Spartina anglica and Spartina alterniflora) based on restriction site-associated DNA (RAD) sequencing. After more than 50 years, the distribution of S. alterniflora has rapidly expanded, while S. anglica has experienced extreme dieback. A total of 212,939 single nucleotide polymorphisms (SNPs) for the two Spartina species were used for analysis. The multilocus genotype (MLG) analysis revealed that clonal reproduction was the prevalent mode of reproduction in both species, indicating that a change in the mode of reproduction was not the key factor enabling successful invasion by Spartina. All genetic diversity indicators  in S. alterniflora populations were at least two times higher than those in S. anglica populations, respectively (p < 0.001). Furthermore, the population genetic structure and stronger patterns of climate-associated loci provided support for rapid adaptive evolution in the populations of S. alterniflora in China. Altogether, our results highlight the importance of genetic diversity and local adaptation, which were driven by multiple source populations, in increasing the invasiveness of S. alterniflora.Biological invasions have become one of the greatest threats to global biodiversity and ecosystem conservation. Most previous studies have revealed how successful invasive species adapt to new environments and climate change through phenotypic and genetic evolution. Some researchers suggested that understanding unsuccessful or less successful biological invasions might be important for understanding the relationships between invasion adaptability and climate factors. We compared the sexual reproduction ability, genetic diversity, and gene × environment interaction in two intentionally introduced alien species in China ( Ambrosia artemisiifolia invaded European, Australian and Chinese areas with high levels of genetic diversity due to multiple introductions  between the European native species S. maritima and the alien species S. alterniflora , global positioning systems (GPSs), and on-site investigations in 2007, S. anglica in Liaoning, 10 ha in Hebei, 3 ha in Shandong, 1 ha in Jiangsu, and 1 ha in Guangdong. The authors found that populations of S. alterniflora were also distributed in these provinces and occupied larger areas than S. anglica. S. alterniflora in Liaoning but no S. anglica. In 2019, we found and identified S. anglica in Dandong and Huludao cities of Liaoning Province. We chose four populations of S. alterniflora, which are distributed from north to south in China, for comparison with the two populations of S. anglica. We used restriction site-associated DNA (RAD) sequencing  Does the successful invasive species S. alterniflora have higher genetic diversity than the less successful invasive species S. anglica? (3) Do the highly successful invasive species S. alterniflora and less successful invasive species S. anglica differ in their levels of genetic adaptation? We also expected to provide evidence to clarify the role of genetic diversity in the colonization or invasion failure of species.Moreover, the distribution areas of quencing  to improSpartina from six sites (14–15 leaf samples per site) along the coastline of China in October 2018  was used to identify S. anglica and S. alterniflora  tree construction with 1,000 bootstrap by MAFFT v. 7 . Based on the published nrITS sequences of 145 species of rniflora . We confFFT v. 7 .Spartina. This DNA was double-digested with EcoRI and Mse l  and ligated to two-end adapters, allowing the resulting amplified fragments to bind to Illumina flow cells and uniquely identify the individual. All libraries with insert sizes of 300–500 bp were isolated by gel extraction and sequenced on the Illumina XTen platform using 150 nt paired-end reads . A total of ∼260 GB of data was obtained from Illumina sequencing of 88 samples from six populations. The data for each sample were split and filtered using the process_radtags module of Stacks 2.3e . We finally used the cstacks, ustacks, and populations modules in Stacks to obtain SNPs. We obtained the loci using the following limits in the populations module: 0.01 as the minor allele frequency, 75% as the percentage of individuals sharing the locus within the population, and appearance of the locus in all populations. Linkage disequilibrium (LD) between each pair was tested using VCFTOOLS v.0.1.15 , average nucleotide diversity (π), average observed heterozygosity per locus (Ho), and average expected heterozygosity per locus (He) were calculated using populations in Stacks version 2.3 based on the raw dataset  using populations in Stacks version 2.3. The inbreeding coefficient (FIS) was often used to quantify the partial asexual reproduction population  to calculate the index of association (IA), the standardized index of association (rd), and their p-values  tree of all individuals, and minimum spanning networks (MSNs) of each MLG based on distance matrices.Second, the genetic clones of  dataset , 2015, w dataset  threshol dataset , 2015. Sp-values . We also of loci . The rd ize bias . Clone cH) (E). The parameter G/N was also estimated, and while it could be biased, it is frequently used  , SimpsonH) , and evetly used . G repreSpartina populations, we used ADMIXTURE version 1.3.0  , retaining 100 PCs in the analysis. Discriminant analysis was performed using 50 PCs (>90% of variance explained), and one, four or five eigenvalues were retained and examined. The optimal number of PCs to be retained in the final analysis was identified by the optim.a.score function with twenty independent runs.To infer the genetic structure of on 1.3.0  to estimon error . We creas (DAPC)  was perfs (DAPC) . The numS. alterniflora. A principal component analysis (PCA) was performed by TASSEL 5  within the “vegan” package (v.2.5–7)  in R 4.1TASSEL 5 . VariancTASSEL 5 . The impTASSEL 5 . Then weTASSEL 5 .qvalue less than 0.01 was considered as an outlier (The outlier loci were detected using two population differentiation (PD) analysis methods and two environment association (EA) analyses. The first PD method was implemented in ARLEQUIN , which i outlier .K). A fitted model was then used to perform association testing based on the ridge_lfmm function after ridge estimation. Finally, any obtained p-values were transformed into qvalues. The qvalues less than a cutoff of 0.01 were indicative of candidate loci. A Benjamini and Hochberg (1995) false discovery rate (FDR) correction of 5% was applied to p-values using the qvalue package in R . A batch file estimated the BayesFactor with 100,000 MCMC steps. Regarding interpretation , a Bayesage in R . Binomia2 and identified its related functions in plants according to literature. Then, we calculated correlations between allele frequencies of outlier loci and climate variables to identify putative genes associated with local adaptation.To gain insights into the potential adaptive significance of outlier loci, we obtained the flanking sequence of outlier loci identified by at least two methods from the RAD library. Functional annotation was performed using the online Basic Local Alignment Search Tool (BLAST) database. We searched the Genbank database using BLASTn with >60% sequence similarity. If the sequence was related to a protein, we further searched for its molecular function from UniProtS. anglica and 58 individuals of S. alterniflora. We obtained a total of 1674,211,878 reads  after demultiplexing and removing reads of low quality. The number of reads for the samples varied from 3,632,316 to 52,525,600, with the average number of reads per sample being 19,025,135. Our analysis with ustacks showed that the number of ddRAD tags recovered from these samples varied from 248,767 to 1,385,706. Using cstacks, we obtained 4,664,866 loci from the 30 S. anglica and 58 S. alterniflora samples. After the initial filtering steps, 212,939 SNPs were retained, followed by removing sites showing linkage disequilibrium, and 169,522 SNPs were retained for all samples. Based on the dataset with 169,522 SNPs, a total of 90,671 SNPs were identified in the S. alterniflora subset, and 10,542 SNPs were identified in the S. anglica subset, with a minimum allele frequency of 0.01  reads  were obtained from the ddRAD library constructed for 30 individuals of  of 0.01 .cutoff_predictor, we found 32 MLGs among 58 individuals of S. alterniflora . Other populations with low genotypic diversity were QZ  and HLD (S. anglica), which had G:N ratios of 28.6 and 40%, respectively. In contrast, populations DD (S. anglica) and TG  had G:N ratios of 100%  from the expected products of panmixia for populations HLD (S. anglica), CX, QZ, and FCG   only for populations DD (S. anglica) and TG  (FIS) of all S. anglica populations (DD and HLD) and the QZ and FCG populations of S. alterniflora were negative, showing a clear trend of heterozygote excess  , indicatniflora) , indicate excess . In addie excess . MLG 54 e center . Ten inddividual .S. alterniflora populations than in S. anglica populations in terms of nucleotide diversity , observed heterozygosity  and expected heterozygosity  (p < 0.001), except He and π between HLD and DD , Ho between FCG and QZ  . TukeyHSK = 5) were identified using ADMIXTURE based on the lowest cross-validation error . The total explained variation was 0.784, and the precipitation of the warmest quarter  was the most important predictor , the mean temperature of the wettest quarter (BIO8), and the precipitation of the warmest quarter (BIO18), which were significantly associated with genetic variation among populations of redictor .S. alterniflora and S. anglica, respectively, using four methods , and the two EA methods identified 3,619 outliers . Only one outlier was identified by all four methods, 91 outliers were identified by three methods, and 848 outliers were identified by two methods , and the two EA methods identified 31 outliers (12 from BAYENV and 19 from LFMM). None of the outliers were identified by all four methods, and only three outliers were identified by three methods; 60 outliers were identified by two methods .A total of 7,868 and 238 outliers were identified in  methods . For the methods . For the methods . The proS. anglica. Fifteen loci obtained from the S. alterniflora subset were annotated , and negative FIS, was found in populations of both S. alterniflora (FCG and QZ) and S. anglica (HLD). No reticulation of the minimum spanning networks  was found in population DD of S. anglica, we still suspected that sexual reproduction rather than clonality was the main mode of reproduction in this population in consideration of the high number of genotypes, non-significant IA and rd, and lack of a large excess of heterozygotes. Negative FIS was usually interpreted as a signal of clonal reproduction in previous studies  clonal populations might be impacted by genetic drift (IA and rd) when evaluating the mode of reproduction. Inconsistencies in FIS, IA, and rd were also found in population CX, which might be a partially clonal population with significant indices of association but a non-negative FIS (0.029). The results of reproduction analysis based on molecular genetic information in our study did not support the finding of previous studies that S. anglica mainly relies on asexual reproduction for population regeneration (S. anglica being located at a northern latitude (N 39.87°) with a lower annual mean temperature (9.3°C) and maximum temperature (28.88°C), while the population site in the study of S. anglica is in cool regions   , and higEngland) . Lower gEngland) . This reatitudes .Pueraria montana var. lobata  of each population reflected lower genetic diversity of the failed invader S. anglica  relative to the successful invader S. alterniflora. . Even though population FCG had extremely low genotype diversity, its genetic diversity was significantly higher than that in populations of S. anglica  of S. alterniflora generated a genetic admixture, and genetic variation remained high compared to those in the native populations  than observed heterozygosity , which may have been a reflection of earlier population bottlenecks  by LFMM. To reveal signatures of adaptation within two montane bumble bee species, Rapid local adaptation of invasive alien species usually includes two aspects: phenotypic plasticity and genetic evolution . Early sS. alterniflora than in S. anglica, some of which were successfully annotated, provided evidence for rapid local adaptation in this study. It supports hypothesis of previous studies that rapid adaptive evolution is a key mechanism driving successful invasion of S. alterniflora along the coast of China through reciprocal transplant experiments , we cannot directly demonstrate that rapid adaptive evolution was one of the main factors which promoted the success of S. alterniflora invasion. However, the evidence of the correlation between gene and environment of S. alterniflora revealed in this study might provide genetic support for previous research conclusion. Additionally, annotated outlier loci are worthwhile of further study in consideration that the functional protein-coding genes obtained from four populations of S. alterniflora might be critical for understanding processes influencing the range expansion of invasive species.To conclude, we explored aspects of invasiveness from an evolutionary perspective through a genomic comparison of SRR18493548-SRR18493635.The data presented in the study are deposited in the GenBank repository, accession numbers FL and CZ conceived the idea. FL, XL, JZ, and KG collected the samples in the field, performed the experiments, and collected the data. FL analyzed the data and wrote the original manuscript with edits completed by CZ. JL provided comments on the manuscript. All authors contributed to the final version before the manuscript was submitted.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Micropterus velox), a former subspecies of the keystone top‐predator and globally popular Smallmouth Bass (M. dolomieu), is endemic and narrowly restricted to small, clear streams of the Arkansas River Basin in the Central Interior Highlands (CIH) ecoregion, USA. Previous studies have detected some morphological, genetic, and genomic differentiation between the Neosho and Smallmouth Basses; however, the extent of neutral and adaptive divergence and patterns of intraspecific diversity are poorly understood. Furthermore, lineage diversification has likely been impacted by gene flow in some Neosho populations, which may be due to a combination of natural biogeographic processes and anthropogenic introductions. We assessed: (1) lineage divergence, (2) local directional selection (adaptive divergence), and (3) demographic history among Smallmouth Bass populations in the CIH using population genomic analyses of 50,828 single‐nucleotide polymorphisms (SNPs) obtained through ddRAD‐seq. Neosho and Smallmouth Bass formed monophyletic clades with 100% bootstrap support. We identified two major lineages within each species. We discovered six Neosho Bass populations (two nonadmixed and four admixed) and three nonadmixed Smallmouth Bass populations. We detected 29 SNPs putatively under directional selection in the Neosho range, suggesting populations may be locally adapted. Two populations were admixed via recent asymmetric secondary contact, perhaps after anthropogenic introduction. Two other populations were likely admixed via combinations of ancient and recent processes. These species comprise independently evolving lineages, some having experienced historical and natural admixture. These results may be critical for management of Neosho Bass as a distinct species and may aid in the conservation of other species with complex biogeographic histories.The Neosho Bass ( Micropterus dolomieu) and Neosho Bass (M.velox) where their native distributions come together in the Central Interior Highlands ecoregion, USA. We also show strong signatures of genetic differentiation among stream populations due to neutral drift within both subspecies, and we detected adaptive divergence within the Boston Mountains ecoregion of the Neosho native range. Despite differentiation, Neosho Bass stream populations exhibit variable demographic histories, with some populations having been admixed recently, and others historically, with Smallmouth Bass. Our findings provide key insights into levels of genomic diversity and eco‐evolutionary forces driving that diversity, which will be critical for establishing effective conservation and management strategies for these economically important species.Using over 50,000 SNPs obtained from ddRAD‐seq, we detected genomic divergence between the globally popular and ecologicaly important Smallmouth Bass ( The one‐dimensional, dendritic configuration and abiotic heterogeneity of stream ecosystems, including variable flow rates, depths, temperature gradients, nutrient concentrations, and allochthonous and autochthonous inputs  is only partially understood but is likely to have been shaped by both natural and anthropogenic forces. One of the most economically important and globally popular black bass species, the Smallmouth Bass (M. dolomieu), occupies a native range extending from the Laurentian Great Lakes in southeastern Canada to the Central Interior Highlands, USA (CIH). Such a broad, ecologically variable distribution and high levels of range‐wide diversity , consolidating the Northern Smallmouth Bass as synonymous with Smallmouth Bass (M. dolomieu). Recent investigations of morphological and ecological divergence have largely affirmed this taxonomic revision  from 13 streams throughout the Arkansas River Basin (ARB), and the Smallmouth Bass (N = 25) from three tributaries of the White River (WRT), two tributaries of the Missouri River (MRT), and Skiatook Lake (LAKE), an impoundment in northeastern Oklahoma situated outside the native range of Smallmouth Bass that was stocked with a hatchery‐reared strain colloquially known as “Tennessee lake‐strain” sourced from the Cumberland River drainage  from the ARB as an outgroup  from ~25 mg fin clips excised from the upper caudal fin using the DNeasy Blood and Tissue kit . Fin clips were coarsely chopped with sterile razor blades, digested with proteinase K, and treated with 4 μl RNase‐A. Extracts were diluted to ~20 ng/μl with ddH2O, arranged on a 96‐well plate in 50 μl (~100 μg gDNA) aliquots and stored at −20°C before library preparation. We included a single, no‐DNA negative control sample containing only ddH2O.We extracted high molecular weight DNA  barcodes were included in the P5 PstI adaptor for individual sample identification. PCR was performed using the parameters listed in  were pooled and sequenced with 1 × 95 bp chemistry on a single lane of the Illumina HiSeq 4000.Library preparation and sequencing for ddRAD‐seq were completed at Floragenex, Inc.  according to a modified Sequence‐based Genotyping protocol outlined in  according to the  Lozier, . Cluster Lozier,  were use Lozier, .2.4samtoolsv.0.1.16 , and single‐nucleotide polymorphisms (SNPs) were called using Variants were initially filtered based on individual read depth; only sequences with a minimum of 15X coverage were retained. We removed samples from the dataset that had greater than 20% missing genotype calls across SNPs. We then removed SNPs with phred quality scores less than 20 . We created SNP tables using 2.5plinkv.1.90  for individual fish in the program admixturev.1.3.0 . We used stringent criteria to determine pure or admixed origin: individuals were considered putatively pure if q ≥ 0.95 for one inferred cluster at the optimal K.  and (2) putatively admixed individuals (“p‐Admixed”).We screened the full dataset for individuals of putative admixed ancestry, i.e., those of Smallmouth Bass × Spotted Bass or Neosho Bass × Smallmouth Bass hybrid origin, to limit gene flow influence on the assessment of lineage diversification. Our full VCF file was converted to binary pedigree (BED) format in a high‐contig build of p‐Pure dataset to investigate phylogenomic relationships among and within Smallmouth and Neosho Bass. We first assessed allele frequency differentiation in the population structure program admixture, choosing K by minimizing 10‐fold cross‐validation error for K = 1–20. We conducted a parallel phylogenomic analysis using maximum‐likelihood methods in the snphylo pipeline  using Spotted Bass as an outgroup (−o) and setting a linkage disequilibrium threshold (−l) of 0.1. A consensus tree was constructed in figtree v.1.4.2 . We used our full, nonthinned SNP dataset and concatenated SNPs on the same RAD tag to form haplotypes in the radpainter package , 100,000 Markov chain Monte Carlo (MCMC) iterations (−y), and thinning (−z) every 1000 iterations for the full sample set, including p‐Pure and p‐Admixed individuals. We also calculated co‐ancestry matrices separately for the p‐Pure and p‐Admixed groups to eliminate bias due to multiple ancestry; results from the separate matrices were used for population delimitation. We used a full hill‐climbing tree‐building method to construct trees, running 10,000 iterations (−x), providing no value for the initialization parameter (−T). Individuals were collapsed into populations if they formed blocks of high co‐ancestry along the diagonal of the co‐ancestry matrix and if they were monophyletic at deeper nodes in the tree.To delimit populations and assess connectivity among stream sites, we examined haplotype similarity among Smallmouth and Neosho Bass individuals in 2.7p‐Pure and p‐Admixed). Outlier analyses are effective in identifying SNPs that deviate from null expectations of allele frequencies under an island model of migration  or low  differentiation among populations  to identify individual SNPs contributing most substantially to variance along principal component axes. While FST‐based methods may be compromised by the presence of hierarchical population structure . To determine the optimal number of PCs, we assessed Scree plots and selected the number of PCs based on Cattell's Rule , we also created datasets with only shared neutral SNPs (nonoutliers).For each hierarchical analysis, we used default MCMC parameters in R package adegenet v.2.3.1  of all SNPs (given that we filtered for one SNP per RAD tag during bioinformatic processing) and therefore generated arbitrarily large physical and genetic distance values for each SNP according to the custom formula:d = physical/genetic distance, x = numerical label of the RAD tag , and y = nucleotide coordinate of SNPs within RAD tags. Moment statistics were calculated using 1000 bootstrap replicates over 50 cM blocks, and the scaffold tree was constructed using 10,000 data subsets. For populations not included in the scaffold tree, we tested the fit of two‐way admixtures between all pairs of nonadmixed parents (sources), running 100 bootstrap replicates. Significantly admixed populations were used in demographic analyses.We used the full dataset with all inferred populations and created input files in 2.9p‐Pure and p‐Admixed populations, testing nine two‐population diversification‐based demographic models ; two‐population models allow for divergence with and/or without migration between focal populations. Candidate scenarios differed in the timing of migration, i.e., ancient or due to recent secondary contact, and directionality of migration, i.e., symmetric or asymmetric. Model descriptions are given in Table We explored potential demographic scenarios driving observed admixture patterns between a𝛿i assumes SNPs are unlinked and neutral . Parameter estimates for each model were determined through a four‐round perturbed optimization procedure as described in Portik et al. . The de novo reference assembly contained ~12.86 million reads passing quality filters, and these were clustered into 240,085 RAD contigs. An average of 59.2% of reads across all samples aligned successfully to the de novo reference. The full genomic dataset contained 357,123 SNPs. A total of 229,694 SNPs were omitted due to low read depth or phred quality scores below a threshold of 20, leaving 127,023 SNPs before filtering for missing data, linkage, minor allele frequency, and excess heterozygosity.We obtained ~1.76 billion reads across all samples  contained 50,828 SNPs for downstream analyses.We removed three samples  were admixed while 36% (N = 23) were putatively pure . In six Neosho sampling sites , all individuals were putatively admixed  and Lineage 2 (navy blue) emerged within the Smallmouth Bass  and Lineage 4  and K = 5 (CVerror = 0.283) were within 0.008 of the optimal K = 3 , and two were nested in Lineage 2: the LAKE site (SKIA) and the MRT (MISS). The remaining two p‐Pure populations belonged to the Neosho Bass and corresponded exactly to Lineage 3 within the middle ARB (MIDARK) and Lineage 4 encompassing Lee Creek and Mulberry River , the Illinois River system, including the Illinois River, Caney Creek, and Baron Fork Creek (ILLI), and the Illinois Bayou River and Big Piney Creek, AR , including Big Sugar Creek, Buffalo Creek, and Spavinaw Creek, consisted of two monophyletic clades that did not correspond to stream sites and which did not exhibit high ancestry. Thus, these two clades were grouped as a single population. All nodes in the tree for p‐Pure and p‐Admixed were confirmed at 100% bootstrap support  and 50,825 neutral SNPs  were found to be nonadmixed and formed the branch tips of the admixture tree  were significantly admixed based on f3 statistics  was substantially higher than in the opposite direction  was higher than in the opposite direction . The estimate for the period of isolation following divergence (T1 = 1.143) was substantially greater than the time since secondary contact  was greater than the opposite direction (m21a = 0.096 and m21b = 0.011). Timing of each epoch with respect to species divergence was variable, although the second epoch (T2 = 16.128) was estimated to last longer than the first  was much higher than in the opposite direction . The estimate for the period of isolation following divergence (T1 = 0.750) was substantially greater than the time since secondary contact  indicated an approximate 2°C difference in thermal tolerances ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); resources ; visualization (lead); writing – original draft (lead); writing – review and editing (lead). Leah K. Berkman: Conceptualization ; formal analysis (supporting); funding acquisition (supporting); investigation (supporting); methodology (supporting); project administration (supporting); resources (supporting); supervision (supporting); writing – review and editing . Jeff Koppelman: Conceptualization ; data curation (supporting); formal analysis (supporting); funding acquisition (supporting); investigation (supporting); methodology (supporting); project administration (supporting); resources (supporting); supervision (supporting); writing – review and editing . Andrew T. Taylor: Conceptualization (supporting); data curation ; formal analysis (supporting); investigation (supporting); methodology (supporting); project administration (supporting); resources (supporting); writing – review and editing . Shannon K. Brewer: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); investigation (supporting); methodology (supporting); project administration (supporting); resources (supporting); writing – review and editing . James M. Long: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); investigation (supporting); methodology (supporting); project administration (supporting); resources (supporting); writing – review and editing . Lori S. Eggert: Conceptualization ; data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation ; methodology (supporting); project administration (lead); resources (supporting); supervision (lead); visualization (supporting); writing – review and editing .This project was funded by the Missouri Department of Conservation.The authors declare that they have no conflict of interest.fastq.gz) and sample metadata (.xlsx) are available on Zenodo (https://doi.org/10.5281/zenodo.7032495). All code for genomic analyses is available on GitHub (https://github.com/jcg5g9/SMB_Genomics).Raw sequencing data (.Appendix S1: Supporting informationClick here for additional data file.


The genetic makeup of a breed including its genetic differences from other breeds determines its appearance and characteristics, including economically important traits and resistance to pathologies. To date, many loci controlling significant phenotypes have been identified, which is successfully used in the world practice of marker-assisted selection to improve breed properties. The aim of this study was a comparative analysis of frequencies for known causative nucleotide substitutions, insertions and deletions associated with disease and economically important traits in Russianand foreign cattle breeds. As a result, we identified frequencies of these DNA polymorphisms in the populations ofRussian cattle breeds, compared them with those of foreign populations of the same breed, as well as other foreignbreeds. Our results indicate similarities in frequencies for most of such alleles within breeds (populations of Russian andforeign breeding), as well as the relationship between the causative allele prevalence and the presence of phenotypictraits under the effect. We also found an excess of some undesirable alleles in the Russian cattle populations, whichshould be paid attention to when designing breeding programs. We found that the alleles increasing fertility in theHereford breed have a higher frequency in the Russian Hereford population compared to the foreign counterpart.Interestingly, unlike for the European breeds, for Asian Turano-Mongolian Wagyu and Yakut cattle, there was a less clearlink between phenotypic traits and frequencies of known causative alleles. Our work points to specific genetic variantsthat could be used to improve and/or maintain the performance of certain cattle breeds bred in the Russian Federation Common types of genetic variations, such as single nucleotidepolymorphisms, nucleotide insertions and deletions, amongothers, can have “beneficial” or “harmful” effects on animalhealth and productivity . That is why the sequencing of the Bos taurus genomecaused a surge in research on the genetic diversity of cattlebreeds and its relationship with economically important traits,adaptations and diseases, which opened up opportunities to usethe knowledge gained for creating breeds with the necessaryqualities and improving existing breeds . Now, according to the OMIA database, 272 bovine traits areknown to be genetically controlled, including a number ofdiseases. For 175 of them, causative mutations in the codingand non-coding regions of DNA have already been identified,the effect of which is related to various mechanisms, includingchanges in the protein sequence, in the stability, expressionor processing of RNA . Using this information,tests were developed for genotyping pathological mutationsand removing carrier animals from the breeding herd. With this approach, it is possible to identifymutations at an early age for the timely culling of animals orembryos . At the same time, it is worthconsidering that a “harmful” mutation may be “useful” foranother economically important trait .Identification of gene alleles associated with economicallyimportant traits allowed using them for marker-assistedselection .Marker-assisted selection is particularly important for traitsthat become evident with age or only in animals of the samesex, such as productivity or fertility .So far, Russian cattle breeds have been investigated for thepresence of only a few, the most common mutations associatedwith economically important traits and health .The purpose of our work was to analyze the spectrum andfrequencies of known causative DNA polymorphisms in nineRussian cattle breeds using genome sequencing data and tocompare the frequencies of these polymorphisms with thosein worldwide breeds or foreign populations of the same breedsto determine the options for which the selection in Russiancattle could be conducted.The list of single-nucleotide polymorphisms (SNPs),insertions and deletions, clinically and economically importantfor cattle, was compiled based on the information from theOMIA database  andpractical guidance of the Irish Cattle Breeding Federation. The genomic positionsof polymorphisms specified in the Bos taurus UMD3.1assembly coordinates were converted to the ARS-UCD1.2assembly coordinates using liftOver . Forpolymorphisms present in the sample of Russian breeds,reference and alternative alleles were verified for matchingthose specified in the publications. For four substitutions ofthe twelve possible (T↔A and G↔C), such a verification iscomplicated, since: (1) there may be a change in the referenceallele during the transition to a new genome assembly; (2) inthe publication, the allele can be specified for a chain whichis complementary to reference sequence. In such cases, weverified the alleles of polymorphisms in the context of codons(for substitutions in the coding sequence) or proximatesequences. For example, according to Hirano and colleagues(2013) and the OMIA database, replacing the nucleotide Gwith C at the BTA8:83909754 position, leading to thereplacement of valine with leucine, results in perinatal weakcalf syndrome. However, apparently, this replacement wasindicated by the authors for the messenger RNA sequence –since in the assembly ARS-UCD1.2 C stands for the referencenucleotide, being a part of the “AAC” triplet, which, in turn,corresponds to “GUU” mRNA codon, encoding valine. Thus,in the reference assembly of ARS-UCD1.2, the G allele willbe “harmful”.In this paper, we used data on SNPs, insertions and deletionsin the worldwide breeds from the “1000 Bull Genomes”Project , including the resequencingdata of eight Russian breeds obtained earlier, as well as theresequencing data (“.fastq”-files) for the Russian populationof the Aberdeen Angus breed (hereinafter simply Angus),provided by LLC “Miratorg-Genetika”. Of note, some ofthese animals were imported from the USA and Australia (Table 1). Additionally, we also used data on three nativeFinnish breeds provided by the Natural Resources InstituteFinland (Luke). Finland borders with Russia and has a largelysimilar  climate, so the inclusion of Finnishbreeds in the study could shed light on features of the selectivebreeding manifesting in the close natural conditions of thetwo countries.Removal of adapter sequences from raw paired readswas performed using Trimmomatic-0.39. Clean reads werealigned to the ARS-UCD1.2 reference sequence usingBWA-MEM v.0.7.17 . Files containingaligned sequences (“.sam”-files) were then converted to the“.bam” format and sorted using the SAMtools v.1.8 software. Further, libraries belonging to the sameanimal were pooled using the ‘MergeSamFiles’ module ofthe Picard v.2.18.2 package (http://broadinstitute.github.io/picard). Duplicates were marked using the ‘MarkDuplicates’module of the above-mentioned software. The OPTICAL_DUPLICATE_PIXEL_DISTANCE parameter equaling 2500was chosen according to the recommendations of the “1000Bull Genomes” protocol. Base quality score recalibrationwas performed using the ‘BaseRecalibrator’ and ‘PrintReads’modules of the GATK v.3.8 package using data provided by the “1000 Bull Genomes” Project. The variant calling and themerging of the resulting gVCF files were performed usingthe ‘HaplotypeCaller’ and ‘GenotypeGVCFs’ modules of theGATK v.3.8 program, respectively.Extraction of SNPs, insertions, and deletions from genomewideVCF files was performed with the Tabix utility ,using the coordinates of polymorphisms from a previouslygenerated list. The resulting VCF files containing the selectedpolymorphisms were used to calculate the frequencies ofalternative alleles in the samples using the PLINK 2.0 program with the following parameters: --vcf --chrset30 --freq --pheno --loop-cats. The count has been carriedout for (1) breeds bred in Russia ,(2) foreign populations of those breeds (if present), (3) threeFinnish breeds , and (4) a combined sample of all otherworldwide cattle breeds (see Table 1).The presence of allele frequency differences between theabovementioned samples was tested using Fisher’s exact testimplemented in the ‘fisher.test’ R function. Contingencytables 2×2 were composed by counting the number ofreference and alternative alleles in the chromosomal pool ofeach of the two groups studied. Three types of comparisonswere made: (1) between a breed bred in Russia  and a combinedsample of other world breeds; (2) between a breed bred inFinland and the combined sample of the world’s breedsusing the polymorphisms identified in the first type ofcomparisons; (3) only between the Russian population and theforeign population of the same breed. To correct for multiplecomparisons, we used the Storey and Tibshirani method  implemented in the ‘qvalue’R function .Our list of clinically and economically importantpolymorphisms contained 193 SNPs and 63 insertions/deletions. A search in the VCF files revealed in Russian breedsthe presence of 38 SNPs and one insertion from the abovementionedlist (Supplementary Table 1)1, which correspondedto at least 21 phenotypic traitshttp://vavilov.elpub.ru/jour/manager/files/Suppl_Igoshin_engl.pdfSupplementary Tables 1 and 2 are available in the online version of the paper:When comparing 15 populations for 39 polymorphisms with a global sample, in229 cases statistically significant (q < 0.05) differences inallele frequencies were found . The mostsignificant differences with the total sample of worldwidebreeds were observed for foreign populations of Angusand Hereford breeds . Of theRussian populations, the Yakut breed had the largest number(16 loci) of differences from the worldwide sample. Of theFinnish breeds, the Northern Finncattle had the largest number(20 loci) of such differencesThe most significant (q = 4.24E–286) allele frequencydifference from the global sample was observed for the foreignAngus population for SNP rs109688013 in the melanocortin-1receptor gene MC1R, carriers of the alternative allele C ofwhich have a black coat color . Thedifference from the worldwide sample for this locus wasalso statistically significant for most of other populations aswell, with the exception of the Northern Finncattle, as wellas Russian and foreign Wagyu populations. In particular,the difference at this SNP was the highest among 39 locifor the Russian population of Angus (q = 6.01E–35), bothpopulations of Herefords (q = 6.22E–37 for foreign and7.34E–07 for Russian), for Altai (q = 1.99E–06), Kholmogory(q = 9.27E–12) and Yaroslavl (q = 2.76E–06) breeds. Inforeign and Russian Angus populations, the frequency of theC allele coding for black color reaches 0.973 and 0.989, whilein other worldwide breeds it is 0.339. In the populations ofAltai, Kholmogory, Yaroslavl breeds, Russian and foreignHerefords, it has a frequency of 0.026, 0.828, 0.772, 0 and0.019, respectively. In Finnish breeds, the frequency of theC allele varies from zero in Western Finncattle to 0.052 inEastern Finncattle and 0.258 in Northern Finncattle.Of the remaining loci, the greatest difference in the studiedbreeds from the global cattle population was observed forpolymorphisms associated with milk traits, coat color andbleeding disorders. Thus, the Russian Wagyu population hadthe most significant (q = 6.44E–21) allele frequency differencefrom the worldwide sample for 15 bp insertion located atBTA27:16305660, which disrupts the F11 gene function and,as a result, leads to a deficiency of blood coagulation factor XI,encoded by this gene . In Russian Wagyupopulation, the frequency of this insertion reaches 0.25,while in the global cattle population it is close to zero. Themost significant differences from the worldwide sample forthe foreign Wagyu population (q = 2.60E–05) and the Yakutbreed (q = 2.21E–18) were observed for SNP rs210634530in the gene of microphthalmia-associated transcription factor MITF, which is associated with the ‘white spotting’ phenotype. The frequencies of the ‘whitespotting’-associated allele T in the Yakut breed and foreignWagyu population are 0.083 and 0.111, respectively, whilein the worldwide sample it reaches 0.65. In the Buryat andKalmyk breeds, the most significant difference  had SNP rs109191047 in thegrowth hormone gene GH1, associated with the compositionof milk . The frequency of G alleleincreasing the milk fat and protein content is 0.100 in theworldwide population, while in the above-mentioned breedsit reaches 0.526 and 0.500, respectivelyComparisons between the Russian and correspondingforeign populations, made for the Angus, Hereford andWagyu breeds, revealed four loci, statistically significantly(q < 0.05) differing in allele frequencies. Of these, three SNPs had differingfrequencies in the Russian and foreign Angus populationsand specified the alleles of the kappa-casein gene CSN3.One SNP located in the CAPN1 gene (rs17872050) differedbetween the Hereford populations and was associated withmeat tenderness. Taking into account the frequency differencesat the nominal significance level (p < 0.05), eight additionalloci can be noted (Table 2), among which the V311A missensesubstitution (BTA26:34340886T>C) in the NHLRC2 genediffering between the Angus populations and in homozygotesleading to notomelia, a type of polymelia in which theadditional limb is located along or near the midline of theback .Breed-specific genetic featuresThe gene pool of farm animals is formed under the influenceof factors such as selection for productive traits, adaptation toenvironmental conditions, hybridization, de novo mutations,the founder effect and genetic drift .As we showed above, a significant part of the polymorphismstaken into the analysis in the studied breeds differs infrequencies from the “worldwide average”, reflecting thegene pool features of particular populations. For example, theYakut cattle shows the highest divergence in allele frequenciesamong Russian breeds, expressed both in a greater number ofdiffering loci and in a greater significance of these differences,which is consistent with the data on phylogeny of this breedand the analysis of its population structure .Some of the polymorphisms studied make a definitivecontribution to characteristic features of the breeds. Forexample, the content of the MC1R gene allele rs109688013-Cin the breeds coincides well with the typical color of theirrepresentatives. Thus, in Angus having a black coat color,the frequency of this allele is close to one. In Yaroslavl andKholmogory cattle, rs109688013-C also predominates,apparently defining black and black-mottled coats, mainlycharacteristic of these animals. At the same time, in Herefords,which are not characterized by a black color, the frequency ofthe C allele is close to zero. Similarly, there is a link betweencolor and the frequency of the C allele in populations ofFinnish breeds. In breeds that have mainly lighter coats ,it is low (0.053 in Eastern Finncattle) or zero (in WesternFinncattle). In Northern Finncattle, which has a predominantlywhite coat color , thefrequency of rs109688013-C is 0.258. Breeds for which thered  or brown  colors are typical havers109688013-C in low frequency (0.03–0.08). However, inWagyu populations, which are usually characterized by blackcolor, the frequency of this allele is far from one and has valuesof 0.42 in Russian population and 0.67 in foreign population,probably reflecting the genetic characteristics of Turano-Mongolian breeds. This discrepancy is also observed in Yakutcattle, in which a black-and-white color is common, but thefrequency of rs109688013-C is vanishingly small. Given thegenetic divergence of Turano-Mongolian breeds from otherbreeds, it can be assumed that other loci are involved in thecontrol of body coloration.Also, coat color is associated with the SNP rs210634530in the MITF gene, the T allele of which defines the ‘whitespotting’ phenotype. The highest frequency of rs210634530-Tis observed in populations of Hereford cattle (fixed in theRussian sample and 0.92 in the foreign population), which ischaracterized by a white head and belly. In addition, this alleleprevails in the populations of Kholmogory, Yaroslavl, Altaiand Kalmyk breeds, which have white spotting in color, as wellas in Angus. In other populations, the frequency of the T allelevaries from low (Yakut breed) to moderate .The link between the content of rs210634530-T and coat colorcan be demonstrated for Finnish breeds. As mentioned above,many individuals of Western Finncattle and Eastern Finncattlehave a white muzzle, back, and belly. Northern Finncattle haseither a white or, less often, black-and-white coat. It shouldbe noted that in addition to SNP rs210634530, additional lociappear to be involved in the control of the ‘white spottingʼphenotype , therefore, the link betweenthe frequency of rs210634530-T and coat color may not beso straightforward.Some of the genetic features of the breeds are not quiteobvious at first glance. For example, both Russian and foreignpopulations of Angus and Wagyu have a high (0.89–0.95)frequency of rs43703011-G allele of the beta-casein geneCSN2. Variations of the CSN2 gene at several non-synonymous positions determine its alleles – A1, A2, A3, B, C, etc. Theabove-mentioned allele G of rs43703011 is shared by severalalleles of the CNS2 gene, the most common of which is A2.The so-called A2-milk is considered more preferable forconsumption, due to better absorption and fewer undesirableeffects from the human digestive system .In recent years, breeding programs in many countries haveaimed to increase the frequency of the A2 allele in dairy cattle. Given that Angus and Wagyu are beefbreeds and are not used for milk production, the increasedG allele content they have can hardly be explained by selectionto improve milk quality. The most plausible explanation isselection for meat productivity. Thus, according to Hohmannet al. (2020), the carriage of the A2 allele increases averagedaily weight gain and weaning weight in German Angusand Simmentals. Therefore, increasing the frequency of thers43703011-G allele, and consequently the A2 allele of theCSN2 gene, can be useful for improving not only dairy butalso beef breeds.Some of the variants found are specific to one breedand virtually absent in others. The most breed-specific areclinically important polymorphisms in the F11, IARS andNHLRC2 genes. The previously mentioned insertion inthe F11 gene, leading to a deficiency of blood coagulationfactor XI, is almost exclusively observed in foreign andRussian Wagyu populations. At the same time, among morethan 5 thousand other animals from the “1000 Bull Genomes”Project, this mutation is harbored by only two animals.Association of factor XI activity with ATATGTGCAGAATATinsertion has been initially demonstrated for Wagyu . The homozygous genotype for this mutation isassociated with a blood clotting disorder and an increase inthe duration of bleeding. In the Russian population of Wagyu,its frequency equals 0.25, which is consistent with the dataof early publications on its prevalence in the Japanese blackbreed . At the sametime, in the foreign Wagyu population, here represented bya sample from Australia, this insertion has a frequency of 0.11.Other examples of breed-specific variants are singlenucleotide substitutions in the IARS (BTA8:83909754C>G) andNHLRC2 (BTA26:34340886T>C) genes. BTA8:83909754C>Gmutation in the IARS gene in homozygote leads to perinatalweak calf syndrome and increased prenatal mortality . This variant is specific to Wagyu, andbesides, it was found only in one animal from the “1000 BullGenomes” Project. In the Russian and Australian samples ofthis breed, its frequencies are 0.075 and 0.056, respectively.The BTA26:34340886T>C mutation in the NHLRC2 genementioned earlier, in homozygote leading to notomelia, isbreed-specific for Angus, and was first discovered in thisbreed . Besides Angus, in the sample of“1000 Bull Genomes”, the mutant allele is found only in oneanimal of an unknown (‘crossbreed’) breed. In the Russianand foreign populations of this breed, it has frequencies of0.065 and 0.024, respectively.Differences between Russian and foreign populationsof the same breedThe presence in our analysis of foreign Angus, Herefordsand Wagyu populations can shed light on the features of the selection and adaptation of Russian populations of thesebreeds. Overall, Russian and foreign samples of the samebreed demonstrate similar allele frequency profiles, withstatistically confirmed differences present only in a smallnumber of loci. The differences observed can be explained bymany factors or their combinations. For example, an almostthreefold excess of the BTA26:34340886-C allele  content in the RussianAngus population compared to the foreign one (see Table 2)may be a consequence of the founder effect or genetic driftin general, as well as less intensive efforts for elimination ofthis variant in the Russian herd.Interpopulation differences in the loci associated with reproductionmay result from an adaptation to environmentalconditions. In the Russian population of Herefords, allelesof several polymorphisms that negatively affect the survivalof embryos (rs43705173-G and rs110942700-C) and fertility(rs208753173-G) have a lower frequency compared to theforeign sample of this breed. It can be assumed that the Russiansample of Herefords, in this work represented by a populationbred in Western Siberia since the 1960s , was subject to selection for reproductive performance.This assumption is supported by the data of Afanasyeva andco-authors, according to which in the conditions of the AltaiTerritory, the population of Herefords of Siberian selectionshows a much lower stillbirth rate (1.4 %) compared to animalsof Finnish selection (6.6 %) imported in 2011 . Low temperatures are known to negatively affect thereproduction of cattle, reducing fertility and increasing perinatalmortality . Therefore,population differences at these loci may reflect the processof genetic adaptation aimed at compensating a decrease inreproductive functions caused by cold.Of particular interest are single nucleotide polymorphismsassociated with meat traits and differing in samples of Angus(rs41255587 and rs109221039 in the CAST gene) and Herefords(rs17871051 and rs17872050 in the CAPN1 gene). Forall four SNPs, the foreign populations of these breeds demonstratea higher content of alleles increasing meat tenderness.This is an important gastronomic feature and its improvementis included in the breeding programs of foreign beef breeds. At the same time, we are not aware of extensivebreeding attempts of this kind in Russia, which is probablythe reason for the observed differences between the samples.Therefore, the Russian populations of Angus and Herefordshave the potential for the improvement of meat quality byselection for CAST and CAPN1 alleles.Of the studied loci differing between populations of thesame breed, four SNPs  determining the kappa-casein gene CSN3alleles deserve to be noticed. Their allele frequencies differbetween Russian and foreign Angus populations. These polymorphismsare associated with milk traits, in particular, withthe concentration of kappa-casein in milk and milk coagulationproperties, which is important for cheesemaking. At thesame time, the effect of CSN3 alleles on the productivity ofbeef cattle is poorly understood. Investigations of Tambascoet al. (2003) и Curi et al. (2005) found no association betweenCSN3 alleles and meat traits. Thus, the observed differencescan be attributed to the founder effect, or selection for economically important traits whose associations with CSN3polymorphisms have not yet been identifiedPolymorphisms of clinical significance presentin Russian breedsIn Russian breeds, there is a number of polymorphic variants,in homozygous state causing hereditary diseases, some ofwhich  havealready been discussed above due to their breed specificity.Also, the variants in the ROR2 and LRP4 genes should bementioned that are associated with the manifestation ofinterdigital hyperplasia (proliferation of tissue between thehooves) and syndactyly , respectively. Unlike the F11, IARS and NHLRC2 genes,the “harmful alleles” in ROR2 (rs377953295-A) and LRP4(rs453049317-T) are not breed-specific, and are widespreadboth in Russian breeds and in the rest of the worldwide cattlepopulation. Of the Russian populations, the Kalmyk (0.192)and Altai (0.15) breeds have the highest content of thers37795322-A allele of the ROR2 gene. In the worldwidesample, its frequency reaches 0.13. The rs453049317-Tvariant in the LRP4 gene has the highest frequency in the Altaibreed (0.2) and in the Russian Angus (0.12), while in the restof the worldwide population it is 0.076.Currently, testing for genetic defects is widely used in thepractice of animal husbandry in many countries . For example, testing for mutations in theF11 and IARS genes is included in the genetic screeningprograms recommended by the Australian Wagyu Association(https://www.wagyu.org.au/content/uploads/2020/08/Genetic-Conditions-in-Wagyu-FactSheet-2020.pdf). At thesame time, the elimination of undesirable alleles shouldbe approached with caution. For example, there is an assumptionthat the carriage of mutations associated with syndactylyimproves the milk productivity of cows, which can partiallyexplain the spread of this pathology in cattle .Our analysis showed the allele frequency distributionfor the most clinically and economically important DNApolymorphisms present in Russian cattle breeds. A number ofvariants leading to common hereditary disorders in cattle havesignificant representation in Russian populations, and probablyneed to be eliminated. Also, the differences between Russianand foreign cattle populations at several loci are presumablyof adaptive importance. The data of this study may be usefulin cattle breeding programs aimed at improving the existingcattle breeds, and creating new ones.The authors declare no conflict of interest.Abd El-Hack M.E., Abdelnour S.A., Swelum A.A., Arif M. 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DOI 10.3390/ani10020252.Storey J.D., Bass A.J., Dabney A., Robinson D. qvalue: Q-valueestimation for false discovery rate control. R Packag. version 2.24.0.2020. DOI 10.18129/B9.bioc.qvalue.Storey J.D., Tibshirani R. Statistical significance for genome-wideexperiments. Proc. Natl. Acad. Sci. USA. 2003;100(16):9440-9445.DOI 10.1073/pnas.1530509100.Surzhikova E.S., Sharko G.N., Mikhailenko T.N. Allelic spectrum ofCSN3, PIT-1, PRL genes in horned cattle of Black-and-White breed.Novosti Nauki v APK = Science News of AIC. 2019;3:136-139. DOI10.25930/2218-855X/032.3.12.2019. (in Russian)Tambasco D.D., Paz C.C.P., Tambasco-Studart M., Pereira A.P.,Alencar M.M., Freitas A.R., Coutinho L.L., Packer I.U.,Regitano L.C.A. Candidate genes for growth traits in beefcattle crosses Bos taurus × Bos indicus. J. Anim. Breed. Genet.2003;120(1):51-56. DOI 10.1046/j.1439-0388.2003.00371.x.Tatum J.D. Pre-Harvest Cattle Management Practices for EnhancingBeef Tenderness. Colorado State Univ., 2006.Terletskiy V.P., Buralkhiyev B.A., Usenbekov Y.S., Yelubayeva M.,Tyshchenko V.I., Beyshova I.S. Screening for mutations thatdetermine the development of hereditary diseases in breeding cattle.Aktual’nye Voprosy Veterinarnoi Biologii = Actual Questions ofVeterinary Biology. 2016;3:3-7. (in Russian)Usova T.P., Usmanova N.N., Litvina N.I., Usov N.V. The spread ofBLAD-syndrome of breeding bulls of Holstein breed of Russian andimport selection. Vestnik Rossiyskogo Gosudarstvennogo AgrarnogoZaochnogo Universiteta = Bulletin of the Russian State AgriculturalCorrespondence University. 2017;25:20-24. (in Russian)Vsyakikh A.S., Kurinsky M.S. Imported Сattle in the USSR. Moscow:Kolos Publ., 1976. (in Russian)Watanabe D., Hirano T., Sugimoto Y., Ogata Y., Abe S., Ando T.,Ohtsuka H., Kunieda T., Kawamura S. Carrier rate of FactorXI deficiency in stunted Japanese black cattle. J. Vet. Med. Sci.2006;68(12):1251-1255. DOI 10.1292/jvms.68.1251.Xu L., Bickhart D.M., Cole J.B., Schroeder S.G., Song J., Tassell C.P.,Sonstegard T.S., Liu G.E. Genomic signatures reveal new evidences for selection of important traits in domestic cattle. Mol. Biol. Evol.2015;32(3):711-725. DOI 10.1093/molbev/msu333Yudin N.S., Larkin D.M. Whole genome studies of origin, selectionand adaptation of the Russian cattle breeds. Vavilovskii ZhurnalGenetiki i Selektsii = Vavilov Journal of Genetics and Breeding.2019;23(5):559-568. DOI 10.18699/VJ19.525. (in Russian)Yudin N.S., Voevoda M.I. Molecular genetic markers of economicallyimportant traits in dairy cattle. Russ. J. Genet. 2015;51(5):506-517.DOI 10.1134/S1022795415050087 A., Yudin N., Aitnazarov R., Plyusnina A., Brukhin V.,Soloshenko V., Lhasaranov B., Popov R., Paronyan I.A., PlemyashovK.V., Larkin D.M. Genome-wide genotyping uncovers geneticprofiles and history of the Russian cattle breeds. Heredity (Edinb.).2018;120(2):125-137. DOI 10.1038/s41437-017-0024-3.Zinovieva N.A. Haplotypes affecting fertility in Holstein cattle.Sel’skokhozyaistvennaya Biologiya = Agricultural Biology.2016;51(4):423-435. DOI 10.15389/agrobiology.2016.4.423eng.


Testudo hermanni. We started by performing a ddRAD‐seq experiment on 70 wild individuals of T. hermanni from 38 locations. Results obtained using 3182 SNPs are comparable to those previously obtained using STR markers in terms of genetic structure and power to identify the macro‐area of origin. However, our SNPs revealed further insights into the substructure in Western populations, especially in Southern Italy. A small panel of highly informative SNPs was then selected and tested by genotyping 190 individuals using the KASP genotyping chemistry. All the samples from wild populations of known geographic origin were genetically re‐assigned with high accuracy to the original population. This reduced SNPs panel represents an efficient molecular tool that enables individuals to be genotyped at low cost (less than €15 per sample) for geographical assignment and identification of hybrids. This information is crucial for the management in‐situ of confiscated animals and their possible re‐allocation in the wild. Our methodological pipeline can easily be extended to other species.Assigning individuals to their source populations is crucial for conservation research, especially for endangered species threatened by illegal trade and translocations. Genetic assignment can be achieved with different types of molecular markers, but technical advantages and cost saving are recently promoting the shift from short tandem repeats (STRs) to single nucleotide polymorphisms (SNPs). Here, we designed, developed, and tested a small panel of SNPs for cost‐effective geographic assignment of individuals with unknown origin of the endangered Mediterranean tortoise  The intense harvesting for pet trade, especially before the 1980s when it was not banned yet . Previous genetic studies suggest that individuals can be correctly assigned in most cases to their macro‐area of origin with a small panel of STR markers ; 3. we designed a cost‐effective reduced panel of SNPs with retained informativeness; and 4. we tested the reduced SNP panel in 190 individuals using the KASP‐by‐Design Fluidigm Assays  following manufacturer's instructions. See DNeasy Blood and Tissue Kit (Qiagen) and Quick‐DNA Universal Kit (Zymo Research) were used to extract genomic DNA from FTA cards and whole blood stored at −20°C, respectively, following manufacturer instructions. DNA was quantified with Qubit using the dsDNA BR kit (Invitrogen). ddRAD‐seq libraries were prepared using 2.2process_radtags program in version 1.44 of STACKS . In an additional filtering step, we retained only one SNP per locus  to minimize redundancy of genetic information due to the physical linkage between the markers. This reduced dataset was used for PCA and FST‐based methods. The resulting vcf files were converted to other program‐specific input files using PGDSPIDER v2.0.5.1 . Populations were defined as clusters within the fineRADstructure tree and relatedness plot.The population structure was inferred from the ddRAD‐seq with the package alpha among clusters, as suggested by  from K = 1 to K = 10 with 750,000 MCMC after a burn‐in of 500,000. Structure results were summarized and visualized with the web server CLUMPAK  with the Jombart, . The samFST values were estimated among clusters inferred from fineRADstructure analysis using ARLEQUIN v3.5 . To maintain this level of resolution while reducing the number of markers, we considered one node at the time, and ranked each SNP with the aim of identifying SNPs that were most informative for maximizing genetic differentiation between the two groups separated by the node. A comparable number of SNPs was selected from each node  by running RF with 100, 500, 1000, 8000, 25,000 trees. The mtry parameter (number of features considered at each node) was tested using the function tuneRF. We then ran RF 10 times per node, as suggested in Sylvester et al.  were built to visualize overlapping SNPs selected across all methods within each of the two panel sizes. We calculated expected heterozygosity across seven categories of SNPs selection generated by Venn diagram using ARLEQUIN v3.5  in terms of assignment accuracy. Assignment testing was performed by using the R package 2.5FST approach and these loci were tested with the KASP‐by‐Design Fluidigm Assays  as internal control, samples from captivity (from rescue centers), or from wild populations never typed before : We performed an assignment analysis with STRUCTURE . K was fixed at its optimal value  identified by the The diagram in Figures 33.1full dataset).From the 84 individuals sequenced, 14 samples were excluded due to low coverage and high amount of missing data. For the remaining 70 individuals, the sequencing coverage obtained per individual ranged from 89,897 to 412,840 with an average of 236,722 reads per sample see . After f3.2fineRADstructure using the full dataset showed the presence of eight main clusters , North Calabria (NCA), Central Calabria (CCA), South Calabria (SCA), Sicily (SIC), and Sardinia (SAR). These results support a finer genetic structure than previously suggested using STRs , and Bosco Mesola + Croatia (MEC). The comparison between individuals within these two genetic clusters showed the highest level of co‐ancestry compared to other groups. See The co‐ancestry matrix obtained with s Figure . The lowfineRADStructure utilizes haplotype linkage information, other approaches such as PCA may be biased by the association between markers. Thus, population clustering was further investigated by means of PCA using only independent SNPs . The first three principal components, PC1, PC2, and PC3, together accounted for 52% of the total variance in the dataset, with eigenvalues of 39.4%, 8.4%, and 4.2%, respectively  and the second by south Calabria together with the two islands  to 0.35 . Within the two subspecies, the highest FST value was observed between the two eastern subspecies populations MEC and GRE (FST = 0.47). When populations belonging to different subspecies are compared, FST values reached values between 0.7 and 0.8. All FST values were significant at the nominal significance level of 0.05, and most p‐values were lower than 0.01 .The SNPs markers isolated with the ddRAD‐seq approach appeared thus able to genetically discriminate populations with different geographic distribution, with a precision that was not the same using different statistical approaches. To further confirm that the SNPs markers contained relevant information to discriminate individuals with different geographic origin, at least at the macro‐areas level, we compared the geographic structure inferred with the SNPs with that estimated from a small STR panel of proven efficacy for conservation and forensic applications  thC Figure ; overallt Figure ; (iii) tt Figure  correspo3.3fineRADstructure with the whole dataset , the loci with the highest FST resulting from pairwise comparisons between the two clades at each node were selected. FST values ranged from 1 to 0.39 in the 48 SNPs panel and from 1 to 0.33 for the 96 SNPs panel. Finally, in the last method (RF) the loci with the highest MDA were included. MDA values ranged from 7.99 to 1.45 in the 48 SNPs panel and from 7.99 to 1.26 in the 96 SNPs panel. The distribution of SNPs across the three SNP selection methods is summarized by means of a Venn diagram in In the first method (PCA‐based panel), the highest loadings from the top 5% of the most informative principal components (PC) were selected. For all the seven nodes, PC1 was always the only axis to highlight differences in our data. In the second method  and the Mediterranean Islands . The mean accuracy ranged from 91.6% (PCA) to 97.6%  to 99.1%  > 0.80 as the assignment threshold. Using these parameter values, we were able to assign more than 78% of samples to one of the eight clusters. 32.8% of the tortoises were assigned to the GRE cluster, 24.3% to ITP, 18% to MEC, 2.1% to the SAR, 0.5% to NCA, and 0.5% to CCA, while 21.7% of the individuals were not assigned to any predefined cluster (NA).The genotypes of 189 DNA samples (one sample failed) were assigned to the most probable geographic area of provenance included in the ddRAD‐seq reference and then the STR reference  was available. Both markers assigned all the samples to the same cluster , and three samples assigned to ITP with the STRs were assigned to ITP and CAL with the SNPs .Furthermore, we compared the geographical assignment of samples that were genotyped at 41 SNPs and 7 STR loci see , using fr Figure . By conts Figure . Among tT. h. hermanni subspecies , and only 8% were assigned to the MEC cluster . In the Emilia‐Romagna center, 58% of the tortoises had Greek origins, and 6% were assigned to the MEC cluster. Approximately one‐third of the tortoises from this center was not assigned. The centers of Emilia‐Romagna and Lazio show a similar proportion of unassigned individuals . On the contrary, the two centers show an opposite trend in terms of individuals assigned to the GRE  and ITP  clusters: 58% GRE and 6% ITP for the Emilia‐Romagna center, 11% GRE and 56% ITP for the Lazio center. Interestingly, the tortoises from a wild and isolated population located in a natural reserve with fossil dunes corresponding to the former coastline of the Northern Adriatic Sea (Dune), were mainly assigned to the MEC cluster , and from wild populations never typed before  showed a better accuracy of PCA method over FST and RF. This pattern is in discordance with what was found in Sylvester et al.  and considering that the genotyping cost for a panel with twice as much of SNPs implied a twofold cost increase, we selected the smallest panel for further genotyping to achieve a trade‐off between accuracy and costs.Given the relatively high overall self‐assignment accuracy of the 48 SNPs panel based on T. hermanni's STRs, outsourcing the entire service is extremely expensive , which is significant in the case of noninvasively collected samples. We showed that missing data were below 1.9% for every SNP in our final panel, which is low compared with higher rates of missing data observed especially in STR genotyping of species of conservation concern where the sampling should not be invasive  was available. These results were also confirmed by using a panel of STR markers , or to the lack of major genetic clusters in the reference populations.Assigning individuals to their area of origin was highly accurate (100%) when we considered samples from wild populations with known geographic origin and for which the reference . This pattern confirms the Balkan areas as a source of illegal trading and the presence of hybrids in the Italian rescue centers due to mating occurring in captivity between individuals with different origins , which was never sampled before, were assigned to the genetic cluster presents also in other areas in North‐East Italy (Bosco Mesola) and Croatia. However, we also found non‐native tortoises belonging to genetic cluster observed in Greek individuals.4.4Testudo hermanni harvested for the pet trade, and Italy was a major destination and virtually the only one where local populations already existed and introgression of non‐native genomes could have occurred. Besides the possible translocation in recent and historical times of native individuals across the Italian peninsula and the Mediterranean islands , these measures should be carefully evaluated as a necessary step in conservation plans.Approximately 2 million tortoises were exported from the former Yugoslavia to different European countries in the last century, especially after the Second World War and until the 80’s (Ljubisavljević et al., T. hermanni protection. This study, describing in detail the necessary methodological steps of the process, represents also a useful example for the development of similar tools in other species with similar management needs.In a previous paper we introduced a panel of 7 STR markers useful to reach this goal (Biello et al., The SNP panel introduced in this study allows the genotyping of a large number of samples at low cost. Compared to the development and the typing of a set of similarly informative STR markers, this approach is cheaper, faster; requires less handling; and provides immediate standardization across laboratories. At the moment, however, the reference database of wild populations for the SNPs panel is still smaller and geographically less representative of the distribution range of this species compared to the STRs database, and we therefore recommend, until more wild individuals will be typed, the use of the SNPs panel integrated for difficult and especially forensic assignments with the analysis of the STR markers.Hundreds of tortoises are currently maintained in captivity in breeding and rescue centers, providing a highly valuable source of individuals for reintroduction and reallocation projects. A fraction of these individuals, selected considering their health conditions, sex, age, and genetic compositions, could represent not only a demographic and genetic supplementation of small natural populations, but also the founders of new wild populations in ecologically suitable areas where this species was present in the past. These interventions would favor the well‐being of the captive tortoises but also the human well‐being by creating more natural and biodiverse environments (Fuller et al., None declared.Figure S1–S12Click here for additional data file.Table S1–S4Click here for additional data file.Appendix S1Click here for additional data file.


To analyze an additional set of Y-chromosome genetic markers to acquire a more detailed insight into the diversity of the Croatian population.Rst with AMOVA online tool from the YHRD. The evolutionary history was inferred with the neighbor-joining method of phylogenetic tree construction in the MEGAX software. Whit Athey's Haplogroup Predictor v. 5 was used for additional comparison with regional and other European populations.A total of 518 Yfiler Plus profiles were genotyped. Allele frequencies, haplotype frequencies, and haplotype diversity were calculated by using the STRAF software v. 2.0.4. Genetic distances were quantified by A total of 507 haplotypes were used for genetic STR analysis. An interpopulation study on 17 Y-STR markers showed the lowest genetic diversity between the Croatian and Bosnian-Herzegovinian populations and the highest between the Croatian and Irish populations. Additional interpopulation comparison with the original 27 Y-STR markers (for the population with available data) was also performed. A total of 518 haplotypes were used in the determination of haplogroup diversity. Haplogroup I with its sublineage I2a expressed the highest prevalence. The second most prevalent haplogroup was R, with its major sublineage R1a, except for the subpopulation of Hvar, where E1b1b was the second most prevalent haplogroup. Rare haplogroups also confirmed in this study were L, T, and Q. G1 was detected for the first time in the Croatian population.We obtained a new insight into the differences between examined subpopulations of Croatia and their possible (dis)similarities with neighboring and distant populations. The Y chromosome  ∼ 60 Mb) is relatively small and inherited from father to son unchanged . Except for the small pseudoautosomal regions (PAR), there is no recombination between the X and Y chromosome -3. This 0 Mb is rThe Y chromosome mostly consists of repetitive sequences (around 50%), which are single-base substitutions, Alu elements, and long interspersed nuclear elements (LINEs). Short tandem repeats (STRs) as repetitive elements are the base of population genetic studies. Their average mutational frequency is ∼ 0.2% per generation ,8.Y haplogroup can be defined as a part of the Y-chromosome family related by ancestry and determined by a specific set of Y chromosomal single nucleotide polymorphisms (Y-SNPs). It is important to better understand the demographic processes that shaped modern populations ,9. The lY-chromosome haplogroups can also be successfully predicted from Y-STR markers (Y-STR haplotype) by using Y-STR haplogroup predicting tools. Lately, this method has drawn attention due to its effectiveness in terms of labor, time, and costs . The hapThe main aim of this research is to update the information about Croatian Y-chromosome diversity by using additional Y-STR loci to compare new results with the previously published results generated using Y-STR and Y-SNP markers -20. A seFor this study, buccal swab samples were obtained from 518 unrelated adult male individuals from five different regions of Croatia: Hvar (n = 104), Osijek (n = 110), Pula (n = 99), Varaždin (n = 100), and Split (n = 105).® nexus SX1 PCR thermal cycler  according to the manufacturer’s instructions. PCR-amplified products were separated and detected by using standard protocols for electrophoresis on 3500 Genetic Analyzer (Applied Biosystems). Allele calling was performed with GeneMapper ID-X Software v. 1.4 (Applied Biosystems) by using the custom panel and bin sets.DNA was extracted with QIAsymphony instrument by using the QIAsymphony DNA Investigator Kit and protocol . It was quantified on Rotor-Gene Q real-time PCR cycler (Qiagen) by using Q-Rex Software and Investigator Quantiplex Pro RGQ Kit. Twenty-seven Y chromosome STR loci were simultaneously amplified with Yfiler Plus PCR Amplification Kit . Amplification was carried out following the manufacturer’s protocol. PCR amplification was performed on MastercyclerA total of 507 fully genotyped Y-STR profiles from the present study were submitted to Y Chromosome Haplotype Reference Database (YHRD) with the accession numbers as follows: Hvar , Osijek , Pula , Varaždin , and Split .Allele and haplotype frequencies, the number of alleles and different haplotypes, as well as gene and haplotype diversity were estimated to assess the intrapopulation diversity.ip2)*n/(n-1) was used to calculate haplotype diversity; where n is the sample size and ip is the ith haplotype frequency. Gene diversity was calculated as 1- ∑ ip2, where ip is the allele frequency. Match probability (MP) was calculated with the formula ∑ ip2, where ip is the frequency of the ith haplotype. Discrimination capacity (DC) was determined by dividing the number of haplotypes by the number of individuals in the population  with 10 000 permutations were included for the studied populations. The multidimensional scaling plots (MDS) showing the comparison of population haplotype data from YHRD were generated by using genetic distances.ulations ,26. AssoAMOVA analysis was performed with two population groups. The number of the populations with available data for 27 STR loci was relatively small, especially in the closest Croatian neighborhood. Therefore, the first group was analyzed by comparing the 17 Y-STR loci included in the AmpFLSTR Yfiler PCR Amplification Kit. The second group was analyzed by comparing the whole set of 27 Y-STR loci included in the Yfiler Plus PCR Amplification Kit.The first group of European populations selected for comparison with the population of Croatia by using 17 Y-STRs included Tiroler Unterland, Austria (n = 547); Antwerpen, Belgium (n = 309); Bosnia and Herzegovina (n = 574); Bulgaria (n = 91); Rostock, Germany (n = 598); Greece (n = 191); Hungary (n = 303); Italy (n = 147); Warsaw, Poland (n = 491); Serbia (n = 567); Albania (n = 315); Czech Republic (n = 109), Estonia (n = 123); Ireland (n = 863); Lithuania (n = 531); North Macedonia (n = 493); Norway (n = 1555); Slovenia (n = 294); Sweden (n = 296); and Ukraine (n = 212).The second group of the worldwide populations selected for comparison with the population of Croatia included Croatia ; Slovenia (n = 194); Belgium (n = 160); Hungary (n = 218); Austria (n = 392); Germany (n = 495); Italy (n = 689); North Macedonia (n = 295); Serbia (n = 183); Denmark (n = 177); Ethiopia (n = 290); French Polynesia (n = 81); Ghana (n = 584); India (n = 541); Lithuania (n = 251); Mexico (n = 354); Nigeria (n = 337); Pakistan (n = 280); Poland (n = 612); Russian Federation (n = 958); Saudi Arabia (n = 156); Spain ; Switzerland (n = 724); and United Kingdom (n = 115).Available population data and all related references are included in the YHRD ,26. The Y-chromosomal haplogroup prediction with allele frequencies on 518 Yfiler Plus profiles was performed by using Whit Athey's Haplogroup Predictor v. 5, an algorithm based on the Bayesian allele-frequency approach ,30.A total of 518 haplotypes were detected and used for haplogroup prediction. Eleven haplotypes were considered newly detected microvariants, which required additional analysis for confirmation. Therefore, the remaining 507 haplotypes (the ones without newly detected microvariants) were used for additional statistical analysis. On a sample of fully genotyped 507 Y-STR profiles, 502 different haplotypes were detected, with 497 unique haplotypes and 5 haplotypes appearing twice. In addition, 196 alleles at 27 Y-STR loci were detected . The locThe haplotype diversity for the studied population was 1.0000 ± 0.0014, with DC of 1.00 and MP of 0.01. Genetic diversity ranged from 0.886 for DYS481 to 0.251 for DYS392. The genetic diversity average across all loci was 0.656. With six detected alleles and the lowest genetic diversity, DYS392 was one of the least polymorphic loci in the studied population. Therefore, it was not surprising that, with a frequency of 0.862, allele 11 at DYS392 was the most common allele .Rst = 0.0009, P = 0.3240). The greatest genetic diversity observed for the population of Hvar was compared with the population of Varaždin , the population of Pula , and the population of Osijek . The lowest genetic diversity observed for the population of Osijek was compared with the population of Split  and the population of Pula . The greatest genetic diversity observed for the population of Osijek was compared with the population of Varaždin  and the population of Hvar . The lowest genetic diversity observed for the population of Pula was compared with the population of Osijek . The greatest genetic diversity observed for the population of Pula was compared with the population of Hvar , the population of Split , and the population of Varaždin . The lowest genetic diversity observed for the population of Split was compared with population of Hvar  and the population of Osijek . The greatest genetic diversity observed for the population of Split was compared with the population of Varaždin  and the population of Pula . The lowest genetic diversity observed for the population of Varaždin was compared with the population of Pula . The greatest genetic diversity observed for the population of Varaždin was compared with the population of Hvar , the population of Split , and the population of Osijek .In order to determine additional genetic differences, an interpopulation analysis was done between five regions: Hvar (n = 104), Osijek (n = 109), Pula (n = 94), Split (n = 102), and Varaždin (n = 98). The lowest genetic diversity observed for the population of Hvar was compared with the population of Split  and the population of Serbia . When compared with the present results, other populations with low genetic diversity values include those from Bulgaria , Ukraine , Slovenia , Hungary , Greece , North Macedonia , Italy , Albania , Czech Republic , and Austria . The studied population showed the greatest genetic distance from the populations of Ireland , Estonia , Lithuania , Belgium , Norway , Poland , Sweden , and Germany .In the first group of populations selected for comparison with the population of Croatia by using reduced 17 Y-STR set of markers included 20 populations. The lowest genetic diversity was observed between the currently analyzed population of Croatia and previously published results for the population of Bosnia and Herzegovina , and Slovenia . Other European populations with low genetic diversity values were the populations from Hungary , North Macedonia , Russian Federation , and Poland . A higher genetic distance was observed when the study population was compared with other European and worldwide populations.The second group of selected countries used a set of all 27 Y-STR markers. The selection was limited since this is an expanded panel of Y-STR markers, and data are not available for many populations. A comparison of the data on 27 Y-STR markers for the first selected group of the current study and the previously published data for 23 populations showed the lowest genetic diversity of the Croatian population and the population of Serbia , and between the population of the Varaždin region and the neighboring Slovenian population; Rst = -0.0002, P = 0.4124). These results prove again that the Y-chromosome is expected to show greater geographical clustering than other population markers , Varaždin (n = 100), Split (n = 105), Pula (n = 99), and Osijek (n = 110). Regarding the haplogroup diversity between these five subpopulations, the haplogroup was successfully assigned to all 518 Y-STR profiles . The resFour of the five subpopulations of Croatia showed expected results . High frIn summary, sublineage I2a was generally the most frequent haplogroup in the populations of Croatia in this study, but also in all previously studies ,18,19,32Haplogroup I (previously described as Eu7)  arrived The R1a (previously described as Eu19) , as a leSublineage R1b (previously described as Eu18)  showed aSublineage E1b1b (previously predominantly described as Eu4)  is the mAn approximate comparison between the frequencies of the earlier used Y chromosome lineage (Eu) determined by Semino et al  and the Rare haplogroups discovered in this study were Q, T, L and G1, each present in 1.93%, 0.58%, 0.19%, and 0.19% of all samples, respectively. Haplogroup L is associated with South Asia and India but is also found in low frequencies in Central Asia, Southwest Asia, and Southern Europe. With its alternative phylogenetic name K1a, haplogroup L is closely related to haplogroup T . HaplogrRst statistics was used to compare 27 Y-STR loci data from the studied Croatian population with data of other populations available from YHRD. The results indicate that the Croatian population does not deviate significantly from the neighboring populations of Bosnia and Herzegovina, Slovenia, and Serbia. This proves that the Y chromosome genetic marker has a noticeable geographical background  harbor the ancestral genetic impact of Old European people who settled in Europe approximately 25 000-30 000 years ago and survived the LGM in several different refugia . Our resThe limitation of the study is that some of the regional populations could not be compared with the Croatian population due to the lack of published data. Therefore, two levels of the comparison were necessary. Furthermore, current results for Pula and Varaždin subpopulations indicate somewhat greater similarity to regional populations. Although this could be explained based on historical facts, more detailed structuring of these populations should be performed to confirm the obtained results.