Triticum durum Desf.) has a long-time tradition of growing and breeding. Accessions collected and now preserved ex situ are a valuable genetic resource, but their effective use in agriculture and breeding programs remains very low. In this study, a small number (44) of simple sequence repeats (SSR) molecular markers were used to detect pattern of diversity for 136 accessions collected in South Italy over time, to identify the genepool of origin, and establish similarities with 28 Italian varieties with known pedigree grown in Italy over the same time-period. Phenotyping was conducted for 12 morphophysiological characters of agronomic interest. Based on discriminant analysis of principal components (DAPC) and STRUCTURE analysis six groups were identified, the assignment of varieties reflected the genetic basis and breeding strategies involved in their development. Some “old” varieties grown today are the result of evolution through natural hybridization and conservative pure line selection. A small number of molecular markers and little phenotyping coupled with powerful statistical analysis and comparison to pedigreed varieties can provide enough information on the genetic structure of durum wheat germplasm for a quick screening of the germplasm collection able to identify accessions for breeding or introduction in low input agriculture.In South Italy durum wheat ( As part of a worldwide effort large collections of plant genetic resources have been established to meet future needs for agriculture and breeding and maintained ex situ in genebanks  but, dueTriticum turgidum L. ssp. durum Desf., 2n = 4x = 28; AABB genomes) is one of the most widely cultivated cereal species worldwide. It represents a major renewable resource for food, feed, and industrial raw materials [Durum wheat  are very useful but for the characterization of large collections a huge amount of money and human resources are needed and must deal with little polymorphism, low heritability, and genotype by environment interaction . In addiThe advances in molecular genotyping provide tools easier to handle the study variation in germplasm collections . DNA molTriticum turgidum L. subspecies [To date most studies on Italian durum germplasm have analyzed collections including old and new elite varieties for morphophysiological and qualitative traits ,16, and bspecies .In a recent study  a panel Mediterraneum typicum and Syriacum typicum groups [In Italy durum wheat has a long-time tradition of growing and breeding and accessions collected in Southern Italy, now preserved ex situ, are a valuable genetic resource. A large number of Italian and some north African landraces were grown in Italy since the end 1800 and after 1920 . Almost m groups ,23 whereNew varieties were then obtained from crosses between lines from Mediterraneum (Senatore Cappelli) and Syriacum  groups (1950–1965), from the introgression from tetraploid and hexaploid species and mutagenesis (1965–1973), the introduction of Norin10 dwarfing genes (1970–1980) and of new germplasm mostly from CIMMYT  and from recombination of the whole available tetraploid gene pool (1980–1990), followed ,26,27,28Luckily, as the result of extensive germplasm collecting expedition programs that did start in 1948, durum wheat germplasm from inland South Italy was collected; also in the most remote areas and preserved ex situ collections . Today tIn this study a strategy combining molecular genotyping, phenotyping, and the use of pedigreed varieties is proposed to characterize a durum wheat ex situ germplasm collection: (i) a small number of SSR molecular markers were used to detect pattern of diversity for 136 accessions collected in South Italy over time; (ii) to identify the genepool of origin of these ex situ accessions the same molecular markers were used to establish similarities with 28 Italian varieties with known pedigree; and (iii) phenotyping was than conducted for 12 morphophysiological characters of agronomic interest and the structure of diversity compared to identify the most diverse accessions for breeding purposes or for use in low input agriculture. The results show that a small number of molecular markers and little phenotyping coupled with powerful statistical analysis and comparison to pedigreed varieties can provide enough information on the genetic structure of durum wheat germplasm for a quick screening of the ex situ germplasm collection, in order to identify accessions for breeding or introduction in low input agriculture.The analyzed durum wheat germplasm included 136 accessions collected in Southern Italy from 1947 to 2003, now preserved ex situ in genebanks, and of 28 elite varieties cultivated in the past decades in Italy and representative of the Italian breeding programs. Seed samples for all accessions were obtained by the Institute of Plant Genetics and Crop Plant Research (IPK), Germany; Centro per la Salvaguardia delle Risorse Genetiche Vegetali “Pierino Iannelli”, Università degli Studi della Basilicata (Unibas), Italy; and the Istituto di Bioscienze e Biorisorse del Consiglio Nazionale delle Ricerche (IBBR-CNR), Italy. Passport information  was available for all the accessions. In only a few cases the accession was also identified with a “variety” name. Luckily the accessions of eight very old “varieties” have been collected in 1950 from an institution well-known for durum wheat breeding . But in most cases, none of the information available was useful to distinguish “landraces” from varieties or to identify the gene pool of origin and length of time for evolution at the collection site. The seeds of 28 varieties were obtained from the Centro di Ricerca per la Cerealicoltura-Foggia (CREA-CER), Italy. Year of release, registered pedigree, and breeder for each elite variety were recorded. A complete list of the accessions analyzed is available in For the purpose of the study, accessions were subdivided into groups according to the year in which the collecting expeditions took place: Group1 (number of accessions = 37) comprised of accessions collected in the period 1947–1950; Group2 (number of accessions = 77) included accessions collected from 1973 to 1982; and Group3 (number of accessions = 22) consisted of accessions collected during the period 1983 to 2003. n = 9); (ii) “modern” including genotypes released after the 1980s, mostly selected from crosses between CYMMIT  breeding lines and materials belonging to the former set of varieties (n = 19). According to the 28 varieties’ release year, two temporal groups were identified: (i) “old and intermediate” group (1915–1975) including “old” genotypes selected from indigenous and exotic landraces, and “intermediate” genotypes selected from crosses or mutagenesis involving old materials (an) and degree of polymorphism [In order to estimate molecular diversity, the germplasm collection was characterized using 44 SSRs markers. All the nuclear SSRs markers were selected on the basis of their known genetic locations to give a uniform coverage for all seven linkage groups as well as their temperature of annealing . The DNA samples were diluted to a final concentration of 20 ng/μL with TE (Tris-EDTA) for the following SSRs analysis. Genomic DNA was extracted from leaf tissues at the tillering stage of greenhouse-grown plants using the CTAB (Cetyltrimethylammonium bromide) method  with min2, 1–4 pM of each primer, and 0.5 U of Taq DNA polymerase. PCR was carried out as follows: after 5 min at 94 °C, 35 cycles were performed with 30 s at 94 °C, 30 s at either 55 or 60 °C (depending on the locus), and 1 min at 72 °C, followed by a final extension step of 30 min at 72 °C. The PCR products were detected by capillary electrophoresis using an ABI PRISM 3130xl Genetic Analyzer  and analyzed using the GENEMAPPER V3.7 genotyping software (Applied Biosystems).Multiplex PCR  and multiple sample loading (loading more than one PCR reaction in each well) were used. The reverse primer for each locus was labeled with one of the three fluorophores  and PCR amplification was performed with a final volume of 20 µL containing 30 ng of genomic DNA, 0.2 mM of each dNTP, 2 nM MgClThree plants for each of the 136 accessions from ex situ genebanks and 28 varieties were grown in the greenhouse at the Università degli Studi della Basilicata  during the growing season of 2012 to 2013. Heading date (expressed as days after 10 April) was recorded when the culms showed emerging spikes and plant height (excluding spike) was measured at harvest time. Individual plants were harvested manually in July 2013 and ten quantitative morphological traits were scored according to descriptors for wheat defined by IBPGR  : spike le [Summary statistics for each SSRs locus, including the number of alleles detected and the gene diversity or unbiased expected heterozygosity (He ) were cae .a), gene diversity, or unbiased expected heterozygosity  was visually examined, and the number of clusters was determined as the value of K above which BIC values decreased or increased only subtly. We then applied the dapc function to describe the relationship between the inferred groups. This function constructs synthetic variables and discriminant functions (DFs) that maximize variation between while minimizing variation within groups and computes coordinates along these functions for each individual. In order to obtain reliable group membership probabilities and to avoid overfitting, we retained only the four first principal components (PCs) from the preliminary data transformation step .The level of variation in different germplasm types (landraces and elite varieties) and within temporal groups of accessions was evaluated in terms of number of alleles per locus (Nsity (He ) and ally (He [s ). All thy  and the temporal group to which each genotype belonged.The model-based approach implemented in the software package STRUCTURE  was thenST metrics [ST vary from zero to one; when FST = 0 the subpopulations are identical, while when FST = 1 they are completely differentiated in relation to the fixation of different alleles in the subpopulations.Pairwise F metrics  were als metrics  to estimDifferences of trait means were checked by a nested analysis of variance (ANOVA) based on the following sources of variation, between clusters, between germplasm types (landraces and elite varieties) within clusters, and between accessions among germplasm types within clusters. Principal component analysis (PCA) was performed to determine the overall morphological traits distinctiveness, and to investigate the relationships between the traits. Principal component analysis was performed using the software JMP version 8.0 . a per SSR was 5.50, ranging from two alleles  to 14 alleles (Xgpw2302)  to a high 0.832 (Xgwm6)  . Gene di (Xgwm6) . a per locus was observed in the accessions (Na = 5.39) compared to the varieties (Na = 3.86) . When the whole germplasm collection was subdivided into two groups on the basis of the improvement status of each accession (accessions vs. varieties), a higher mean N = 3.86) . FPTest s Na = 5. comparede = 0.579, Rs = 3.715) to Group2 , followed by a decrease in variation in Group3 . Nevertheless, when considering the varieties, a loss of genetic diversity was observed from the beginning of the 20th century to nowadays germplasm. Indeed, the highest He and Rs were observed in “old and intermediate” genotypes  as compared to “modern” varieties .In order to evaluate how genetic diversity changed over time different groups of accessions were identified and compared. When considering the accessions, a slight, but not significant, increase of diversity was observed from Group1  did cluster in C2 with 10 germplasm accessions  collected in 1950 in Calabria that, according to the passport data information, were also named, “Senatore Cappelli” and the accession named “Bidi” that, according to De Cillis (1927), was from a variety from Tunisia, was not different from the one that originated Cappelli. Russello, a variety selected from a Sicilian local population, was the only variety included in C4 where six out of the ten accessions were collected in Sicily in 1950, including accession TRI3857 also named “Russello”, TRI3851 named “Gioia”, and TRI3855 named “Regina”, which are old Sicilian varieties. It should be noted that, according to molecular data, accessions and varieties with the same “variety” name that cluster in the same group do not have the same genotypes thus cannot be considered “duplicates”. Two old varieties, namely Aziziah, selected from the exotic landraces “Near East”, and Timilia, selected from Sicilian landraces, did cluster in C5 with accession TRI14111, named “Triminia”, collected in Campania in 1982. Capeiti 8, Appulo, Simeto, and Ciccio, that could be traced back to a common origin on the bases of pedigree information (Cappelli × Eiti 6), were assigned to C1 which also included accession TRI16687, named “Trinakria”, a variety that was obtained using Capeiti 8 as parent. Except for the variety Isa, that did cluster to C6 with accessions TRI3518 named “Cotrone”, TRI3519 named “Inglesa”, TRI3520 named “Scorsonera”, and TRI3853 named “Bianmaia”, which are also old varieties, all the remaining 19 varieties were assigned to C2. These genotypes were the most recently released varieties and were related, according to their pedigree information, to Creso, and to the innovative semidwarf materials developed at CYMMIT. Accession TRI13079 named “Creso” and TRI16565 named “Capeiti” were also included in C2.To further describe the population structure of the whole germplasm collection, a Bayesian model-based clustering method implemented in the STRUCTURE program was performed. The number of subpopulations (K) was identified based on maximum likelihood and Delta K values . The DelThe STRUCTURE bar graphic  also proIndividual assignments provided by STRUCTURE differed only slightly from those by DAPC: one of the STRUCTURE clusters (K3) was split into three DAPC groups, while the other three clusters where consistent with the DAPC assignments .ST values. All pairwise FST values were significant and ranged from 0.122 (C5–C6) and 0.502 (C3–C4), as shown in Genetic differentiation among the six DAPC clusters was investigated by computing pairwise FTo get some insight into the underlying causes of the differentiation among clusters, we inspected the associated allele loadings. DAPC is perfectly adapted to this task, as it finds linear combination of alleles (the discriminant functions) which best separate the clusters. Alleles with the largest contribution to this discrimination are the most different among groups. Therefore, the plot of allele contribution  could beXgwm408, located on the long arm of chromosome 5B, mostly contributed to the first principal component with allele 180 and allele 200. The first allele discriminated C4 and C5, which was not represented, from the other clusters, whereas the second allele was not recorded in accessions attributed to C1 and C3. Allele 170 of locus Xgwm282, located on the long arm of chromosome 7A, was detected in all DAPC clusters except for C1 and C3. The locus Xgwm234, on the short arm of chromosome 5B, contributed to the first principal component with allele 224 detected only in accessions belonging to C1, C2, C3, and C6. The same discrimination pattern was observed for allele 175 of the locus Xgpw2302, located on the short arm of chromosome 4A.The locus Xgpw2239, contributed to the first principal component with two alleles, namely allele 196 and allele 198. The first allele was recorded in all clusters except for C4, while allele 198 was detected only in C3, C4, C5, and C6. The allele 219 identified at locus Xgwm285 on the short arm of chromosome 3B was useful to discriminate C1, C2, and C3 from the remaining clusters identified by DAPC analysis, in which this allele was not recorded. Finally, allele 96 at locus Xgwm413 (short arm of chromosome 1B), allele 101 identified at locus Xgpw7148 (long arm of chromosome 3B), and allele 224 for locus Xgpw4004 (long arm of chromosome 5A), showed similar behavior in discriminating DAPC clusters. Indeed, these alleles were all detected in C1, C2, C3, and C6.An additional locus mapped on the short arm of chromosome 4A, Xgwm193, located on the short arm of chromosome 6B, two alleles showed a high contribution; allele 170 was recorded in all DAPC clusters except for C5, whereas allele 172 was identified in accessions assigned to all clusters except for C4. A similar level of discrimination was observed for an additional locus mapped on the short arm of chromosome 6B, namely Xgwm518. Allele 120 was observed in all clusters except for C4; allele 170 was useful to discriminate C5, whose accessions were not characterized by this allele, from the remaining clusters. The locus Xgpw2103, located on the short arm of chromosome 7A, contributed with allele 232 and allele 234. The first allele was not recorded in C3, whereas the second allele was not detected in C1, C4, and C5. Finally, allele 102 identified at locus Xgwm413 (short arm of chromosome 1B), characterized genotypes attributed to C1, C2, C5, and C6.When considering the second principal component, four loci showed the largest contribution to the discrimination among clusters. Regarding the locus To identify the relationship between molecular SSR diversity and phenotypic characters of agronomic interest, a greenhouse phenotyping was conducted. Twelve morphophysiological characters were scored on three plants for each accession and variety.n = 21.73) than in varieties . Similarly, the number of fertile spikelets per spike was higher in accessions (n = 17.50) when compared to the varieties (n = 15.69). By contrast, kernel number per spikelet was lower in accessions (n = 1.68) than in varieties (n = 2.21). Additionally, higher values were recorded for both kernel weight/spike and 1000 kernels weight in accessions  in comparison to the varieties .The differences among the means of 12 characters were tested by a nested analysis of variance (ANOVA): between clusters, between germplasm type (accessions and varieties), and between accessions among germplasm type . Differep < 0.001) between clusters for all the traits except for kernel width and kernels number/spike. Additionally, means were highly significant (p < 0.001) for all the traits between germplasm types within clusters and between accessions among germplasm types within cluster  was run. Means were significantly different  analysis on all traits revealed four principal components (PC1–PC4) explaining 90% of total variance . The firPrincipal components analysis (PCA) analysis based on the measured morpho-agronomic traits was also able to differentiate contrasting wheat groups as identified using the DAPC analysis. Genotypes that did cluster in C1 and C2 with the DAPC analysis were also included in the same group in PCA analysis and, according to the first PCA loadings, were early heading and short plant height, while genotypes in C3, C4, C5, and C6 showed late heading, tall plant height, and low kernel number/fertile spikelet and kernels number/spikelet .The recent history of durum wheat has been characterized by the replacement of landraces at first with selected pure line varieties and after with modern cultivars obtained by crosses between lines that did also include lines from mutagenesis, exotic germplasm, and different wheat species. Luckily ex situ collections preserve precious genetic resources collected over time that include germplasm from different genepools. Since in most cases the passport data do not include genetic information, their effective use in agriculture and breeding programs is very low.The identification of useful genotypes is largely improved if the genetic structure of the germplasm collection is known. In the present study, we combined molecular genotyping and plant phenotyping with the comparison to well-known pedigreed varieties to describe the genetic structure of durum wheat germplasm collected in Southern Italy and preserved ex situ.n = 16) or in C1 (n = 2), while most accessions cluster in four different groups  that include “landraces” or varieties from pure line selection largely representative of the Italian–Mediterranean genepool. In ex situ accessions a small increase of diversity was observed from Group 1 (n = 37 accessions collected from 1947 to 1950) to Group 2 (n = 77 accessions collected from 1973 to 1982). Group 2 accessions, which were included in all clusters, were “old” and “new” germplasm grown at the same time in the same area at the time when intense varietal substitution was in progress. As expected, the amount of collected diversity decreased in the following time (n = 22 accessions collected from 1983 to 2003) when most obsolete varieties had been already replaced. Clustering also showed that the choice of collecting sites was very careful to avoid the most recently introduced varieties. Out of 136 accessions, only 10 did cluster in C2 that included most “modern” varieties, and 23 in C1 which includes varieties derived from hybridization.Overall, gene diversity for SSR markers was 0.607, and within the range of previous studies in durum wheat ,10,11,47It has been argued that the selection pressure applied in breeding programs may have reduced the level of genetic diversity in durum wheat germplasm ; in our Our findings on the elite germplasm agree with previous reports by Medini et al. , Reif etThe now available high-density consensus maps of durum wheat coupled with sequencing information provide an advanced tool ,53 for dDiscriminant Analysis of Principal Components and STRUCTURE analysis were very informative and complementary about the structure of the durum wheat ex situ germplasm collection and were useful to identify the most diverse accessions for utilization in low input agriculture and in breeding programs. For instance, accessions were included in all clusters, whereas modern varieties from hybridization, including in their pedigree the innovative semidwarf CIMMYT materials, were assigned to only two clusters (C1 and C2). These findings are consistent with the repeated use of a few founder genotypes that played a relevant role in the creation of the genetic basis of modern genetic pools, in turn becoming progenitors of new elite varieties and completely replacing traditional varieties or landraces ,54. The New variability is needed to face the challenges of modern low input agriculture. Accessions from the ex situ collection that are genetically distant, as those included in different clusters, could be useful for this purpose. Pedigreed varieties were useful to identify the gene pool of origin of the accessions that did cluster in different groups. It should be noted that the varietal purity was higher for those released more recently, while genotypes associated with older releases might be different from accessions with the same name. In some cases, Saragolla, a brand new variety that has been released in 2004 with an old name, or variety and accessions with the same name as “Russello”, might be selected as pure lines from the same “landrace” (Russello 329-S.97-S.G.7) , or the In a recent study , a panelVrn-B1, affecting the vernalization response, was found to be tightly linked to the marker Xgwm408 [Vrn system are the most important to determine the rate of generative development in wheat and, thereby, its flowering time [Xgwm282 [Xgwm234 was found to be significantly associated with dough strength [Xgwm285 was located at only 2.1 cM from the tsn2 gene, conditioning resistance to race 3 of the fungus Pyrenophora tritici-repentis (Died.), causal agent of tan spot in durum wheat and inducing necrosis [Discriminant analysis of principal components can be also used to identify the alleles with the largest contributions to the discriminant functions, as an approach to detect putative patterns among the genes determining the group differentiation . A plot  Xgwm408 ,56. Noteing time ,56,57. A[Xgwm282 , whereasstrength ,60 and tnecrosis . Since tXgwm193 encompasses the peak of the QGpc.ndsu.6Bb QTL for grain protein content. Thus, it could be useful to use this SSRs marker to select for the high-GPC allele in Marker-Assisted Selection programs [Xgwm518 was found to be linked to the QLr.caas-6BS.2 QTL for resistance to leaf rust caused by Puccinia triticina Westend [Xgwm413 to be associated to a QTL for grain weight on the short arm of chromosome 1B [Similarly, when considering the alleles’ contribution to the second principal component, we noticed that the marker programs ,63. Fina Westend  and the osome 1B . These tGenetic groups identified by DAPC were also differentiated on the basis of morphological traits, as confirmed by PCA. Moreover, accessions and varieties assigned to the same cluster, although genetically related on the base of molecular markers, exhibited different morphological traits. These results could be helpful to select, especially from C3 and C4, early-heading landraces well adapted to the Southern Italian semi-arid conditions, where earliness allows farmers to better tackle the drought season . Within Both the morphological traits and SSR markers used in the present study were equally appropriate to provide a first overview of the genetic diversity levels and of the population structure within the whole durum wheat germplasm collection from Southern Italy.Evolution through natural hybridization is suggested for old varieties recently reintroduced in agriculture and subjected to careful conservative selection by pure line.Despite similar level of diversity for molecular markers in ex situ accessions and varieties DAPC did show that they cluster in different groups, thus representing different genepools capable to provide different sources of genes.The analysis of diversity based only on molecular markers must be supported by passport data and or some phenotyping or knowledge in crops. In crops where plant breeding played a relevant role, social factors that promote seed exchange might be as significant as ecological factors in determining the distribution of genetic diversity and variation in such “ex situ” collections might be heavily structured.The germplasm evaluation highlighted the existence of a broad genetic base in ex situ accessions and a narrowing of diversity in varieties due to breeding activities. The investigation of population structure suggested the genetic potential of landraces for the detection of unexplored sources of variation and allowed to identify groups of accessions differentiated both at molecular level and for morphological, phenological, and qualitative traits not directly evaluated and potentially useful in sustainable production systems.The generated knowledge about the levels of diversity and population structure could be an important contribution for parent selection in durum wheat breeding programs and for germplasm management and conservation.


Salvia officinalis and S. fruticosa at morphological, genetic and epigenetic levels. SSR results revealed that hybrid individuals were characterized by diploid set of chromosomes suggesting homoploid hybridization. A well-defined group that mostly comprised of F1 generation individuals was detected. For the majority of analysed morphological characteristics, hybrids were placed in-between parental taxa, while at the same time, values of different genetic parameters were mostly higher in hybrids than in parental species. The results revealed a high contrast in the levels of phenotypic variability and epigenetic excitation between parental taxa. Environmental niche modelling confirmed that in the studied location S. officinalis experiences optimal climatological conditions, while S. fruticosa struggles with unsuitable conditions. Very low levels of gene flow between the parental species were detected. In addition, contrasting levels of epigenetic excitation in the studied groups clearly demonstrated the importance of an epigenetic response to an altered environment and confirmed the trans-generational nature of the epigenetic changes.The inheritance of phenotypic, genetic and epigenetic traits in hybridization events is difficult to predict, as numerous evolutionary, ecological, and genetic factors can play a crucial role in the process of hybridization. In the middle Adriatic island of Vis, we investigated hybridization between  Hybridization is one of the fundamental forces of evolution, and this process can result in a diverse range of outcomes, including hybrid divergence and sympatric speciation, introgression, hybrid zone formation, erosion of species boundaries and possible species extinction through assimilation3. New lineages of hybrid origin are characterized either by allopolyploidy, meaning that the complete genomes of the parental species have joined, or homoploidy. As defined by Nieto Feliner et al.4, homoploid hybrid speciation “is the formation of a new-hybrid-species, independent from its parents, via hybridization with no whole-genome duplication and thus no increase in ploidy”. Although this definition seems to be straightforward, key aspects of hybridization process are under debate5. Schumer et al.5 proposed following criteria that must be met to confirm inter-species homoploid hybridization: reproductive isolation of hybrids must be recognized, there must be evidences that this isolation was triggered by hybridization itself and hybridization must be confirmed at a genomic level. However, in a recently published paper, Nieto Feliner et al.4 argues these criteria, especially the importance of reproductive isolation as a crucial criterion as an evidence of occurrence of a homoploid hybrid speciation.Introduction of plant species into new areas occurs naturally, but the frequency of this phenomenon has increased dramatically because of human activity during the last few millennia. Consequently, hybridization between congeneric species occurs more often, causing significant genetic changes in both the native and alien species7. Such expression of different phenotypes in changing environments may be caused by alteration of gene expression without changes in the primary sequence of the DNA, i.e., epigenetics9. Molecular mechanisms that are considered responsible for modifications in gene expression include histone and chromatin modifications and methylation, all of which are directed by small RNAs10. Thanks to transgenerational inheritance of epigenetic variants, phenotypic responses to an altered environment remain memorized and at disposal for generations to come11. Interspecies hybridization triggers not only genetic but also epigenetic changes through modification of DNA methylation patterns13. Consequently, new phenotypic variants found in hybrid offspring more often originate from epigenetic than genetic recombination, and thanks to their transgenerational heritability, they can play a very important role in the process of speciation15.Once an organism finds itself in an altered environment, its genotype can produce numerous phenotypes, of which some will presumably be better adapted to the new ecological conditions. This phenomenon, known as phenotypic plasticity, has an integral role in an organism’s attempt to cope with environmental variation and enables plants to respond to such variations within their lifetimeSalvia is the largest genus of the Lamiaceae family16. Thirty-six taxa are present in Europe, and these taxa are divided into seven sections17. The Salvia section includes 13 species, with the common or Dalmatian sage  as its type species. Another easily recognizable member of this section is the Greek sage (S. fruticosa Mill.). Both species are well known due to their high essential oil content and commercial value20. The natural distribution range of common sage includes the eastern Adriatic coast from the Gulf of Trieste in the north to northwestern Greece in the south22, as well as parts of the central and southern Apennines25. Greek sage extends from Cyrenaica, Sicily, and southern Italy through the southern part of the Balkan Peninsula to West Syria26. Because both species have been highly prized for their numerous beneficial healing properties, Phoenicians, Greeks and Romans have introduced these species into many Mediterranean countries throughout history26. Ancient Mediterranean cultures were prone to transfer and grow useful plants near their settlements and colonies scattered across the Mediterranean basin. Consequently, sub-spontaneous populations and dubiously native populations of certain species can today be found in different locations where the ancient colonies once settled29.With approximately 1000 recorded species, the genus 30, where the plant grows sympatrically with common sage. Because of the well-organized Greek colony Issa that was founded on the island of Vis in the 4th century BC33, it can be assumed that this disjunct population of Greek sage located approximately 500 km from the nearest population of its type is of sub-spontaneous origin rather than being a member of an indigenous flora.In the central Adriatic region, a small and isolated population of Greek sage can be found on the island of Vis (Croatia)Salvia has been previously recorded ; thus, it appears that erosion of species boundaries between closely related species of the section is present. Hybridization between common sage and Greek sage has never been documented to occur spontaneously in nature but was artificially performed for breeding purposes in Israel36. An obtained commercial sage hybrid  was well suited for intensive agricultural conditions in Israel, but in terms of essential oil quality, it was more similar to common sage than Greek sage19.Hybridization within the section 26, hybridization between these species in the region seems unlikely because of the different flowering periods37. However, in the western coastal area of the Island of Vis, common and Greek sage have overlapping flowering periods from late April to early May, and hybridization between the two species is frequent, as a full spectrum of intermediate individuals sharing morphological traits of both parental taxa can be observed. Greek sage and hybrids grow in dense and admixed stands, whereas common sage is present sporadically. However, in the surrounding area, common sage grows abundantly as a typical representative of the indigenous flora. In addition, it is important to note that both species share the same number of chromosomes (2n = 14)26 and rely on the same pollinator assemblage39.Although the distributions of both species overlap in parts of southern Albania and northern Greece41 to population and conservation genetics42. However, unlike universal molecular markers (e.g. AFLP43) that require no prior knowledge regarding target species genome, microsatellites are species-specific and require de novo isolation for any species being studied for the first time44. Also, in situations of weak population differentiation, an analysis based on limited number of microsatellite loci lacks the statistical power for individual population assignment45. Since this limitation can be overcome only by significant increase of the number of used loci46 which may be technically challenging and time consuming45, the utilization of AFLP technique (Amplified fragment length polymorphism) in such situations represents a good alternative45. However, AFLPs are also characterized by some major drawbacks when compared to microsatellites. The most important ones are homoplasy47 and their dominant nature45. While the homoplasy (i.e. either non-homologous fragments share the same or very similar size or independent fragment loss occur) decreases the resolution and reliability of the analysis47, AFLPs dominancy makes it impossible to assess the levels of departure from Hardy-Weinberg equilibrium45. In summary, microsatellites and AFLPs both have certain advantages against each other that should be weighted when choosing the most appropriate genotyping technique for a specific study. The MSAP technique48 (Methylation sensitive amplification polymorphism) as the most widely used tool in epigenetic studies of natural populations49, is a modification of the AFLP method. In the majority of MSAP based studies, the rare cutter EcoRI restriction enzyme was used as in the AFLP analysis, while the frequent cutter MseI was replaced by HpaII and MspI methylation-sensitive restriction enzymes50. Since these two methods are technically very similar and their results can be easily compared to each other, they are methods of choice in studies of both genetic and epigenetic diversity of the same target species.Because of their high variability, codominant inheritance and high reproducibility and reliability, for the last two decades microsatellites have been one of the most popular molecular markers used in many different areas, from genome mappingThe main goal of this study was to perform a comprehensive analysis of hybridization between common sage and Greek sage at morphological, genetic and epigenetic levels. For this purpose, in sampled individuals, we analysed 39 morphometric traits and performed DNA analysis using microsatellite and AFLP nuclear genetic markers as well as MSAP epigenetic markers. By exploring the different aspects of the hybridization event, we aimed to (1) obtain insight into the extent of hybridization between common sage and Greek sage on a phenotypic level; (2) determine the prevalence of hybridization and assess the nature and levels of inter-species gene flow (if present); (3) assess the levels of methylation variability in hybrids and parental groups; and (4) discuss the complexity of the interactions among the environment, phenotype, genotype and epigenotype through the prism of core vs. marginal population.The mean Shannon’s diversity index based on 23 qualitative traits was significantly different among taxa (P < 0.05), with means  of 0.155, 0.417 and 0.684 for common sage, hybrids and Greek sage, respectively . All pairwise differences in mean intraspecies Gower’s distances were significant at P < 0.05.A neighbour-net diagram based on Gower’s distance matrices of combined qualitative and quantitative traits unambiguously separated the parental taxa from each other and from the hybrids Fig. . The hybAll seven microsatellite loci were successfully amplified in all samples. For each individual, analysis revealed either one (i.e. homozygous state) or two (i.e. heterozygous state) allele per locus. Overall, 64 alleles were detected, out of which 48 were present in common sage, 24 in Greek sage and 52 in hybrids, with a considerable number of alleles shared between taxa  and expected heterozygosity (HE) were significantly higher in common sage  and in the hybrids  than those in Greek sage . Significant and positive FIS values were detected in both parental species , whereas hybrids exhibited a negative but not significant FIS value of −0.147 , while the value was lowest between the hybrids and Greek sage (0.115).As expected, the highest pairwise a priori identified as hybrids, wide range of admixture proportions were observed. The average estimates of the likelihood of the data, conditional on a given number of clusters, ln[Pr(X|K)], increased up to K = 3, with a standard deviation substantially higher at K = 3 compared to that at K = 2. The highest ΔK was obtained at K = 2 (177.54), and the second highest ΔK was obtained at K = 3 (4.41).The results from the Bayesian analysis implemented in STRUCTURE strongly supported the presence of two clusters representing the parental species Fig. . In indi1 hybrids with posterior probabilities (PP) ranging from 0.608 to 0.990. A single hybrid individual was classified as an F2 (PP = 0.715), and three individuals were classified as back-crosses to Greek sage (BC2) . All common sage and Greek sage samples were classified as pure parental  with posterior probabilities ranging from 0.994 to 0.999 and 0.906 to 0.998, respectively. Assignment of individuals into classes  carried out using NEWHYBRIDS was also indicated in the neighbour-net diagrams . According to Shannon’s information index, a significantly (P < 0.05) higher level of diversity was detected in the group of hybrids as compared to that of the parental groups, while no significant difference in levels of genetic diversity was observed between the parental groups .Average Dice’s genetic distance at the taxon level was significantly higher (P < 0.05) in both common sage and the hybrids than that in the Greek sage , while at the inter-taxa level the greatest genetic distance was expectedly observed between the parental taxa (0.836), while the least was between the Greek sage and the hybrids (0.421)  in the AFLP and MSAP analyses. The results from STRUCTURE and NEWHYBRIDS based on the AFLP dataset strongly supported the results obtained by microsatellite analysis ], increased up to K = 3, with the standard deviation substantially higher at K = 3 compared to K = 2. The highest ΔK was obtained at K = 2 (3204.10), and the second highest ΔK was obtained at K = 3 (609.19).The average estimates of the likelihood of the data, conditional on a given number of clusters, 1 hybrids in the SSRs result were now recognized as a back-cross to common sage (BC1) and a back-cross to Greek sage (BC2). The individual classified as an F2 hybrid in the SSRs result was now recognized as a back-cross to common sage (BC1).In the STRUCTURE results, two parental clusters were well recognized, while in the group of hybrids, a wide range of admixture proportions can be observed. The results obtained by NEWHYBRIDS were for the most part congruent with the NEWHYBRIDS results for the microsatellites, with the exception of three individuals within the group of hybrids. Two individuals recognized as FAnalysis of MSAP data revealed that the highest proportion of polymorphic loci could be observed in the group of hybrids, followed by Greek sage and common sage , as was also the case with the AFLP results  and the least distance between Greek sage and the hybrids (0.565)  of the total genetic variability was attributed to variability detected within groups, rather than among them (Supplementary Table ln[Pr(X|K)], increased up to K = 3, with a standard deviation substantially higher at K = 3 compared to K = 2. The highest ΔK was obtained at K = 2 (1778.52), and the second highest ΔK was obtained at K = 3 (664.64).The average estimates of the likelihood of the data, conditional on a given number of clusters, Results obtained by STRUCTURE for the MSAP dataset were consistent with STRUCTURE results for microsatellite and AFLP analysis as the parental species were well recognized. All individuals belonging to different generations of hybrids as defined by NEWHYBRIDS analysis based on microsatellites were again characterized by a range of admixture proportions Fig. .S. officinalis  and 13 for S. fruticosa  were excluded from further analysis. For both species, models with the smallest AICc values were selected. All bioclimatic variables and relative contributions to the Maxent model of the selected variables are listed in Supplementary Table S. officinalis) or 250 km (for S. fruticosa). However, since both species are characterized by relatively small distribution areas that extends in narrow coastal areas, such result was inevitable, because reduction of number of occurrences to avoid ENM residual autocorrelation would lead to insufficiently large data set for this analysis. Besides, this result have not any influence on comparison of two ENMs which illustrates strong spatial segregation of S. officinalis and S. fruticosa environmental niches and positioning of hybrid population examined in this paper 1) far away from the environmental niche of S. fruticosa and 2) within the environmental niche of S. officinalis.Based on the multicollinearity test, 12 bioclimatic variables for 53. The long persistence of the species in this region led to its adaptation to local ecological conditions and resulted in it reaching a phenotypic optimum. On the other hand, the population of Greek sage is far away from the Eastern Mediterranean where it grows abundantly26. According to MaxEnt analysis  and calyx lobe length (CLMI) were significantly longer in the hybrids than in either of the parental taxa  ca. 2500 years ago when Ancient Greeks started their colony on the island33 and consider that even today the population is comprised of a limited number of individuals, the detected levels of genetic variability are not surprising.Analysis of seven microsatellite loci revealed that the common sage group was characterized by significantly higher levels of genetic diversity than those of the Greek sage group (Supplementary Table ). Common sage is characterized by greater genetic variability and participates in a hybridization event with more alleles than that of Greek sage, resulting in a high similarity of allelic richness levels between the hybrids and common sage. As already mentioned, only three out of the 26 hybrids were identified as backcrosses to Greek sage. Although such a result implicates that the gene flow is oriented towards the Greek sage as a sink population, only three detected BC2 individuals make such a conclusion speculative. However, such orientation of the gene flow would be in accordance with a neutral model which suggests that in a hybridization between the native and non-native species, the gene flow is expected to be biased towards the non-native species63. In addition, the model proposes that the asymmetry of the inter-species gene flow arises because of “a demographic imbalance between the two species at the wave front, where the invading species is at lower densities63”, thus supporting our assumptions regarding the non-native origin of the Greek sage in the studied location. Similar patterns of introgression were also detected by some other researches65. Based on obtained results it is possible to discuss the nature of homoploid hybridization in accordance to Schumer et al.5. The case study here presented obviously meet only one criterion without any doubts, as the genome level hybridization was confirmed. The criterion implying the necessity of reproduction isolation of hybrids from parental species was not met, although such a small number of back-crosses leaves some doubts. Finally, even if the back-crosses were not detected, based on implemented research strategies it would be impossible to address the issue of the origin of the reproduction isolation.In the group of hybrids, the highest levels of genetic diversity and allelic richness were observed . Based on both the AFLP and MSAP results, three groups of individuals clearly differentiated from each other. The results from STRUCTURE for both AFLP and MSAP and from NEWHYBRIDS for AFLP Fig.  are full66. When comparing differences between genetic and epigenetic distances  higher values for the group of hybrids than those of the parental species, and such results are not surprising, as similar results were observed in other researchces Fig.  within ices Fig. . Such re71. The obtained results are well supported and visualized by the Neighbour-Net diagram  in late April. The morphological characteristic used in the field for identification of parental species and hybrids was the calyx shape . To meet the second criterion, three groups  were sampled in unequal proportions and in limited numbers. Since studied species do not naturally reproduce clonally and are characterized by distinctive bush-like habit, there was no risk of sampling genetically identical individuals. All of the samples were collected from neglected and abandoned vineyards and olive groves surrounding a local settlement, on a site of S-SW exposition, at the altitude ranging from 30 to 110 m. The majority of observed individuals was grouped in a large stand and a typical distance between sampled individuals ranged from 5 to 10 meters. The sampling was performed in a way that the entire stand was equally sampled.In defining the sampling area, the main criteria were that all samples must be (1) within a pollinator’s range and (2) strictly from the area of sympatry. Because the expected foraging distances for honey bees is greater than 1 kmSalvia36, 23 qualitative and 16 quantitative traits were selected and measured from digitized photographs using a Dino-Lite pro Digital Microscope and DinoCapture 2.0 software .Based on a previous taxonomic revision of the section Total genomic DNA was extracted from silica-gel-dried leaf tissue using a GenElute Plant Genomic DNA Miniprep Kit . The DNA concentrations were measured using a P330 Nanophotometer .83 were amplified. Details on the polymerase chain reaction (PCR) conditions and cycle profiles may be found in Radosavljević et al.62. Polymerase chain reaction products were analysed by capillary electrophoresis on a ABI 3730xL DNA analyser  provided by Macrogen DNA service , and alleles were scored using GeneMapper 4.0 (Applied Biosystems).For fine-scale analysis of the population genetic structure of the sampled groups, seven microsatellite loci  that were previously characterized in common sage and successfully cross-amplified in Greek sageet al.84 with some modifications. For selective amplification, four primer combinations were used as follows: VIC-EcoRI-ACG + Tru1I-CGA, NED-EcoRI-AGA + Tru1I-CAC, FAM-EcoRI-ACA + Tru1I-CAC and PET-EcoRI-ACC + Tru1I-CGA. Amplified PCR products were analysed using an ABI 3730xL analyser (Applied Biosystems), and the GeneMapper 4.0 software (Applied Biosystems) was used for final allele scoring. Reactions of genomic DNA restriction, adapter ligation, and preselective and selective amplification were performed using a GeneAmp PCR System 9700 . The selection of markers for further statistical analysis and the estimation of the error rate per primer combination were performed as suggested by Herrmann et al.85.For a genome-wide assessment of the genetic diversity, the AFLP technique was used based on the protocol of Carović-Stanko et al.86. Analysis and scoring of the amplified PCR products were the same as in the AFLP analysis.To assess the level of epigenetic variability of the studied groups, MSAP analysis was applied in accordance with Nuskern 87 (1972) as well as separately for each species. Repeated measure analysis of variance was performed using PROC GLM in SAS88 with species membership as categorical independent variable and Shannon’s information index of each qualitative trait as response variable. Post hoc Bonferroni’s adjustments were used to compare the mean values of Shannon’s information index between species at a significance level of P < 0.05. Univariate analysis of variance was performed using PROC GLM in SAS88 to test for differences in mean values between species  for 19 quantitative morphological traits (response variables). Post hoc comparisons of the means were performed using Tukey’s Studentized Range test at P < 0.05.Shannon’s information index as a measure of qualitative trait diversity was calculated for each trait according to Lewontin89 was calculated between all pairs of individuals with the algorithm implemented in PAST ver. 2.0190. Pairwise Gower’s distance matrices among individuals were calculated separately based on qualitative and quantitative data and compared by calculating the correlation coefficients and performing Mantel’s test91. The randomization procedure as implemented in NTSYS-pc92 included 1,000 permutations. To test the significance of the differences in mean intraspecies Gower’s distances among the species, Kruskal-Wallis  and Wilcoxon rank-sum  non-parametric tests were performed using SAS Release 9.288. A neighbour-net diagram, which is well suited to depict reticulate relationships93, was constructed from Gower’s distances using SplitsTree 494.To combine the information from the qualitative and quantitative traits into a single distance measure, Gower’s distance95 was used to estimate population genetic parameters  and to test genotypic frequencies across all loci for conformance to Hardy-Weinberg (HW) expectations. As a measure of the number of alleles per locus is independent of sample size, allelic richness (Nar) was calculated using the FSTAT v.2.9.3.2 package97, and the number of private alleles (Npr) per species was assessed using MICROSAT98. The estimates of Nar, HO and HE in each species were compared by repeated measure analysis of variance followed by post hoc Bonferroni’s adjustments using PROC GLM in SAS88. The species membership was used as categorical independent variable and the estimates of Nar, HO and HE as response variables. The private allelic richness (Npar) for each species was calculated by averaging the number of private alleles across possible subsets of sampled chromosomes while adjusting for differences in sample size across species. The calculations were performed using the rarefaction procedure as implemented in ADZE 1.099.GENEPOP 4.0Dpsa)100 as implemented in MICROSAT98. A neighbour-net diagram was produced as described previously. Bootstrap support was obtained using 1,000 replicates generated by MICROSAT and subsequently used in the cluster analysis performed by the neighbor-joining method as implemented in the NEIGHBOR and CONSENSE programs in the PHYLIP ver. 3.6b software package101. The significance of the differences in mean intraspecies distances among species was assessed as previously described.Genetic distances between pairs of samples were calculated using the proportion-of-shared-alleles distances (102 (AMOVA) was used to partition the total genetic variance into within and among species in Arlequin ver. 2.000103. Pairwise comparisons examined with analysis of molecular variance resulted in values of ϕST that are equivalent to the proportion of the total variance that is partitioned between the two species.Analysis of molecular variance104. Given a value for the number of clusters, this method assigns individual genotypes from the entire sample to clusters so that linkage disequilibrium (LD) was maximally explained. Thirty runs for each cluster (K) ranging from one to 11 were performed on the Isabella computer cluster at the University of Zagreb (Croatia), University Computing Centre (SRCE). Each run consisted of a burn-in period of 200,000 steps, followed by 106 Monte Carlo Markov Chain (MCMC) replicates assuming an admixture model and correlated allele frequencies. The choice of the most likely number of clusters (K) was performed by comparing the average estimates of the likelihood of the data, ln[Pr(X|K)], for each value of K, as well as by calculating an ad hoc statistic ΔK based on the rate of change in the log probability of data between successive K values, as described by Evanno et al.105 and implemented in STRUCTURE HARVESTER v0.6.92106. Runs were clustered and averaged using CLUMPAK107.A model-based clustering method was applied to infer the genetic structure and define the number of clusters using STRUCTURE ver. 2.3.3108 was used to assign individuals to one of six classes: two pure  and four putative  hybrids. The program ran without any prior information regarding the putative hybrid status of collected individuals and with the uninformative Joffrey’s prior option for both mixing proportions and allele frequencies. The results were based on the average of five independent runs consisting of a burn-in phase of 100,000 steps and 500,000 MCMC sweeps. Following the suggestions of Anderson and Thompson108, individual genetic assignment to classes was based on a minimum posterior probability threshold (Tq) of 0.50.The Bayesian method implemented by NewHybrids 1.1AFLP-amplified fragments were scored for the presence (1) or absence (0) of homologous bands to create binary matrices. The AFLP diversity within species was estimated by determining the percentage of polymorphic bands (%P) and Shannon’s information index. The mean values of Shannon’s information index among species were tested as previously described.109 , and a neighbour-net diagram was constructed as previously described. Bootstrap support was obtained using 1,000 samples in the cluster analysis performed by the neighbour-joining method as implemented in TREECON for Windows version 1.3 b110. The significance of the differences in mean intraspecies distances among species was assessed as previously described. Analysis of molecular variance and the analyses in Structure and NewHybrids were performed using the same parameters as previously described.Pairwise genetic distances were calculated using Dice’s coefficientEcoRI/HpaII and EcoRI/MspI reactions, four distinct profiles were obtained as follows: (I) fragments present in both profiles (1/1), unmethylated state; (II) fragments present only in the EcoRI/MspI profiles (0/1), hemi- or full-methylation of internal cytosine (HMeCG or MeCG-sites); (III) fragments present only in EcoRI/HpaII profiles (1/0), hemi-methylation of external cytosine (HMeCCG-sites); and (IV) fragments absent in both profiles (0/0), uninformative state. A ‘Mixed-Scoring 2’ approach as implemented in R script MSAP_calc.r111 was used to generate a matrix of binary data for each of the three classes of markers corresponding to three subepiloci: u-subepilocus (coding of unmethylated sites), m-subepilocus (coding of HmeCG or MeCG-sites), and h-subepilocus (coding of HMeCCG-sites). Further analyses were performed in the same manner as in the case of the AFLP data.In the MSAP analyses, after the 112. The analysis was based on 90 occurrence records for common sage and 70 occurrence records for Greek sage. From the WorldClim dataset, 19 bioclimatic variables for the present time and at a resolution of 30 arc-seconds were downloaded113. To test the multicollinearity among the bioclimatic variables for each species separately, “usdm” R package114 was used to calculate variance inflation factors (VIF) and only the bioclimatic variables with VIF < 10 were used for further analysis. For a selection of an appropriate value of Maxent’s regularization multipliers for obtained sets of bioclimatic variables, ENMTools was used115. After the environmental suitability model was constructed, ENM residuals  were used to construct Moran’s I correlogram  for multiple distance classes) as implemented in SAM v4.0 software116. Obtained environmental suitability models were visualized in ArcGIS ver. 10.1. .To test the presumption that the central Adriatic region is well suited for survival of common sage, but not Greek sage, environmental-niche modelling for both species was independently performed using MAXENT ver. 3.3.3k.Supplementary Information


Neothraupis fasciata joined with species distribution modelling to evaluate whether: (1) the distribution of genetic variability shows a pattern expected by the isolation-by-distance model; (2) the influence of the Pleistocene climate changes on species distribution; and (3) climate/climatic stability (hypothesis of climatic stability) as a predictor of population genetic diversity. Based on two molecular datasets (ND2 and FIB-5), the isolation-by-distance hypothesis was not supported. The mitochondrial haplotype network indicated the existence of historically isolated populations at the southern range of the species distribution, and recent population expansion was identified by both neutrality tests and extended Bayesian skyline plot analysis. Thus, the climatic changes during the Pleistocene might have promoted the reconnection of the partially isolated southern populations, which may have persisted in the plateaus during the cycles of savanna contractions. Subsequently, this species (re)colonized northern areas of the species present distribution, following the continuous vegetation on the São Francisco and Central plateaus about 60 kyr, and also reached the Amazonian savannas likely via the central corridor. Thus, our results indicated that the intrinsic relationship between the relief heterogeneity (plateaus and depressions) and the climatic fluctuations, mainly in the Pleistocene, promoted population reconnection and demographic expansion of N. fasciata.We performed phylogeographic and genetic structure analyses of  It is also postulated that during the Pleistocene the forest and nonforest vegetation range dynamics may have led to the establishment of biogeographic corridors currently connecting the isolated savanna blocks in northern Amazonia Forest (Llanos and Amazonian savannas) and Cerrado [Tertiary geological events and the Quaternary climatic changes have been considered the key events related to the biodiversity diversification e.g. –5). Spec. Spece.ge.g. ,3,6–12. .g. [see ). Events.g. [see ,14, here Cerrado ,15–20. T LGM, c.  kyr the  Cerrado , understsee leading-edge model [see rear-edge model [see [The demographic history of the South American species occurring in open vegetation formations was probably influenced by the climatic fluctuations, with expected events of population expansion  follge model ,25), in ge model ,11. Cycldel [see ). On thedel [see –27. Addidel [see ,29. Finadel [see ,31.Neothraupis fasciata  to evaluate the response of this savanna-adapted bird to environmental changes during the late Quaternary (last 120 kyr). Commonly known as a White-banded Tanager, it is a passerine species from the Thraupidae family, and is the only representative of the genus. This tanager is widely distributed and very common in the undisturbed Cerrado, occurring mainly in cerrado sensu stricto and grassy cerrado [N. fasciata and the distribution of the genetic variability was not explained by the isolation-by-distance hypothesis, habitat heterogeneity or by the core-periphery effect [Herein, we performed phylogeographic and population genetic analysis of  cerrado –35. This cerrado ,37, has  cerrado . A previy effect . Similary effect . These fBased on molecular data sets and ecological niche modeling, we evaluated: (1) the isolation-by-distance hypothesis, with an increase in genetic differentiation among locations being expected due to an increase in geographic distances; (2) the influence of late Quaternary climate changes on the species distribution and demographic history, with events of population expansion being expected in response to the increase in suitable areas and a reduction in genetic diversity following population expansion; and (3) climate/climatic stability (hypothesis of climatic stability) as a predictor of population genetic diversity, with greater genetic diversity in stable areas being expected than in unstable ones.N. fasciata from fieldwork of the Laboratório de Genética e Biodiversidade da Universidade de Brasília, scientific articles, museums, and using the two online databases . We filtered the 176 unique occurrence points found by excluding redundant records in each pixel on the scale of the bioclimatic variables, resulting in 158 points. A set of 19 bioclimatic variables and elevation for the present were downloaded from WorldClim website in a 2.5 arc-min resolution (http://www.worldclim.org/). The highly correlated variables (|r|≥0.75) were eliminated, and variables with |r|<0.75 were retained using findCorrelation function available on caret (Classification And Regression Training) package [The species distribution models were estimated compiling occurrence records of  package  in R 3.4 package . The coret al. [The maximum entropy approach implemented in MAXENT 3.4.1  was usedet al.  for the To convert habitat suitability into presence/absence binary maps, we used the equal training sensitivity and specificity threshold. For Holocene and LGM we combined the two binary maps obtained with the CCSM4 and MIROC-ESM general circulation models into a single map, in which only areas with predicted occurrence in both projections were highlighted as potential distribution area. Stable areas over the last 120 kyr were delimited based on binary maps and were defined as areas with potential distribution concordant in all projected periods.https://earthexplorer.usgs.gov/), which provides a high level of detail in global topographic data 30 arc-sec, suitable for various regional and continental applications [N. fasciata, we evaluated the continuity of the plateaus, based on the categories by Riitters et al. [To explore the relief, we used the Global Multi-resolution Terrain Elevation Data 2010  and H63apud  and a frapud . The ND2DnaSP 5.10 [We edited the sequences in GENEIOUS 6.0.6  and coded double peaks in both strands of FIB-5 sequence electropherograms using the IUPAC nucleotide code ambiguous positions. In the FIB-5 sequences, we identified alleles with different sizes (14 bp), generating sequences with mixed traces  in heteraSP 5.10  with defDnaSP.The number of polymorphic sites, number of haplotypes, number of unique haplotypes, haplotype and nucleotide diversities were estimated using DnaSP . We estiST) in ARLEQUIN 3.5.1.3 [ST were done using Tamura-Nei model for ND2 alignment and Jukes-Cantor model for FIB-5 alignment. The best-fit model of evolution of each dataset was selected according to the Bayesian Inference Criterion in JMODELTEST 2.1.10 [K) set to 12, and 20 replicates for each K-value.Genetic differentiation between all sample sites was estimated using Φ-statistics . We conducted the test using the corrected genetic distance (PhiST/(1-PhiST)) and the logarithm of the geographic distance in IBDWS 3.23 [To verify whether the genetic differentiation among each pair of individuals sampled in the Cerrado is associated with the geographic distance, we performed a Mantel test. The Euclidean geographic distances were obtained using an on-line distance calculator from the Instituto Brasileiro de Pesquisas Espaciais  the response variables.DnaSP using 10,000 coalescent simulations and a significance level of 0.05. We also inferred the dynamic of population size as a function of time performing a coalescent extended Bayesian skyline plot (EBSP) analysis implemented in BEAST 1.8.4 [-9 per base pair per year for ND2 [-9 per base pair per year for FIB-5 [We performed Tajima´s D  and Fu´sST 1.8.4 . We used for ND2  and 3.6xor FIB-5 . We usedor FIB-5  using a ST) and the mitochondrial haplotype sharing between pairs of populations to evaluate whether the proposed biogeographic corridors served as dispersal routes for N. fasciata, considering: 1) MACA and PNCG—as an indicator of historical connection throughout the Andes region and/or Madeira River; 2) MACA and PATO, AGCV, or EEAE as an indicator of historical connections across central Amazonia; and 3) MACA and URUC or PNCM as an indicator of historical connection along the Atlantic coast. Genetic differentiation was estimated using F-statistics as previously described.We evaluated the genetic differentiation  and FIB-5 (487 bp) sequences for all 105 or FIB-5 . Sequencor FIB-5 . Haplotyor FIB-5 .The ND2 haplotype network was represented by one broadly distributed and common haplotype connected by a few mutational steps with several less frequent haplotypes in a star-like pattern . This paThe results of Mantel tests for both ND2  and FIB-5  indicated that the geographic distances do not correlate with the genetic differentiation among sample locations. The analyses of the influence of geographic gradients in genetic diversity using the ND2 indicated a negative correlation between nucleotide diversity and latitude , characterized by a south-to-north trend of declining genetic diversity . For FIBBased on the genetic structure found, neutrality tests were carried out considering all sample sites as a single genetic population. Deviations from neutrality were found in Tajima's D , Fu's Fs , and R2  tests for the ND2, suggesting recent population expansion. For FIB-5, deviation from neutrality was found for Fu's Fs , but not for Tajima's D  and R2  tests. EBSP analysis considering both markers together showed a signal of population expansion starting about 60 kyr .Neothraupis fasciata based on samples from a wide geographic area in the Cerrado. Our data support a single population (supported by BAPS results) with a weak genetic structure , in which southernmost locations showed more genetic variability than those sampled in the northern Cerrado. Considering that areas of greater genetic diversity are commonly associated with the origin center of a species  is restricted to southern South America [In the present study, we assessed the levels of genetic diversity and structure in es e.g. ,27,76), ,76, Neotreas see . These h America .N. fasciata does not seem to be related to the isolation-by-distance hypothesis. This is an unexpected result, given the low dispersal from the birth area reported for this species . Howeve meters; . The iso meters; . Therefo meters; . ConsideThe Cerrado landscape is characterized by large blocks of plateaus  separateN. fasciata. The shifts in the distribution of habitat suitability for N. fasciata presumably accompanied the shifts in the distribution of the Cerrado itself, since this species is strictly associated with cerrado sensu stricto and grassy cerrado [e.g. [Our paleodistribution models also showed an increase in suitable area and connection from LIG to LGM, and a decrease and fragmentation from the LGM to the Holocene, followed by an expansion from the Holocene to the present distribution of  cerrado –35. In tdo [e.g. ,10,11,84N. fasciata underwent a recent population expansion event starting about 60 kyr , expansion , or even constant effective population size   (see ).sensu stricto vegetation, while interplateau depressions have younger soils and, consequently, a more heterogeneous landscape than the plateaus (see Silva 1995 for a review). As in the plateaus, intraplateau depressions are composed of planning surfaces covered predominantly by Oxisols, which favor the establishment of cerrado sensu stricto-like vegetation in response to climatic changes. In this regard, the interplateau depressions are possibly more prone to vegetation range shifts due to climatic changes than the plateaus, as denoted by our paleomodelling results and the literature that have associated the Cerrado stable areas with plateaus and residual relief [We found greater genetic diversity in those individuals from the southern sample sites: (I) PNCG, (II) PNSC + PNEM, and (III) PGSV + BRMI, and a low number sharing haplotypes with each other . These rl relief ,19,81–83N. fasciata sample locations categorized as unstable are in the northern part of the distribution, and the effects of range expansion may act as a confounding factor. Therefore, additional sampling mainly in unstable areas is necessary to disentangle the effects of population range expansion and historical stability.We observed higher genetic diversity and a signal of historical differentiation between populations located in different stable areas. Thus, the mitochondrial results reinforce the hypothesis that high levels of divergence and population structure are expected across refugia  and the N. fasciata dispersal may have occurred mainly through central Amazonia corridor, besides we were not able to dismiss the use of the other hypothesized corridors. Additionally, the niche models indicated a region of habitat suitability concordant with the low precipitation belt during the LIG. In accordance, Bueno et al. [et al. [N. fasciata. The pattern observed in our results and the growing literature about the importance of the central Amazonian corridor  high highN. fN. fasciata located in the southern part of the species distribution (likely its center of origin) might have persisted in partially isolated populations restricted to the plateaus during the cycles of savanna contractions. Subsequent events of savanna expansion promoted the reestablishment of gene flow among these isolated populations and the increase in the distribution range through the (re)colonization of the northern portion of the species distribution. Thus, the intrinsic relationship between the relief heterogeneity (plateaus and depressions) and the climatic fluctuations, mainly in the Pleistocene, promoted population reconnection and demographic expansion of N. fasciata, reinforcing previous studies (e.g. [Genetic groups of es (e.g. ) that suS1 Table(DOCX)Click here for additional data file.S2 TableThe average Area Under the ROC curve (AUC) for the replicate runs (standard deviations in brackets) is given for each model. The average test omission rate (OR) is also given for each model.(DOCX)Click here for additional data file.S3 TableSee (DOCX)Click here for additional data file.S4 TableSignificant p-values after False Discovery Rate correction are marked with one asterisk (*).(DOCX)Click here for additional data file.S1 FigWarmer colors represent areas of higher habitat suitability.(DOCX)Click here for additional data file.S2 FigNeothraupis fasciata occurrence points and predicted Holocene distribution using (A) MIROC and (B) CCSM4 General Circulation Models. Warmer colors represent areas of higher habitat suitability.(DOCX)Click here for additional data file.S3 FigNeothraupis fasciata occurrence records and predicted Last Glacial Maximum distribution using (A) MIROC and (B) CCSM4 General Circulation Models. Warmer colors represent areas of higher habitat suitability.(DOCX)Click here for additional data file.S4 FigWarmer colors represent areas of higher habitat suitability.(DOCX)Click here for additional data file.


The pig breeding industry has undergone a large number of mergers in the past decades. Various commercial lines were merged or discontinued, which is expected to reduce the genetic diversity of the pig species. The objective of the current study was to investigate the genetic diversity of different former Dutch Landrace breeding lines and quantify their relationship with the current Dutch Landrace breed that originated from these lines.Principal component analysis clearly divided the former Landrace lines into two main clusters, which are represented by Norwegian/Finnish Landrace lines and Dutch Landrace lines. Structure analysis revealed that each of the lines that are present in the Dutch Gene bank has a unique genetic identity. The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former lines. The Dumeco N-line, which is conserved in the Dutch Gene bank, is poorly represented in the current Dutch Landrace. All seven lines (the six former and the current line) contribute almost equally to the genetic diversity of the Dutch Landrace breed. As expected, the current Dutch Landrace breed comprises only a small proportion of unique genetic diversity that was not present in the other lines. The genetic diversity level, as measured by Eding’s core set method, was equal to 0.89 for the current Dutch Landrace breed, whereas total genetic diversity across the seven lines, measured by the same method, was equal to 0.99.The current Dutch Landrace breed shows a high level of admixture and is closely related to the six former Dutch Landrace lines. Merging of commercial Landrace lines has reduced the genetic diversity of the Landrace population in the Netherlands, although a large proportion of the original variation is maintained. Thus, our recommendation is to conserve breeding lines in a gene bank before they are merged. The pig is a major livestock species, which in 2016 accounted for 37% of the meat production worldwide [Over the past decades, the commercial breeding industry has seen considerable business consolidation through mergers and take-overs, which have resulted in a limited number of remaining internationally operating breeding companies . ConsequThe process of consolidation of breeding lines in domestic farm animals is most advanced in poultry, where both for broiler and laying chickens, the global market relies on just a handful of breeding lines/populations. Currently, the global poultry breeding market is primarily covered by just a few breeding companies, which has led to a loss of genetic diversity in these breeds . Pig breTraditional pig breeds and pure breeding lines are valued resources, not only for meat production, but also for cultural, historical, sociological, and environmental aspects. The underlying genetic variation may disappear, or may already have disappeared, from the global highly productive breeds that dominate modern intensive livestock production systems. Thus, the continued merging of breeding companies increases the concern of losing essential genetic variation .The consolidation of breeding lines is often poorly documented, with public records usually limited or absent. Even for breeding lines listed by the FAO, which include data on their current status and vulnerability, information is often limited or outdated. A post hoc evaluation of loss of diversity in the aftermath of company mergers by genotyping is further hampered by the absence of reference samples from the pre-merger breeding lines. Here, we present a relatively well-documented case of the merging of a number of breeding associations that operated at the national level (the Netherlands) into an internationally operating breeding company (Topigs Norsvin). Although consolidation affected all breeding lines owned by the breeding companies, we will focus on one particular breed in this paper, i.e. the Dutch Landrace.Our objective was to investigate the consequences of merging and discontinuing breeding populations on the genetic diversity of the Dutch Landrace breed over the past decades. To achieve this objective, we used genotype data of boars from the former Dutch Landrace breeding lines that have been conserved in the Dutch Gene bank to quantify their relationship with the current Dutch Landrace breed, and to estimate the loss (if any) of genetic diversity as a result of the merging of lines.http://www.genebankdata.cgn.wur.nl/). This practice has been continued by Topigs  during the last two decades, resulting in a unique collection of material from breeding lines that were either discontinued or altered by merging lines. The timeline of consolidation of the different Landrace breeding lines in the Netherlands since 1960s is illustrated in Fig. The Dutch Landrace breed originated from the original native Landrace pig, with infusions of the German Landrace and the Danish Landrace around 1900 . By 1933The Centre for Genetic Resources, the Netherlands (CGN) of Wageningen UR, i.e. the Dutch Gene bank, stores cryopreserved genetic material, primarily semen, from the former pig breeding associations in the Netherlands. From 1995 to 2003, CGN collected genetic material from six Landrace breeding lines of breeding associations that existed at that time. Merging of Dutch Landrace lines was in full progress and consequently the number of animals was already reduced. To select the group of boars, from the available animals, with minimal kinship and maximum diversity, optimal contributions were estimated using Gencont . From 20The 187 animals were genotyped using the PorcineSNP80 BeadChip . All samples had a genotype call rate higher than 90%. For quality control, SNPs with a GenCall score lower than 0.20, a minor allele frequency lower than 0.02 and a per SNP genotype call rate less than 100% were removed from further analyses, the latter because some of the subsequent analyses cannot deal with missing genotypes. Imputing missing genotypes was not appropriate for this dataset, since it requires more animals for each of the lines involved to be genotyped. In addition, applying a call rate threshold of 100% left a sufficient number of SNPs in the dataset for subsequent analyses. The final dataset included 42,655 SNPs with calls for all 187 animals.Fst, as defined by Weir and Cockerham [To examine relatedness between the Landrace lines, a principal component analysis (PCA) was performed using the prcomp function in R . To idenockerham , using tockerham .The contribution of breeds to genetic diversity was analysed using the marker-estimated kinships and the core set method of Eding et al. . In thisIn the core set method of Eding et al. , the conThus, the contribution of each breed to this core set depends on both the between- and within-breed components of genetic diversity. However, this contribution is not the only one that determines the relative importance of a breed to total genetic diversity. A breed that only contributes a small amount to the core set (e.g. when their within-breed kinship is high) can, nevertheless, increase the total genetic diversity considerably, e.g., when its across-breed kinships are low. Therefore, the average kinship coefficient of the core set when the breed is included is compared to the average kinship coefficient of the core set when the breed is excluded .The required kinship coefficients were obtained by first computing the genomic relationship matrix , using default settings [https://may2017.archive.ensembl.org/Sus_scrofa/Info/Index). The candidate genes were characterized using the PANTHER Classification System version 14.1 (http://geneontology.org/) [Selection signatures were detected for each pairwise comparison between the current TN and the six former lines, by using the settings . SNPs wigy.org/) , in partThe current Dutch Landrace (TN: Topigs Norsvin N-line) is the result of the consolidation of six former Landrace lines that existed from the 1960s until early 2000 . The PCA clearly indicates a division of the seven Landrace lines into two main clusters; on the one hand, the former Norwegian/Finnish Landrace lines , which were introduced in the Netherlands between 1970 and 1980, and, on the other hand, the former Dutch Landrace lines   to the genetic diversity in the overall Landrace population is in Table Fst) in pairwise comparisons between the current TN and the six former lines. Outlier  SNPs are an indication of regions that are potentially under selection. The 10log Bayes factor values for each SNP are shown in Fig. As breeding lines are merged, selection continues, although in some cases the breeding goal may be different in the consolidated line compared to the parent lines. SNP genotypes were used to estimate allele frequency differentiation  was moderate to low. Wilkinson et al. reported a mean Fst value of 0.156 between three British Landrace lines [Fst values ranged from 0.020 to 0.256 [Fst values (0.02 to 0.10) found in our study are at the lower end of this range. According to Willing et al. [Fst can be accurately calculated based on small sample sizes  if the number of markers examined is large, i.e. larger than 1000.Genetic differentiation between the lines  [RAB39A [ENSSSCG00000012012 [BECN1, which is a muscle-related gene [GARS and NOL10, which are associated with meat quality [KIAA0513, which is associated with the male reproduction trait “Seminiferous tubule diameter” [GLO1, which is assumed to be involved in fatness [GOT1 and PKHD1L1, which have been reported as candidate genes for intramuscular fat content [TSPAN11, which was associated with metabolic body weight in a study on Holstein dairy cows [WDR92, which is associated with total fat in Duroc and Yorkshire F2 intercrosses [We detected no over-representation of any GO biological process among the candidate genes in our study. It should be noted that most traits that are under selection in pigs are complex traits that are regulated by many genes . We idenLAPTM5 ; CIB4 (s (sheep) ), the im [RAB39A ), and inted gene , 41, GAR quality , 43, andiameter” . In the  fatness , is impo fatness , and is  fatness ; GOT1 an content  and vari content , respectiry cows . In the As shown by our results, differences can be pronounced even between populations that have common origins, which stresses the value of gene banks to record and preserve variation that is lost in the process of merging, even over short periods of time.The breeding industry has undergone a strong consolidation process in the past decades and this will likely continue –54. EconThe current Dutch Landrace (TN line) shows a high level of admixture and is closely related to the six former Dutch Landrace lines. However, the merging of commercial Landrace lines has reduced the genetic diversity of the Landrace population in the Netherlands, and the DN line is poorly represented in the current Dutch Landrace. Thus, it is recommended to conserve selection lines in a gene bank before merging. Our findings also showed that the merging of lines results in a large proportion of the original variability being maintained.Additional file 1: Figure S1. Inferred ancestry of the 34 TN line individuals. Each bar is an individual.Additional file 2: Table S1. Outlier SNPs identified  by BayeScan in the pairwise comparison between the current TN and the six former lines. SNP ID, SNP identifier; Chromosome, chromosome number; Position, position on the chromosome; prob, posterior probabilities; log10(PO), the logarithm to 10 of Posterior Odds; qval, q-value the threshold for significance of signatures of selection; alpha, a locus-specific component indicating the strength and direction of selection, a positive value of alpha suggests diversifying selection, whereas negative values suggest balancing or purifying selection; fst, fixation index; RS ID, accession number of the SNP; Gene Name (5 kb upstream/downstream), name of a gene that is located within 10 kb (5 kb downstream/upstream) of the SNP outlier; GO BP (GO-slim), Gene Ontology slim biological process term; General Term GO BP, general Gene Ontology biological process term.


The expansion of genetic and genomic testing across medical specialties and the changing workforce demographics of certified genetic counselors (CGCs) have led to concerns of a workforce shortage. We assessed the number of genetic counselors working in the Southern United States—a rural and medically underserved region—using various online and professional resources. We identified 683 practicing genetic counselors across the Southern U.S. and 160 specializing in prenatal genetics. CGCs were concentrated in urban areas; counties with a CGC had a significantly higher proportion of minority residents and median household income than counties without a CGC. There is an average of 2.97 prenatal CGCs per 5000 high-risk births in the South. Alternative delivery models are needed to increase access to counseling services in the Southern U.S., particularly for low income households and those of high risk pregnancies. Increased provider education and patient educational materials can help facilitate informed decision-making in prenatal settings as genetic technologies gain a stronger foothold and bring value to medical practice. Advances in genetics and genomics research, new testing technologies, electronic medical records, and general public interest have combined to usher in the precision medicine movement. Yet the expansion of genetic and genomic testing may pose greater burdens on providers, particularly on genetic counselors, to adequately educate patients and promote informed decision-making. In particular, the limited number of certified genetic counselors (CGCs) and the lack of diversity in the profession has raised concerns regarding access and widening health disparities in light of increasing demands ,2. CGCs It is estimated that more than 4000 genetic counselors are currently practicing in the United States ,4. Most Although it has long been widely recognized that there are disparities in access to genetic counselors ,11, therThe Southern U.S. may experience greater problems with access to genetic counselors than other regions of the country. Approximately 123 million people, or 38 percent of the U.S. population, reside in the South . ApproxiIn this study, we examined the availability of genetic counselors working in the Southern U.S. and the demographics in areas with and without practicing CGCs. We specifically explored the accessibility of prenatal CGCs in this region, given the already limited access to prenatal care. Understanding the distribution of CGCs in the south US, and the populations that are affected can inform development of alternative delivery strategies to improve access to genetic services and health equity in CGC-underserved regions.Research Design: We conducted a geospatial analysis of genetic counselors’ work location with Southern states’ population, level of urbanization, median household income, racial demographics, and number of births. Based on the U.S. Census Bureau’s defined regions, the ‘South’ includes 17 states/regions  . The stuData Collection: To identify where genetic counselors work, we used three databases. The National Society of Genetic Counselors (NSGC) website has a “Find a Genetic Counselor” tool  that helWhile several types of health professionals may provide genetic counseling services, such as clinical social workers, physicians, and nurses, we included only board-certified genetic counselors in our analysis. The NSGC tool explicitly notes whether the counselors are board-certified are not. The Healthgrades tool, however, is less clear about board certification and therefore, we confirmed board certification through the ABGC’s database.Several data points for each counselor were recorded in Excel (Microsoft) file, including state, specialty , whether they offered telecounseling or see patients in-person, their employer, and their zip code. We did not store names or personal contact info nor did we contact any genetic counselors identified in the search. We recorded five-digit zip codes in a separate excel spreadsheet and matched each zip code with its respective county and latitude and longitude coordinates in order to import the data to GIS mapping tool.Identifying State/County Demographics: To identify the demographic characteristics of counties in the southern U.S., we relied on data from the American Community Survey (ACS) of the U.S. Census Bureau . We usedWe recorded data on total state and county populations to calculate the number of CGCs per 1,000,000 residents in each state and the median population of counties with and without CGCs. To assess income, we used ACS county and state level data on “Median Household Income in the Past 12 Months (in 2016-Inflation-Adjusted Dollars).”In addition, we used ACS data on race to examine the racial make-up of counties with and without practicing genetic counselors. The ACS classifies the racial demographics of a state and county into seven categories: “White alone”, “Black or African American alone”, “American Indian and Alaska Native alone”, “Asian alone”, “Native Hawaiian and Other Pacific Islander alone”, “Some other race alone”, and “Two or more races”. Given the low prevalence of some populations in the South we grouped “American Indian and Alaska Native alone”, “Asian alone”, “Native Hawaiian and Other Pacific Islander alone”, and “Some other race alone” into one category—“Some other race alone”. We also renamed “Two or more races” to “Mixed race” for clarity.Finally, we used the ACS’s state-level data on "Women 15 to 50 Years Who Had a Birth in the Past 12 Months by Marital Status and Age". The data are divided into three age groups: “15 to 19 years old”, “20 to 34 years old”, and “35 to 50 years old”. We recorded the total number of births across all age groups and the total number of births specifically for women over 35 years old, as these would be considered high risk pregnancies and typically referred to genetic counseling according to clinical guidelines ,26,27,28Data Analysis: Summary statistics were computed for population density, income, and race prevalence. We used ArcGIS Pro (ESRI) to create a regional map of CGCs’ practice location across the South after converting each CGC’s zip code location into geographic coordinates (latitude and longitude).Number of practicing genetic counselors: We identified a total of 683 practicing CGCs in the Southern United States . A totalOverall Access to Genetic Counselors by State & County: To assess workforce density and general access to CGCs, we first assessed state population data to determine the number of CGCs per 100,000 residents in each state . The aveIn contrast, the smallest populated states in the South had the highest number of practicing counselors per 100,000 people. Namely, the District of Columbia had 2.43 counselors and Delaware with 1.18 counselors per 100,000 people. One exception was West Virginia—the third smallest state in the South (by population) had the lowest number of counselors (0.11) per 100,000 people.p < 0.001).Geographical Proximity: We identified the county of practicing CGCs based on zip code of practice location and used this as a measure of access based on geographical proximity. However, we recognize that counties may differ with respect to size, public transportation, walkways, and development, which may impact accessibility to GCGs for residents. As shown in p < 0.001). For example, in Georgia, Louisiana, and Mississippi, the proportion of minorities in counties with a CGC was greater than 50%, while the proportion of minorities in counties without a CGC was less than 35%. South Carolina was the only state where the proportion of minorities was larger in counties without a CGC (34%) than in counties with a CGC (31%).Income & Race: We assessed the racial demographics and income of counties with and without practicing CGCs  and founp = 0.00019)  . Median Access to prenatal genetic counselors: It is difficult to estimate the appropriate number of CGCs given the wide range of practice settings and clinical indications for their services. Since state population data of the number of births from advanced maternal age pregnancies are available, we narrowed our analysis to practicing prenatal CGCs in each state in order to assess need versus supply. As counseling is indicated for other high risk pregnancies for a history of miscarriage or family health history, we will underestimate the high risk prenatal population warranting counseling services. As the field of genetics and genomic clinical applications continues to rapidly expand, access to expert providers is critical to the appropriate utilization of these applications, particularly CGCs ,12,29. TThe concentration of CGCs in urban areas is likely no different than other regions in the U.S., as this is typically where academic medical centers and large medical practices or hospital centers are located. Since CGCs cannot always bill for their services and receive reimbursement , they tyThe southern states continues to face challenges regarding access to maternal care and poorer maternal and child outcomes. Access to prenatal care in the southern US is limited, particularly in rural areas, with the number of board-certified obstetrician/gynecologists decreasing ,31 and aOne potential approach to increasing access to genetic services, especially for patients living in rural areas, is telehealth or telemedicine ,41,42. TAs many health providers are struggling to keep up with the scientific advances and new clinical applications in precision medicine ,60, educInterestingly, we identified  many health providers that were not board-certified, but that self-reported they could provide genomic services or genetic counseling. It is possible that these health professionals  are filling in gaps where we observed shortages of CGCs. However, the differences in training and lack of certification may yield differences in quality and scope of counseling practices offered by non-CGCs.The limited and unequal geographic distribution of CGCs across the South may impact access to prenatal screening and other needed counseling services. A crucial step to reducing the health disparities gap in genomics will be to improve access to expert providers, as well as educational resources for both nongenetics providers and patients. While developing and implementing alternative delivery strategies will prove crucial to closing the health disparities gap in the genomics field, some thought should also be given to the establishment of new genetic counseling training programs in the Southern US. Of the 45 genetic counseling training programs in the U.S. in good standing, 13 are based in the southern U.S.  and noneSome limitations of this study should be noted. The data sources used to identify CGCs practicing in the southern U.S. each have limitations. The NSGC “Find a Genetic Counselor” tool only includes genetic counselors that are members of the society, which makes it possible our study excluded prenatal genetic counselors that are not members of the society. Moreover, for a small number of cases, five-digit zip codes were not all available for all counselors in the NSGC list and two states had more than 100 genetic counselors, but the tool only lists the first 100 counselors. In addition, some counselors may be a member of NSGC but are not practicing. Genetic counselors may choose not to include their information in the ABGC directory. With respect to Healthgrades, how often the website is updated and if the information is confirmed is unclear. Some academic institutions may not list mid-level healthcare providers in their directory, and therefore we may be underestimating the number of practicing CGCs. Other employment settings such as genetic testing laboratories and telegenetics companies were not queried. Finally, we only considered the high risk category of AMA given the availability of data for this variable. However, there are many other risk factors that are important to consider in determining counseling needs for prenatal care.With the expanding use of genetic and genomics technologies across medicine, accessibility to CGCs is critical. With the changing workforce demographics of CGCs and unequal geographic distribution of the current workforce, access is quite limited, and even more so in underserved regions of the U.S. Thus, greater effort is needed to increase access to CGCs, potentially through telemedicine and greater training of providers about genetics, patient resources, and referrals.