberibacter asiaticus, and Candidatus Liberibacter americanus, the causal agents responsible for Huanglongbing (HLB) disease [D. citri acquire the bacterial pathogen when feeding on infected plants. Once the bacteria are inside the vector, they replicate, invade the salivary glands, and thus enable Ca. Liberibacter to infect other citrus plants through psyllid feeding [D. citri represents a dangerous threat to regions that are still free of this disease [The Asian citrus psyllid (ACP),  disease . Nymphs  feeding –4. Citru feeding , as most feeding . Affecte feeding , 8, and  feeding . At pres feeding , 11. The disease .D. citri was first described in Taiwan in 1907, and it is thought to be native to Southwestern Asia [D. citri evolved in India, in association with a species of orange jessamine Murraya [D. citri was first reported in South America in 1942, and now is common to citrus growing regions in Brazil, Argentina, Venezuela, Paraguay, and Uruguay [ern Asia . Specifi Murraya , 15. It  Murraya , 17. D.  Uruguay . It is a Uruguay .D. citri is being actively managed across the commercial citrus growing regions of the U. S. A., including Arizona, California, Florida, and Texas, as well as non-commercial regions. In Florida alone, citrus losses associated with D. citri and citrus greening has been estimated to exceed U. S. A. $3.6 billion in a 5-year period [D. citri is paramount to help reduce the spread of citrus greening disease.ACP has also invaded the Pacific Islands of Hawaii, American Samoa, Northern Mariana Islands, and Guam and is present in the following states of the mainland United States: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina and Texas , 21,22. r period , 24, andD. citri, can help on the identification of the geographical origin of the invader, and can potentially provide insights of invasion routes. This information can also be useful for biological control programs and other pest management programs [D. citri from 15 different countries was studied using mitochondrial cytochrome oxidase I (mtCOI) sequencing by Boykin et al. [D. citri [D. citri. The authors suggested that further sampling, especially from the Middle East and Asian countries was needed to better define the genetic diversity of this insect.Knowledge of the genetic variation and population structure of an invasive pest species, such as programs . Mitochoprograms , 27, 28.D. citri . HoweverD. citri, identifying the source or pathway of the Asian citrus psyllid is difficult. Additional data from geographic regions where this pest occurs is needed. The objective of this study is to: (I) examine the genetic variation of D. citri using COI sequences in samples from different geographic locations and (II) to examine these collections for population structure in order to further populate the dataset used for the pathway analysis of this pest. This information would aid in decision-making concerning pest management programs.Because of the limited knowledge on the genetic diversity of D. citri samples were collected from different citrus host-plants, and made available to us by collectors from 18 different countries between 2008 and 2012  and (reverse: DCITRI COI-R 5’- TCA ATT GGG GGA GAG TTT TG-3’) designed by Boykin et al. [D. citri.Total genomic DNA was isolated from each individual using the DNeasy Blood and Tissue Kit . The primers  contained 16.77μl of DNAase free water, 2.50μl of 10X Taq Buffer , 2.60μl of dNTP mixture 2.5μM each (TaKaRa), 0.13μl TaKaRa Ex Taq polymerase (5U/μl), 1μl of each primer at 10μM and 1μl of DNA template for a total volume of 25μl. The amplification was conducted in Applied Biosystems  Gene Amp® PCR System 9700 thermal cycler and the following cycling parameters were used: 94°C for 2 minutes, followed by 35 cycles of 30 seconds at 94°C, 30 seconds at 53°C, of 1 min at 72°C extension and a final extension of 72°C for 10 minute, as described in Boykin et al. .TS2 imager . Before sequencing, the PCR products were purified with Exo-SAP-ITTM , using the company protocol. Sequencing of PCR products was performed using bidirectional sequencing and 3’ BigDye-labeled dideoxynucleotide triphosphates , and run on an ABI 3730XL DNA Analyzer with the ABI Data Collection Program v. 2.0 at the Huck Institute’s Nucleic Acid facility at Pennsylvania State University. Sequences were edited using Sequencher® v. 5.0 . The alignment was constructed in MEGA v. 6.0 [The PCR products were stained with Blue/Orange 6X loading Dye  and visualized with ethidium bromide on 1.5% electrophoresis agarose gels at 90V for 90 minutes in 1X TAE buffer. Documentation of these gels was performed using UVP Gel-Doc-itA v. 6.0 , using CA v. 6.0 . SequencHaplotypes (h) were identified using DnaSP v. 5.10.01 . DNA PaiPachypsylla venusta) was selected as an out-group for the analyses (accession number: NC_006157). Additionally, a phylogenetic analysis using maximum likelihood methods was performed in PhyML v. 3.0 [JModelTest 2  was usedL v. 3.0 , using tThe final DNA sequence alignment of mitochondrial COI from 1,108 individuals was 678bp and contained a total of 29 polymorphic sites, 12 parsimoniously informative sites, and no indels. There were 18 synonymous changes and 11 non-synonymous. Unique sequences were submitted to GenBank (accession numbers: MH001373-MH001384). This resulted in 28 haplotypes shown by samples gathered from among the 26 locations analyzed for the study . Of thesD. citri. This analysis revealed strong support for genetic clusters and groups. In the Bayesian analysis, two major genetic clusters were observed (A and B). Cluster A contains 14 haplotypes and shows strong support, and cluster B contains 14 haplotypes with moderate support (.67). Three genetic groups  were observed in cluster B. Each of these three groups showed strong branch support , they are locally restricted to a single collection site each. This group included individuals from 14 of the 26 countries; U. S. A. 230/246 (93.49%), Mexico 40/40 (100.0%), Belize 9/9 (100%), Costa Rica 10/10 (100%), American Samoa 22/22 (100.0%), Saudi Arabia 8/8 (100.0%), Iran 10/10 (100.0%), Brazil 4/16 (25.00%), Puerto Rico 14/62 (22.58%), Pakistan 9/381 (2.36%), India 4/14 (28.57%), China 11/75 (14.67%), Indonesia 4/13 (30.77%), Reunion 1/7 (14.29%).Group B2 includes five haplotypes . The most common and widespread haplotype is Dcit-02. The other four haplotypes are no more than two mutational steps away from Dcit-02, and are limited to a single collection site each. This group contain 7/7 (100.0%) of the individuals from Paraguay, 15/15 (100.0%) of the samples from Argentina, 6/6 (100.0%) of the samples from Uruguay, 61/75  of the specimens from Taiwan, 2/246 (0.81%) of the samples collected in the US, 10/10 (100.0%) of the samples from Vietnam, 9/13 (69.23%) of the specimens from Indonesia, 31/31 (100.0%) of the samples from Thailand, 3/3 (100.0%) of the individuals from Singapore, 5/5 (100.0%) of the individuals collected in Mauritius, and 6/7 (85.71%) of the specimens from Reunion.Group B3 included haplotypes Dcit-03, Dcit-13, Dcit-20 and Dcit-23. The most common haplotype is Dcit-03. The three other haplotypes were not more than six mutational steps away and were limited to a single collection site each. This group contains 13/246 (5.28%) of the individuals from the US, 48/62 (77.42%), of the individuals from Puerto Rico, 8/11 (78.73%) of the samples collected in Guadeloupe, 72/72 (100.0%), of the individuals gathered in Barbados, and 19/19 (100.0%), of the individuals from Trinidad.Using natural geographic barriers, previously posed hypotheses, and observed patterns of distribution, the data were delineated into five populations: North and Central America (NCA), South America (SAM), The Caribbean (CAR), South West Asia (SWA) and South East Asia (SEA). The AMOVA confirmed the significance of population structure for the five geographic populations . The AMOIn all, 413 individuals were successfully sequenced. A total of 16 haplotypes were observed in this region. We observed a higher nucleotide diversity (0.00447), and a lower haplotype diversity (0.218), as compared with the estimated overall nucleotide and haplotype diversity . The mosEight of the sixteen haplotypes were exclusive to Pakistan psyllids (n = 381). Dcit-11 was observed in three individuals from Pakistan and Dcit-16 was observed in two. The remaining five haplotypes  seen in samples from Pakistan were recovered from a single individual each. Among the 14 samples from India, six haplotypes were seen in seven psyllids and were found to be exclusive to that country. Haplotype Dcit-23 was observed in two individuals. The remaining five haplotypes (Dcit-24-28) were recovered from a single individual each. All the individuals from Saudi Arabia (n = 8) and Iran (n = 10) showed haplotype Dcit-1. Demographic analyses for this population revealed significant values for Tajima’s D , Fu and Li’s D (-5.64: P<0.02), and Fu and Li’s F (-5.17: P<0.02) . For SWAIn all, 148 sequences were analyzed, and a total of six haplotypes were recovered for psyllids from this region. The haplotype (Hd) and nucleotide (μ) diversity for this region was of 0.354 and 0.00088, respectively. Haplotype Dcit-2 was observed in 118/148 (79.72%) of the SEA individuals, and it was the only haplotype observed in individuals from Taiwan (n = 4), Vietnam (n = 10), Singapore (n = 3) and Mauritius (n = 5). In China, five haplotypes were identified, three of which were exclusive to this country  and made up a small percent of the sample . The remaining haplotypes, Dcit-1 and Dcit-2, were observed in eleven individuals (14.67%) and fifty-nine (78.66%) individuals, respectively. Haplotype Dcit-18 was only observed in 9/31 (29.03%) individuals from Thailand. Demographic analyses in this region using haplotype (Hd) and nucleotide (μ) diversity revealed non-significant values for Tajima’s D (-0.94), Fu and Li’s D (0.08), and F (-0.31). The Fst value for this region was significant (P<0.05), and relatively high (0.80376), suggesting the existence of population structure.A total of 327 specimens were successfully sequenced for collections from this region, and seven haplotypes were observed. Six of the haplotypes were recovered from psyllids gathered in a single collection site each. The haplotype and nucleotide diversity of this population was of 0.734 and 0.00294, respectively. The majority of the samples from this region were observed in group B1 311/327 (95.1%). Haplotype Dcit-1 was the most common haplotype in this group; it was seen in 92.66% of the NCA individuals. This haplotype was seen in psyllids from the five geographical regions analyzed. In Edinburg, Texas, U. S. A., 13/119 (10.92%) individuals showed haplotype Dcit-3, consistent with samples gathered in the Caribbean. Haplotype Dcit-4 was only observed in 6 individuals and was unique to a specific location in Yucatan, Mexico. In Los Angeles, California, U. S. A., two individuals showed haplotype Dcit-19. Haplotype Dcit-7, Dcit-20 and Dcit-21 were observed in a single individual each, from psyllids gathered in St. Lucie, Florida, Edinburg, Texas and Los Mochis, Sinaloa, Mexico respectively. Demographic analyses using haplotype (Hd) and nucleotide (μ) diversity estimates resulted in non-significant results for Tajima’s D (-1.51), Fu and Li’s D (-1.37), and F (-1.71). The Fst value for this region was significant (P<0.03), but relatively low (0.10132), suggesting no population structure.Sequences from 56 individuals were included in the analyses. Only three haplotypes were recovered from individuals gathered in this geographic region. A total of 71.43% of the individuals carried haplotype Dcit-2, the most common haplotype found in SEA. This haplotype occurs in 44/56 of the analyzed individuals. These individuals were found in Argentina, Brazil, Paraguay, and Uruguay. Haplotype Dcit-2 is included in group B2. The second most common haplotype, Dcit-6, was found in all individuals from Colombia (12/12) and is common to SWA collections. Haplotype Dcit-6 in included in cluster A. Four individuals, all from a single location in Brazil, carried haplotype Dcit-1. Haplotype Dcit-1 is found in group B1. Demographic analyses in this population using haplotype (Hd) and nucleotide (μ) diversity revealed no significant values for Tajima’s D (1.60), Fu and Li’s D (0.88), and F (1.28). The Fst value for this region was significant (P<0.05) and relatively high (0.81727), suggesting the presence of population structure.For the Caribbean region, sequences from a total of 164 individuals were analyzed, and three haplotypes were observed. Haplotype Dcit-3 was observed in 89.63% of the collected CAR samples. Additionally, this haplotype was recovered from 13 individuals from Texas. In Puerto Rico, fourteen individuals showed haplotype Dcit-1, which also occurs in NCA, SAM, SWA and SEA. Three individuals (27%) from Guadeloupe exhibited haplotype Dcit-13, which was unique to this region. Demographic analyses in this population using haplotype (Hd) and nucleotide (μ) diversity revealed no significant values for Tajima’s D (-1.12), Fu and Li’s D (0.89), and F (0.66). The Fst value for this region was significant (P<0.002), but relatively low, suggesting no population structure for this geographic region.D. citri in worldwide populations [D. citri is a single species and not a species complex. However, the population structure indicates barriers to gene flow. The numerous private haplotypes observed suggest differentiation and limited gene flow between the five different populations.A total of 1,108 DNA sequences from the COI mitochondrial region were analyzed, revealing the existence of 28 haplotypes and a haplotype and nucleotide diversity of 0.723 and 0.00294, respectively. Our results are in accordance with previous reports of the relatively low genetic diversity of ulations . BecauseWolbachia. Recent studies indicated that 66% of all insect species may be infected with this bacterium [Wolbachia has been found infecting D. citri. Infection with Wolbachia often causes reproductive abnormalities including cytoplasmic incompability (CI) [Wolbachia strains may lead to reproductive isolation between populations [D. citri and Wolbachia strains has been observed: U. S. A., Mexico, Belize and American Samoa D. citri samples were infected with Wolbachia strain ST-FL, as well as samples from Pakistan and Colombia [Wolbachia strain. Similarly, D. citri samples from China, Singapore and Argentina carried Wolbachia strain ST-173, a different strain than the one from SWA and NAM. Another strain of Wolbachia was associated with samples from the Caribbean, Puerto Rico, Trinidad and Barbados [Wolbachia strains.It is possible that this genetic isolation is caused by geographic barriers or biotic factors, such as infection with the endosymbiont acterium , and Wolity (CI) . It has ulations . An assoColombia . These rBarbados . These rD. citri populations. The parasitoid Tamarixia radiata has been extensively used as a biological control agent for D. citri [T. radiata has been successfully introduced to several countries, including Reunion Island, Taiwan, Mauritius, Guadeloupe, Florida, the Philippines and Indonesia, with varied results in parasitism rates of D. citri [T. radiata effectively reduced psyllid populations and helped to mitigate the impact of citrus greening [T. radiata (originating from Taiwan and South Vietnam) was released in Florida, the reported parasitism was lower than 20% [T. radiata and D. citri [T. radiata populations, as a biological control agent, are more effective for D. citri management when matched with pests that originate from the same geographic region. However, other ecological factors should be considered to understand variation in rates of parasitism [The information provided in this study is also important for the development of effective biological control programs to control D. citri , 49. T. D. citri , 50. In greening . Howeverthan 20% , 53. TheD. citri  may imprrasitism .D. citri. Previous analyses in the geographic origin of D. citri had proposed Dcit-01 as the ancestral haplotype [D. citri.Based on the phylogenetic tree and haplotype network it was not possible to determine the ancestral haplotype for aplotype , 29, basSWA collections exhibited relatively high nucleotide diversity but low haplotype diversity. The haplotype network shows a star-shaped network for this region with a common central haplotype (Dcit-06), and a relatively high number of low-frequency haplotypes. These observations suggest that this is an expanding population, possibly caused by a recent introduction . The divConsistent with results from previous analyses, Dcit-01 was the most geographically distributed haplotype in this study, and was present in all five geographical regions analyzed. However, it was more frequent in NAM populations (92.66%), when compared with the other geographical regions , suggesting a single introduction for NAM. This haplotype was the most frequent haplotype for all the collections from the New World , and it was also present and frequent for some countries in the Old World. In Saudi Arabia and Iran, Dcit-01 was the only haplotype found. It is possible that Dcit-01 originated in SWA, where it is relatively frequent. However, more sampling from this region is necessary to confirm this.Haplotype Dcit-02 was the most common haplotype for SEA and SAM populations, suggesting that this Asian region is the geographic origin for South America psyllid populations. However, haplotype Dcit-01 was also present in Brazil, where it also co-occurred with Dcit-02, suggesting two independent introductions for this country. It is possible that Dcit-01 was introduced from SWA, or from China or Indonesia, where this haplotype co-occurs with Dcit-02. In addition, haplotype Dcit-06 was found in twelve individuals from Colombia, suggesting a possible third introduction for South America.Haplotype Dcit-03 was found mostly in the CAR region with thirteen individuals showing this haplotype from Texas collections. Dcit-03 clustered within group B3 along with other haplotypes recovered from Texas (Dcit-20), Guadeloupe (Dcit-13) and India (Dcit-23). Since a closer relative has been found in India (Dcit-23), SWA could be a possible origin for this haplotype. Because this haplotype was found in some of the Texas samples, it is possible that a second introduction from the Caribbean to the U. S. A. occurred.Even though relatively low genetic diversity was observed, the geographical distribution of the different haplotypes in our study support the existence of (1) five different populations within the different geographic regions: South West Asia (SWA), South East Asia (SEA), North and Central America (NCA), South America (SAM), and the Caribbean (CAR), and (2) four major haplotype groups . A substantial portion of the haplotypes found in this study was unique for each region suggesting limited gene flow among the five geographic regions.


Salvia rosmarinus). Most of the materials were collected in Italy and France. High‐resolution melting curves were compared each other computing their Euclidean distances and estimating the differences within their principal component as a measure of genetic diversity. Mantel correlation results combined to linear discriminant analysis allowed examined populations to be divided in four principal groups corresponding to four geographic areas, with few interesting and discussed exceptions. As rosemary propagates by seeds coming from insect mediated pollination, steady wild populations can be expected to be in panmictic equilibrium. Gained results confirmed and extended precedent characterization of rosemary genotypes and are compatible with the distribution of other Mediterranean species, as well as with the presence of a glacial refugium in the north‐east area of Sardinia previously described. As the officinal use of this aromatic shrub is spreading, characterization and conservation of wild Mediterranean germplasm is gaining strategic importance. A core collection of 100 genotypes was pointed out as suitable for a cheaper biodiversity ex situ preservation as well as for subsequent metabolic and linkage disequilibrium analyses.In the present work, variability of both cytoplasmic and nuclear microsatellite traits was investigated with the aim of characterizing a set of rosemary germplasm resources ( More recently  has a wide natural distribution area, throughout the Mediterranean, and is cultivated all over the world mainly as food flower and ornamental. Otherwise, S. jordanii is an Iberio‐Maghrebian endemic species, while S. granatensis is an endemic species restricted to the southern part of the Iberian Peninsula  amplified yielded a total of 17 different alleles; their combination produced 10 different haplotypes. The relationships between haplotypes, their frequencies and geographic distribution were determined, as well as the distribution of haplotypes within populations. The results support the hypothesis that the diversification center of S. rosmarinus is in the western part of the Mediterranean basin. The effect of glaciations on Mediterranean plant species, rosemary in particular, and the role of specific areas as refuges for genetic diversity are discussed.Thanks to Mateu‐Andrés et al. , some knS. rosmarinus is the study of Xanthopoulou et al.  analysis was developed for detection of genetic variants in nucleic acid (dsDNA) sequences; HRM a post‐polymerase chain reaction method based on thermal denaturation (melting) Simko, . The preu et al. , who shoIn all the cited works, HRM was used to characterize haploid cytoplasmic loci so that HRM profile could always easily be reported to a known allele. In the present work, we characterized genetic variability between and within wild rosemary populations using HRM for both cytoplasmic and nuclear microsatellite markers. A rapid data analysis workflow was used mainly based on measuring Euclidean distance of melting profiles. This method was previously tested as suitable for scoring diversity in heterozygous rosemary . This collection is not aimed at preserving the species, that is not in danger of extinction, but in preserving and studying genetic biodiversity within the species itself. Concerning the management of this collection, we needed to establish priority for genotypes to be conserved as well as to define subsets of genotypes on which deeper and wider metabolic analysis should be done.22.1Sampling was carried out across the Mediterranean basin to obtain a representative pool of rosemary genotypes. The collection started in 2013 and continued through the years till 2016 and had the main objective of biodiversity preservation and characterization. The 364 individuals tested here have been picked up in 39 natural areas mainly distributed in Tyrrhenian coasts and isles of France and Italy and in Ionian and southern Adriatic coasts of Italy. Eight to ten genotypes have been collected from each area as reported in Supporting Information Table 2.2In order to isolate total DNA from leaf tissues, DNeasy Plant mini Kit (Qiagen—Germany) was used according to manufacturer's instructions. Twenty milligram of lyophilized leaves were disrupted by TissueLyser (Qiagen—Germany) and treated as recommended. Total DNA was eluted in two steps using 70 and 50 µl of elution buffer and collecting both eluates in the same tube. DNA quality and yield were checked out by UV‐vis spectrometer, evaluating absorbance spectra in the wavelength range between 220 and 350 nm. DNA integrity was evaluated on random and bulked samples by agarose gel electrophoresis.Salvia officinalis L.  and for 10 cycles. Melting was conducted after denaturing at 90°C and renaturing PCR products at 50°C for 2 min. Acquisition was made during cycling amplification, acquiring luminescence each cycle, and in the denaturation phase, acquiring luminescence each 0.1°C. The size of amplified fragments was verified loading some of the products (random and bulked) on agarose gel.For HRM analysis, reactions were built up by Corbett CAS 1,200 robotic instrument in a total volume of 10 µl, using an amount of 5 ng of template DNA per reaction and a final concentration of 50 nM of each primer. SsoFast™ EvaGreen® Supermix (Biorad—USA) was used according to manufacturer's instructions. A touchdown amplification protocol was used decreasing annealing temperature by 0.5 degrees per cycle starting from a temperature 4.5 degree above recommended annealing temperature (2.3F) over temperature (T) curve ; the fluorescence  was used for subsequent Mantel correlogram (using vegan package in R) and principal coordinate analysis (PCoA) using DARwin software  are similarity values (S) usually computed using the formula: Also, raw normalized data were exported in DARwin, where an average dissimilarity matrix was built up by Euclidean function  were also performed considering exclusively the data concerning ccpm series microsatellite.k = 120 and used as independent variables for subsequent linear discriminant analyses. LDA was performed at first grouping genotypes by populations, and then repeated twice grouping genotypes according to first results as better specified hereafter. All the three LDA were also performed only using the four PC1 scores coming exclusively by cytoplasmic markers in order to have indication about diversity in pollen and seed dispersion.To better describe and analyse diversity of melting profiles of each molecular marker in each genotype, PAST . 154 (2.6%) curves that came from inefficient or not specific fragment amplification were therefore scored as not available data. The observation of the reaction ring after run and during first screening of results revealed that inefficient amplification was mainly due to insufficient sample loading and/or inefficient adhesion of film cover. The presence of secondary peaks the major part of HRM profiles deriving from nuclear traits confirmed a high level of allogamy in analyzed genotypes.Results were observed in Rotor Gene Software both by “melting analysis” modality and by “HRM analysis” modality. A total of 5,824 melting curves with their respective amplification profiles were observed, coming from the amplification of 364 different samples by means of 16 primers pairs. Among these 5,824 melting curves, 5,670 (97.4%) were considered a result of successful and specific amplification as they were amplified with the same CT . A glance to the melting curves confirmed that all those gained from each primer pair were comparable (similar rtD) ranged from 0.08 to 0.95, with a global average value of 0.57. From these distances 38 average intra‐population and 703 average inter‐population genetic distances were computed. The sample PORTO was excluded from this analysis. The intra‐population genetic distances, corresponding to a geographic distance of 0 km, ranged from 0.22 (CIR population) to 0.56 (GON population) and showed an average value of 0.34 with a standard deviation of 0.10. A negative (slope = −9 × 10−5) but weak (R = 0.1488) correlation was found between these values and the respective distance from the diversification center of the species localized in the southern east of the Hiberian peninsula by Mateu‐Andrés et al.  . So, 16 PC1 scores  and 12 PC2 scores (deriving only from the nuclear microsatellite) were transformed and used as independent variables for subsequent linear discriminant analyses.These variables explained, on average, 95.35% of observed variability.Linear Discriminant Analysis of genetic characterization of 364 rosemary genotypes grouped by populations evidenced the existence of three principal nest , and TOR population coming from Sicily, apparently clustered within Liguria/France group.MAR and SPIE populations, coming from south west of Sardinia, apparently clustered within Southern Italy Group.Few exceptions can be observed:As told, MAR, SPIE and TOR population are 100% genetically distinguishable from all the other populations .A dendrogram was also built using average PC scores of the 39 population Figure , to focuResults for a core collection including 100 individuals, suitable for subsequent Linkage disequilibrium studies, are shown in Figure Deeper insights came from the comparison of genetic distances measured by cytoplasmic and nuclear markers in 12 pairs of populations very close each other , as expected between populations originated from geographic isolation of groups from the same original population. On the other hand, the populations pairs TOR‐STEF (separated by land) and MATI‐LEV (separated by sea) were characterized by a relatively high cytoplasmic genetic distance , as expected when seeds or vegetative material imported from a different geographic region give rise to a new population.4Hymenoptera apoidea, Lepidoptera in general and Sphingidae in particular) and even hummingbirds have been observed while visiting rosemary flowers outside from the natural Mediterranean distribution area.Rosemary pollination is mediated by insects of several orders  indicates the size of the patch . The presence of this area of biodiversity storage can explain the diversity we found among Sardinian populations in the present study.The presence of two distinct sub‐clades among Sardinian population is compatible with previous findings in other Mediterranean species by Melito et al.  who repo Diadema  reportedThe nature of the data gained in this study does not allow to establish the age in which direct connections have occurred between populations SPIE and MAR  and the other southern Italy rosemary populations. Further analysis on coding sequences could help understanding, as an example, if the boundaries of the Carthaginian Empire in 400 a.C. or more recent events in the peculiar history of S. Pietro island between Genoa and Tunis have determined this exception in homogeneous geographic genetic distribution.aemorhous mexicanus, P.L.Statius Muller, 1776) have been observed eating rosemary seeds in Georgia (USA) and goldfinches  in Texas (USA). Searching for migration routes of sparrow‐like birds in Italy, we found that tree pipits , yellow wagtails  and European serins  have been observed moving between Liguria and Sicily  have been observed moving along Thirrenian coast as well, but not specifically between Liguria and Sicily , and TOR population coming from Sicily, in the France/Liguria group, many hypotheses can be done. The two populations share an uncommonly high similarity at cytoplasmic level (90%) that could be explained only by a movement of seeds or other vegetative parts between the cited places. Rosemary seeds are not known as principal seeds in bird nutrition, but sparrow‐like birds have been observed while eating seeds on rosemary bushes. In particular, house finches (HAmong the 39 populations here analyzed, 13 had already been characterized for their volatile composition in a previous work (Li et al., Regarding the management of the rosemary collection in our institute, we evidenced the presence of many important genotypes different from others already described in other works and collected elsewhere. In particular, the genetic characterization evidenced that southern Italy populations are very different from rosemary collected elsewhere and feature this particular collection. Building of core collection can be the first fundamental step for a correct study of statistical association between morpho‐physiological traits and genetic markers. A core collection should be characterized by the maximum possible genome‐wide variability and by lack of structure, that should ensure low statistical association between randomly chosen distant traits (Frankel & Brown, The authors declare that this piece of written work is the result of their own independent scholarly work, and that in all cases material from the work of others is acknowledged, and quotations and paraphrases are clearly indicated. No material other than that listed has been used. This written work has not previously yet been published. The authors declare that they have no conflict of interest.A.N. conceived and designed the study. C.C. performed population retrieval and sampling across Mediterranean. L.D.B. and I.M. performed DNA extraction and HRM. A.N. performed data analyses. All the authors participated in writing the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


We used a high marker density to represent a high consistency of LD between populations, and lower marker densities to represent situations with a lower consistency of LD between populations. Markers and causal loci were selected to have either similar or different allele frequencies in the two populations.Generally, populations differ in terms of environmental and genetic factors, which can create differences in allele substitution effects between populations. Therefore, a single genotype may have different additive genetic values in different populations. The correlation between the two additive genetic values of a single genotype in two populations is known as the additive genetic correlation between populations and thus, can differ from 1. Our objective was to investigate whether differences in linkage disequilibrium (LD) and allele frequencies of markers and causal loci between populations affect the bias of the estimated genetic correlation. We simulated two populations that were separated by 50 generations and differed in LD pattern between markers and causal loci, as measured by the LD-statistic Our results show that genetic correlations were underestimated only slightly when the difference in allele frequencies between the two populations was similar for the markers and the causal loci. A lower marker density, representing a lower consistency of LD between populations, had only a minor effect on the underestimation of the genetic correlation. When the difference in allele frequencies between the two populations was not similar for markers and causal loci, genetic correlations were severely underestimated. This bias occurred because the markers did not predict accurately the relationships at causal loci.For an unbiased estimation of the genetic correlation between populations, the markers should accurately predict the relationships at the causal loci. To achieve this, it is essential that the difference in allele frequencies between populations is similar for markers and causal loci. Our results show that differences in LD phase between causal loci and markers across populations have little effect on the estimated genetic correlation.The online version of this article (10.1186/s12711-018-0434-6) contains supplementary material, which is available to authorized users. Alleles in different populations are often expressed in different environments and genetic backgrounds. Because of genotype by environment interactions and non-additive genetic effects, these differences result in different allele substitution effects between populations –3. In adKnowledge of the genetic correlation between populations helps to understand the differences and similarities in genetic architecture of complex traits between populations , 6. For To estimate a genetic correlation between two populations, it is essential to know the relationships between individuals from the two populations. Traditionally, relationships between individuals are based on pedigree information, which generally is only available within a population. The current availability of genome-wide marker panels has opened up new opportunities to estimate genetic correlations between populations of distantly related individuals, such as between breeds e.g. , 12, linBecause causal loci are generally unknown, genomic relationships have to be based on marker information. It is expected that this creates bias in the estimates of genetic correlation, because the strength and phase of linkage disequilibrium (LD) between causal loci and markers differ between populations in humans , livestoThe objective of this study was to investigate whether differences in LD patterns between populations and differences in allele frequencies of markers and/or causal loci between populations affect bias of the estimated genetic correlation. We simulated two populations that were separated by 50 generations using scenarios that differed in consistency of LD between populations, defined as the correlation in LD phase between the two populations, and in allele frequencies of markers and causal loci between the populations. We used marker-based relationship matrices to estimate the genetic correlation.Two populations were simulated using the QMSim software . The simmentclass2pt{minimneration .Fig. 1ScThe last generation of the historical population was divided randomly into two equally-sized populations , markers and 2000 causal loci were selected from all segregating loci. Three marker panels were constructed: a high-density panel (HDP) with 200,000 markers, a medium-density panel (MDP) with 20,000 markers, and a low-density panel (LDP) with 2000 markers. Each of the smaller marker panels was a subset from the larger marker panels.Markers and causal loci were selected to have either similar or different allele frequencies in populations As a first step, markers were selected from the segregating loci. To select markers with similar allele frequencies in the two populations, (1) loci had to segregate in both populations, (2) er chips –30. For As a second step, causal loci were selected from the segregating loci that were not selected as markers. To select causal loci, the same criteria and cut-off values were used as for markers, with one exception. In all scenarios, causal loci did not have to segregate in both populations, since some causal loci are known to be at least partly population-specific . Causal The LD pattern and consistency of LD between the populations was investigated. Within each population and between all causal loci and markers less than 10 cM apart, the parameter lculated :\documens et al. . The cons et al. . This inFor each causal locus, allele substitution effects were sampled from a bivariate normal distribution, with a mean of 0, a standard deviation of 1, and a correlation between the populations of 1, 0.8, 0.6, 0.4, 0.2 or 0. For each individual, its allele counts for the causal loci  were multiplied by the corresponding allele substitution effects and the results were summed over loci to calculate the additive genetic value (AGV) of the individual. The AGV were scaled to a mean of 0 and a variance of 1 across all individuals. Since allele substitution effects were sampled independently from allele frequency, the correlation between AGV of populations The simulation of phenotypes was replicated 50 times for each scenario. For each replicate, markers and causal loci were selected for three scenarios; (1) similar allele frequencies between populations for both markers and causal loci, 2) similar allele frequencies between populations for markers and different allele frequencies between populations for causal loci, and (3) different allele frequencies for both markers and causal loci. Within each scenario, phenotypes were simulated for each of the six genetic correlations. Scripts and seeds to simulate the data are in Additional file  similar ), The additive genetic correlation between populations was estimated using the following bivariate model:software . For thesoftware  to reducThe genomic relationship matrix  , 38, 39,ument}A) . To inveBefore regressing ollowing :\documenollowing , 38:\docionships , 41. Forcument}A . RegressThe criteria for selecting markers and causal loci resulted in clear differences between the scenarios with similar and different allele frequencies in the two populations Fig.  and see. As desiThe proportion of the phenotypic variance explained by the markers, known as the genomic heritability , was cloWith relationships based on causal loci, all estimated genetic correlations were unbiased, irrespective of whether causal loci had similar or different allele frequencies in the two populations Fig. . This waWith relationships based on markers, all estimated genetic correlations were slightly to severely biased. The bias was very small when the difference in allele frequencies between the two populations was similar for the markers and the causal loci. For example, when marker-based relationships were not regressed towards the pedigree relationships, genetic correlations were underestimated by only ~ 2.5% for HDP, ~ 3% for MDP, and ~ 11% for LDP markers Fig. a, c. TheAcross all scenarios, regressing ion Fig. . At a hiIn general, standard errors of the mean across replicates for the estimated genetic correlation were small for all scenarios (~ 0.02), and tended to be slightly larger for lower true genetic correlations. Moreover, standard errors were slightly larger when the difference in allele frequencies between populations was not similar for markers and causal loci Fig. b vs a, cEstimates of the genetic variance are biased when the regression of true relationships on marker-based relationships is not equal to 1 . We inveRegression coefficients of between-population relationships deviated more from 1, especially at low marker density. When the difference in allele frequencies between the populations was similar for markers and causal loci, the regression coefficients were equal to ~ 0.8 for HDP and MDP and 0.67 for LDP markers  marker effects. Surprisingly, our results showed that estimated genetic correlations were similar with HDP and MDP markers, and only slightly lower with LDP markers. This can be explained by the potential of marker-based relationships to accurately predict the relationships at the causal loci, which is essential to estimate without bias the genetic (co)variances and the genetic correlation between populations. A lower consistency of LD between populations results in a smaller variation in between-population relationships , 50, botThe consistency of LD between populations does affect the correlation between the relationships at the causal loci and at the markers Figs. , 7, i.e.In our study, the analysis with the HDP markers represented a situation with the highest consistency of LD between populations, and the analysis with LDP markers represented a situation with the lowest consistency of LD between populations. Therefore, the effects of marker density and consistency of LD between populations were confounded. The combined impact of marker density and consistency of LD appears to be limited, because the bias in the HDP and MDP scenarios was similar and only a little stronger in the LDP scenario. The impact of marker density can be reduced by regressing the genomic relationships towards the pedigree relationships. If the causal loci are known and the regression coefficients are calculated using relationships at causal loci, we showed that this regression completely removed the bias in estimated genetic correlations based on HDP and MDP markers. This suggests that the slight bias in the HDP and MDP scenario was due to marker density, and that differences in LD between the populations had almost no effect.In order to investigate whether the simulated genome represented livestock genomes, we compared the distribution of allele frequencies and LD pattern with real genomes, as suggested by Daetwyler et al. . The simWe simulated causal loci that were spread randomly across the genome, which is not always the case in real populations. When causal loci are enriched in regions with either high or low LD, (co)variance estimates can be over- or underestimated , 55. HowAs explained above, the genetic correlation is equal to the correlation between allele substitution effects when allele substitution effects are independent of allele frequency. Differences in allele substitution effects between populations result from non-additive genetic effects and from differences in allele frequencies, and/or from genotype by environment interactions –3. To daContrary to our simulations, selection generally occurs in livestock populations. Selection creates negative correlations between causal loci, known as the Bulmer effect . HoweverFurthermore, the Bulmer effect is a transient phenomenon that depends on the type and intensity of selection. Hence, the additive genetic variance as affected by the Bulmer effect does not represent a fundamental biological property of a population, but it can result from selection decisions that may fluctuate over time. The biologically relevant quantity is the genic (co)variance, which is always twice the Mendelian sampling (co)variance. The relevance of the genic (co)variance follows from the decomposition of the additive genetic value of an individual into the Mendelian sampling deviations of its ancestors , \documeent}2σm2 . This ilGenerally, marker panels are designed such that the markers have intermediate allele frequencies across multiple populations –29. HencFor an unbiased estimate of the genetic correlation between populations based on marker information, it is important that marker-based relationships accurately predict the relationships at causal loci, i.e., entclass1pt{minimaAdditional file 1. Allele frequency distribution of markers and causal loci. Figure S1: Allele frequency distribution of HDP markers for the two populations. Figure S2: Allele frequency distribution of causal loci for the two populations.Additional file 2. Programs and seeds to simulate data. This file contains the input file used for QMSim, the Fortran-programs to select markers and causal loci for the different scenarios, the Fortran-program to simulate phenotypes and the seeds for the different programs in each of the replicates.


Ctenosaura pectinata. However, the extent of this phenomenon along the geographic distribution of the species is unknown. Here, we use multilocus data together with detailed geographic sampling to (1) define genotypic clusters within C. pectinata; (2) evaluate geographic concordance between maternally and biparentally inherited markers; (3) examine levels of introgression between genotypic clusters, and (4) suggest taxonomic modifications in light of this information. Applying clustering methods to genotypes of 341 individuals from 49 localities of C. pectinata and the closely related C. acanthura, we inferred the existence of five genotypic clusters. Contact zones between genotypic clusters with signatures of interbreeding were detected, showing different levels of geographic discordance with mtDNA lineages. In northern localities, mtDNA and microsatellites exhibit concordant distributions, supporting the resurrection of C. brachylopha. Similar concordance is observed along the distribution of C. acanthura, confirming its unique taxonomic identity. Genetic and geographic concordance is also observed for populations within southwestern Mexico, where the recognition of a new species awaits in depth taxonomic revision. In contrast, in western localities a striking pattern of discordance was detected where up to six mtDNA lineages co-occur with only two genotypic clusters. Given that the type specimen originated from this area, we suggest that individuals from western Mexico keep the name C. pectinata. Our results have profound implications for conservation, management, and forensics of Mexican iguanas.Introgression, the transmission of genetic material of one taxon into another through hybridization, can have various evolutionary outcomes. Previous studies have detected signs of introgression between western populations of the Mexican endemic and threatened spiny-tailed iguana,  The role of introgression, or gene flow between divergent lineages  in shapiCtenosaura pectinata, distributed in the lowlands of the Pacific slope and the Balsas Depression in Mexico  within the range of what we currently know as C. pectinata. He stated that C. acanthura was the most widely distributed, inhabiting both the western and eastern coasts of Mexico. He indicated that C. pectinata and C. brevirostris had approximately the same distribution on the western foothills of Mexico, with “Colima” as type locality. Ctenosaura parkeri was only known from two collecting sites in Jalisco and Nayarit. Ctenosaura brachylopha was described as inhabiting the northern states of Nayarit and Sinaloa. Without giving any justification, C. brachylopha, C. brevirostris and C. parkeri with C. pectinata and restricted the name C. acanthura for iguanas from the Gulf of Mexico area.These previous molecular studies in ted taxa . This isC. pectinata genetic diversity and taxonomy is warranted. Taxonomic modifications should rely on morphological information, a multilocus approach, and comprehensive geographic sampling  define genotypic clusters; (2) investigate the levels of geographic concordance between mtDNA lineages and genotypic clusters; (3) evaluate evidence for introgression between clusters; and (4) re-define taxonomic entities based on maternally and biparentally inherited markers, and compare these to previous taxonomic hypothesis.Here, we use multilocus data from individuals sampled across the ranges of C. pectinata and C. acanthura. The narrow area of sympatry between C. pectinata and C. hemilopha in northern Mexico was excluded to avoid the inclusion of C. hemilopha alleles in the analyses (KT003209–KT003210), and microsatellite data  with 317 individuals out of 368, represented in both datasets. This study comprises samples from 53 out of 67 localities sampled in the above-mentioned studies; individuals from 49 localities were included in the microsatellite dataset. In some instances, individuals failed to amplify for mtDNA in earlier studies, but were successfully genotyped . Fragment size was visualized with the GeneMapper software version 4.0 .A thorough description of the sampling and laboratory methods can be found in The School of Biological Sciences Ethical Review Committee at the University of East Anglia approved this study as stated in an “Approval letter” to EZ. All efforts were made to minimize stress when taking blood samples, which were obtained under the permits SEMARNAT SGPA/DGVS/08239, SGPA/DGVS/02934/06, 03563/06 to VHR.K) without geographic restrictions. No coherent geographic structure was detected at K = 15, thus higher values of K were not tested. The FCT index  was used to select the best grouping, that is, the most suitable K. This index reflects the among-group component of the overall genetic variance. We selected the number of groupings that maximizes this component, meaning that under that scenario the groups of populations are maximally differentiated from each other  replicates after a burn-in period of 1,000,000. The most likely number of clusters was inferred with the method of The possible number of genotypic clusters under a scenario of admixture was inferred with STRUCTURE 2.3.2 . Simulatunlikely , thus weFST values between the resulting groups were calculated with Arlequin 3.5. Effective population size was estimated with the coalescent method implemented in NeEstimator v2  that each individual in a pair of clusters (X and Y) falls into each of six hybrid classes: pure cluster X, pure cluster Y, F1, F2, cluster X backcross, or cluster Y backcross. Five independent MCMC analyses were run for each pair of neighboring clusters with at least 300,000 iterations after 10,000 burnin sweeps. To evaluate if the MCMC reached convergence, we observed the NewHybrids graphical output and visually assessed whether the complete-data log-likelihood trace increased and stabilized in a parameter space region. P(z) values were averaged among the five independent runs. An individual was considered as belonging to a given class if it is assigned with P(z) > 0.8 (We estimated the posterior probability z) > 0.8 .KT003209–KT003210, we constructed a haplotype network , the null hypothesis of independence of genotypes at one locus from genotypes at another locus could not be rejected. Pairwise FST values showed a wide range of genetic differentiation among localities, from non-differentiation (FST = 0) to a high degree of differentiation  between the SAMOVA groups is shown in SAMOVA analyses with microsatellite data showed a FCT change <1% under K (ΔK) value. However we suspect that ΔK under K = 7 is an artifact resulting from the large variation in likelihood values obtained with the previous K, K = 6 . We then recalculated ΔK. This time K = 4 showed the highest ΔK . Both SAMOVA and STRUCTURE support the distinction between *Nuc 4 (Nuc 4) and *Nuc 5 . Taking into account the results of SAMOVA and STRUCTURE we recognize a total of five microsatellite genotypic clusters within the entire distribution of canthura  and 2.The microsatellite genotypic clusters detected with STRUCTURE (Nuc 1–Nuc 5) and SAMOVA (*Nuc 1–*Nuc 5) are geographically localized . The limThere are different levels of geographic concordance between the distribution of mtDNA lineages North A, North B, North C, North D, Colima, Balsas, Guerrero, Oaxaca and South as described by FST values between these genotypic clusters are the lowest observed in the pairwise comparisons  and microsatellite markers in this area (Nuc 2 and Nuc 3) have been previously detected and described . In the parisons .Genotypic cluster Nuc 3 overlaps with Nuc 4, which is formed by individuals collected in southeast Mexico with mtDNA haplotypes belonging to the Guerrero, Oaxaca and South mtDNA lineages. Most of the individuals were assigned to one of the pure categories in the SAMOVA equivalents *Nuc 3 and *Nuc 4 . Only twC. acanthura, collected in eastern Mexico. It is geographically concordant with the distribution of a mtDNA lineage closely related to the Southern mtDNA lineage  co-occur with only two nuclear clusters (Nuc 2 and Nuc 3). This discordance between maternally and biparentally inherited markers in C. pectinata might be the result of several processes acting alone, in concert or at different points in time. For example, a suitable explanation might be a scenario of short term refugia where populations decline throughout the range, resulting in isolation, followed by recent range expansion and male biased dispersal . Maternal lineages are more deeply structured than the genotypic clusters. The distribution of the maternally and paternally inherited markers and the high number of sampled admixed individuals suggest that, although there is some substructure in the area, gene flow among populations has been ongoing. Given that the holotype locality is labeled as “Colima”  we suggeectinata .C. acanthura also form a coherent nuclear cluster (Nuc 5) that is concordant with a mtDNA lineage closely related to the South clade  and with only morphological data at hand. Further research is needed to determine if coloration patterns or morphological characters of individuals outside the contact zones provide information for their assignment to a specific genotypic cluster.Ctenosaura pectinata sensu lato faces illegal hunting, poaching and habitat loss ; Haplotypes produced by Click here for additional data file.10.7717/peerj.6744/supp-2Supplemental Information 2K = 10; (C) microsatellite genotypic cluster defined with SAMOVA under K = 5 and (D) STRUCTURE under K = 4; (E) substructure estimated with STRUCTURE in a reduced data set . In STRUCTURE plots, the Y-axis represents proportion of ancestry. As this cannot be calculated with SAMOVA, values are always shown as 1. Each bar represents an individual. White bars are missing data(A) MtDNA lineage of each individual as defined in haplotype networks calculated by Zarza, Reynoso & Emerson ; (B) SAMOVA mtDNA groups detected under Click here for additional data file.10.7717/peerj.6744/supp-3Supplemental Information 3Ctenosaura brachylopha. Photo Credit: Eugenia Zarza.Here, we propose that populations from northern Mexico are referred as Click here for additional data file.10.7717/peerj.6744/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.6744/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.6744/supp-6Supplemental Information 6Click here for additional data file.


Saccharina japonica in the Northwest Pacific (NWP) are still poorly surveyed. In this study, we analyzed the evolutionary history of S. japonica using two mitochondrial markers and 24 nuclear microsatellites. A STRUCTURE analysis revealed two partially isolated lineages: lineage H, which is scattered along the coast of Japan; and lineage P, which occurs along the west coast of the Japan Sea. Ecological niche modeling projections to the Last Glacial Maximum (LGM) revealed that the southern coasts of the Japan Sea and the Pacific side of the Oshima and Honshu Peninsulas provided the most suitable habitats for S. japonica, implying that these regions served as ancient refugia during the LGM. Ancient isolation in different refugia may explain the observed divergence between lineages P and H. An approximate Bayesian computation analysis indicated that the two lineages experienced post‐LGM range expansion and that postglacial secondary contact occurred in Sakhalin. Model projections into the year 2,100 predicted that S. japonica will shift northwards and lose its genetic diversity center on the Oshima Peninsula in Hokkaido and Shimokita Peninsula in Honshu. The range shifts and evolutionary history of S. japonica improve our understanding of how climate change impacted the distribution range and diversity of this species and provide useful information for the conservation of natural resources under ongoing environmental change in the NWP.Studies of postglacial range shifts could enhance our understanding of seaweed species’ responses to climate change and hence facilitate the conservation of natural resources. However, the distribution dynamics and phylogeographic diversification of the commercially and ecologically important kelp  Nevertheless, genetic data  combined with ecological niche models (ENMs) allow us to infer lineage diversity and postglacial range dynamics. The overall aim of this study was to improve our understanding of how 22.1S. japonica using a plant genomic DNA kit  according to the manufacturer's instructions.Between 2011 and 2013, we sampled 20 populations along the NWP coast from Sakhalin, Russia  to Gangneung, South Korea  .The mitochondrial protein‐coding .1 Hall,  and geneIn total, 768 individuals from 35 populations were genotyped at 24 genome‐wide microsatellite loci , number of haplotypes (h), haplotype diversity (Hd), and nucleotide diversity (Pi) with dnasp v.5 , number of private alleles (NP), and observed and expected heterozygosities  were calculated using genalex v.6.41 ) and the Delta K (ΔK) method  were performed to ensure the convergence and consistency of the results.We estimated the population genetic structure using two different Bayesian clustering methods in FST in arlequin v.3.5  was conducted to partition the genetic variance using arlequin v.3.5.Population genetic differentiation was estimated by calculating the 2.5diyabc v.2.1.0  at t2 2 distance were chosen for two sample statistics.We created a reference table consisting of 102.6S. japonica occurrence records were based on field observations, previous studies, and online databases . All 23 geophysical, biotic, and climate variables provided in the original bio‐oracle v.1.0 dataset    of the microsatellite loci ranged from 0.34 to 0.94 . However, null alleles were not overrepresented on any particular locus or populations. No deviations from Hardy–Weinberg equilibrium (HWE) were observed in the populations or loci. Linkage disequilibrium was not detected between pairs of microsatellite loci after Bonferroni sequential corrections (adjusted 2) Rice, . The polHO: 1.61; HE: 0.62; AR: 5.17) and HA  in the Oshima Peninsula, Hokkaido, and in SA in the Shimokita Peninsula, Honshu . All estimates showed that three populations  from the Oshima Peninsula and the Shimokita Peninsula had higher diversities  than other populations  had the highest nucleotide diversity Pi = 0.00158) and population SA  had the highest haplotype diversity (Hd = 0.81) . In total, 16 haplotypes were shared among at least two populations, and the remaining 62 haplotypes were unique to a single population. The most frequent haplotype, H12, was shared by 311 individuals and accounted for 40.65% of all samples. Moreover, H54, which accounted for 23.27% of all samples, was mainly observed in the samples from Far Eastern Russia  plots showed that the most likely number of genetic groups was S. japonica, they also indicated that the populations on the west coast of the Japan Sea (group P and cluster 4) formed a diverged genetic lineage.Bayesian analyses of population structure using concatenated mtDNA sequences detected four genetic clusters: cluster 1 included populations in Hokkaido, Korea, and the east coast of Sakhalin; cluster 2 was observed only on the northwestern coast of Hokkaido; cluster 3 consisted of populations around Honshu Island; and cluster 4 contained most of the populations from the west coast of the Japan Sea and the west coast of Sakhalin   was 13,500  generations, which corresponded to 27,000 years BP (Supporting Information Table t1) was 3,590  generations, which corresponded to 7,180 years BP  was consistent with the species’ current distribution  and the Pacific coastlines of the Oshima Peninsula (Hokkaido) and the Shimokita Peninsula (Honshu) , and disconnected from the surviving populations (ancient H lineage) along the Pacific coast of eastern Japan.Within a species, disjunct distributions of divergent genetic lineages reflect the occurrence of vicariant processes  could expand to Sakhalin Island by the Tsushima Warm Current, and subsequently colonize the west coast of the Japan Sea by the Limen Cold Current after the LGM  from shared ancestral polymorphisms (inherited from a common ancestor) without referring to a coalescent analysis  are valuable resources for the long‐term management and breeding purposes of kelp aquaculture, and can be specifically collected and preserved ex situ.S. japonica along the south coast of the Japan Sea provided suitable habitat only for a short time period as the climate warmed  should be declared as valuable conservation units. Moreover, the kelp populations in these regions mainly include subspecies of S. japonica (S. japonica var religiosa), which are more resistant to high temperature  and along the Pacific coastlines of the Oshima Peninsula (Hokkaido) and Shimokita Peninsula (Honshu) during the LGM. The restriction of S. japonica to isolated refugia and the opening of the Tsushima Strait after the LGM likely played major roles in shaping the present distribution and phylogeographic diversification of this kelp. Most of the present range comprises the postglacial expansion zones, and the secondary contact zone between the two diverged lineages (H and P). Many populations of S. japonica are on the verge of local extinction due to climate‐induced increases in sea temperature. Further studies are needed to explore whether this seaweed has sufficient adaption or acclimation potential to mitigate the predicted climate change‐induced range shift or whether the species is at risk of losing its centers of genetic diversity.The kelp No authors have any conflict of interest to declare. Click here for additional data file.