BSA), which works with selected and pooled individuals, has been extensively used in gene mapping through bulked segregant analysis with biparental populations, mapping by sequencing with major gene mutants and pooled genomewide association study using extreme variants. Compared to conventional entire population analysis, BSA significantly reduces the scale and cost by simplifying the procedure. The bulks can be built by selection of extremes or representative samples from any populations and all types of segregants and variants that represent wide ranges of phenotypic variation for the target trait. Methods and procedures for sampling, bulking and multiplexing are described. The samples can be analysed using individual markers, microarrays and high‐throughput sequencing at all levels of DNA, RNA and protein. The power of BSA is affected by population size, selection of extreme individuals, sequencing strategies, genetic architecture of the trait and marker density. BSA will facilitate plant breeding through development of diagnostic and constitutive markers, agronomic genomics, marker‐assisted selection and selective phenotyping. Applications of BSA in genetics, genomics and crop improvement are discussed with their future perspectives.Biological assay has been based on analysis of all individuals collected from sample populations. Bulked sample analysis ( To ensure enough power in statistical analysis, a large number of samples should be combined with sequencing or a high density of genetic markers. As almost all the traits with agronomic values are genetically complex, which are affected by many genes, environments and their interactions  are analysed, has been proposed  segregants from segregating populations derived from bi‐ or multiparents and (ii) variants from any populations of a species including those with diverse genetic background.et al., et al., Bulked segregants may come from populations derived from biparental, three‐way, four‐way and multiparental crosses, including those developed with special designs such as diallel design, North Carolina Design (NCD), multiparent advanced generation intercross , and DHs (doubled haploids), among which RIL and DH populations consist of individual homozygous lines so that they can be maintained by selfing and evaluated in multiple environments across years and locations.Biparental populations have been most frequently used in BSA with any segregants or phenotypic contrasting extremes. This type of populations includes FAs one of the two major types of multiparental populations, NAM is developed by crossing a common line with a diverse panel of lines, followed by generating a set of RIL populations. Different from NAM, a MAGIC population starts with multiple biparental crosses, and by the end, a composite hybrid is derived to include all the parental lines, from which a set of RILs or DHs is developed.2, F2:3, RIL, DH) or multiparental populations, by testcrossing each individual within a population with their parental lines, F1, or testers. In this case, genotyping is performed for the individuals that are used for testcrossing, while phenotyping is conducted on the testcross progeny. Several mating designs, including diallel, NCD I, II and III, and triple test cross, can be explored for generating testcross populations. The testcross populations can be used for understanding genetic mechanisms of important phenomena such as hybrid performance, combining ability and heterosis.Several types of testcross populations can be derived from biparental  strategy. To cover all the genetic loci across the genome, a large number of mutants need to be developed, which is time‐consuming and also very expensive. Mutant libraries have been used to discover rare mutations in extensively pooled DNA in rice  Xu, , 2016, wet al., et al., et al., et al., et al., et al., Two contrasting sampling methods, trait‐based sampling and marker‐based sampling, have been used in BSA Figure . The foret al., The power of BSA largely depends on sampling‐related factors, particularly sample sizes including entire population size and tail size  Figure . In segret al., et al., For the complex trait controlled by minor genes, other factors associated with the target genes, such as gene number, gene effect, gene interactions and the relative positions on chromosomes, should be taken into account to determine the required population size , optimum tail size would be 20%–30% of the entire population  is available or reliable estimation of allele frequencies is not possible, BSA using a single bulk can be performed by comparing the available bulk with a phenotypic control that is randomly selected from the individuals under a normal environment . The barcode sequences can be designed follow the instructions (http://comailab.genomecenter.ucdavis.edu/index.php/Barcodes). The total number of available barcodes is determined by barcode length and the number of indices. Dual indexing, namely two indices used in multiplexing, further increases the total number of samples that can be pooled. With multiplex sequencing, a large number of samples can be simultaneously sequenced during a single experiment, while multisample pooling improves productivity by reducing time and reagent use. Illumina now provides a 384‐sample kit which allows as many as 96 samples to be analysed in one run. For single nucleotide polymorphism (SNP) genotyping, up to tens or even hundreds of samples can be labelled individually but mixed and analysed as one sample  can be attached to each sample so that multiple samples can be pooled in one sequencing run but can be distinguished and sorted during data analysis  and pooling samples into a single sequencing channel  technology has been developed. First, DNA barcodes are attached to proteins collectively via ribosome display or individually via enzymatic conjugation. To construct a random single‐molecule array, the barcoded proteins are then assayed en masse in aqueous solution and subsequently immobilized in a polyacrylamide thin film for amplification and sequencing  as a novel concept for protein multiplexing without mixing has been used to quantify proteins in the serum of patients with breast cancer and healthy controls, with six candidate biomarkers identified , 71.6% of the genes (filtered‐gene set) can be covered using 15‐day‐old seeding of maize , namely mass spectrometry (MS) and Edman degradation. Because of its sensitivity and efficiency, MS is not only becoming the main tool to study the primary structure of proteins, but also as a central technology for proteomics. Protein quantity can be determined by adding stable isotopes or mass tags into different samples, allowing equivalent peptides (or peptide fragments) to be identified by a specific increase in mass. When proteins and peptides are labelled by selective tags, such as isotope‐coded affinity tags  through analysis of total glucosinolate concentration and its individual components in both leaves and seeds of a DH mapping population , the genetic and metabolic basis of glucosinolate accumulation was dissected in oilseed rape/canola  technologies, BSA has been used for quick discovery of associated markers and candidate genes by sequencing the parents and bulks of phenotypic extreme individuals from the segregating populations, through BSA based on DNA‐ and RNA‐seq. As a typical example for NGS‐assisted BSA method, 430 extreme sensitive and 385 extreme tolerant rice seedlings to low temperature were selected from a very large Fde novo transcriptome assembly, a similar pipeline as DNA‐seq BSA can be developed to enrich molecular markers and identify eQTL and candidate genes through RNA‐seq BSA. In maize, this method was used to rapidly and efficiently map genes for mutant phenotypes, in which 32 mutants (gl3‐ref/gl3‐ref) and 31 nonmutant siblings (gl3‐ref/Gl3‐B73 or Gl3‐B73/Gl3‐B73) were selected with the gl3 locus mapped  , large‐scale proteome identification and quantification can be achieved  may specify a range of phenotypic expressions that are called the norm of reaction, or plasticity, which is simply the expression of variability  will be able to query how gene expression at different developmental stages across different spaces and environments respond to GEI Xu, . To dissArabidopsis, where a poor correlation has been observed between variation for flowing time (FT) scored in field experiments and FT variation observed under greenhouse conditions . By the end, the capacity of the chip should be significantly increased to allow the whole proteome to be represented and less abundant proteins to be detected.Compared with DNA‐microarrays, developing protein microarrays requires a lot more steps in its creation and faces many challenges  in many cases. BSA by sequencing (BSA‐seq) will become more attractive with the development of dedicated software for BSA‐seq data analysis, novel techniques for analysis of low‐frequent or rare variants, new approaches to accurate estimates of haplotypes, and new sequencing technologies allowing generation of longer sequencing reads to facilitate the reconstruction of haplotype information (Schlötterer Figure S1 Evolution of genetic markers and marker analysis.Click here for additional data file.Appendix S1 Populations in genetics, genomics and crop improvement.Click here for additional data file.


Lolium perenne L.) plants. A total of 2,708 data files, derived from liquid chromatography coupled to high resolution mass spectrometry (LCMS), were selected to assess the effectiveness and efficiency of applying high throughput metabolomics to survey chemical diversity in plant populations. The data set was generated from 23 ryegrass populations, with 3–25 genotypes per population, and five clonal replicates per genotype. We demonstrate an integrated approach to rapidly mine and analyze metabolic variation from this large, multi-batch LCMS data set. After performing quality control, statistical data mining and peak annotation, a wide range of variation for flavonoid glycosides and plant alkaloids was discovered among the populations. Structural variation of flavonoids occurs both in aglycone structures and acetylated/malonylated/feruloylated sugar moieties. The discovery of comprehensive metabolic variation among the plant populations offers opportunities to probe into the genetic basis of the variation, and provides a valuable resource to gain insight into biochemical functions and to relate metabolic variation with higher level traits in the species.Metabolomics provides a powerful platform to characterize plants at the biochemical level, allowing a search for underlying genes and associations with higher level complex traits such as yield and nutritional value. Efficient and reliable methods to characterize metabolic variation in economically important species are considered of high value to the evaluation and prioritization of germplasm and breeding lines. In this investigation, a large-scale metabolomic survey was performed on a collection of diverse perennial ryegrass ( Lolium perenne L.) is a widespread species in pasture and amenity settings throughout temperate latitudes. Compared to other forage species such as Festuca spp., the value of perennial ryegrass generally lies in its rapid establishment, long growing season, high palatability, and digestibility for ruminant animals , hydrophobicity based) coupled to high resolution electrospray ionization (ESI) mass spectrometry (LCMS) was employed , to enabIn perennial ryegrass, metabolic research interests have focused on plant-endophyte interactions , with lim/z and 4.58 the retention time in minutes. Accordingly, CN denotes peaks from the negative ESI.Thirty-two thousand, two hundred and seventy-three peaks from positive ESI and 20,838 peaks from negative ESI were detected from 1,331 to 1,377 samples, including controls, respectively. For the convenience of discussion, a nomenclature for peaks is assigned as for example, CP434.2167_4.58, where CP represents the C18 column in positive ion mode, 434.2167 the measured n = 71) are differentiated from all experimental samples   in a space spanned by PC1 (5.2%) and PC2 (4.1%). The separation of control samples demonstrates the effectiveness of the peak filtering procedures for correcting the batch effect which was the predominant variation prior to filtering . Similar results were found for 15,486 CN peaks .After applying a series of quality control filtering, including peak merging, batch effect normalization, and de-isotoping procedures 28,043 CP peaks were retained. Overall quality was evaluated by principal component analysis (PCA) where control samples  was applied to retain peaks of biological interest among the 23 populations. This led to 17,579 CP peaks from 1,162 PG samples. To assess the technical variability and evaluate the quality of individual peaks, we first calculated the coefficient of variation (cv) of the five replicates in each of 233 genotypes, and the median value of cv among all genotypes. Among all 17,579 CP peaks, 42.1% of peaks exhibited cv < 0.3 , indicating appreciable variation but an adequate number of peaks measured with certainty to justify peak annotation.Excluding the control samples, a statistical filtering  isotopic peak presence, e.g., to annotate the peak of interest CP697.1593_4.15, its isotopic peak (CP698.1630_4.15) must be present in the peak annotation table; (2) use of the correlation structure of top ranked peaks to enable a group of peaks, often co-eluting, to be annotated simultaneously rather than on an individual basis; and (3) search in the raw data generated from highly expressed samples and compute the mean mass spectrum of the eluting peak for spectral interpretation. Any peaks deficient in these aspects of information are largely excluded from discussion in this study. As an example , the extracted ion chromatogram (EIC) of m/z 697.1593 ± 20 ppm was retrieved from a sample with a strong peak intensity and the mean mass spectrum was constructed from individual peaks. The mass spectrum of peak 1 at 4.15 min (249 s), with co-eluting ions m/z 287.05, 449.11 indicates the presence of aglycone kaempferol (K) and the release of hexose (m/z 162) moieties; the loss of m/z 248.05 indicates the presence of a malonyl hexose (maHex). We assume m/z 162 as glucose (Glc) as it is the most commonly found hexose in plant flavonoid O-glycosides. Therefore, CP697.1593_4.15 can be annotated as K-Glc-maGlc, where maGlc represents maHex. A loss of m/z 146 can be attributed to rhamnose (Rha) because it is the only deoxyhexose known to form natural flavonoid conjugates.Data quality should comprise both the reproducibility of peak intensity and the reliability of peak identity. However, in large scale metabolomics studies peak complexity often confounds peak identification due to a mixture of compounds within a peak or the difficulty in peak alignment. In this study, as illustrated in p-value > 0.05) among the 23 populations  were removed. 17,579 CP peaks and 12,238 CN peaks were consequently retained to investigate the pattern of variation among the populations.The median peak intensity among the five clonal replicates was used to assess clonal variation among the 233 genotypes . Those peaks that are invariant . Both populations are of North African origin, ‘PG238’ being a Moroccan ecotype and ‘Tunisia’ a Tunisian ecotype. All other populations are European and New Zealand cultivars or breeding lines.Based on the 17,579 CP peaks the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) clustering of 23 ryegrass populations reveals ‘PG238’ and ‘Tunisia’ are clearly differentiated from the others , 551 (77.5%) were found to be significant and likewise, 86.5% (365/422) of the CN peaks were significantly different. Here, we only focus on the intense peaks (which likely give rise to more informative spectra), and use a ranking algorithm  many are only present or absent in ‘PG238.’ An example is peak CP667.1858_5.46 , which can be annotated as isorhamnetin-acetylglucosyl-rhamnoside. Our annotation reveals that the top ranked CP/CN peaks represent a class of flavonoids and their derivatives, which can be characterized by typical fragment ions in mass spectra. Briefly, the backbone of this class of molecules can be identified by the characteristic accurate m/z in positive ion mode, with m/z 287.05 of kaempferol (K), m/z 303.05 of quercetin (Q) and m/z 317.06 of isorhamnetin (I), in protonated form [M+H]+, respectively. These three main flavonols are commonly found in Lolium spp., often existing as O-glycosides , I-acGlc (CP521.1286_5.89), Q-acGlc-Rha (CP653.1703_5.11) and Q-acGlc , are only present in ‘PG238.’ Among the top ranked peaks (Supplementary Data 1 and Data 2) are also fragment peaks, which are correlated and co-eluting, for example CN300.0272_5.55 is a fragment peak from CN505.0985_5.54.Among the top ranked CP/CN peaks , and K-Glc . K-Glc-maGlc can also be confirmed by its presence in the top CN peaks as CN695.133_4.13. Notably, among three isomeric peaks both CN651.1567_5.07 and CN651.1564_5.29 are present only in ‘PG238,’ whereas CN651.1563_4.13 is absent. Interrogation of the raw data indicated the two peaks present in ‘PG238’ are Q-derivatives; and the absent peak is the K-derivative (K-Glc-maGlc).Another group of metabolites that are absent in both ‘PG238’ and ‘Tunisia’ include K-Glc-maGlc  and annotated the peak as thesinine. This peak formed its own group in peak clustering analysis (Supplementary Data 2). However, thesinine rhamnoside (The-Rha), detected as CP434.2167_4.80, among the top ranked peaks , is only present at a very low level in ‘Tunisia.’Further to this, CP288.1590_5.17 is present only in ‘Tunisia.’ We predicted its formula as CTo summarize, ryegrass populations can be effectively separated by mass signals without metabolite identification. Subsequent peak annotations revealed structural differences in flavonoids amongst the ryegrass populations, where flavonoid glycosides uniquely present in ‘PG238’ are all Q  and I derived , but not K derived. K derived flavonoid glycosides including K-Glc-maGlc and K-Glc were found absent in both ‘PG238’ and ‘Tunisia.’ The unique presence of thesinine in the ‘Tunisian’ population indicates its specific regulation of thesinine, whose relation to The-Rha remained to be explored.Populations ‘PG238’ and ‘Tunisia’ have very different metabolic features from the other 21 populations, evident by the qualitative variation amongst the top 50 ranked CP/CN peaks. To further assess quantitative differences in metabolic profile among the rest of the perennial ryegrass populations, we exclude the two populations from the following analysis.Supplementary Figure 3). While discriminate analysis may overfit as it aims to maximize separation between groups, all the genotypes from a given population formed a single cluster suggesting good data quality overall. Using UPGMA clustering analysis we investigated relationships among the 21 populations, revealing that the metabolic profile is strongly associated with population provenance . Largely, two sub-clusters are formed comprised of populations in cluster A of New Zealand (NZ) origin and in cluster B of European origin. More specifically, both the populations ‘Option’ and ‘Premium’ were cultivars bred in Northern Europe; and ‘GA214’ and ‘FLp0962’ were developed from similar European accessions. Within cluster A there is some differentiation between ‘Samson,’ a cultivar bred from NZ-adapted material, and cultivars and breeding lines wholly- or partly derived from germplasm originating in the North West of Spain. ‘PG2020’ originates from crosses between ‘PG238’ and breeding lines containing NZ and North West Spain germplasm. ‘Hilary’ is a selection from the older NZ cultivar ‘Ruanui.’ Encouraged by the distinct patterns that are associated with breeding history we then aimed to identify metabolites specific to populations.Linear discriminate analysis was initially applied to all of the 1,057 samples (genotypes × reps) with 17,579 CP peaks partly revealing population structures (Supplementary Data 3) is The-Rha . The-Rha is represented by the peak CP434.2167_4.80 and its fragment peak CP288.159_4.80. Another isomer of The-Rha, CP434.2167_4.58, highly correlated with CP434.2167_4.80 , is also present in the top list. Two isomers of E/Z-thesinine-O-4’-alpha-rhamnoside have been structurally elucidated in ryegrass .Using the same ranking algorithm described we identified the top 50 differential CP and CN peaks among these 21 populations. The most varied metabolite, as shown on the top ranked list  demonstrates the challenges in assigning peaks to metabolites. One challenge is that a peak may represent a mixture of compounds due to insufficient chromatographic separation. Peaks in this category include CP859.1917_5.21 (a mixture Q- and K-derivatives), CN695.1332_4.03, CN695.1468_4.24 [a mixture of chlorogenic acid (CGAs) derivative and K-derivative]. Other challenges are due to the limited in-source fragmentation for annotation, for example CP659.3629_8.94 and CP261.2209_8.94 and the peak mis-alignment, see discussion as follows.Annotating the two sets of top ranked peaks , when all 20 samples from ‘Samson,’ run in 18 different batches, are taken together it becomes impossible to differentiate the retention time of the two peaks across batches . As a result, six peaks were assigned in peak detection instead of two. Chromatographic peak mis-alignment may be unavoidable for closely eluted peaks in multi-batch experiments. Different peak alignment algorithms may be employed to evaluate their performance but can be time-consuming for large data sets (see Discussion). We undertook peak aggregation analysis by summing up all the detected isomeric peaks when the identification of isobaric metabolites among samples is evaluated as uncertain.While it is challenging to establish systematic approaches to evaluate the reliability of peak identification among a large number of samples, our prior knowledge of the chemical structures of The-Rha in ryegrass  offers aFigure 8). This confirmed the observation based on individual peak analysis, that ‘PG238’ samples had high levels of Q and I. Figure 8C also confirms the extensive variation of K compared to Q and I among the populations , with ‘Tunisia’ having the lowest level of K and ‘PG2020’ having the largest within-population variation. However, it was noted that K, exhibiting complex chromatographic behavior, was not differentiated from its isomeric flavone luteolin using this coarse-level quantification.We performed a peak aggregation analysis of quercetin (Q), isorhamnetin (I), and kaempferol (K) (Supplementary Data 4). CGAs are abundant in perennial ryegrass which has three isomers  can be seen among samples. However, in total 55 peaks of 353.0867 (±10 ppm) were detected here due to misalignment from 3.5 to 4.5 min across samples. Peak aggregation analysis of CGA (CN353.0875) demonstrates that there is no difference among the 21 populations . Of the four peaks among the top list only CN353.0875_5.39 shows a distinct profile, with the highest level present in ‘PG2020’ and lower or at the baseline noise level among other populations. The EIC data interrogation showed this peak can be sufficiently separated from the other three peaks. CN353.0875_5.39 is a relatively weak peak but consistently present in ‘PG2020’ genotypes . CN353.0875_5.39, co-eluting with 515.12, could be annotated as CGA glycoside due to the neutral loss of m/z 162.Peak aggregation analysis was also performed for CGAs, which are represented by peaks CN353.0873_3.84, CN353.0874_4.06, CN353.0873_4.20, and CN353.0875_5.39 and their derivatives , with 18 ppm error from the calculated m/z 333.1234, [M+H]+, and present as a single chromatographic peak in individual samples. CP333.1174_5.28 is not on the top 50 significant peaks (Supplementary Data 3) but among the list of significant peaks based on empirical Bayes, suggesting more informative metabolic variation can be mined from the data.Perloline, one of the known alkaloids present in the Supplementary Data 5) is most abundant in ‘Samson,’ but both compounds are present at various levels, even down to noise level, in other populations.To summarize, plant alkaloids and kaempferol derived flavonoids are notably varied among the 21 populations. The population-level variation of The-Rha and perloline revealed here expands our knowledge on the magnitude of their variation in ryegrass. A CGA glycoside was found to be unique to ‘PG2020’ but CGAs were invariant among the populations. Structural variation of sugar moieties in flavonoids may contribute to ryegrass population differentiation. As examples, K-Glc-maGlc is most abundant in ‘Tolosa’ and K-Glc-feruloylGlc (Supplementary Data 6). The results demonstrated two metabolites (K-Glc-maGlc and CGA glycoside) identified and annotated in this study were reproducible between two environments indicating the robustness of our statistical data mining procedures. The magnitude of population-level variation documented here serves as a resource to research how these metabolic variations are associated with high level traits or modulated by genetic regulation..As a further note, to assess whether these population-level variations are consistent across seasons and under various environments we have conducted peak validation with samples harvested in a different year  of targeted ions or predicted retention time, to arrive at MSI (Metabolomics Standard Initiative) level 2 identification  located in the linkage group 4 , and in contrast, functions of this alkaloid were reported in literature Intraspecific differences were observed earlier by The-Rha, as a metabolic trait, is subjected to genetic control in  group 4 . AlthougPhenotypic characterization often remains simplistic and the main bottleneck to understanding the genetic basis of traits . MetabotSupplementary Table 1). Seeds of all ryegrass material were treated by heat and fungicide to ensure that they were endophyte-free. All plants were grown in pots and placed outdoors at AgResearch Grasslands Research Centre, Palmerston North, New Zealand.Twenty-three populations were selected from a large collection of breeding lines, cultivars and wild ecotypes, which was designed for genetic association mapping. In this paper we refer to cultivars, breeding lines or ecotypes as populations, and each individual plants from population as genotypes due to the nature of high heterozygosity in perennial ryegrass. These 23 populations were selected to ensure that more than three genotypes were included from each population and five clonal replicates of each genotypes were available for population-level analysis. In total 233 genotypes from these populations were selected for this investigation (m/z 60–1200.Plants were harvested after 3 weeks regrowth (to mimic rotation grazing) by cutting all the tissues 4 cm above the pot surface, predominantly consisting of the leaf blades. Samples in this study were harvested in May 2012, late autumn in New Zealand. Control samples were derived from a bulk sample consisting of a collection of ryegrass materials that may contain endophyte. These control samples were formed to monitor systematic variations such as run order effect and batch effect. Sample preparation and Metabolomics lab analyses followed protocols previously reported . BrieflySupplementary Table 1) selected for this investigation were run in 36 randomized batches over 9 months. Data files generated from all the genotypes discussed above, and control samples from each batch, were subjected to data analysis. In total, 1,260 data files (plus 71 controls) in positive ionization mode and 1,297 data files (plus 80 controls) in negative ionization mode, were analyzed.The large number of biological samples had to be assayed in multiple batches. The biological samples (m/z window and eluting at approximately the same retention time (±0.2 min); in addition, the peak intensity of the group must be highly correlated (Pearson’s correlation coefficient r > 0.9) among all the samples, and the ratios of the detected peak intensities must satisfy the criteria: [M]/[M + 1] > 2 and [M + 1]/[M + 2] > 2. Software is available from https://github.com/AgResearch/peakOmics. Only the monoisotopic ion [M] of the series was retained for subsequent data analysis.The profile (positive and negative ionization) mode data were converted into centroid mode using ProteoWizard tools . Peak deF-test, p-values < 0.05) was further used to remove peaks still with the significant batch effect.Batch effect was corrected using a parametric empirical Bayes methods  using ban = 5), the most varied replicated groups were identified (cv > 0.2). The outliers were removed within the replicated groups if the modified Z-scores were >3.0, where modified Z-score is defined as Zi = 0.6745∗[xi – median(x)]/mad(x) and where mad is the median of absolute deviation  were also removed.Among the replicates  was created for annotation purposes throughout the study. Different annotation strategies were used for different classes of compounds. For flavonoid glycosides, in-source fragmentation patterns usually provide sufficient information for the annotation  a peak annotation table (including detected notation . In thisnotation  along wiMC conceived the scope of the paper, conducted data analysis and implemented computer code to support data analysis and peak annotation. KF oversaw LCMS lab operations. MC and KF carried out peak annotation. CJ undertook funding acquisition, project coordination and management. AS provided plant resources. All authors conducted data interpretation and contributed to writing the paper.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.


Caucasotachea vindobonensis were conducted. Surprisingly, reconstruction of ancestral areas suggests that extant C. vindobonensis probably originated in the Balkans and survived there during the Late Pleistocene glaciations, with a more recent colonization of the Carpatho-Pannonian and the Ponto-Caspian regions. In the Holocene, C. vindobonensis colonized between the Sudetes and the Carpathians to the north, where its recent and current distribution may have been facilitated by anthropogenic translocations. Together, these data suggest a possible non-steppic origin of C. vindobonensis. Further investigation may reveal the extent to which the steppic snail assemblages consist partly of Holocene newcomers.Existing data on the phylogeography of European taxa of steppic provenance suggests that species were widely distributed during glacial periods but underwent range contraction and fragmentation during interglacials into “warm-stage refugia.” Among the steppe-related invertebrates that have been examined, the majority has been insects, but data on the phylogeography of snails is wholly missing. To begin to fill this gap, phylogeographic and niche modeling studies on the presumed steppic snail  Until recent anthropogenic habitat destruction, steppe and related xeric grasslands covered almost 10% of the land in Eurasia, stretching from the Pannonian Basin and Black Sea coast to Mongolia and Manchuria . The orihttp://ec.europa.eu/environment/nature/legislation/habitatsdirective/docs/2007_07_im.pdf).All types of steppes and xeric grasslands sustain rich communities of plants and animals, with many species tightly related to these types of environments e.g., . Many ofOur knowledge of the phylogeography of European taxa of continental provenance (mainly steppic) is relatively low  comparedAmong the steppe-related invertebrates that have been examined, the majority have been insects, especially butterflies and beetles , and so Caucasotachea vindobonensis  (Cepaea vindobonensis) as a study system. Based on the distribution, frequent in the Pannonian Basin and in the Pontic region but rarer towards the Balkans and the north-western edge of the distribution   , then we expected the molecular genetic diversity to peak in the Ponto-Caspian zone and be lower in the supposedly peripheral Balkan and north-western areas. If correct, steppic gastropods (at least this species) could be another exception to the “southern refugia” principle . VerificC. vindobonensis is one of the most common and widely distributed land snails in the central, south-eastern and eastern parts of Europe . The concentration of the reagents used for the amplification of all markers and the cycling profile for PCR were as in Small fragments of snail foot tissue were used for DNA extraction using a Nucleospin Tissue kit (Macherey-Nagel). Amplification and sequencing of fragments of a mitochondrial cytochrome oxidase I (COI) gene fragment was performed using either the primers of COI sequences were collected from 145 snails . Sequences were checked and aligned using BioEdit v.7.0.5.2  and ClusFor some analyses, sequences were grouped according to their geographic provenance based on previously deducted , distincTo test if examined populations experienced expansion and/or contraction events in their history, the mismatch distribution (MD)  was calchttp://www.atgc-montpellier.fr/phyml/ (http://www.atgc-montpellier.fr/sms/) using Akaike information criterion (AIC). Branch support was obtained by the approximate likelihood-ratio test (aLRT)  approach implemented in PhyML 3.0 software  and the r/phyml/ . The bett (aLRT) , which it (aLRT) . The nult (aLRT)  of the Rt (aLRT) .http://popart.otago.ac.nz/) for the haplogroups defined from the topology of ML tree (as above).Haplotype networks were constructed using the median-joining algorithm (MJ)  in the shttp://www.reelab.net/lagrange/configurator) using the ultrametric tree described above, and the haplotype-distribution matrix , Natural History Museum Vienna (NHMW), Munkácsy Mihály Museum, Békéscsaba (MMM), The Zoological Museum of the Zoological Institute of Russian Academy of Sciences, St. Petersburg (ZIN), Zoological Museum of Moscow State University (ZMMU), and Museum and Institute of Zoology Polish Academy of Sciences (MIZ PAS) and from the private collections of Frank Walther  and Sergei Kramarenko . See the detailed list of overall 393 distribution records in www.worldclim.org). Although MaxEnt is more robust in controlling for correlations between variables than stepwise regression (r > 0.75) are recommended to be excluded from the analysis . For the projections, we used the predictions of two different global circulation models (MIROC-ESM and CCSM4).The discrimination ability of the model was evaluated by area under the curve (AUC) measure. The value of AUC varies between 0.0 and 1.0, where 1.0 is considered to be a perfect prediction, while a value ≤0.5 is considered not being better than a random prediction . The disC. vindobonensis are summarized in Standard genetic indices for The distribution of pairwise differences  was multThe ML tree suggests the presence of two main lineages, each of which consist of two haplogroups, respectively  and 3. HTo identify the ancestral region, six combinations of the two range and three dispersal constraints were compared. The model with a range constraint of maximum three sub-areas and lower migration rate (0.25) between non-adjacent areas  had the highest likelihood ; howeverC. vindobonensis yielded a good fit for the known area, although it also predicts suitable areas to the Apennine Peninsula, the Iberian Peninsula, and northern Anatolia, where the species does not occur , mean temperature of warmest quarter (bio10), precipitation seasonality (bio15), and precipitation of warmest quarter (bio18). The models received excellent support values  following the nomenclature of ot occur . Potentiot occur . InteresC. vindobonensis was formerly classified in the genus Cepaea, and therefore, it was considered the easternmost member of that western European genus , which provide further support for this assumption. Based on scattered fossils, it seems that at the end of the Pliocene, the range of this species already included Daghestan (Caucasus Mts)  . All othsus Mts) , Bulgarisus Mts) , and sousus Mts) .C. vindobonensis first expanded in the Balkans, followed by the expansion into Carpatho-Pannonian and Ponto-Caspian regions. The most recent expansion, probably during the Holocene, was the expansion to the north of the Carpathians.We can speculate that this species went through numerous range expansions and contractions during the Pleistocene, in concordance with glaciations and warmer periods. COI data implies the presence of at least two refugia during the last glaciation, both of which could have been in the Balkans. Demographic analyses suggest that C. vindobonensis had survived in an Adriatic refugium, it might have next expanded to the Po Valley and Apennine Peninsula—but it did not. Colchis as a refugium is questionable, as there are no past or current signs of C. vindobonensis being present there; however, it is within the area of origin for Caucasotachea snails. Predicted Last Glacial Maximum range did not include the Pannonian Plain, the Podolian Upland, the Ponto-Caspian Plain, and the areas north of them. Based on this, we suppose that the colonization of the Carpatho-Pannonian and the Ponto-Caspian regions started not earlier than the late Vistulian  glacial. The difference in the two regions’ genetic heterogenity is due to the fact that the former one could have been colonized from both Balkan refugia, whereas the latter one only from the Eastern Balkan refugium. This multiple colonization of the Carpatho-Pannonian area, and presence of haplotypes from diverged lineages there, could bias expansion time estimates in that area.According to our reconstruction of historical areas, there were potential refugial areas during the LGM: (i) along the western coast of the Black Sea (Thrace); (ii) in western Transcaucasia (Colchis); and (iii) in the Adriatic Basin , they showed that Pontic populations of this species had reduced allelic diversity and increased levels of inbreeding in some groups and high levels of isolation of populations compared with Pannonian (Austrian) populations. This genetic pattern is in contrast with the expectations of an east-to-west expansion  and concordant with a recent review of steppic species phylogeography in Eastern Central Europe  and mesic environments , could also be interpreted as the strong argument against its strict steppic affinity. C. vindobonensis is perhaps better considered a species of southeast European  origin, associated with rather more humid habitats characteristic for river valleys and gorges. It has probably expanded into more xeric environments of similar climatic conditions and plant communities in central and eastern Europe, including steppes. This pattern is supported by the fact that the species is currently more abundant in Pontic and Pannonian regions than in the Balkans and is still expanding to the north, e.g., in Poland  markers or a histone intron that has been used fairly widely in the phylogenetics of land snails e.g., . We alsoomic DNA  and so aomic DNA . Thus, tomic DNA . Relatinomic DNA , and relomic DNA . In our omic DNA .C. vindobonensis populations are structured spatially, but there are also signs of interregional gene flow. Mitochondrial haplogroups generally link snails from distinct geographic localities clustered in defined geographic regions . Moreover, in several cases, the same haplotypes were found in snails from highly distant localities belonging to different regions, especially in both the Carpatho-Pannonian and north-western populations. This is consistent with one of major routes of expansion/migration of steppic fauna and flora via the Moravian Gate, localized between the Carpathians and the Sudetes  must have been settled from the reverse direction—from the Pannonian lineage via Poland but surprisingly not from adjacent populations in Pontic region. There are also other examples of rare haplotype sharing between geographically distant populations from Carpatho-Pannonian and Balkan, Balkan and Ponto-Caspian, Carpatho-Pannonian and Ponto-Caspian, and north-western and Ponto-Caspian regions. These shared haplotypes could be just examples of ancestral polymorphism present in various populations within the species or else they could also be explained in the light of dispersal of individuals.The geographic distribution of haplotypes showed that es e.g., . Anotheres e.g., . ApparenC. vindobonensis in some locations  is likely due to human translocations. A role of humans in the current distribution of other snails has been suggested previously  can disperse passively by humans (anthropochory) even over large distances. Certainly, anthropochory is the most likely explanation for populations in the Moscow vicinity, ~550 km from the nearest natural populations of this snail in southern Russia  origin for –C. vindobonensis does not follow the pattern previously identified in other strict steppic taxa, which were rather more common across Eastern Central Europe during glacial periods than interglacials. However, as C. vindobonensis was not found to be a steppic species in a strict sense , further studies of other steppic mollusks, especially those with ranges restricted to steppic region  are necessary to investigate if this is a general pattern. For the moment, we hypothesize that steppic snail assemblages are formed by species of heterogenous origin: many Holocene newcomers, like C. vindobonensis and species are of actual steppic provenance.–C. vindobonensis populations from different regions in central, eastern, and south-eastern Europe is rather low and unfavorable areas like high mountain ranges do not constitute effective barrier for gene flow among distant localities. This is also discordant with general patterns found for steppic taxa.The genetic distinctiveness of –Expansion of this species during prehistoric and current times could have been facilitated by unintentional man-made transfers of snails across large distances.Electronic supplementary material The online version of this article (doi:10.1007/s13127-017-0337-3) contains supplementary material, which is available to authorized users.Supplementary figure 1Caucasotachea vindobonensis populations. Values indicate branch supportsMaximum likelihood phylogenetic tree reconstructed on haplotypes found in Supplementary figure 2Caucasotachea vindobonensis populationsMismatch distribution calculated for four regional groups of Supplementary figure 3Caucasotachea vindobonensis. Warmer colors indicate more suitable climatic conditions, while crosses indicate the subsample of presence points used for modeling (for more detail see The potential distribution of Supplementary file 1Supplementary table 1Supplementary table 2Supplementary table 3Supplementary table 4Supplementary table 5


The role of marginal populations for the long-term maintenance of species’ genetic diversity and evolutionary potential is particularly timely in view of the range shifts caused by climate change. The Centre-Periphery hypothesis predicts that marginal populations should bear reduced genetic diversity and have low evolutionary potential. We analysed temporal stability at neutral microsatellite and adaptive MHC genetic variation over five decades in four marginal Atlantic salmon populations located at the southern limit of the species’ distribution with a complicated demographic history, which includes stocking with foreign and native salmon for at least 2 decades. We found a temporal increase in neutral genetic variation, as well as temporal instability in population structuring, highlighting the importance of temporal analyses in studies that examine the genetic diversity of peripheral populations at the margins of the species’ range, particularly in face of climate change. The importance of marginal populations for the long-term maintenance of species’ genetic diversity and evolutionary potential has long been discussed135The genes of the major histocompatibility complex (MHC), which are some of the most studied in relation to adaptive genetic variation101416182024Salmo salar) is probably the species which has suffered the most dramatic decline382641424445Salmonids are good study models of marginal populations because most of their current species’ range was recolonised from a few refugia after the last glaciation2728303132In Europe, the current distribution of the Atlantic salmon seems to reflect a pattern of postglacial recolonisation from multiple refugia2848Here, we examined temporal and spatial patterns of neutral and adaptive genetic diversity measured by microsatellites and MHC-linked markers in four marginal salmon populations in the Iberian refugium with a complicated demographic history, to test the general hypothesis that marginal populations have temporarily unstable genetic diversity and low adaptive potential. To this end, we compared spatial and temporal patterns of variability at neutral markers and markers linked to genes under selection (MHC class I and class II).A) between temporal samples of the same rivers (0.057 to 0.177) were of a similar order to genetic distances between river samples (0.060 to 0.215). The NJ-phenogram, although with low statistical support, suggested that samples from the rivers Ason and Deva tended to group by river and not by decade, whereas samples from the rivers Nansa and Pas were intermingled, with a tendency to associate by decade instead of river , SsaD485 (P = 0.998), Ssa1438 (P = 0.984) in 1960, all under positive selection and Ssa197 (P = 0.997), SsaD485 (P = 0.997) in 1980, also under positive selection.Individuals captured by anglers in the rivers Asón, Deva, Nansa and Pas  and Sasa-UBA-3′UTR (0.30). Allelic drop out for Sasa-DAA-3′UTR was higher than reported for the neutral microsatellites , whereas the ADO rate for Sasa-UBA-3′UTR was more similar (2.96). A total of 22 different alleles were found for the Sasa-UBA-3′UTR locus, and 15 for the Sasa-DAA-3′UTR locus. The large range of Sasa-DAA-3′UTR allele sizes (207 bp-367 bp) may have contributed to the greater rate of ADO, particularly in historical samplesSasa-UBA-3′UTR locus or the Sasa-DAA-3′UTR locus. Analysis of linkage disequilibrium of class I and class II linked markers for all samples revealed no linkage disequilibrium of these loci . Observed heterozygosities ranged from 0.17–1.00 across all samples (Sasa-DAA-3′UTR locus (Ho and Ar: s = 8 P = 0.0042) and a decrease in Sasa-UBA-3′UTR in the rivers Nansa (Ho and Ar: S = −4 P = 0) and Pas . According to LOSITAN, Sasa-UBA-3′UTR locus was under positive selection in 1960 (P = 0.995) and 1980 (P = 0.995). In contrast to neutral microsatellites, significant correlations in allele frequencies were observed between adjacent temporal samples for all comparisons, except for those involving the Deva 1960s sample  but not those of Sasa-DAA-3′UTR (class II). This is perhaps not surprising given the differences in response to selection previously observed between both markersSasa-DAA-3′UTR did not indicate any clustering of samples, by river or decade. Such a pattern of variation could reflect adaptation to local conditions in these marginal populations, an scenario that might be expected given the homing behavior of Atlantic salmon and their tendency to form locally adapted populations5256Peripheral  populations tend to be genetically and morphologically distinct as a consequence of their isolation and typically smaller size, and are considered particularly valuable because they can help preserve the evolutionary potential of the species48et al.Adipose fins from dead adult Atlantic salmon, captured by anglers in the rivers Asón, Deva, Nansa and Pas . We then used CLUMPPA distanceSTRUCTURE v2.3.3Sasa-UBA-3′UTR and Sasa-DAA-3′UTR data were analysed separately (as ino and He) for each locus in each sample. Allelic richness was calculated at each locus for each sample using FSTAT version 2.9.3ely as in. GENEPOPST, infinite alleles mutation model, 99% confidence interval and false discovery rate of 0.1%. All populations were tested for recent bottlenecks using BOTTLENECK v.1.2.02All markers, neutral and MHC-linked microsatellites, were tested for neutrality using Lositan84How to cite this article: Ciborowski, K. et al. Temporal and spatial instability in neutral and adaptive (MHC) genetic variation in marginal salmon populations. Sci. Rep.7, 42416; doi: 10.1038/srep42416 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Seriola lalandi), and captive bred fingerlings are being shipped from the Southern Hemisphere to be farmed in the Northern Hemisphere. It was recently proposed that Pacific S. lalandi comprise at least three distinct species that diverged more than 2 million years ago. Here, we tested the hypothesis of different “species” in the Pacific using novel genomic data (namely single nucleotide polymorphisms and diversity array technology markers), as well as mtDNA and DNA microsatellite variation. These new data support the hypothesis of population subdivision between the Northeast Pacific, Northwest Pacific and South Pacific, and genetic divergence indicates restriction to the gene flow between hemispheres. However, our estimates of maximum mtDNA and nuclear DNA divergences of 2.43% and 0.67%, respectively, were within the ranges more commonly observed for populations within species than species within genera. Accordingly our data support the more traditional view that S. lalandi in the Pacific comprises three distinct populations rather than the subdivisions into several species.Captive breeding programs and aquaculture production have commenced worldwide for the globally distributed yellowtail kingfish ( Seriola lalandi Valenciennes, 1833, Carangidae) has recently commenced around the world. Global aquaculture production of the Seriola species exceeds 150,000 tonnes per year1, where yellowtail kingfish contributes to the second highest production within the Seriola species2. At present yellowtail kingfish aquaculture is practised either in sea cages or in land-based facilities and in many different countries including Japan3, Australia5, New Zealand6, USA7, Mexico8, Chile9, Netherlands10 and South Africa11. A recent study reported the potential expansion to further countries, such as China and Spain2. Recent population genetic studies have proposed that globally distributed S. lalandi is a complex of at least three closely related species with a restriction of gene flow across the equator, and with a divergence of about two millions years12. If so, it could be of concern that there is international trade of yellowtail kingfish fingerlings and eggs across the geographical boundaries including across the equator from the Southern to Northern Hemisphere14. For example Australian yellowtail kingfish fingerlings have been exported commercially from the Southern Hemisphere to the Northern Hemisphere  and Chilean yellowtail kingfish fingerlings have been exported across the hemispheres to Mexico for commercial aquaculture in 201315. These exchanges or movements of potentially different breeding material have the potential to impact not only local natural wild populations adapted to local regions18 but also have the potential produce genetic incompatibilities from aquaculture crosses20. Indeed, a significant reduction of fitness and viability in F2 generation as a consequences of hybrid breakdown of cichlid fish was reported by Stelkens et al.21, and others reported a significant decrease of fitness with increasing genetic distance22 and significant F2 hybrid breakdown23 in the copepod Tigriopus californicus. Given the worldwide distribution of yellowtail kingfish and commercial movement of fingerlings around the world it is important to determine with accuracy the genetic relatedness and consider the possible genetic “compatibility” of different populations around the world.Commercial aquaculture of yellowtail kingfish  sequence analysis, or combination of mtDNA and a few nuclear genes for the assessment of genetic diversity and species delimitation24. However, the effectiveness of mtDNA and microsatellite loci data in population genetic analysis is questionable due to their low representation of genomic information. Moreover, mtDNA represents only maternal inheritance and may provide a different image of population structure than that provided by biparentally inherited nuclear DNA, as evident in many sea turtle species and white sharks Carcharodon carcharias26. While other potential limitations of mtDNA relate to clonal inheritance, neutrality and erratic evolutionary rates suggested that mtDNA may no longer be considered as an ideal marker for evolutionary studies27, it still represents a powerful tool for phylogenetic studies, particularly when relating new research to previously published data. The use of DNA microsatellite data has also been challenged for genetic analyses on the grounds of its extremely high mutation rate, size homoplasy, irregularities in mutation process and degradation over time29; it is questionable how well evolutionary rates for DNA microsatellite sequences reflect those across the whole genome30. Likewise, it is of concern how well just a few nuclear genes reflect whole genome evolution and divergence due to the possibility of locus specific anomalous evolutionary rates32. Therefore, to address these concerns arising from the use of traditional markers to resolve phylogenetics, we carried out partial genome sequencing of S. lalandi from different Pacific regions to reassess the genetic structure of Pacific S. lalandi populations. This allowed comparison of diversity estimates obtained by traditional methods  with new estimates, provided for the first time, from partial genomic sequencing.Most previous population genetic studies of Analysis of the mtDNA cytochrome oxidase subunit I (COI) fragments sequenced in this study identified 15 haplotypes containing a total of 28 polymorphic sites  Pacific and Northwest (NW) Pacific Fig. , but witMultiple sequence alignment confirmed there was no deletions or insertions within the analysed COI sequence fragment. There were five nucleotide positions  indicated relative low values in Chile and higher values in Mexico  among regions where 83% of the total variation were observed among regions . Pairwise comparison of FST indicated significant differences among populations, except among the Australian populations  showed the same trends  using data from the present study formed a single major group of the ted Fig. . Howeverion Fig. ; subgrouion Fig. . The samion Fig. , noting L = 220.83, max. ln L = 229.41, likelihood ratio = 17.16, P < 0.001; using pooled data from the current study and published).Generalized Mixed Yule Coalescent (GMYC) analyses, a likelihood method for delimiting species, indicated that while the Northern and Southern Hemisphere groups have genetic differences, all these lineages, assessed by clade support values, are from a single species group with four clades  with 95% highest posterior density (HPD) of 0.5–6.9 MYA again using pooled data from the current study and published , although samples from the same hemisphere were more closely related  could be assumed in all populations. However, when considering the differences in terms of allele type and frequencies, there were significant differences among populations  and 15% of the molecular variance occurred among populations. Pairwise FST comparisons between populations varied from 0.001 to 0.227 for microsatellite data and were statistically significant for most pair combinations  were broadly similar among the six locations Table . The FISAnalysis of microsatellite data using “STRUCTURE” software indicated the presence of two genetic clusters (K = 2) with a high probability of membership coefficient , whereas less variation was observed within populations. Pairwise comparison of FST values also indicated significant differences among hemispheres and regions but not among populations within Southern Hemisphere where values were close to zero  of single nucleotide polymorphisms (SNPs) was attributed to among regions  of SNPs from five sampling locations were sorted into three clusters where Southern Hemisphere locations  made one tight cluster and the Mexican population and Japanese population made another two separate clusters, which represent the separation of three regions  among regions. Pairwise comparison of FST among populations indicated significant and higher values among the locations from different hemispheres and regions, whereas values were lower (close to zero) among the populations within Southern Hemisphere (Table AMOVA test for DArT markers (presence/absence of restriction fragments) indicated significant population differentiation  grouped into one cluster and the Mexican and Japanese populations grouped into another  were present only in Japan and not in other populations, similarly, another three markers distinguished the Mexican population. Conversely, no single South Pacific population had markers restricted only to that population, i.e. all markers from a given South Pacific population were found in each of the other South Pacific populations. When combining the populations in the South Pacific and comparing them to the combined Northern Hemisphere (Japan and Mexico), 20 markers were identified in the Northern Hemisphere which were not present in the South Pacific. Whereas, only two markers were identified in the South Pacific that were not present in the Northern Hemisphere , the NE Pacific (USA & Mexico), the South Pacific  Oceans and South Africa. This interpretation aligns with previous reports of global genetic differences in the S. lalandi species complex based on mtDNA and microsatellite loci in the NE Pacific, NW Pacific and South Pacific populations12. S. lalandi is known to be a long distance migratory fish: a study on kingfish migration reported that they were able to move >2,000 km from the tagged site, but were recaptured within the same hemispheric locations34. Mobility of S. lalandi was also suggested by genetic differences among years in Chile35. However the present data suggest very limited mixing between hemispheres; this may be due to the geographical barriers related to crossing the equator; factors such as the surface gradient of water temperature and salinity that are not preferred by the S. lalandi, as discussed in previous reports12.The present mtDNA and DNA microsatellite data indicate the presence of diverged genetic lineages of et al.8 report that S. lalandi from Chile is not significantly different from the S. lalandi in New Zealand waters. Nugroho et al.24 and Miller et al.14 also reported that the S. lalandi population in Australian waters is not significantly different from that in New Zealand. Martinez-Takeshita et al.12 considered fish from Chile, New Zealand and South Africa to be one species. Results from the present study revealed that there is no evidence of major genetic differentiation among two Australian populations (NSW & SA), whether considering mtDNA or nuclear DNA data. Furthermore, the current study observed that there is no significant differentiation among the South and East coast Australian, New Zealand and Chilean populations, suggesting S. lalandi from the South Pacific Ocean is from one genetic lineage. The GMYC analyses did indicate the South African population was a separate genetic lineage from South Pacific samples, even though some haplotypes were shared between South Pacific locations and South Africa. However there were only ten samples available from South Africa, and more samples are needed to confirm the differences between South Africa and other Southern Hemisphere populations.Even though the South American and Australian populations are separated by the whole of the Pacific Ocean, there appears to be close similarity among the Southern Hemisphere populations. Purcell 12. Although the geographical distance between Japan (NW Pacific) and USA (NE Pacific) is less than the distance between Australia and Chile, there may be a limitation/barrier to gene flow between NW and NE Pacific locations. Rohde and Hayward36 reported that Eastern Pacific Barrier is a 100% effective barrier to dispersal of species of Scomberomorus, Grammatorcynus and their parasites. Martinez-Takeshita et al.12 considered the South Pacific offers many island stepping stones for this inshore pelagic species which facilitate migration, stepping stones which are less abundant in the North Pacific. Moreover, Sepulveda and Gonzalez35 provided evidance of the migration of genetically different populations over the time and space in South East Pacific Ocean.This picture for the Southern Hemisphere contrasts that from the Northern Hemisphere where mtDNA and nuclear DNA data revealed fish from Japan as a somewhat separate genetic lineage from those in the NE PacificST values than those estimated from the nine microsatellite loci used in the present study and also, the FST values observed from SNPs were greater than the values observed for DArT markers. Likewise AMOVA analyses for SNPs and DArT data gave much greater FST estimates, at least two fold of between region variation than those obtained from microsatellite loci. Again, as for FST values, greater between region variance using AMOVA was evident for SNPs than DArT. A set of DArT markers could be identified that categorically distinguished between the Mexican and Japanese populations and among the Northern and Southern Hemisphere populations.Our partial genome sequencing data comprised SNP data and absence/presence of restriction fragments (i.e. the DArT data) were available for Mexico, Japan, Chile and two Australian populations. Broadly, the genome data analysed by a variety of tests supported patterns evident from the mtDNA and DNA microsatellite data, namely major differences between hemispheres but little variation within the Southern Hemisphere. There were some grouping differences regarding Japan which probably reflects the small number of Japanese samples. In general, the SNPs and DArT markers provided larger Fet al.12 and others8 have proposed a revision to the taxonomy of S. lalandi, specifically that is should be split into three species, namely, resurrection of Seriola aureovittata for NW Pacific, Seriola dorsalis for NE Pacific, and leaving S. lalandi as the Southern Hemisphere species. The most straight forward tests for species division are those based just on percent DNA sequence homologies. Considering many different fish species, the recorded average genetic distance between species (within genera) for mtDNA COI gene for marine fish was at least above 9.54%38, and at least 5.71% for freshwater species39. However, the maximum COI sequence divergence observed between the populations in the present study was just 2.43% (present and published data pooled), which is prima facie evidence that all the populations studied in the present study are from a single species. The maximum nuclear DNA divergence was 0.67%, which is comparable to that between humans and Neanderthals40 and there is some debate as to whether humans and Neanderthals are one or separate species42. According to the conventional molecular clock of 2% sequence divergence per million years for mtDNA43, S. lalandi populations seem to have diverged about 1.2 MYA between the two hemispheres using our data; this estimate of 1.2 MYA suggests a younger divergence, by about 50%, than previously reported12.Recently, Martinez-Takeshita More involved tests for species delimitation are available, but do require a greater number of assumptions. Generalized mixed Yule Coalescent analyses indicated all populations belonged to one species group. On the other hand, a divergence time of 0.5 to 6.9 MYA between Hemispheres was estimated from Bayesian inferences in BEAST.et al.12 may be premature.Overall, most of the analyses, both simple and complex point to relatively recent divergence and relatively little genetic difference . The converse does not seem to be the case, that there is consistent and categorical evidence for major genetic change consistent with speciation. For major taxonomic revisions, consistency of the data are probably required and until this evidence is available, the revision proposed by Martinez-Takeshita 45. However the rate of nucleotide substitution in mtDNA observed in the current study was only about 4 times greater than the nuclear DNA in S. lalandi. This apparent inconsistency between kingfish and primates indicates a need for more work to properly calibrate the evolutionary rate of kingfish mtDNA sequence divergence over time, and may be needed prior to relying on kingfish mtDNA for major taxonomic revision.Nuclear genome sequences were reported to have a lower rate of nucleotide substitution than the mtDNA, about 10-fold lower in primates2 breakdown would help resolve this matter, and until then any conclusions regarding F2 breakdown would only be of a speculative nature.As to whether genetic incompatibilities could arise from mixing Southern and Northern Hemisphere stocks, on one hand our revised estimates of divergence, would lessen this risk as previously considered. On the other hand, our finding using genomic data of many categorical differences could indicate adaption to different local conditions in the different hemispheres, or they could just reflect genetic drift. Perhaps direct empirical testing of FFin tissue samples of yellowtail kingfish were collected from six different populations representing New South Wales, Australia , South Australia , Chile (n = 50), Japan (n = 15), Mexico (n = 76) and USA (n = 26). Tissue samples were collected into tubes containing 80% ethanol and were stored at 4 °C. Genomic DNA was extracted and purified using the DNeasy Blood and Tissue kits  and the NucleoMag® 96 Tissue kit  following the manufacturers’ protocol and extracted DNA was stored at −18 °C for future analysis.et al.37. PCRs were carried out in 25 µL reaction volumes containing 0.5 µL of DNA (50 ng/µL), 12.5 µL of 2× AmpliTaq Gold 360 master mix, 1.0 µL of each gene-specific primer (10 pmol/µL) and 10 µL of PCR-grade H2O. Amplifications were performed using an Eppendorf Mastercycler® nexus . The PCR cycle program consisted of an initial denaturation step at 95 °C for 10 min, followed by 35 cycles of 95 °C for 30 s, 50 °C for 30 s, and 72 °C for 30 s, and a final extension of 72 °C for 10 min. PCR products were subjected to standard DNA sanger sequencing  from both directions using the gene-specific PCR primer pair mentioned above (FishF1 and FishR1).A length of 655 bp fragment from the COI gene was amplified by polymerase chain reaction (PCR) using gene-specific primers, sense primer FishF1 5′-TCAACCAACCACAAAGACATTGGCAC-3′ and anti-sense primer FishR1 5′-TAGACTTCTGGGTGGCCAAAGAATCA-3′ designed by Ward S. lalandi  were selected from twenty five published microsatellite primer pairs47. Microsatellite primers were combined into two multiplex PCR sets and DNA amplification was achieved using the Qiagen Multiplex PCR PLUS Kit  in 13.5 µL reactions, each containing 1.25 µL of 10x primer mix, 6.25 µL of Multiplex PCR Master Mix, 2.75 µL of RNase free water, 1.25 µL of Q-Solution and 2.0 µL of approximately 20 ng template DNA. Amplification was performed using an Eppendorf Mastercycler® nexus  with cycling conditions as follows: initial denaturation at 95 °C for 15 min, followed by 35 cycles of 95 °C for 30 s, 57 °C for 90 s, and 72 °C for 30 s; with a final extension at 68 °C for 30 min. PCR products were separated by capillary electrophoresis on an AB3500 Genetic Analyser (Applied Biosystems) at the University of the Sunshine Coast, Australia.Nine established microsatellite markers were used to genotype the 231 specimens. Six markers  were selected from PstI-SphI method was selected. DNA samples were processed in digestion/ligation reactions principally as per Kilian et al.48 but replacing a single PstI-compatible adaptor with two different adaptors corresponding to two different restriction enzyme overhangs. The PstI-compatible adapter was designed to include Illumina’s flowcell attachment sequence, sequencing primer sequence and “staggered”, varying length barcode region, similar to the sequence reported by Elshire et al.49. Reverse adapter contained flowcell attachment region and SphI-compatible overhang sequence. Only “mixed fragments” (PstI-SphI) were effectively amplified in 30 cycles of PCR using the following reaction conditions: an initial denaturation step at 94 °C for 1 min, followed by 30 cycles of 94 °C for 20 s, 58 °C for 30 s, and 72 °C for 45 s; with a final extension at 72 °C for 7 min.Genomic DNA from five populations were selected for genome-wide markers development; Japan (n = 2), Mexico (n = 38), Chile (n = 20), New South Wales (n = 48) and South Australia (n = 44). DNA aliquots were sent to DArTseq™  for markers development. Four methods of complexity reduction were tested (data not presented) and the After PCR equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina) bridge PCR followed by sequencing on an Illumina Hiseq. 2500. The sequencing (single read) was run for 77 cycles, producing 77 bp reads. Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the primary pipeline the fastq files were first processed to filter away poor quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence. In that way the assignments of the sequences to specific samples carried in the “barcode split” step were very reliable. Approximately 2,500,000 sequences per barcode/sample were identified and used in marker calling. Finally, identical sequences were collapsed into “fastqcoll files”. The fastqcoll files were “groomed” using DArT PL’s proprietary algorithm which corrects low quality base from singleton tag into a correct base using collapsed tags with multiple members as a template. The “groomed” fastqcoll files were used in the secondary pipeline for DArT PL’s proprietary SNP and silicoDArT  calling algorithms (DArTsoft14). For SNP calling all tags from all libraries included in the DArTsoft14 analysis are clustered using DArT PL’s C++ algorithm at the threshold distance of 3, followed by parsing of the clusters into separate SNP loci using a range of technical parameters, especially the balance of read counts for the allelic pairs. Additional selection criteria were added to the algorithm based on analysis of approximately 1,000 controlled cross populations. Testing for Mendelian distribution of alleles in these populations facilitated selection of technical parameters discriminating well true allelic variants from paralogous sequences. In addition multiple samples were processed from DNA to allelic calls as technical replicates and scoring consistency was used as the main selection criteria for high quality/low error rate markers. Calling quality was assured by high average read depth per locus .https://blast.ncbi.nlm.nih.gov/Blast.cgi). COI region sequences were evaluated and aligned using ClustalW multiple sequence alignment program in BioEdit sequence alignment editor V.7.2.550. Published sequences were also collated by directly acquiring homologous sequences from GenBank ; sequences were trimmed to 604 bp to correspond to the sequences generated in the present study. Also we regenerated DNA sequences from published haplotype frequencies as found in Sepúlveda and González35; for example, the sequence of the “H1” haplotype was reported along with its frequency (which was 80%) from a total of 291; thus we inferred there were a total count of 233 “H1” haplotypes.Two hundred and forty-four mtDNA sequences generated in this study were trimmed to 604 bp (see Table ST) were estimated as indicators of genetic differentiation between all samples groups using Arlequin 3.5.2 and significance was assessed using 1,000 permutations52. DNA polymorphism/divergence analysis were conducted to estimate the average number of nucleotide differences (k), nucleotide diversity (π) and haplotype diversity (h) using DnaSP v. 5.053.Analysis of molecular variance (AMOVA) test and pairwise genetic differences (F54 to find the best model of sequence evolution. The best model for COI was determined to be the “K2 + G: Kimura 2-parameter + Gamma” model which was selected based on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values55. Phylogenetic tree analyses were conducted with aligned sequences from the present study (244 sequences) and previously published (341 sequences) COI gene sequences from S. lalandi and from related Seriola species as outgroups  using Neighbour-Joining (NJ)56, Maximum Parsimony (MP)57 and Maximum Likelihood (ML)58 methods, with 1,000 bootstrap replicates in MEGA 7.054.Prior to the construction of phylogenetic trees, an assessment was made using MEGA 7.059. Bayesian phylogenetic inferences were characterized with HKY + G (Hasegawa-Kishino-Yano + Gamma) model, assuming coalescent constant population priors with relaxed lognormal clock60 in BEAST v2.4.661. Single representatives of given COI haplotypes sequences (to avoid many zero-length branches) together with five outgroup sequences  were used in phylogeny analysis. BEAST was run for 10 million of Markov-Chain Monte Carlo (MCMC) chain length , which reached convergence with effective sample size (ESS) > 200 for all parameters. Independent GMYC analyses for the trees generated from BEAST were conducted using “rncl”62 and “splits”59 packages in R63 to discover species.To test how many different species were evident from the COI sequences, a species delimitation analysis was carried out using the Generalized Mixed Yule Coalescent (GMYC) approach61, setting birth-death tree prior, since this is well suited and used for multi species data with varying degrees of lineage divergence64. The uncorrelated log-normal relaxed molecular clock was selected for node ages estimates. Monophyly constraints were enforced for all clades represented by calibration points. Calibration points were set to 55.0 MYA (SD = 1.0) to calibrate the ancestral node of the genus Seriola from the fossil record66 that has already been used in time-calibration studies67 and 52.0 MYA (SD = 1.0) for the split between Seriolina nigrofasciata and Elagatis bipinnulata derived from phylogenetic data68 and TIMETREE database70 and thus here included S. nigrofasciata and E. bipinnulata  as outgroups. Three independent runs were implemented with 10 million MCMC steps and sampled every 1000 generations. A burn-in of 10% was used and the convergence of all parameters was assessed (ESS > 200) using the TRACER v1.671. Subsequently, TreeAnotator v2.4.661 was used to generate the consensus tree with median node age and FigTree v1.4.372 was used to visualise the annotated tree.Divergence time estimates were assessed using two approaches. First, molecular dating for COI haplotype sequences were conducted using Bayesian inferences in BEAST v2.4.650. The number of base differences per site was averaged over all corresponding sequence pairs for the full COI data set and calibrated according to Brown et al.33.The second approach to estimate the divergence time among populations was to calculate pairwise sequence divergence percentages (1-sequence identity) using BioEdit v7.2.5g), mean number of alleles per locus (Na), observed heterozygosity (HO) and expected heterozygosity (HE) were estimated with 10,000 permutations and Hardy-Weinberg equilibrium (HWE) tests were performed with 1,000,000 Markov Chain steps and 100,000 dememorization steps using Arlequin V. 3.5.252. AMOVA test and population level of inbreeding (FIS) were performed using Genetic Analysis in Excel (GenAlEx) V. 6.574. Microsatellite data were used to conduct Bayesian cluster analysis using STRUCTURE software V. 2.3 program75, assessing individuals to genetic clusters without a priori population definition and using ‘admixture’ ancestry model with correlated allele frequencies. The number of assumed genetic clusters (K) was set to vary between 1 to 6 and the program setup for five independent runs with burn-in period of 100,000 iterations and 100,000 MCMC (Markov Chain Monte Carlo) iterations. The most likely number of K was determined using the STRUCTURE HARVESTER program76.Molecular diversity indices including gene diversity  data were filtered to remove any loci which had missing marker information for more than 5% of the samples within each population, as well as those with multiple markers in the same locus, to allow assumption of HWE and single-loci markers in downstream population structure and sequence homology analyses. In addition, samples with missing data in more than 100 markers were filtered out. Markers unique to each population were determined as those present in at least 95% of the individuals in the population, while present in not more than 5% of the individuals of all other populations. Data filtration and analysis was performed in the R statistical programing language v3.2.174 and Arlequin V. 3.5.252 for population differentiation. In addition, a Bayesian model-based cluster analysis was performed for three independent runs with K from 1 to 5 and each run with a burn-in period of 100,000 iterations followed by 100,000 MCMC iterations, assuming an admixture ancestry model and correlated allele frequencies using STRUCTURE software V. 2.3 program75. The results of STRUCTURE were subsequently analysed to determine the most likely number of K using the STRUCTURE HARVESTER program76.Both DArT and SNPs data were subjected to AMOVA test using Genetic Analysis in Excel (GenAlEx) V. 6.5v) across both alleles of single-loci markers, divided by the total number of alleles in the markers (2n) multiplied by the tag length (l), as presented in Equation 1. The overall sequence homology within and between populations was then calculated as the mean of homologies from each pairwise comparison of individual samples from a single or compared populations, respectively.Since each single-loci DArTseq™ marker comprised of a single polymorphic base (SNP) in a consensus sequence of fixed tag length (70 bp in this case), the sequence homology (in percentage) across one allele of a polymorphic marker can be defined as (1–1/70 bp) × 100. Therefore, the overall sequence dissimilarity between samples could be calculated as the total number of varying SNPs (SEquation 1. SNP-based sequence homology calculationGenomic DNA variation confirmed Seriola lalandi comprises three different populations in the Pacific, but with recent divergence


The Khon Mueang represent the major group of people present in today’s northern Thailand. While linguistic and genetic data seem to support a shared ancestry between Khon Mueang and other Tai-Kadai speaking people, the possibility of an admixed origin with contribution from local Mon-Khmer population could not be ruled out. Previous studies conducted on northern Thai people did not provide a definitive answer and, in addition, have largely overlooked the distribution of paternal lineages in the area. In this work we aim to provide a comprehensive analysis of Y paternal lineages in northern Thailand and to explicitly model the origin of the Khon Mueang population. We obtained and analysed new Y chromosomal haplogroup data from more than 500 northern Thai individuals including Khon Mueang, Mon-Khmer and Tai-Kadai. We also explicitly simulated different demographic scenarios, developed to explain the Khon Mueang origin, employing an ABC simulation framework on both mitochondrial and Y microsatellites data. Our results highlighted a similar haplogroup composition of Khon Mueang and Tai-Kadai populations in northern Thailand, with shared high frequencies of haplogroups O-PK4, O-M117 and O-M111. Our ABC simulations also favoured a model in which the ancestors of modern Khon Mueang originated recently after a split from the other Tai-Kadai populations. Our different analyses concluded that the ancestors of Khon Mueang are likely to have originated from the same source of the other Tai-Kadai groups in southern China, with subsequent admixture events involving native Mon-Khmer speakers restricted to some specific populations. The area of northern Thailand situated in proximity to southern China, northern Myanmar and northern Laos hosts several ethnicities who can be linguistically classified in four groups: Austroasiatic, Tai-Kadai (TK), Hmong-Mien and Sino-Tibetan. The languages belonging to the Austroasiatic subfamily Mon-Khmer (MK) are spoken today by populations, e.g. Lawa (LW) and Mon (MO), historically and archaeologically recorded as the native inhabitants of this area before the arrival of Tai-Kadai people form southern China 2,000 years ago –3. OtherLinguistic similarity between populations has often been used to disentangle patterns of relationship, under the assumption that a common language implies a common origin ,8. HowevAmong the multi-ethnic groups in northern Thailand, the Khon Mueang (KM) are the most represented, with a total number of individuals reaching 6 million . KM is tIt is widely accepted that genetic markers can be efficiently used to reconstruct past populations’ history and interactions –20. ThreA previous study, based on comparisons among autosomal Short Tandem Repeat (STR) loci, suggested an admixed origin for KM, with a higher contribution from the TK than from the MK groups . On the A sample of 519 males belonging to 24 populations from northern Thailand was subdivided into three groups: Khon Mueang (KM), Mon-Khmer (MK) and Tai-Kadai (TK) . The DNAIn order to investigate the origin of the KM populations from both maternal and paternal perspectives using a simulations based analysis, we assembled two datasets in which we collected genetic information for 17 Y-STRs loci: DYS19, DYS388, DYS389a, DYS389b, DYS390, DYS391, DYS392, DYS393, DYS426, DYS434, DYS435, DYS436, DYS437, DYS439, DYS460, DYS461, Y-GATA-A10 and mtDNA HVR-I sequence . A totalfind.clusters function in adegenet 1.3–1 [We employed a discriminant analysis of principal components (DAPC)  on both et 1.3–1  and compΦst distances constructed on the mtDNA dataset using Arlequin 3.5 [https://github.com/dipetkov/eems. We averaged three runs each with 200, 300, 400 and 500 demes to produce the final EEMS surface, as the number of demes simulated during the grid construction phase can influence the scale of the deviation from overall migration detected [rEEMSplots package in R v 3.2.2.An exploratory analysis such as the DAPC however do not account for geographic location of the samples, thus precluding the visualization of geographic locations where gene flow between populations is either hindered or facilitated. Estimated Effective Migration Surfaces (EEMS) which employed individual based migration rates can be used to visualize zones with higher/lower migration with respect to the overall rate . The regquin 3.5  and on tdetected . Each siIn order to unravel the origin of the KM population, we employed an Approximate Bayesian Computation (ABC) approach on both the HVR-I and Y-STR datasets. We first constructed two competing models , which aβ coefficients of the regression model. To evaluate the stability of the models’ posterior probabilities, we considered different thresholds by considering different number of retained simulations for LR . To generate the simulated datasets, we used the software package ABCtoolbox, running 500 000 simulations for each model. To calculate the models’ posterior probabilities, we used R scripts from http://code.google.com/p/popabc/source/browse/#svn%2Ftrunk%2Fscripts, modified by SG. To summarize the genetic information contained in both the HVR-I and Y-STR datasets we calculated two arrays of statistics within and between populations. For the mitochondrial DNA dataset, we considered the number of haplotypes, the number of private polymorphic sites, Tajima’s D, the mean number of pairwise differences for each population, the mean number of pairwise differences between populations and pairwise Fst. When we analysed Y-STR, we used as summary statistics the mean and the sd. over loci in each population of four parameters: the number of alleles, haplotype diversity, modified Garza–Williamson index and the allelic range. Finally, as the genetic heterogeneity was observed in KM populations [In the admixture model, the KM population originated as a consequence of an admixture event from the parental populations, the MK and TK groups. The tree-like model postulates instead a recent separation of the KM and TK populations and a split of this combined population from the MK ones further back in time. For both the admixture and the tree-like models, we assumed constant effective population sizes based on historical records  and thatulations , we repeThe haplogroup frequencies in each studied population are listed in K in both uniparental datasets , O-M117 (O2a2b1a1) and O-M111 (O1b1a1a1a1a). These lineages are also prevalent in Chinese and other Southeast Asian populations –35. The In agreement with earlier studies of mtDNA and autosomal STRs variation –22 that To investigate the origin of the KM, we proposed two demographic models, which summarized previous hypothesis on their origin. The first scenario depicted an admixture event involving local MK populations and migrating TK groups, while a second tree-like model suggested that the KM originated following a recent split from the ancestors of modern TK populations . We thenS1 FigThe names of the lineages are reported on the right while the markers that define them are shown along the branches of the tree. Lineage names with an asterisk refer to internal nodes of the tree.(PDF)Click here for additional data file.S2 Figfind.clusters on both Y-STR and HVR-I datasets: points represents the mean of the iterations and bars the standard deviation . Scatterplots of the DAPC conducted on the Y-STR and HVR-I datasets when individual sequences where grouped based on populations .BIC resulting from averaging 5 runs of (PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLS)Click here for additional data file.