Bos primigenius) of Eurasian taurine cattle experienced two notable prehistorical declines in effective population size associated with dramatic climate changes. The modern Yakutian cattle exhibited a higher level of within-population variation in terms of number of SNPs and nucleotide diversity than the contemporary European taurine breeds. This result is in contrast to the results of marker-based cattle breed diversity studies, indicating assortment bias in previous analyses. Our results suggest that the effective population size of the ancestral Asiatic taurine cattle may have been higher than that of the European cattle. Alternatively, our findings could indicate the hybrid origins of the Yakutian cattle ancestries and possibly the lack of intensive artificial selection. We identified a number of genomic regions under selection that may have contributed to the adaptation to the northern and subarctic environments, including genes involved in disease resistance, sensory perception, cold adaptation and growth. By characterizing the native breeds, we were able to obtain new information on cattle genomes and on the value of the adapted breeds for the conservation of cattle genetic resources.Northern Fennoscandia and the Sakha Republic in the Russian Federation represent the northernmost regions on Earth where cattle farming has been traditionally practiced. In this study, we performed whole-genome sequencing to genetically characterize three rare native breeds Eastern Finncattle, Western Finncattle and Yakutian cattle adapted to these northern Eurasian regions. We examined the demographic history, genetic diversity and unfolded loci under natural or artificial selection. On average, we achieved 13.01-fold genome coverage after mapping the sequencing reads on the bovine reference genome (UMD 3.1) and detected a total of 17.45 million single nucleotide polymorphisms (SNPs) and 1.95 million insertions-deletions (indels). We observed that the ancestral species ( Bos taurus) have adapted to a wide variety of biogeographic zones and sociocultural environments as a result of natural and human-derived selection  and the Board of Agricultural Office of Eveno-Bytantaj Region, Sakkyryr, Sakha, Russia.DNA extracted from blood samples was available for the two Finnish cattle breeds (Eastern Finncattle and Western Finncattle) and one Siberian breed (Yakutian cattle) from a previous study . Five un1. We used Picard tools to remove PCR duplicates from the aligned reads and then used the uniquely mapped reads for variant calling.For short read alignment, the bovine reference genome (UMD 3.1), including regions that were not assembled into chromosomes , were do∗ DP)) > 0.1)”, “SB ≥-1.0, QUAL < 10,” and “QUAL < 30.0 || QD < 5.0 || HRun > 5 || SB > -0.10” and from the indels with the parameters “QD < 2.0,” “FS > 200.0,” and “ReadPosRankSum < -20.0.” All the variants that passed the above filtering criteria were used in the downstream analysis and compared to the cattle dbSNP150  v2.6-4 according to the GATK best practices pipeline  for downdbSNP150  to identP-value, 0.05; at least 5 mapping entries). Out of four indel classes , we annotated frameshift indels in exonic regions using default parameters in ANNOVAR. Frameshift indels may change amino acid sequences and thereby affect protein function.ANNOVAR  was usedWe investigated for all identified SNPs the signatures of selection using SFS-based α statistics in SweeD  with defThe average pairwise nucleotide diversity within a population (π) and the proportion of polymorphic sites (Watterson’s 𝜃) were computed using the Bio::PopGen::Statistics package in BioPerl (v1.6.924) . PCA was∗2+4+6’. To scale the PSMC output to real time, we assumed a neutral mutation rate of 1.1 × 10-8 per generation and an average generation time of 5 years  of SNPs  SNPs were found to be novel when compared to NCBI dbSNP bovine build 150. At the breed level, 2.8, 2.6, and 4.5% of the total SNPs in the Eastern Finncattle, Western Finncattle and Yakutian cattle, respectively, were novel. Furthermore, out of the novel SNPs identified for each breed, 258,409 (83.3%), 219,234 (81.5%), and 528,763 (95%) were breed-specific SNPs in Eastern Finncattle, Western Finncattle, and Yakutian cattle Figure , respectIn total, we detected 2.12 M indels, 79.72% of which were found in the dbSNP build 150, with 20.28% being novel Figure . At the In our data, on average, 0.65% of the SNPs were detected in exonic regions, 25.1% in intronic regions, 72.6% in intergenic regions, and 1.65% in UTRs and in regions upstream and downstream of genes Table . In geneThe functional categories of the indel mutations are presented in Table We computed the genotype concordance between the SNPs detected by our SNP calling pipeline and the previous SNP genotype study ; IlluminThe GO enrichment analysis of 1,331, 1,170, and 1,442 genes containing >5 nsSNPs  is shown in Supplementary Figure n = 611), Western Finncattle (n = 544), and Yakutian cattle (n = 629)  number of selection signals and chromosome 25 the lowest (n = 43). Considering a 150-kb window centered on the candidate site, Western Finncattle exhibited the highest number (n = 371) of candidate genes with selection signatures, followed by Eastern Finncattle (n = 331), while Yakutian cattle exhibited the lowest number (n = 249) , Western Finncattle , and Yakutian cattle  exhibited the strongest signatures of selection. Of the genes with the strongest signatures of selection, one gene each from Eastern (ENSBTAG00000045571) and Western Finncattle (ENSBTAG00000012326) and three genes from Yakutian cattle  lacked gene descriptions , Western Finncattle , and Yakutian cattle .We examined the candidate selective sweep genes in each breed. A number of genes potentially associated with cold adaptation  were preIFNAR1, IFNAR2, IL10RB, and NOD2), neuronal and brain development (OLIG1), growth (ACTA1) and meat quality  (Supplementary Data ULBP3), sensory perception , meat quality  and disease resistance  , sensory perception , meat quality  and milk production (MUC1)  than in Eastern Finncattle  and Western Finncattle , and these results were inconsistent with those of previous studies based on autosomal microsatellite and SNP data sets, which showed that Finncattle were more diverse than the Yakutian cattle , were higher in the Yakutian cattle   , experieigenius) . After tWe also used the ∂a∂i program to reconstruct the recent northern European cattle demographic history (from 418 kya to the present). The parameters Ta, Td, nua, nu1F, and nu2Y in the demographic model are shown and explained in Supplementary Figure B. taurus) breeds that have adapted to the northernmost cattle farming regions, even subarctic regions. The contemporary genetic resources of the Eastern Finncattle, Western Finncattle and Yakutian cattle breeds studied are the result of a complex process of genetic and demographic events that occurred during the domestication and selection and even the evolution of the ancestral species of northern Eurasian taurine cattle, namely, the near-eastern aurochs (B. primigenius).To our knowledge, this is the first whole-genome sequence-based report on the genetic diversity of Eurasian native cattle (B. primigenius) experienced two notable prehistorical population expansions, after which the population size declined gradually. The first marked decline in the effective population size (Ne) occurred during the Middle Pleistocene period starting after ∼1 Mya, which may have been associated with reduction in global temperatures and even with negative actions of humans on the auroch population  corresponded well to numbers found typically in European taurine animals. In contrast, we found that the Yakutian cattle exhibited a higher number of SNPs on average per individual (7.12 M SNPs) than the number of SNPs detected in European and Asiatic humpless cattle to date . Accordi-3, 1.512 × 10-3, and 1.728 × 10-3, respectively, and the proportions of polymorphic sites (𝜃) were 0.001445, 0.001398, and 0.001588, respectively] exceed those typically found in European taurine cattle breeds  values were 1.559 × 10e breeds . We obse0 × 10-3 . We obse0 × 10-3 , where l0 × 10-3 .B. primigenius) that lived in the East Asian region during the arrival of near-eastern taurine cattle . Among these three breeds, Western Finncattle have been subjected to the most intensive artificial selection for milk production characteristics, while the production selection program of Eastern Finncattle was stopped in the 1960s, when the census population size of this native breed declined rapidly. Currently, in vivo and in vitro conservation activities are being implemented for Eastern Finncattle (and for Western Finncattle and Yakutian cattle). In addition, although Yakutian cattle had the highest number of genes containing SNPs  among the three breeds, the GO analysis indicated that this breed had the lowest number of significantly enriched GO terms . This difference between the native Finnish cattle and Yakutian cattle can be due to the differences in the selection histories of these breeds.The high number of SNPs and high genomic diversity found in the Yakutian cattle may be due partly to the breed’s selection history: the artificial selection by humans has not been intensive . The Yak-3; 2,091 and 1.792 × 10-3; 1,478 and 1.113 × 10-3, respectively), which is in contrast to the number of SNPs and π-estimates obtained for the entire genomes of the breeds. Great variations in the number of olfactory receptor genes and structural variations in these genes among mammalian species and even individuals within species  have been interpreted as reflecting the effects of environmental factors on the genetic diversity of this multigene family and demonstrate the importance of these genes from the evolutionary point of view . This observation was consistent with previous cattle sequencing analyses  and sugg of view . Therefo of view . Cattle  of view . Compare of view . The Yak of view  and can We performed genome-wide selection-mapping scans for the three northern cattle breeds and found a great majority of SNPs exhibiting selection signatures in non-coding genomic regions. This finding indicates that selection occurs specifically via the regulatory elements of genomes [see also ]. We fouNRAP and IGFBP5, both of which have been previously identified as candidate genes for muscle development and meat quality in cattle , influencing the oxidative stress response, is an example of the genes with significant selection signatures in Yakutian cattle, Siberian human populations  for this study can be found in European Nucleotide Archive (ENA) under the accession number JK designed the study and revised the manuscript. MW performed the bioinformatics and statistical analyses and drafted the manuscript. JK, RP, IA, and ZI collected the samples. RP, KP, IA, YM, and ZI participated in the experimental design and paper revision. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Prunus rootstocks and five scion cultivars from three subgenera  for genome-wide SNP identification and to assess genetic diversity of both Chilean and Spanish germplasm collections. A group of 45,382 high quality SNPs  were selected for analysis of this group of 58 accessions. These SNPs were distributed in genic and intergenic regions in the eight pseudomolecules of the peach genome (Peach v2.0), with an average of 53% located in exonic regions. The genetic diversity detected among the studied accessions divided them in three groups, which are in agreement with their current taxonomic classification. SNPs were classified based on their putative effect on annotated genes and KOG analysis was carried out to provide a deeper understanding of the function of 119 genes affected by high-impact SNPs. Results demonstrate the high utility for Prunus rootstocks identification and studies of diversity in Prunus species. Also, given the high number of SNPs identified in exonic regions, this strategy represents an important tool for finding candidate genes underlying traits of interest and potential functional markers for use in marker-assisted selection.Genotyping-by-Sequencing (GBS) was applied in a set of 53 diploid  Prunus is a genus belonging to the subfamily Prunoideae of the family Rosaceae1. Several species of this large genus, known as stone fruits, are among the most important for the world fruit industry, providing edible and tasty fruits highly appreciated by consumers . Cherries and plums are well adapted to the cooler temperate areas of the world, while almonds, apricots and peaches are grown in warmer temperate regions with Mediterranean climate. Nevertheless, all of them require adequate winter chilling to achieve an effective fruit set and production2.6, such as blossom, fruit set, size, sugars and other fruit quality parameters, as well as tolerance to biotic and abiotic stresses. While new stone fruit scion cultivars development is addressed by numerous breeding programs around the world, only very few of them aim to the development of new Prunus rootstocks.In modern stone fruit production, trees require to be grafted onto a rootstock well adapted to the soil most prevalent conditions. It means that the tree is composed of two genetically distinct partners joined by grafting of the aerial part (scion) on the rootstock, the later including part of the trunk and roots. The scion is the fruiting cultivar while the rootstock is responsible for water and nutrient uptake. In addition, some of the most important agricultural traits of the tree may be substantially influenced by the rootstockPrunus rootstock breeding program in 2010, with collaboration of the ‘Estación Experimental de Aula Dei-Consejo Superior de Investigaciones Científicas’ (EEAD-CSIC). From 1950, the survey and establishment of Spanish Prunus germplasm collections were conducted by EEAD-CSIC aiming to preserve and use this material in breeding programs to obtain new stone fruit rootstocks, with specific adaptation to Mediterranean environments7. Furthermore, new rootstocks generated by CEAF and EEAD-CSIC have the potential to be graft-compatible with scions from different species8, which is a desirable characteristic for stone fruit producers. Therefore, better understanding of the molecular background of the material currently released by and/or used in rootstock breeding programs is of great importance.In Chile, the ‘Centro de Estudios Avanzados en Fruticultura’ (CEAF) started a Prunus rootstocks in breeding programs depends upon accurate and unambiguous characterization9. In addition, the knowledge of the genetic diversity and relationships among the cultivated species of Prunus is important to recognize gene pools, to identify pitfalls in germplasm collections, and to develop effective conservation and management strategies2. Current trends in breeding stone fruit rootstocks are based on the production of interspecific hybrids, aiming at combining favorable traits from different species10. Genetic characterization of diversity and relationships at the interspecific level include studies on the systematic relationships within Prunus using allozyme polymorphisms11, chloroplast DNA variation14, Internal Transcribed Spacer (ITS) sequence variation of nuclear ribosomal DNA15, ITS and chloroplast trnL–trnF spacer sequence variation16 and Amplified Fragment Length Polymorphisms (AFLPs)2. Molecular characterizations and estimation of relationship specifically between Prunus rootstocks have been performed using molecular markers, such as Random Amplification of Polymorphic DNA (RAPD)18 and Single Sequence Repeats (SSRs)21. In recent years, advances in next-generation sequencing (NGS) have enabled the use of Single-Nucleotide Polymorphisms (SNPs) as other important type of molecular marker.Effective utilization of 23. SNPs have been indicated as the major factors in the creation of phenotypic variation and their effect on functional changes of genes is used as a tool in functional genomics of organisms24. For Prunus species, the availability of the peach genome sequence26 enables anchoring of SNPs identified through NGS to corresponding positions in the genome, identification of SNP-carrying genes and prediction of the effect of SNPs.In the last decade, SNPs have become the markers of choice in molecular genetics due to their frequency in genomes and high-throughput, cost effectiveness for their detection using various approaches and platforms27. In this approach, single and/or double-digestion of DNA with restriction enzymes is used to produce a reduced representation of the genome of each sample28. Single and/or double-digest GBS generate massive datasets of SNPs for a range of applications and is widely applicable in both model and non-model organisms31. It has been described that, compared with single-digest GBS, double-digest protocol greatly simplifies quantification of the library prior to sequencing and could generate a suitable and uniform complexity reduction of the genome28.Genotyping-by-Sequencing (GBS) is one of the NGS approaches, which enables the simultaneous discovery and genotyping of thousands of SNPs in a set of multiplexed samplesPrunus species, single-digest GBS has been used for identification of a high number of SNPs for linkage maps construction35 and analysis of population genetic structure38. The use of double-digest GBS in Prunus has not been reported as far as the authors know. Also, and despite their importance, molecular characterization of Prunus rootstocks using GBS or other NGS tools has not been published. In this work, we used double-digest GBS for the identification of SNPs from 58 diploid accessions, most of them rootstocks and interspecific hybrids, members of three different Prunus subgenera . Some of these accessions are representatives of the most important breeding programs of the world (released during the last two decades), such as INRA (France) and UC Davis (USA) programs, and they are extensively used by the fruit industry. Genetic relationship between accessions, the prediction of SNP effects and the identification of SNP-carrying genes is presented. The usefulness of the presented information in understanding the genomic and phenotypic differences among Prunus accessions and its potential towards substantial improvement in knowledge about the genome structure of accessions from Prunus subgenera is discussed.In 39 and IRSC 9 K peach SNP array v140 that were updated with the Peach v2.0 as a reference genome (www.rosaceae.org)41. Only a group of 49 SNPs were in common between our study and the cherry 6 K SNP array v1, while 75 SNPs were common between our study and the IRSC 9 K peach SNP array v1 (data not shown).Double-digest GBS produced between 6,801,412 and 14,293,180 read pairs, with an average of 11,117,746 reads per individual. This extremely deep sequencing led to a mean depth per SNP of 1,323 across the entire dataset  and transversions (Tv) Fig. . TransitAmygdalus subgenus, while those showing the highest percentage of heterozygous sites were the Prunus-Amygdalus hybrids AG-‘030104’ (41.22%), ‘AG-030107’ (41.23%) and ‘R R’ (41.64%). The two duplicates of ‘Adara’ and ‘Citation’ shared similar results, while the two ‘Mariana 2624’ samples were not concordant.Percentage of heterozygous positions is presented in Fig. Amygdalus, Prunus and Cerasus; Table Amygdalus consisted of 31 accessions divided in two subclusters, one formed by four accessions (Subcluster A) and other formed by 27 accessions (Subcluster B). Subcluster A was comprised of four accessions, all Prunus-Amygdalus hybrids. Subcluster B is divided in two groups, B1, with 25 accessions, and B2, with two accessions. Group B1 is further divided in two subgroups, with ‘Carmel’ (P. dulcis), and ‘Hansen 2168’ and ‘Hansen 536’ (both P. dulcis × P. persica hybrids) being separated from the main subgroup. All accessions in the main subgroup have P. persica in their genetic background. Accessions with P. davidiana in their genetic background were grouped more tightly, separated from accessions with P. dulcis in their background. Group B2 is formed by two samples of ‘Citation’, a Prunus-Amygdalus hybrid , one from CEAF and the other one from CSIC.An UPGMA dendrogram grouped accessions into three major clusters, which, in most cases, were in agreement with their taxonomic classification ]. In the Subcluster D, accessions with P. cerasifera in their genetic background are grouped separately from other accessions. Two unexpected results were observed in the Subcluster D, with ‘Larry Ann’ not grouping with ‘Angeleno’, both P. salicina, but instead grouping with ‘Mariana 2624’ (P. cerasifera × P. munsoniana) accessions. Also, ‘Mariana 2624’ accessions from both CEAF and CSIC were clustered together, but results suggest that they are not the same accessions. Two interspecific hybrids, ‘R 20’ (P. besseyi × P. cerasifera) and ‘Isthara’ [ × (P. cerasifera × P. persica)], were grouped in this subcluster. Nanking cherry (P. tomentosa) was also grouped in the Subcluster D, although it was expected to be grouped with accessions from subgenus Cerasus.Cluster Cerasus was comprised of five accessions, with ‘Bing’ and ‘Mazzard F12/1’ (P. avium) being closely clustered and separated from ‘Pontaleb’ . ‘Maxma 14’ and ‘Maxma 60’, interspecific hybrids considered to be of P. mahaleb × P. avium parentage, were grouped with P. avium accessions.Cluster Prunus accessions suggested the maximum ∆K-value of K = 3 , 17 in the second population (Prunus) and five in the third population (Cerasus). Eleven accessions were classified as admixed and nine of these accessions showed approximately 50% of membership to both Prunus and Amygdalus populations , while the duplicates of ‘Adara’ and ‘Citation’ were identical.Population genetic structure among the studied = 3 Fig. , as seenPrunus-Amygdalus accessions was located in the central area of the PC1 and PC2 plot, and the Amygdalus, Prunus and Cerasus clusters diverged from it , in the vicinity of cluster Cerasus; ‘Ishtara’, near members of subgenus Prunus; and Nanking cherry, in the middle, closer to the cluster Prunus than to the cluster Cerasus.The first two components of a principal component analysis (PCA) described 55.2% and 21.4% of the variance, respectively. Results supported phylogenetic Fig.  and popuDetailed analysis of the classification of SNPs along the eight peach pseudomolecules was performed using data from only 55 unique accessions; one of the two samples of the accessions analyzed in duplicate was included in the study; e.g., ‘Adara’ CSIC, ‘Citation’ CEAF and ‘Mariana 2624’ CSIC.Amygdalus in blue, Cerasus in red and Prunus in green). Prunus-Amygdalus hybrids and Nanking cherry were included only in the analysis of 55 accessions. A non-uniform pattern of SNP distribution along the eight pseudomolecules was observed when all 55 accessions were considered as well as when accessions within a subgenus were considered. SNP distribution along pseudomolecules was correlated with gene distribution.SNP location and gene density (gray) along the eight pseudomolecules of the peach genome, visualized in Circos plot, are presented in Fig. From the initial set of 45,382 SNPs identified, 4,302 9.7%) were detected in intergenic regions ; followed by SNPs with a low (14% of the SNPs with synonymous substitution); moderate (9.9% of the SNPs could have a non-synonymous substitution); and high impact (0.1% of the SNPs with disruptive impact on the protein). SNPs with a modifier effect had a downstream gene variant, with a default length of 5 kbp downstream of the most distal polyA addition site, as the most frequent variant (39.0%) , while Prunus and Cerasus accessions show the variation (T/T). Out of the 119 genes, only 36% had hits in KOG database . This included 53 diploid Prunus rootstocks and five scion cultivars. Some of the accessions included in this work are extensively used by the stone fruit industry around the world. In fact, millions of trees on these rootstocks are sold and planted every year and they remain in the orchard for 10–20 years, depending on the fruit species. That makes this research relevant through time and to broad audience, and the results provide valuable insight into genetic diversity and relationships at the interspecific level within Prunus.The discovery and identification of genomic variants such as SNPs, together with the determination of their location in the genome, can provide valuable information for breeding programs. In plants, many traits of interest have been linked with SNPsPrunus for phylogenetic and population structure analysis in apricot37 using 90 accessions of different origins and DNA digestion using ApeKI restriction enzyme. Single-digest GBS was also used for phylogenetic analysis using 11 Japanese plum cultivars to explore their natural allelic diversity in relation to the most important phenology events and fruit quality traits38. In this work, we used a double-digest strategy (PstI/MspI) and deep coverage paired-end sequencing for genome-wide representation of each Prunus sample. In our experience and in other published reports, paired-end sequence reads can be more accurately mapped onto the reference genome when compared to single-end reads45, which results in a significantly greater number of quality SNPs and lower amounts of missing data.SNPs identified from GBS have been previously used in Prunus. In fact, the genus Prunus is well enabled with the availability of several reference genome sequences49. However, it must be noted that each genome is prone to harbor insertions and deletions with respect to genomes from other species. For example, in the first work published using GBS, Elshire et al.27 observed that BLAST results indicated that the majority of non-aligning reads represented maize sequences that were absent in the reference genome version used for the analysis. In peach, some genes have been reported as mutated when they were compared with similar sequences from other Prunus species54. Nevertheless, in spite of these differences, results obtained in this study further corroborate the high degree of synteny found among Prunus species reported previously57. Even though our analysis included species from three different Prunus subgenera, we observed an exceptionally high read mapping rate to the reference genome.The availability of a reference genome sequence allows for a positional reference for each SNP for genome-wide analysis. The group of SNPs identified in the present study could prove useful for various marker-based applications in 39, and 75 SNPs with the IRSC 9 K peach SNP array v140. This could be explained by technical differences in the two genotyping approaches where peach SNP array does not contain A/T nor C/G SNPs, for example, and because the accessions used for SNP identification are different. Also, GBS allows for discovery of the SNPs that are different between the genotyped samples and the reference genome, therefore reflecting the difference between peach genome sequence and the material analyzed. Among newly discovered SNPs, transitions were more frequent than transversions which was previously observed in different plant species60. This is to be expected because transitions are less likely to result in amino acid substitutions and are therefore more likely to persist as silent substitutions in populations. The Amygdalus subgenus exhibited the lowest percentage of heterozygous SNPs, while Prunus-Amygdalus hybrids had the highest number of heterozygous SNPs. This is logical when the reference genome is considered, where material closer to P. persica is less heterozygous, e.g. ‘Pomona’, a pure P. persica. Other accessions with a low percentage of heterozygous SNPs, ‘Pontaleb’, ‘Mazzard F12/1’ and ‘Bing’, belong to Cerasus subgenus. These results could be explained with variations in the extent of genetic diversity across stone fruit species, ranging from a narrow genetic base in peaches; intermediate for apricots, sweet cherries and sour cherries; and higher genetic variability in almonds and plums62.In comparison to previous studies where genotypes were collected by a community SNP array, only a group of 49 SNPs were in common with the cherry 6 K SNP array v1Prunus accessions into three major genetically distinct groups was consistent with their expected pedigree relationships and parentage, which is in agreement with previous studies featuring accessions useful as rootstocks for different Prunus species21. Classification of hybrids between Prunus and Amygdalus, e.g. ‘Ishtara’, clearly reflected their origin with the proportion of genetic content belonging to both Prunus and Amygdalus subgenus. An interesting result was observed for Nanking cherry (P. tomentosa), classified as a member of the section Microcerasus within the subgenus Cerasus1 or the subgenus Prunus63. The P. tomentosa accession was grouped within Prunus in our study, although the structure results reveal similarities with all three subgenera . Mowrey and Werner11 suggested P. tomentosa being more primitive than other Prunus species, which could explain our results.Classification of 58 Solanum44 using SNP from ESTs of Solanum habrochaites and S. lycopersicum. In the peach genome, most of the regions with low number of SNPs were associated with the putative position of the centromere of each pseudomolecule26. One exception to the SNP density being associated with gene density is the bottom region of pseudomolecule 4, which had high number of SNPs in our material, but low gene density  were identified in 4,884 genes in the peach genome, with 53% of SNPs located in exons. The number of SNPs located in genic regions is higher than previously observed in two sweet cherry cultivars and their progeny34, where 65.5% of SNPs were located in genic regions and 49.8% were located in exonic regions. Differences could be due to the use of different restriction enzymes, different version of the peach genome (ApeKI and Peach v1.0 in the previous work and PstI/MspI and Peach v2.0 in this work) and material analyzed. Despite the differences in the methodology between the studies, the percentage of SNPs located in exon regions was similar, with less SNPs identified in intergenic regions and higher number of SNPs located in intron and 5′- and 3′-UTR in this study. This information might be useful for future applications because SNPs in the upstream, downstream, and 5′- and 3′- UTR regions might affect transcription and/or translation. However, the actual SNP effects have to be confirmed on case-by-case basis68 because not all mutations are functionally important and different proteins and domains differ in how well they tolerate mutations69.The association between single nucleotide change and gene function has been reported for a number of traits70, soybean71 and pear72. In contrast, the SNPs with impact on protein efficiency and loss-of-function, that have a direct impact on gene function with adaptive interference during the course of selection, were reported in a smaller proportion. To the best of our knowledge, this is the first work using members from different subgenera which were analyzed using GBS and where SNPs were classified according to their putative effect on annotated genes. Consequently, it was not possible to compare our results with those from other related works. Similar distribution of SNPs in respect to their putative effect was observed in a collection of bean accessions70 by using the Diversity Arrays Technology methodology (DArT), also based on genome complexity reduction using restriction enzymes (PstI/MseI) and SNP detection through hybridization of PCR fragments73.As expected, the SNPs classified as modifier in our study were more abundant that the other categories, while the high impact SNPs were the smaller proportion. The same was observed in other crops, for example beanPrunus subgenera, with nucleotide changes observed for some subgenus and not for others. Detailed analyses are needed, but this information will facilitate investigation of the consequences of predicted SNPs and their biological role. Involvement of SNPs in genes reported to be playing a role in metabolism, cellular processes and signaling, and information storage and processing could be addressed in detail at breeding level.The group of 128 high impact SNPs identified in this study, which are located in 119 genes, could have a direct effect on the gene functionality in the group of accessions analyzed. These SNPs caused either stop codon gain, splice donor and acceptor variants or stop codon loss. By performing KOG analysis Fig.  of thesePrunus rootstocks. This resource provides foundation for analysis of the genetic diversity among the different interspecific hybrids and species in the germplasm collections of CEAF and EEAD-CSIC for their conservation, management and utilization in current or future rootstock breeding programs. Informative SNPs identified in this study, particularly in coding and non-coding regulatory sequence components of various genes, once validated, can be utilized as potential markers in genetic and association mapping for identifying major trait-regulatory candidate genes/QTLs in Prunus.The 45,382 GBS-derived SNPs identified in this study represent a valuable resource for molecular characterization of commercial and selected Prunus rootstock collections: 33 accessions coming from CEAF in Chile and 25 from EEAD-CSIC in Spain. These accessions belong to three Prunus subgenera: Amygdalus (n = 25), Prunus (n = 17), Cerasus (n = 6) and 10 hybrids between subgenera Prunus and Amygdalus. Detailed information about accessions is provided in Table The group of 58 diploid accessions used in this study Table  are partFor genomic DNA extraction, young leaves of each accession were collected and stored at −80 °C until use. The DNeasy Plant Mini kit (Qiagen) and the NucleoSpin Plant II kit (Macherey-Nagel) were used for DNA extraction for samples from CEAF and EEAD-CSIC, respectively, according to the manufacturer’s instructions. DNA quality was examined by 1% agarose gels and DNA quantity was determined by spectrophotometry .PstI (methylation sensitive) and MspI , as described by Poland et al.28 in cereals. A total of 200 ng of intact genomic DNA was digested and ligated to custom designed adapter sequences. A total of 58 GBS libraries were sequenced on an Illumina HiSeq2500 using a 2x125 bp paired-end read module across 2 high-output lanes. Raw sequence data was demultiplexed and preprocessed for errors using the Stacks demultiplex tool74. Sample specific sequences were aligned to the eight pseudomolecules representing the eight chromosomes of the peach genome assembly (Peach v2.0)26 with the GMAP/GSNAP release 816.1675. The resulting variant call file (.vcf) was filtered for SNPs with a minimum depth (DP) of six, and present in at least 80% of the accessions. Mean coverage of each GBS SNP was determined by creating a.BED file from the final SNP set and generating a bed graph with the genomecov function of bedtools v. 2.28.076, and intersecting the bedgraph with the SNP.bed file with the intersect function in bedtools. The mean coverage of each sample was determined with in house scripts. SNPs were extracted using the GBS pipeline implemented in TASSEL 5.2.5 software77 and accessions were called using minor allele frequency (MAF) > 0.05.GBS was carried out at Clemson University Genomics Computational Laboratory . A reduced representation GBS library was prepared using restriction enzymes www.perl.org) with Peach v2.0 as reference. The physical position of each SNP was used to identify common markers among this study, the RosBREED cherry 6 K SNP array v139 and the IRSC 9 K peach SNP array v140.SNPs were labeled according to the pseudomolecules in the peach genome (Pp01 to Pp08), followed by the physical position in base pairs (bp). Location of each SNP within genic  and intergenic regions was determined using a custom Perl script (http://sniplay.southgreen.fr)78.Transitions/transversions and percentage of heterozygous positions were determined using SNiPlay3 80. Accessions were subdivided into different populations according to their maximum membership probability among the populations and the membership probabilities threshold of 0.80. Furthermore, principal components analysis (PCA) was performed on genotype scores using the PCA function in TASSEL and visualized using Infostat v201781 to confirm population structure among accessions.An UPGMA dendrogram was constructed using Archeopteryx software within TASSEL82 was used to plot the histograms of both gene and SNP density for each pseudomolecule of the peach genome sequence26. Gene and SNP density were assessed and plotted in a window of 1000/kb. In a first analysis, SNP density was determined for 55 accessions . In a second analysis, accessions were separated by subgenus  and intergenic regions for the three subgenus  and for the group of 55 accessions analyzed in this study.Circos software v 0.69–368 based on the P. persica gene annotation (www.rosaceae.org). Whenever multiple transcripts for a gene exist, the effect on each transcript was analyzed. The SNP predicted effects were categorized by impact, as modifier (with impact on noncoding regions), low (synonymous substitution); moderate (non-synonymous substitution); or high (disruptive impact on the protein). To investigate the putative function of the genes containing high impact SNPs, a eukaryotic orthologous group (KOG) analysis was carried out using tools from Join Genome Institute .Prediction of SNP effects was performed using SnpEff v 4.3eSupporting Information.


Pinus halepensis), a drought resistant conifer that has colonized many habitats in the Mediterranean Basin, as the model species. We used a nested sampling approach that considered replicated altitudinal gradients for three contrasting sites. We genotyped samples at 294 SNPs located in genomic regions selected to maximize outlier detection. We then applied three different statistical methodologies—Two Bayesian outlier methods and one latent factor principal component method—To identify outlier loci. No SNP was an outlier for all three methods, while eight SNPs were detected by at least two methods and 17 were detected only by one method. From the intersection of outlier SNPs, only one presented an allelic frequency pattern associated with the elevational gradient across the three sites. In a context of multiple populations under similar selective pressures, our results underline the need for careful examination of outliers detected in genomic scans before considering them as candidates for convergent adaptation.Finding outlier loci underlying local adaptation is challenging and is best approached by suitable sampling design and rigorous method selection. In this study, we aimed to detect outlier loci  at the local scale by using Aleppo pine ( Finding the genetic basis of local adaptation is a topic of crucial importance in evolutionary biology as it allows for the study of the mechanisms of natural selection ,2. HowevPinus halepensis, Mill.). Aleppo pine has a circum-Mediterranean distribution that is both geographically fragmented and vast, spanning 3.5 million hectares [In the present study, our objective was to shed new light on the genomic signatures of local adaptation in Aleppo pine , two Italian populations , and two Spanish populations , totaling 476 individuals from seven populations  were doFor every genotyped tree, 50 mg of needles were dried with silica gel and ground in a QIAwell  plate homogenized with a mixer mill MM300  under liquid nitrogen. DNA extractions were carried out with the Invisorb DNA plant HTS 96 kit  following the manufacturer’s instructions. DNA was quantified with Nanodrop 10,000 .Aleppo pine populations were successfully genotyped at 294 SNPs (conversion rate of 76.56%) using a 384-plex SNP assay with Illumina VeraCode technology as described in Pinosio et al. . SNP genLoci for which SNPs were found putatively under selection using at least two different methods were annotated from homology with other plant species using Geneious version 6.1  by searcWe used two Bayesian outlier approaches, Bayenv2 ,35 and BFST was computed among all seven populations with Genepop v4.1 [K) set from 1 to 10; number of iterations per K set to 10; number of steps set to 100,000 with a burn in period of 10,000 to minimize the effect of the starting configuration, and with an ancestry model of admixture. The convergence toward reliable allele frequency estimates in each genetic group and membership probabilities of individuals to a genetic group was assessed by checking that the alpha parameter was relatively constant, as indicated in the STRUCTURE manual.To confirm that the population pairs/triplets were sampled from the same phylogeographic clusters, population genetic structure was estimated in two ways. First, pairwise pop v4.1 . Second,pop v4.1 , with thFST index of genetic variation can also be viewed as the proportion of variance explained by principal components [K) was established by running the PCA analysis in PCAdapt [K displaying most of the cumulative explained variance was chosen based on a scree plot, which presents the percentage of variance explained by each principal component (PC) in decreasing order. The choice of K was also corroborated by plotting individuals on the first two components  to verify if the level of clustering was consistent with the chosen value for K. To account for missing data, the correlation matrix between individuals was computed using only the markers available for each individual. The Mahalanobis distance was used to identify the outliers by measuring how distant a data point is from the multivariate space’s centroid , considering the covariance structure of all of the data points in the sample. By default, alleles displaying MAF < 5% were removed in the PCAdapt analyses.The rationale of this method is that the mponents ,54,55,56 PCAdapt  with a lq-values based on the p-value distribution.Both Manhattan and Quantile—Quantile plots were used to visualize the distribution of the data. The presence of outliers was confirmed by applying a false discovery rate (FDR), defined as the percentage of false positives among the list of candidate SNPs. The FDR was set at 10%, as recommended by Luu et al.  using thXtX in Baypass to help define a cut-off point for the Bayenv outputs, for which standardized methods exist neither for the XtX statistics nor for the Bayes factors. The comparison aimed to also compare the outliers identified by the two methods.BayenvBayesian approaches have been shown to perform well in situations where there is a non-uniform population structure ,57. The XtX and evaluates the association of ecological variables with genetic marker differentiation. Bayenv2 first estimates the empirical pattern of covariance in allele frequencies between populations, and uses this as the null model to test each individual SNP for selection. To minimize the stochasticity in estimating the null model, three covariance matrices were produced from 100,000 iterations each, and the mean of these matrices  was then used. In order to test whether the mean covariance matrix represented the true variance of allele frequencies well across populations, it was compared to a pairwise FST matrix using a Mantel test in R (R Core Team 2013) with 1000 permutations. The method described in Coop et al.  and implXtX averages across multiple samples from the MCMC. Outlier XtX values from the main XtX distribution were considered as potentially under selection. Finally, these values were compared to the ones estimated using Baypass in order to ascertain significance. Indeed, in theory, Baypass and Bayenv2 both produce XtX statistics and can be easily compared against each other. However, Bayenv2 does suffer from a lack of pre-defined methods to establish outliers based on XtX statistics, which would have been difficult to do without the results from Baypass to use as a guideline. (2) Finding SNPs that show a significant correlation between an environmental variable and allele frequencies. This was carried out by comparing, via Bayes factors, a model that allows for a linear relationship between allele frequency from the 294 SNPs and the 20 environmental variables with a null model based on the covariance matrix. This approach was repeated three times for each combination of SNP-environmental variable tested for association to account for instability between independent runs [BaypassThis covariance matrix, which takes into account correlations due to population structure, was then used as the null model in two analyses aimed at detecting SNPs under selection. (1) Detecting outliers through differences in overall genetic differentiation as in classic outlier tests by estimating the ent runs . The meaent runs  criterioXtX statistics and determine a cut-off point for detecting potentially significant outliers by applying a false discovery rate threshold.The Baypass method is based on a very similar model as Bayenv2, but is suggested to be an improvement in two aspects: (i) It has a higher accuracy in the estimation of the baseline  allele frequency through the use of a hierarchical Bayesian model; and (ii) the use of simulated pseudo observed datasets (PODs) from the posterior predictive distribution in order to calibrate the XtX differentiation were produced from the SNP data. The correlation matrix was then visualized as a hierarchical cluster tree in order to be compared to a neighbor joining tree of pairwise FST done in Genepop v4.1 [XtX. The quantiles of these empirical XtX distributions were used to calibrate the XtX observed for each locus in the original data, and the 99% quantile of the XtX distribution from the POD analysis provided a 1% threshold XtX value (the default cut-off in Baypass). This threshold was then used to discriminate between outlier and neutral SNPs. Baypass is expected to outperform Bayenv2 because it uses the analyzed data to provide hyperparameters for the outlier detectors. However, as the two methods have not been used in conjunction, we ran them concomitantly to compare how these different features will impact outlier detection.Baypass  was run pop v4.1 . Geneticpop v4.1 . Second,Baypass was also used to detect outlier SNPs showing a significant correlation between an environmental variable and allele frequencies. This was carried out by comparing a model that allows for a linear relationship between allele frequency and environmental variable with a null model using only the correlation matrix. For this purpose, Baypass was run using an importance sampling estimator (IS) model based on Coop et al. . The empIn the two Bayesian analyses, SNPs with a minimum allele frequency (MAF) below 5% were kept  as their removal is not recommended because some of these might be under selection.FST were all significant except between the two Italian populations  and the variance across the iterations increased (K = 2 with STRUCTURE), while PC2 separates the French populations from the other two to a lesser degree , and that it was unnecessary to use more PCs .Confidence in the mean covariance matrix representing the true variance of allele frequencies across populations was assessed by the similarity between its heat map and that of pairwise T values , that waXtX analysis were: SNP 4 (seq-9882-801), SNP 149 (seq-8671-529), SNP 316 (seq-10373-2483), and SNP 378 (seq-2_3941_01-381) (BaypassThe four top outliers from the _01-381) , left. W_01-381) .BaypassFST  , right.When Baypass was used to detect SNPs showing significant correlation with environmental variables based on eBPis > 3, 17 SNPs were detected see . These wEight SNPs were identified as outliers in at least two methods : SNP 4, These SNPs belonged to six different loci, of which two could be annotated using the BLAST search : seq-988XtX statistics (SNP 149 and SNP 378), while Bayenv2 detected two additional ones (SNP 4 and SNP 316). Of the eleven outliers detected by PCAdapt, only SNP 4 was in common with the XtX outliers found in Bayenv2 . This family of proteins act as auxin transporters and constitute key regulators in multiple developmental events ranging from embryogenesis through morphogenesis and organogenesis to growth responses to environmental stimuli [Locus annotation could be obtained only for one of the two top-candidate SNPs, SNP 4, which is located in a non-coding region of a PIN-like protein  the evolutionary history of the species ; (ii) the sampling size in terms of individuals per population ; and (iii) the sampling size in terms of replicated environmental gradients . When detected, a convergent pattern of microevolution at local geographical scales may be attributed to parallel selective pressures at the locus, shared ancestral standing variation, or spread of the selected alleles via gene flow. Understanding the origin of convergent evolution can assist in dissecting the basis of local adaptation, and thereby in predicting adaptive response to selection ,70. In AWith the combination of the approaches used here , eight SNPs were identified as outliers by at least two methods, of which only two (SNP 4 and SNP 378) had their allelic frequencies associated with elevational gradients, and one (SNP 4) across all population replicates. Looking for signal of convergent evolution at local or regional scales is an attractive and powerful approach to understand the molecular basis of adaptation. However, false positives could be linked not only to the statistical methods used, but also to the sampling. The outcome of our study cautions against considering outlier markers detected by genomic scans as automatic candidates for convergent adaptation in populations submitted to similar selective pressures.


Panicum hallii.Understanding how and why genetic variation is partitioned across geographic space is of fundamental importance to understanding the nature of biological species. How geographical isolation and local adaptation contribute to the formation of ecotypically differentiated groups of plants is just beginning to be understood through population genomic studies. We used whole genome sequencing combined with association study of climate to discover the drivers of differentiation in the perennial C4 grass P.hallii revealed complex population structure across the species range. Major population genomic separation was found between subspecies P.hallii var. hallii and var. filipes as well as between at least four major unrecognized subgroups within var. hallii. At least 139 genomic SNPs were significantly associated with temperature or precipitation across the range and these SNPs were enriched for non-synonymous substitutions. SNPs associated with temperature and aridity were more often found in or near genes than expected by chance and enriched for putative involvement in dormancy processes, seed maturation, response to hyperosmosis and salinity, abscisic acid metabolism, hormone metabolism, and drought recovery.Sequencing of 89 natural accessions of P.hallii. Population subgroups within P.hallii may represent early stages in the formation of ecotypes. Climate associated loci identified here represent promising targets for future research in this and other perennial grasses.Both geography and climate adaptation contribute significantly to patterns of genome-wide variation in The online version of this article (10.1186/s12864-018-5179-7) contains supplementary material, which is available to authorized users. Understanding how and why genetic variation is partitioned across geographic space is of fundamental importance to understanding the nature of biological species –3. For wPanicum hallii Vasey, is a perennial C4 grass and a member of the Panicoid grasses, which includes sorghum, maize, and sugarcane [P. hallii is a diploid model system for the closely related tetraploid bioenergy crop switchgrass (Panicum virgatum). Beyond having a favorable ploidy for genetics, P. hallii has a short generation time, a small stature, a moderate sized genome (~ 550 Mbp), and a primarily self-fertilizing mating system [P. hallii has two major ecotypic subspecies (varieties), P. hallii var. hallii  and P.hallii var. filipes (hereafter var. filipes) [var. hallii is typically found in drier habitats across the range of the species, which stretches from Arizona to central Texas and south into northern Mexico. P. hallii var. filipes, like lowland switchgrass, is typically found in more mesic habitats and is restricted to the southern Rio Grande Valley and the Gulf-Coast of South Texas and Northern Mexico. In general, var. hallii is smaller in stature, has fewer tillers, has larger seeds, and flowers earlier than var. filipes [Hall’s panicgrass, ugarcane . P. hallg system , 11. Jusg system ), P. halfilipes) , 14. Sim filipes , 15. The filipes .var. hallii is further subdivided into two subgroups, one occurring primarily in low elevation regions of Texas, with the other occupying higher elevation sky islands of Chihuahuan and Sonoran deserts. Thus, the population structure of P. hallii is likely more complex than the major division between the two recognized varieties. Despite this new insight, the previous study by Lowry et al. [P. hallii. Further, the previous study only had one accession from northern Mexico, which represents a large portion of P. hallii’s range, and only three accessions of var. filipes.A recent microsatellite-based population structure analysis by Lowry et al.  suggestey et al.  utilizedP. hallii from 89 locations to better understand genomic variation and patterns of population structure and across its range. With whole genome resequencing, we were afforded the opportunity to identify potential candidate genes and genomic regions that could be involved in local adaptation to different ecoregions. Overall, our goals were: 1) To characterize fine-scale population structure and hybridization between and within P.hallii subgroups; 2) To identify regions of the genome associated with local adaptation to major climate variation in temperature and precipitation; and 3) To provide a genomic resource for future studies into the population genomics of perennial grasses.In this study, we conducted whole genome resequencing of accessions of P. hallii has revealed that much of the large pericentromeric region of the genome consists of a low-recombination region with a high level of repetitive DNA. Such large pericentromeric regions are common across plant species [P. hallii accessions. Given that an appreciable rate of erroneous variant calls likely still occurs in the data, we estimated that the upper bound for pairwise diversity (π), based on callable sites (~ 46% of the genome), as 0.008 within var. filipes and 0.007 within var. hallii. Further, pairwise divergence (Dxy) was greater for comparisons between var. filipes and any var. hallii groups than between any of the var. hallii groups  and var. filipes (N = 11). Within var. hallii, linkage disequilibrium decayed (r2 < = 0.2) at around 6 kb while within var. filipes, linkage remained high (r2 > 0.3) at distances greater than 25 kb , and three more geographically overlapping groups. A small group is restricted to central Texas (blue) that overlaps with a second group that ranges from central Texas south into northern Mexico (yellow). A third var. hallii subgroup occurs in south Texas (purple) and overlaps to a great extent with the range of var. filipes. Interestingly, the degree of divergence between P.hallii var. hallii subgroups (max Dxy = 0.008), which are not formally recognized as subspecies, approached the level of divergence found between recognized subspecies var. filipes and var. hallii  was not significant across the entire P. hallii collection after controlling for the effects of population substructure (see r = 0.580) than within the smaller South Texas group (Mantel’s r = 0.728). We hypothesize that genetic diversity across P.hallii is the result of some isolation by distance combined with hierarchical structure that developed as a result of expansion from southern glacial refugia following the last glacial maximum.Geography is correlated with genetic distance across the range of lii Fig. . Correlature see . Howeverture see , red, MeVar. filipes is found only in the coastal part of the range in South Texas, whereas var. hallii is found throughout the study area with the exception of some parts of the Gulf Coast region .Population structure within P. hallii individuals and for the set of only var. hallii plants . While 41 SNPs were associated with aridity across the combined group, there were no SNPs associated with aridity in var. hallii alone, indicating this association may be primarily driven by differences between subspecies. Many climate associated SNPs in both analyses fell within genic or 1 kb promoter regions of the genome. In the analysis of all plants, for at least three climate variables - isothermality, minimum temperature of the coldest month, and aridity – associated SNPs were more often found in genes than expected by chance  suggesting many may be functional in nature. To test for the effect of recent strong selection on genes containing climate-associated variants we calculated both variable-site nucleotide diversity (πvar) and Tajima’s D. We found no evidence of reduced diversity or an excess of rare variants in climate-associated genes. Variable site π was similar in climate and non-climate associated genes . Tajima’s D was close to zero and similar between the gene groups .We conducted a genome-wide association study (GWAS) of high frequency MAF > 10%) SNPs against each major climate variable . Geography explained only 5.1% of variation in the same SNP set. Most other climate variables including mean daily temperature, isothermality, annual precipitation and aridity explained between 12 and 15% of candidate SNP variation, geography explaining between 5 and 11% of variation  to calculate the proportion of variation in each set of candidate SNPs (for each climate variable) that can be explained separately by climate and by geography . SNPs in genes associated with daily temperature range were enriched for functions related to hormone metabolism and biosynthesis. Lastly, SNPs associated with isothermality were enriched for involvement in drought recovery. An isothermality associated SNP fell within Pahal.D02753. The Arabidopsis ortholog of this gene (AT1G70670) is a negative regulator in abscisic acid (ABA) signaling , which P. hallii revealed that it is partitioned into at least five subgroups. This revelation of complexity of population structure in P. hallii parallels recent findings in its close relative switchgrass (P. virgatum). The literature on switchgrass typically partitioned that species into upland and lowland ecotypes [var. hallii and var. filipes. However, just as in this study recent population structure analyses and new collections in the southern US have revealed that other major groups also exist within P. virgatum [Population structure analysis of ecotypes , 23, whivirgatum , 24.P. hallii and other plant species suggests that many widespread species exist as subspecies complexes, with groups at different stages of the speciation continuum occurring across their range [var. hallii and var. filipes, are at least partially reproductively isolated. This idea is supported by Lowry et al. [var. hallii and var. filipes. There may also be some reproductive isolation between the four subgroups identified within P. hallii in this study. Admixture between the varieties appears to occur at a relatively low rate, despite having overlapping geographic ranges. Future studies should quantify reproductive isolating barriers between these groups and sample more individuals within populations, especially those in sympatric regions, to establish the extent of gene flow between the various groups.Overall, the divided population structure of ir range . The mosy et al.  who idenP. hallii may also provide insights into general biogeographic divisions across its range. In particular, there is a strong break in population structure that runs diagonal through West Texas from northeast to southwest,  contained climate associated SNPs. P. hallii is exposed to variable levels of drought stress throughout its range during the summer and becomes dormant in the winter months. Population subgroups, perhaps nascent ecotypes, would be predicted to have appreciable differences in both drought tolerance and avoidance response (dormancy). One candidate gene  containing climate-associated variation is likely to be involved broadly with drought recovery through the regulation of the key stress hormone ABA.The putative function of many loci associated with climate variation in P.hallii. These population subgroups were partially overlapping across the species range and may represent early stages in the formation of subspecific ecotypes. Genomic variation across subgroups was significantly driven by adaptation to broad-scale climate variation in temperature, precipitation and aridity. Genes containing climate-associated SNPs most likely function in stress response regulation, tolerance, and recovery. These genes provide targets for further research into the molecular basis of adaptation along ecological gradients, particularly in perennial grass species.Using population genomic sequencing and analyses we identified multiple population subgroups within the perennial C4 grass Many of the accessions used in this study were collected from field sites as seeds or live plants in the years 2010–2014. The authors thank the following for providing seed collections and permission for our collections: The Ladybird Johnson Wildflower Center, The Brackenridge Field Laboratory, The Nature Conservancy of Texas, The State Parks of Texas, The Kika de la Garza Plant Materials Center, members of Christine Hawkes laboratory, and The University of Arizona and Arizona State University Herbaria. Seeds were collected from private lands through permission granted by the Texas Ecolab project. Seeds were imported from Mexico by under a University of Texas herbarium permit, number SGPA/DGVS/04170/14.P. hallii and to avoid biases introduced by strong pairwise among deme population structure [A single accession was sampled from each population to maximize sampling across the geographic range of tructure . Becausetructure . All accWhole genome sequencing was performed at two sequencing centers. Eighty lines were sequenced at the Joint Genome Institute  on either the Illumina HiSeq2000 or HiSeq2500 platforms. The remaining libraries were sequenced at the University of Texas at Austin. Sequencing was paired-end at 150 bp read length.https://www.ncbi.nlm.nih.gov/sra). Accession numbers are listed in Additional file Short read data has been archived in the NCBI Short Read Archive  using BWA MEM (v0.7.12) and the parameters (−M –p –t8). Before alignment, the genome was repeat masked for 24-mers occurring >= 650 times. This masked approximately 5% of the genome. BAM files were screened for PCR duplicates and areas around indels were realigned using GATK IndelRealigner [p > 0.05. SNPs were only retained that had a greater than 90% call rate across all individuals and that had at least two individuals supporting each homozygous allele.Reads were aligned against the v2 draft ealigner . SNPs weealigner  with a mN = 15,714 nucleotide positions). We also observed unusually high numbers of heterozygous calls in pericentromeric regions of the genome. This is likely due to inaccuracy of alignment in these areas, which are known to often be repeat-rich in plants [P. hallii at microsatellite markers was about 5% [P. hallii genome (www.phytozome.org).The reference genome line (FIL2) was included in the original sample set, and as a conservative control for sequencing or assembly errors calls at any site where an alternate allele was called in the re-sequenced FIL2 line were considered missing data  to control for possible linkage disequilibrium. We designate this set of markers as ‘neutral SNPs’.A set of synonymous SNPs were isolated for use in analyses of population structure. Using VCFtools v0.1.11 , we genehttp://tree.bio.ed.ac.uk/software/).We analyzed the neutral SNPs with RAxML v8.0.6  to generK = 2–8. Each run had a default 10,000 burn-in MCMC iterations and an additional 10,000 iterations following. Results for all runs were summarized using Structure Harvester [The neutral SNPs were also used to analyze population clustering with STRUCTURE . We convarvester  and plotarvester . The numarvester  was 3. HIsolation by distance (IBD) was tested using a Mantel test, in the R package vegan, of the matrix of geographic distances between sampling locations vs. the matrix of genetic distances between individuals. Genetic distance between individuals was calculated as 1-(probability of identity by state) for neutral SNPs in TASSEL . Geographttp://www.worldclim.org/bioclim. Climate variable values were extracted for a set of 127 P.hallii known sampling locations using the data portal at http://dataportal-senckenberg.de/dataExtractTool/. To choose climate variables for association analysis, we used principal components analysis to determine the axes of greatest climate variation across the study area and to identify variables with the least amount of correlation. First, we determined the principal components of the set of all precipitation variables (bio12-bio19) and identified the individual variables that loaded most heavily on the first three principal components . Aridity is calculated as mean annual precipitation divided by mean annual potential evapo-transpiration. Data for climate variables isothermality, minimum temperature of the coldest month, and precipitation in the warmest quarter were transformed to improve normality before analysis.Full BioClim environmental data (19 variables) was downloaded for the study area from N = 3,059,501). To control for population structure, we tested three linear models: one that incorporated a random effect kinship matrix, one that used the STRUCTURE population membership covariates , and one that used both [p-value distributions. Because use of a model with Q coefficients alone provided stronger control for structure than using a K-matrix alone, we chose the former for all subsequent analyses. We used a Bonferroni corrected significance threshold of p < 1.63 × 10− 8 for associations. Enrichments for SNP position (genic/non-genic) and type (synonymous/non) were conducted using Fisher exact tests or the chi-squared approximation in R. We repeated the association tests within var. hallii alone, excluding all var. filipes individuals. In that analysis, there were 2,241,150 SNPs with MAF > 10%. We used a Bonferroni corrected significance threshold of p < 2.23 × 10^− 8.We used TASSEL to test the association between SNPs and each selected climate variable. To avoid bias we only analyzed SNPs with a minor allele frequency (MAF) of 10% or greater  of latitude and longitude of all plant collection sites as a predictor variable. Missing genotype values were imputed as heterozygous. Significance of each constrained axis of variation (climate variable and geographic PC) was measured using 999 permutation tests. RDA was executed using the vegan package in R . For theP.hallii reference proteins were paired with their best Arabidopsis protein matches using BLAST (blastp) at an e-value cutoff of 10− 3. This set of proteins (including duplicates) was used as the background gene set against which to test hypotheses of significant enrichment of GO terms for each gene set of interest. Subsequent GO enrichment tests for genes near climate associated SNPs were done using the R BIOCONDUCTOR package topGO [P-values <= 0.05  terms among associated SNPs for each climate variable. To annotate GO terms, all ge topGO . We usedAdditional file 1:Figure S1. Linkage vs. physical distance (bp) between markers in the genome. Figure S2. Plots of STRUCTURE membership at various k values. Figure S3. Analysis of climate variation via principal components. Figure S4. Heatmap of 19 Bioclim variables by collection site. Figure S5. Maps of variation in climate variables across the range of P.hallii. Figure S6. Manhattan plots of SNP association with climate variables for P.hallii. (PDF 1693 kb)Additional file 2:Table S1. GO terms significantly associated with each climate variable. Table S2. Candidate genes containing climate associated SNPs. Table S3. Collection locations and SRA accession numbers for sequenced plants. Table S4. RDA analysis of SNP variation. Table S5. Analysis of isolation by distance. (XLSX 26 kb)


FMAR  values at most loci with enough high coverage depth and low level of sequencing noise. These results indicate the potential advantage of the custom MPS–SNP assays and Ion Torrent PGM platform for forensic study.The custom-designed single nucleotide polymorphism (SNP) panel amplified 231 autosomal SNPs in one PCR reaction and subsequently sequenced with massively parallel sequencing (MPS) technology and Ion Torrent personal genome machine (PGM). SNPs were chosen from SNPforID, IISNP, HapMap, dbSNP, and related published literatures. Full concordance was obtained between available MPS calling and Sanger sequencing with 9947A and 9948 controls. Ten SNPs  with low coverage or heterozygote imbalance should be optimized or excluded from the panel. Sequence data had sufficiently high coverage and gave reliable SNP calling for the remaining 221 loci with the custom MPS–SNP panel. A default DNA input amount of 10 ng per reaction was recommended by Ampliseq technology but sensitivity testing revealed positive results from as little as 1 ng input DNA. Mixture testing with this panel is possible through analysis of the  Within forensic community, standardized methods of PCR amplification and capillary electrophoresis (CE) of short tandem repeats (STRs) are usually applied in forensic laboratories ,2. HowevIn the present study, a custom SNP panel containing 231 forensic polymorphic SNPs covering 22 human autosomes was sequenced using Thermo Fisher Ion Torrent PGM system. Libraries were constructed using Ampliseq technology. The MPS–SNP panel was evaluated by concordance study, analysis of population samples, sensitivity testing, as well as mixture testing.http://hapmap.ncbi.nlm.nih.gov/), dbSNP (http://www.ncbi.nlm.nih.gov/snp/), and related published literatures [In this study, public SNP databases of HapMap  was adopted for genome-wide SNP screening. SNPs with MAF above 0.3 in Beijing Han population of China (CHB) and show little frequency variation (Fst < 0.06) among main populations  were included. SNPs should be located in the intron area and follow HWE.http://projects.tcag.ca/variation/); have appropriate GC content (between 30% and 60%) in the flanking sequences; and have no long homopolymers (>5 nt) or Indels in the flanking sequences of targeted SNPs (±20 bp).All selected markers should keep a minimum distance of 5 Mb from each other and most commonly used forensic STR loci (listed in STRBase website). SNPs should locate away from copy number variants (CNV) regions, which are defined in the Database of Genomic Variants (The following steps of SNP screening were applied:http://www.ampliseq.com) for multi-primer designing. And a single pool with amplicon length of 125–175 bp which is suitable for degraded DNA testing was provided and chosen for the following study.A formatted BED file including the final candidates was submitted to Life Technologies’ AmpliSeq primer designing tool  . DNA wasControl samples of 9947A and 9948  were adopted as reference samples for concordance and accuracy testing, and Sanger sequencing was adopted as validation method. The two libraries were pooled together and sequenced on Ion 314 chip in duplicate. Serial dilutions of control DNA 9948 were performed to generate DNA concentrations of 10, 5, 2, 1, 0.5 and 0.2 ng/µL for sensitivity testing. And 1 µL of each concentration was added in the library construction PCR system, thus the DNA input for sensitivity testing ranged from 10 to 0.2 ng. Libraries of the six dilutions were sequenced in parallel with Ion 316 chip in duplicate. Two reference samples (9947A and 9948) with ratios of 100:1, 10:1, 5:1, 1:1, 1:5, 1:10 and 1:100 were prepared for mixture study. For the 1:1 ratio, 5 ng of each DNA was mixed together. For the ratio of 100:1 and 10:1, 50 and 500 pg of 9948/9947A were added to 5 ng of 9947A/9948, while for the 5:1 ratio, 2.5 ng of 9947A/9948 was mixed with 500 pg of 9948/9947A. Therefore, the DNA input ranged from 3 to 10 ng for library-construction system and libraries of the seven mixtures were sequenced with Ion 316 chip in duplicate.Ampliseq technology was adopted for library construction. Chemistry of Ion AmpliSeq Library Kit 2.0  and the primer pool including targeted-SNP primer pairs  were used for library preparation. The total library-PCR system was 20 µL, with 4 µL of 5× Ion AmpliSeq HiFi Master Mix, 10 µL of 2× custom primer pool, 1 µL of above prepared DNA and 5 µL of nuclease-free water. The library-PCR procedures were as follows: 2 min at 99 °C, 17 cycles of 15 s at 99 °C and 4 min at 60 °C followed by a 10 °C hold. According to our pre-testing results, 22 PCR cycles were adopted when library DNA amount was lower than 1 ng. The amplicons were treated with 2 µL FuPa reagent  to partially digest primers with incubation for 10 min at 50 °C, 10 min at 55 °C, 20 min at 60 °C and hold up to 1 h at 10 °C. Library-barcoding was labelled with the Ion Xpress™ Barcode Adapters  and library purification was performed with Agencourt AMPure Reagents .Ion Library TaqMan Quantitation Kit  on 7500 Real-time PCR System  was adopted for accurate library quantification. All library samples of a run were pooled in equimolar prior to sequencing. Pooled libraries were subjected to emPCR on Ion OneTouch™ 2 instrument  with the Ion PGM™ Hi-Q™ OT2 Kit , and cycling setting was selected as “PGM: Ion PGM™ Template OT2 200 Kit for Hi-Q™”. The optimal amount of library corresponds to the library dilution point that gives percent of ion sphere particles (ISPs) was between 10% and 30% . TemplatFMAR  was adopted to evaluate the performance of SNP calling, both for heterozygotes and homozygotes. FMAR was calculated as the biggest reads among the four bases  dividing the total detected reads at the base position. According to our previously published study [FMAR for accurate heterozygotes calling should range from 50% to 60%, while for homozygotes, it should be above 90%.Parallel sequencing was performed on Ion Torrent PGM with Ion PGM™ Hi-Q™ Sequencing Kit. Sample preparation and sequencing chip information are listed in Supplementary Table S1. Raw data were collected as DAT files and signal processing, base-calling and barcode de-convolution were processed with Ion Torrent Suite Server v4.4.0 . Homo genome of GRCh37/Hg19 was adopted as reference sequence. Coverage Analysis (v4.0-r77897), HID_SNP_Genotyper.42 (v4.2), and Variant Caller (v4.0-r76860) plug-ins with default settings were applied for data analysis. BAM files were checked with IGV_2.3.59 software. And parameter of Arlequin 3.5 , PowerMaP>0.05) and Fst < 0.06 which performed with population difference testing among African American, European American, and East Asian populations. After applying the location and sequence criteria detailed in Part 2.1, 85 auto-SNPs were left as candidate targets. Until this point, a total of 236 auto-SNPs were chosen and accordingly formatted BED file of them were submitted to Thermo Fisher AmpliSeq primer design online tool. An approach including 231 SNPs with amplicon length of 125–175 bp was provided. Five SNPs were deleted from the final panel, as either the primer designing was difficult or the primer was not compatible with others. Detailed information including chromosomal position, unmodified primer sequences, start and stop position of these final targets is listed in Supplementary Table S2. Primer sequences were modified by the manufacturer  using a proprietary method, prior to being synthesized for library construction with the AmpliSeq Library Kit.In this study, 129 polymorphic universal SNPs from database of SNPforID  and IISNControl samples of 9947A and 9948 were used for concordance study. MPS results were compared with Sanger sequencing. Unmodified primers in Supplementary Table S2 were used for Sanger sequencing. HID_SNP_Genotyper.42 (v4.2) plug-in and Chromas software were used for genotyping of MPS data and Sanger sequencing data. All results of 9947A and 9948 are listed as Supplementary Table S3-1 and Supplementary Table S3-2, respectively.FMAR values of heterozygotes at rs430046 (r192) and rs4606077 (r116) were between 60% and 90%. In Table S3-2, the same five SNPs (rs1498553 (r143), rs1478829 (r087), rs7141285 (r174), rs2189011 (r102) and rs12714757 (r043)) were detected with coverage depth below 100× and rs1498553 (r143), rs1478829 (r087), rs7141285 (r174) were even detected with coverage depth below 20× and labelled with “NN”. FMAR values of heterozygotes at rs4530059 (r178), rs334355 (r123) and rs2920816 (r157) were between 60% and 90%. However, no inconsistent result was observed between available MPS calling and Sanger sequencing.In Supplementary Table S3-1, coverage depth of rs1498553 (r143), rs1478829 (r087), rs7141285 (r174), rs2189011 (r102) and rs12714757 (r043) were below 100× and coverage depth of rs1498553 (r143) is only 2× which is not available for genotyping calling. The noise ratio was calculated as the number of reads with nucleotide calls that differed from the SNP genotypes  dividing the total reads. Supplementary Figure S1 lists the noise ratio data of 9947A and 9948 at the 231 SNPs. The noise ratio range was from 0% to 4.1041% for 9947A, while it was from 0% to 6.4796% for 9948.FMAR values 60%–90%). SNPs of rs1478829 (r087) and rs1498553 (r143) were detected with heterozygotes imbalance and lower coverage reads (<20), indicating the designed primers are insufficient and improper. For the three SNPs of rs7141285 (r174), rs12714757 (r043) and rs2189011 (r102), lower coverage reads with ideal allelic performance were obtained, suggesting improving the concentration of primers in the library pool may optimize sequencing performance.Fifty blood samples of unrelated individuals were typed twice with the custom panel. Libraries of 10 ng initial DNA were prepared and sequenced for intensive study. FMAR values of heterozygotes distributed between 50% and 60%, and of homozygotes were above 90%. Several genotypes were observed with FMAR values between 60% and 90%, probably caused by occasional sequencing effect. The mean coverage depth and the median coverage depth among the 221 SNPs were 2 532× and 2 755×, respectively, while the lowest average coverage was 1 163× at rs1454361 with a standard deviation (SD) of 466.8, as well as the highest coverage was 4 150× at rs2730648 with SD of 1 940. Mean noise ratio ranges from 0% to 5.8% (rs10495407).The 10 problematic SNPs in Libraries of series dilutions  of a control male DNA 9948 were prepared. When initial DNA was above 1 ng, recommended PCR cycle of 17 was adopted; and when DNA was equal to or lower than 1 ng, five more cycles were added for library-construction PCR. The six libraries were sequenced on Ion 316 chip in duplicate.FMAR values of the 221 SNPs at the six different concentrations. The allelic balance of heterozygotes varied more in experiments with lower amounts of DNA as expected. For sample S1 (10 ng) and S2 (5 ng), all SNPs can be accurately genotyped with ideal FMAR values; for sample S3 (2 ng), three heterozygotes showed a little bit allelic imbalance . However, when DNA ranges from S4 (1 ng) to S6 (0.2 ng), more and more SNPs were detected with FMAR values between 60% and 90%, and some of them were hard to define accurate genotypes. Results illustrated that at least 1 ng initial DNA should be prepared for library construction with this custom assay.The calling rate of S1 (10 ng) to S5 (0.5 ng) was always 100%, and of S6 (0.2 ng) was 98.64% with the analytical threshold of 20×. All the obtained MPS genotypes were consistent with the Sanger results listed in Table S3-2. FMAR values of different ratios where the genotypes of the two individuals differed. FMAR and observed FMAR values of mixtures with genotypes mentioned in FMAR values (R2 = 0.9331), which indicates that the assay generated a loyal representation of DNA samples. Most of the noise ratio data of the two control samples  in many loci were lower than 1%, making them possible to identify mixtures of 1:100 with a high degree of certainty if the total reads was high enough.Seven mixtures of two references (9947A and 9948) ranging from 1:1 to 1:100 were made and each mixture was sequenced twice on Ion 316 chip. Fifty unrelated individuals  of Chinese Han population were sequenced twice with the custom panel on Ion 318 chip. Forensic parameters of the 221 SNPs (the 10 poorly performing SNPs were excluded) were analyzed with Arlequin 3.5  and SNPAThe newly developed panel we present here allows simultaneous sequencing of 231 SNPs chosen from SNPforID , IISNP , HapMap,FMAR was adopted for allelic performance of targets. FMAR should be 50%–60% for accurate heterozygotes calling and above 90% for homozygotes and Y-SNPs [FMAR values above 60%. And with the 50 sequenced individuals, seven SNPs  were detected with mean FMAR values at heterozygotes of 60%–90%. Inconsistent FMAR values were always observed at them. No exact reason was obtained to explain this phenomenon. Primer re-designing of them or exclusion from the assay may improve the whole performance of the custom MPS–SNP panel.In this study, HID_SNP_Genotyper.42 (v4.2) plug-in was mainly applied for MPS–SNP genotype calling. Sometimes, it interpreted the sequence data wrongly and gave incorrect genotypes ,25. Thusd Y-SNPs ,25. WithGenotypes at SNP rs1498553 of 9947A and at rs1498553, rs1478829 and rs7141285 of 9948 were labelled with “NN” as observed coverage depth below the analytical threshold of 20×, indicating that the primer efficiency for library construction was too low. With the 50 tested individuals, two more SNPs (rs12714757 and rs2189011) were detected with mean coverage reads below 100× . With thWith sensitivity testing, accurate genotypes were readily obtained from 10 to 2 ng with recommended library preparation condition. With modified library-PCR conditions, sequencing of 1 ng DNA was able to accurately genotype the targets with a high degree of coverage and an ideal allelic performance. The allelic balance suffered when the amount of input DNA was lower than 1 ng; in other words, the optimal amount of input DNA in the custom MPS–SNP panel was above 1 ng. From FMAR to estimate the SNP performance of mixtures. MPS data can give coverage reads at each bases at each position in detail, providing a more secure basis for analyzing mixtures [FMAR values would become 50% (aa:bb) or 75% (aa:ab or ab:bb)  . The perg et al.  indicateWith forensic application, the CDP of the 221 independent auto-SNPs was 1–1.49E−62 and CPE for duo cases was 1–02.98E−12 while for trios was 1–3.51E−23. Above SNPs constitute an excellent panel for individual identification including paternity testing, and the unlinked status of the subset of SNPs we have identified also makes them useful for complex kinship analysis.


Bacillus anthracis is the wide-spread, so-called Trans-Eurasian clade systematically categorized as the A.Br.008/009 group sharing two defining canonical single-nucleotide polymorphisms (canSNP). In this study, we genome-sequenced a collection of 35 B. anthracis strains of this clade, derived from human infections, animal outbreaks or soil, mostly from European countries isolated between 1936 and 2008. The new data were subjected to comparative chromosomal analysis, together with 75 B. anthracis genomes available in public databases, and the relative placements of these isolates were determined within the global phylogeny of the A.Br.008/009 canSNP group. From this analysis, we have detected 3754 chromosomal SNPs, allowing the assignation of the new chromosomal sequences to established sub-clades, to define new sub-clades, such as two new Spanish, one Bulgarian or one German group(s), or to introduce orphan lineages. SNP-based results were compared with that of a multilocus variable number of tandem repeat analysis (MLVA). This analysis indicated that MLVA typing might provide additional information in cases when genomics yields identical genotypes or shows only minor differences. Introducing the delayed mismatch amplification assay (DMAA) PCR-analysis, we developed a cost-effective method to interrogate for a set of ten phylogenetically informative SNPs within genomes of A.Br.008/009 canSNP clade strains of B. anthracis. By this approach, additional 32 strains could be assigned to five of ten defined clades.The largest phylogenetic lineage known to date of the anthrax pathogen  Bacillus anthracis, the causative agent of the zoonotic infectious disease anthrax has a broad geographic distribution, mostly affecting regions in Africa, South America and Asia [B. anthracis is divided into three major canonical clades (branches), namely the A-, B- and C-branches [and Asia . Enzootiand Asia  and Austand Asia . While rand Asia ,5 and Geand Asia , the disand Asia  and Franand Asia . The globranches . Among tbranches  because branches  or Ameribranches  are alsoB. anthracis in general, and also that of canSNP clade A.Br.008/009, has seen a major revision [Recently, the phylogeny of revision , expandirevision . About hrevision  were groB. anthracis, we have now analyzed 35 strains from our strain collections on a genomic level. This new information was used to describe the phylogenetic placement of these isolates within the A.Br.008/009 clade, and to design and test new SNP-based assays. With the objective of generating an increasingly detailed picture of the phylogenetic population structure of the Trans-Eurasian A.Br.008/009 canSNP clade of B. anthracis strains were handled in biosafety level 3 (BSL-3) laboratories at the Bundeswehr Institute of Microbiology (IMB) or University of Hohenheim. Cultures of B. anthracis from the strain collections  with 100 ng of input-DNA was prepared. High-quality paired-end reads (Q > = 30) were assembled de novo using an in-house script based on the SPAdes (version 3.11.1) assembler  to creatB. anthracis, were aligned against the chromosome of B. anthracis ‘Ames ancestor’ (NC_007530) as a phylogenetic outgroup using Parsnp (parameters -c -e -u -C 1000). To export the identified SNP-positions, HarvestTools (version 1.0) from the same software suite was used to create a vcf- listing all SNP-positions. In order to enhance data quality, chromosome regions with closely adjacent SNPs (<10 bp distance), as well as positions harboring undefined nucleotides (“N”), were removed. This curated vcf-file was used as an input for HarvestTools to compile a multi-FASTA file out of the chromosome-dataset, comprising the concatenated SNPs as a multiple-sequence alignment. This concatenated sequence information was used to calculate a Maximum Likelihood tree in MEGA 7 [B. anthracis chromosomes are compiled in For rapid core chromosome multiple-alignment, the Parsnp tool from the Harvest Suite was used . For thin MEGA 7 ,19. The B. anthracis DNAs, novel PCR assays were designed and tested. The new assay method is a probe-less real-time qPCR hybrid based on the Melt-MAMA test [Ct-values) were recorded and compared between paired reactions. The respective lower Ct-value indicated preferred amplification; thus, the allelic state of the forward primer determined the scored SNP state . This was quantified by subtracting the Ct-value of the PCR ANC forward primer from that of the PCR with DER forward primer. Thus, positive numeric values indicate ANC, and negative values DER allelic states. DMAA SNP-primer oligonucleotides for new SNPs and established canSNPs were designed surrounding the SNP positions with the Primer-BLAST tool of NCBI [B. anthracis Ames ‘Ancestor’ chromosome (accession # NC_007530) as a reference. DMAA-SNP primer sequences for real-time PCR-assays are listed in In order to validate clade-specific SNPs identified by whole genome SNP-discovery, and to determine the distribution of these SNPs in additional AMA test , previouAMA test ,13 or otAMA test , and an AMA test . Our hybAMA test  and the  of NCBI  using thMLVA was done as described previously . BrieflyB. anthracis strains and 75 chromosomal sequences of the A.Br.008/009 clade isolates from public databases against the Ames ‘Ancestor’ reference genome, a total of 3754 chromosomal SNP positions were identified. From the mapping of 35 newly sequenced The Ames ‘Ancestor’ genome served as the root of this tree and is at a 200 SNP distance from the basal node of the A.Br.008/009 polytomy . The assFurther analysis of the A.Br.008/009 canSNP clade by individual sub-branches comprising newly sequenced chromosomes allows for additional observations. Only one of the newly sequenced strains, BUL 40 from Bulgaria, grouped within the A.Br.105 (Tsiankovskii) clade. This isolate was located at a distance of 41 autapomorphous  SNPs from the polytomy leading to A.Br.105 A and forNotably, the closest relative of strain BUL 40 (63 SNPs distance) was strain LP50_3Ya, isolated from Yakutia in Russia a, ratherWithin clade A.Br.127 (Pasteur), the German isolates formed a closely related group , exhibitStrain A166 (France) of clade A.Br.140  differedClade A.Br. 161 (Heroin) was separated from the A.Br.008 polytomy by three synapomorphous SNPs  clade d. StrainAn unassigned lineage . In the near future, however, with the increasing use of long-read sequencing technologies (such as nanopore sequencing), this hurdle certainly will disappear.Thus, it appears that in some instances there is conflicting information between typing information gained by MLVA or SNP-analysis, respectively. This might be explained by the fact that MLVA is not a strict phylogenetic approach, but rather a tool to elucidate population structure by interrogating for categorical differences in individually evolved repeat numbers (which might result in size homoplasy) . In otheB. anthracis strains of the A.Br.008/009 canSNP lineage into relevant sub-clades from Newly identified SNP positions from our genome data analysis (FS2), which were considered potentially useful for grouping additional, non-genome-sequenced Of three A.Br.118 (STI) strains, all were, indeed, different instances of the STI vaccine strain in our collection. A single additional A.Br.Pasteur isolate was from France. All 13 identified A.Br.Pasteur-BUL strains were from Bulgaria . HoweverFurther, strains from our collection, typed by DMAA, were from Germany, Bulgaria or Namibia . Given tBesides the ease of implementation, there is an additional advantage of the DMAA PCR SNP-typing approach to methods based on melt-temperature analysis. This is, DMAA can differentiate DER/ANC pyrimidine or purine pairs  canSNP lineage which has worldwide significance. From 35 newly and 75 published genomes we designed and tested new SNP-based PCR-assays which allowed attributing additional 32 strains to various sub-clades within the A.Br.008/009 canSNP lineage.In conclusion we analyzed and refined the phylogenetic population structure of the