Through the local selection of landraces, humans have guided the adaptation of crops to a vast range of climatic and ecological conditions. This is particularly true of maize, which was domesticated in a restricted area of Mexico but now displays one of the broadest cultivated ranges worldwide. Here, we sequenced 67 genomes with an average sequencing depth of 18x to document routes of introduction, admixture and selective history of European maize and its American counterparts. To avoid the confounding effects of recent breeding, we targeted germplasm (lines) directly derived from landraces. Among our lines, we discovered 22,294,769 SNPs and between 0.9% to 4.1% residual heterozygosity. Using a segmentation method, we identified 6,978 segments of unexpectedly high rate of heterozygosity. These segments point to genes potentially involved in inbreeding depression, and to a lesser extent to the presence of structural variants. Genetic structuring and inferences of historical splits revealed 5 genetic groups and two independent European introductions, with modest bottleneck signatures. Our results further revealed admixtures between distinct sources that have contributed to the establishment of 3 groups at intermediate latitudes in North America and Europe. We combined differentiation- and diversity-based statistics to identify both genes and gene networks displaying strong signals of selection. These include genes/gene networks involved in flowering time, drought and cold tolerance, plant defense and starch properties. Overall, our results provide novel insights into the evolutionary history of European maize and highlight a major role of admixture in environmental adaptation, paralleling recent findings in humans. The spread of a species outside its native range depends on its ability to face new environmental challenges. Despite a loss of diversity associated with recurrent introductions, domesticated species offer excellent examples of rapid expansion and adaptation to new climatic and ecological conditions. This is exemplified in maize, which was first domesticated in a restricted area of Central Mexico but now displays one of the broadest cultivated ranges of all crops. Here, we focused on the largely overlooked history of European maize, which was introduced from American sources. We sequenced 67 genomes from both continents. The data suggest two independent European introductions and also the admixed origin of three groups: maize from the US Corn Belt, European Flints and Italian material. We found modest genetic footprints of bottlenecks from European introduction. We detected signs of past selection at genes and gene pathways involved in adaptation to abiotic (drought/cold) and biotic (pathogens/herbivores) challenges. Our results provide novel insights into the evolutionary history of European maize and highlight a major role of admixture in environmental adaptation. Expansion of species to conditions that differ from their native range depends on their abilities to adapt to new environments. Depletion of genetic diversity of introduced—founder—populations may however alter this ability. Despite loss of diversity associated with population bottlenecks during their domestication , domestiIn plants, while numerous studies have documented the early demographic and selective history of domesticated plants ,7, a hanMaize was domesticated around 9,000 BP in the tropical lowlands of Mexico ,11. DomeZmCCT has been shown to contribute to day-length adaptation [Vgt1 [ZCN8 [Previous studies have genotyped maize from the Americas with MaizeSNP50k genotyping array , Genotypaptation , and Vgton [Vgt1  and ZCN8t1 [ZCN8  are assoTo document routes of introduction, patterns of admixture and the selective history of European corn and its American counterparts, we performed whole-genome sequencing of 67 maize lines from the two continents at mid-depth (18x). We purposely sampled inbred lines directly derived from landraces both to avoid the confounding effects of recent breeding selection and to account for geographical information. With >22 million SNPs identified, we (1) describe the genome-wide distribution of heterozygosity along chromosomes, (2) assess proposed sources of European maize, (3) measure the impact of introduction bottlenecks, (4) explore admixture events, (5) track signatures of selection at the level of genes and gene networks, using latitudinal and longitudinal contrasts.With the purpose of tracing the origins of European maize and investigating its demographic and selective history, we combined genetic, historic and geographic information to select a sample of 67 maize lines. This sample is representative of European maize diversity and encompasses all possible American introduction sources. We specifically targeted lines directly derived from traditional populations (landraces), for which we have established the geographic origin from historical records. Such lines are less heterozygous than landraces and therefore more amenable for high-throughput sequencing (HTS) genotype calls. They are also less affected by recent and intense selection than elite lines, and therefore particularly relevant for conducting population genomic analyzes with a historical perspective.a priori  of 67 lines totaling 2,654 Gb of sequences. We aligned them to the B73 v2 reference genome . The perThe maize genome has undergone several rounds of duplications and contains large proportions (85%) of transposable elements . This geTripsacum dactyloides [Tripsacum dactyloides, half of which (48.5%) were located in genic regions.We obtained a dataset of 22,294,769 Single Nucleotide Polymorphisms (SNPs) after filtering, of which 86.0% were located outside genes, 5.4% in exonic regions and 8.6% in intronic regions. In total 34,350 out of the 39,423 genes of the maize genome were covered by at least one SNP. The percentage of missing data per line ranged between 18.2% and 32.1% . 2,189,2tyloides  to the BProportion of heterozygous sites (SNPs x lines) was initially comprised between 26.5% and 37.5%, but decreased after filtering to a range of 0.9% to 4.1% . To testWe further estimated miscalling of both homozygotes and heterozygotes by comparing our HTS genotype calling to that obtained with the MaizeSNP50k genotyping array , our MaiFinally, we assessed the power of our HTS approach over MaizeSNP50k SNP to analyze maize genetic diversity by comparing folded Site Frequency Spectra . The twoIn sum, our HTS approach combined multiple advantages: high SNP density; a low error rate for homozygous calls; and a realistic description of allele frequencies, including low frequency variants that are likely to have arisen recently and are therefore informative for assessing both recent ancestry and selection.a priori  with groups that are the most closely related to putative sources—ENFs and Spanish lines for EFs, ANFs and Caribbeans for CBDs, EFs and Spanish for Italians). We found significant negative values for EFs and CBDs, confirming their admixed origins . Results for Italians were more equivocal .We further tested admixed origins of EFs, CBDs, and Italians by computing three-population tests , European Flints (EFs)+ Italians, Corn Belt Dents (CBDs), and Spanish + Tropicals  lines formed an independent cluster  from the remainder of the sample (π/bp) of the non-genic compartment for the 5 genetic groups: Tropicals displayed the highest level of genetic diversity of all 5 groups , followed by EFs , CBDs , SpanIt , and NFs . Distributions of nucleotide diversity for all genetic groups are shown in FastStructure analysis revealed a model maximizing the marginal likelihood at ropicals . However cluster . Inspecte sample . The CBDe sample . We estiFinally, we performed a more detailed inspection of admixture of CBDs and EFs using FastStructure. While American Northern Flints contributed less to the CBDs than the Tropicals , the conD.One of our main goals was to test whether introductions of European corn were associated with a bottleneck. We first examined the Site Frequency Spectrum (SFS) of our American sample and compared it to the expected SFS under the most recent demographic model proposed for American maize . We founπ and θ, at non-genic SNPs (π = 0.00842 and 0.00783 and θ = 0.00926 and 0.00736 for American and European samples respectively), which represented a significant loss of diversity in Europe (−11); (2) an increase in D at non-genic SNPs ; (3) a deficit of rare variants in the European material compare with the American material (We found subtle footprints of this introduction bottleneck: (1) a decrease in nucleotide diversity indexes, material . Genome-Structure analyses identified 5 genetic groups  distribuD that detects selection within a group. Genes passing the 5% threshold for all 3 statistics were considered as candidate genes.In this differential selection approach, we employed the non-genic data as a control for forces other than selection, which includes demography and regional genomic variation in mutation and recombination rates. We screened the genome for selective footprints using a combination of 3 statistics: Fst and XP-CLR that detect extreme differentiation of allele frequencies between groups , and Among 9 pairwise comparisons  we detecWe assessed for each pairwise comparison the proportion of non-genic windows (used as controls) that passed the 5% threshold of all 3 statistics. Depending on the comparison, the proportion varied between 0.072% and 0.210%, with an average of 0.134%. Considering these proportions are representative of the expectation under a null hypothesis, we multiplied them by the total number of genes tested in each comparison and obtained between 13 and 74 DS_candidates. These numbers provide estimates of the expected number of false positives. However, because non-genic windows probably contain selected features, they are likely overestimated. Even so, i.e. structure , it accounts for inter-group effects and reveals intra-group effects, i.e. only genes with consistent effects across groups are detected. We found 401 and 424 GWA_candidates associated with latitude and longitude respectively, with 42 genes in common between DS_candidates and GWA_candidates  framework to test for the effect of individual genic SNPs on latitude/longitude while accounting for group membership tructure  and relandidates .SU1 revealed reduced diversity in the CBDs as compared with the NFs  at the gene network level rather than at the individual gene level, following a method proposed by . In thisi.e. heterozygous segments. We applied our method to the subset of 57 first-cycle inbred and SSD lines. Deviations were detected either because heterozygosity extended over an unexpectedly high number of adjacent sites (in a single or multiple lines) or because heterozygosity was unexpectedly conserved across lines (at a single or multiple sites). We retained 17,959 segments genome-wide, each encompassing at least 5 SNPs. Among these, 6,978 exhibited significantly elevated heterozygosity relative to that of the entire genome (0.41%) as determined by an exact Bernoulli test procedure followed by an FDR control at a nominal level of 5%  spanning 10 SNPs and 12 heterozygous genotypes. A single line was heterozygous for 5 of the 12 SNPs, and 7 other lines were heterozygous for 1 of the 12 SNPs. This heterozygous segment overlapped with two genes, GRMZM2G174949 and GRMZM2G174971 . By definition, detection of heterozygosity in DHs may only be explained by alignments artifacts. DHs hence provide an opportunity to test the relative contribution of reads misalignment i.e. deleterious. Interestingly, heterozygous segments encompassed significantly more deleterious mutations than the rest of the genome. Genic SNPs outside those segments contained 7.07% of predicted deleterious variants vs 7.57% within segments .We used SIFT  to prediDuring its spread through the Americas and subsequent introduction to Europe, maize has adapted to a vast range of latitudes. Here we have investigated how distinct migration routes and admixture histories have contributed to shape maize genetic diversity and adaptation on the two continents. For this purpose, we selected an original sample of 67 lines that were directly derived from landraces. These lines have not been affected by modern breeding, and are therefore suitable to address questions about evolutionary history prior to extensive modern breeding. We have generated whole genome sequencing to produce a dataset of SNPs with reduced ascertainment bias and allele frequencies that include low frequency variants .Maize is characterized by high nucleotide diversity (10–20 times greater than humans ), a vastHeterozygotes are much more challenging to call than homozygotes particularly with coverage <20x . ConsistAlthough heterozygotes calling in our HTS dataset was error prone, we have postulated that extended segments of heterozygosity  provide evidence for either mapping artifacts caused by underlying structural variants (duplications) or regions that are resistant to homozygosity. Selection against allele combinations that are highly deleterious when homozygous may indeed help overcoming inbreeding depression. Previous studies have proposed that elevated rate of heterozygosity in low-recombining pericentromeric regions of recombinant inbred lines is a consequence of inefficient purging of deleterious alleles ,37. In cBecause deleterious alleles segregate at low frequencies in the maize genome, they are difficult to use in association mapping frameworks that combine genotypes and phenotypic measures of inbreeding depression . The powIn the Americas, two historical major expansions northwards and southwards from Mexico have been documented  Fig 1)..Fig 1). We have combined evidence from inferred genetic proximities and admixture events revealed by Treemix as well as historical records to propose a model of maize migration in Europe . Our datRegarding a putative third independent introduction of the Italian material from Argentina , our resAltogether our results show that while genetic diversity is significantly lower in Europe than in America , EuropeaOur sample encompassed 5 genetic groups  distribuFst, D) that detect different hallmarks and time scales of selection [−3). Unterseer and colleagues [In total, we retrieved 968 DS_candidates. In order to control the rate of false positives, we based our DS_candidates identification on genome-wide extreme values for three metrics  versus Because polygenic adaptation may involve a collection of mutations with small effects, which collectively have a large effect on a given pathway, we adapted a gene-set enrichment test  to uncovZCN8 and latitudinal variation , a gene from the same family as ZCN8. Here the “tropical” haplotype present in Tropicals strongly differed from the major “NF” haplotype  responsible for the synthesis of a volatile sesquiterpene that attract natural enemies of herbivores upon release [GRMZM2G127336  and 10 Doubled Haploids (DHs) recently derived from Tropical landraces instead. Note that 8 of these SSDs were previously sequenced in . The geoOur final sample encompassed 67 lines including 14 Corn Belt Dents (CBDs), 5 American Northern Flints (ANFs), 8 European Northern Flints (ENFs), 13 European Flints (EFs), 3 Spanish (Span), 5 Italians (It), 7 South Americans (2 and 5 respectively from the Northern and the Southern part), 7 Mexicans (5 lowlands and 2 highlands below/above 1500 meters) and 5 Caribbeans. We referred to Tropicals when combining South Americans, Mexicans and Caribbeans (19 Lines). Sample information with name, origin, provider, and status is available in Genomic DNAs of all lines were extracted from fresh leaf tissue of a single plant using the Macherey-Nagel MaxiKit and sent to Integragen  for library construction and sequencing. Sheared total genomic DNA was used to generate Illumina paired-end libraries (500 bp insert size). Each library was paired-end sequenced (2 x 101 bp) with a target sequencing depth of 15x. Sequencing data volume for each line is detailed in All lines were aligned to the B73 v2. reference genome by combining Bowtie2  and Stam1 and n2 be the counts of most frequent and second most frequent nucleotide variants, we computed the likelihoods of a homozygote (genotype 1/1) or a heterozygote (1/2) as follows: L (1/1) ~ B and L (1/2) ~ B , with the error rate ε = 0.01. If LRT was significant at α = 5%, we assigned the most likely genotype at the SNP.We performed genotype calling for each SNP with Varscan using the following parameters: Phred score above Q20, minimum coverage of 6 reads, α = 5%. For each sample, Varscan provides the number of reads that support variants at a given SNP. We utilized this information to define genotypes by performing a Likelihood Ratio Test (LRT) with one degree of freedom following  with sliAfter individual genotype calling, we considered the genotype information across samples to apply filters and improve genotype-calling accuracy. We retained only SNPs with two variants . Our filters aimed at increasing proper SNP identification, and eliminating false heterozygosity created by misalignment of duplicated  regions. We employed four criteria described below: multiple mapping, sequencing-depth variation, error rate, heterozygosity rate.To avoid false genotype calling arising from genome redundancy, we retained genotypes (lines) for which the percentage of reads mapping to unique genomic positions was >90%. For reads aligned with Stampy, we used an alignment score>11 to define uniquely-mapped reads. We discarded positions that presented both ambiguous (with regards to multiple-mapped reads) and heterozygous genotypes.We eliminated genotyping information for all lines with a low sequencing-depth (<6) at a given position. In addition, we discarded positions with an average sequencing-depth > 28 across samples (28 represents the 99% average upper bound of the coverage). Such an elevated depth likely corresponds to missing portions in the reference genome and/or duplicated regions in our samples.1+n2, ε), with ε = 0.01; (2) the distribution of the error counts among samples significantly differs from a Poisson distribution of parameter the average error count at the position .For each genotype, we evaluated the error rate as the number of reads that differed from the called genotype, as given by the LRT (see above). We dropped positions for which (1) the probability of observing the number of error counts or more is above α = 5% given that B (nWe eliminated heterozygous genotypes located in close vicinity (< 500 bp) to heterozygous insertion-deletions (indels). Local alignments are indeed known to be less accurate in regions encompassing such indels. When a heterozygote was detected among the 67 Lines, we retained only positions for which at least one homozygote of each variant was detected in other lines.We extracted all positions that passed our stringent filters and determined the distribution of missing genotyping information across SNPs as well as the percentage of missing data per line. We annotated SNPs and classified them in two categories: within and outside genes. SNPs within genes were further subdivided into belonging to 5’UTR, 3’ UTR, exonic and intronic regions. Throughout the text, non-genic SNPs are defined as SNPs located >50kb away from annotated genes, and genic SNPs are defined as SNPs located within genes and 10kb on both sides of the genes.To test the accuracy of our genotype calling we employed several approaches. First, we compared the rate of heterozygosity per line before and after applying our customized filters. We computed the per-line heterozygosity as the number of heterozygous sites divided by the number of heterozygous and homozygous sites for the alternative allele . By considering alternative homozygotes only, this estimate compares each line to the reference independently from the level of diversity in the rest of the sample.Second, we used 2 sets of high-confidence genes defined by  as “retaThird, we extracted positions of the Illumina Maize SNP50k array from our HTS data to compare genotypes. For 42 lines, array genotyping was already available  but the Xij stand for the heterozygous status of line i at position j, with Xij = 1 if the line is heterozygous, 0 otherwise. On a given chromosome, positions were assumed to be spread into K contiguous regions, each of these regions being characterized by a specific Heterozygosity Rate (HR), i.e. Xij~B(pk) if position j belongs to region k, where B(.) is the Bernoulli distribution and pk is the HR in region k. The goal of the statistical analysis was to identify regions with associated HR pk significantly higher than the genome wide HR. To this aim, a 2-step statistical analysis was performed.We aimed at identifying regions with heterozygosity patterns deviating from the genome wide expectation across our sample of first-cycle inbreds and Single Seed Descent (SSD) lines (57). In order to do so, we employed a segmentation method on positions with MAF>5%. We considered the following statistical model. Let HR. When the number of regions K is known, the breakpoint detection problem boils down to finding the optimal splitting of the chromosome into K regions along with their associated HR{p1,…,pK}. A combination  is optimal if it achieves the best fit to the data, the fitting being measured through the likelihood. Identification of the combination  optimizing the likelihood was achieved through dynamic programming [K, ranging from 1 to 20,000. We further selected the optimal value K* using the model selection criterion proposed in [In step 1 we applied a breakpoint detection procedure to jointly segment the heterozygous profiles and identify regions with homogeneous gramming ,75. Sincposed in .HR significantly higher than the genome wide HR, noted pg. For a given region, the procedure consisted in testing H0 {pK = pg} vs H1 {pK > pg} using an exact Bernoulli test procedure. To control for multiple testing, a Benjamini Hochberg procedure [HR being unknown, we considered pobs (0.41%) as the best estimate of pg, where pobs is the genome wide averaged HR computed on the first-cycle inbred lines and SSDs (57). Note that pobs was calculated as the number of heterozygotes divided by the number of heterozygotes and homozygotes. The previously described 2 steps procedure was applied to 57 lines (first-cycle inbreds and SSD lines).In step 2, we applied a test procedure to identify regions with a rocedure  was perfFor better visualization, we first merged independently significant (resp. non-significant) adjacent segments; second we removed segments containing a single SNP; and third we repeated the merging. In the end, we retained segments containing at least 5 SNPs. In the following we denoted segments displaying unexpected patterns of heterozygosity as heterozygous segments.For heterozygous segments, we calculated the rate of heterozygosity in our subset of 10 HDs. We also determined the corresponding P-values using the test procedure described above.2 test.We next asked whether heterozygous segments were enriched for deleterious mutations. To this purpose, we extracted genome wide genic SNPs and used SIFT4G_Annotator_v2.3 with the corresponding maize database  to identBecause heterozygotes represented a small proportion of all genotypes, in all subsequent analyzes we considered heterozygotes as missing data and relied on a haploid model.Tripsacum dactyloides HTS data [To evaluate the distributions of allele frequencies in our samples, we generated folded Site Frequency Spectra (SFS) using the DaDi (Diffusion approximation for Demographic inference) python library . When coHTS data  to inferπ[θ [D (D) [π on non-overlapping non-genic windows of 10kb along the genome (with less than 50% missing positions) and applied a pairwise Wilcoxon signed-rank test. Genetic diversity along chromosomes for the American and European samples as measured by π was computed on 50kb overlapping sliding windows with a step of 10kb. We used local linear polynomial fit for smoothing with lowess function of the R Package Stats with parameters 1/500 for smoother span.In order to further test the impact of introduction bottlenecks, we generated SFS on American and European lines (projected down to 29 samples) using non-genic SNPs. We compared our observed American SFS to the predicted “neutral” SFS obtained from a demographic model recently established by  for Amerπ, Wattersπ[θ , and Tajθ [D (D) . In ordeWe assessed the population structure underlying our lines by combining different methods:Genetic differentiation as measured by Fst. We estimated the Fst using non-genic SNPs from the equation of  for all K). It provides admixture proportions of K clusters for each sample. We ran FastStructure using 500,000 non-genic SNPs uniformly distributed along the 10 chromosomes on the whole data set of 67 Lines, with K ranging from 1 to 10. A single run was carried out for each K using default parameters . To choose the appropriate number of clusters, we employed the model complexity that maximizes marginal likelihood. Additionally, we ran separate analyzes on American lines  with K = 2, and European lines (All Europeans) with K = 2 to infer the proportion of admixture of two sets of lines with a recent history of hybridization, the Corn Belt Dents and the European Flints.FastStructure v1.0 . FastStrPrincipal components analysis (PCA). PCA allows samples projection on axes of variation reducing the data to a small number of dimensions. We used EIGENSTRAT v5  on 500,0Tripsacum dactyloides outgroup. We considered all populations as defined in i.e. the value after which the likelihood does not substantially increase. In addition, we computed the three-population test (f3 statistics) introduced by [f3 is used to detect correlations in allele frequencies that are not compatible with evolution following a bifurcating tree. Negative value indicates that a population results from admixture of 2 other populations. A value of Z-score <−1.64 corresponds to a significant P-value at a 5% threshold.TreeMix analysis. Treemix models genetic drift to infer historical splits of populations deriving from an outgroup. But when populations are more closely related than modeled by the resulting bifurcating tree, TreeMix reconciliates the modelled covariance with the observed covariance by placing migration edges along the tree. These edges can originate either from existing populations or from unsampled, more basal populations. They are informative with regards to both the direction and the position of admixture relative to the divergence of the populations. We inferred admixture graphs using Treemix version 1.12  using 10duced by  to furthD—thereafter D [D indicates deviation from the neutral SFS caused by demographic or selective processes.We employed 3 summary statistics to undertake genome scans for selection: Fst , XP-CLR eafter D . Fst andD and Fst values for each genic region encompassing > 7 SNPs and for which missing data did not exceed > 30% of lines in each group. For XP-CLR, genome-wide values were computed on sliding windows of 0.001 cM with 5000 bp steps. Individual SNPs were assigned a position along a genetic map by assuming uniform recombination between mapped markers. We down-weighted pairs of SNPs in high LD (r2> 0.7).We computed vs Corn Belt Dents, Corn Belt Dents vs Tropicals, Northern Flints vs Tropicals within the American continent; Northern Flints vs European Flints, European Flints vs SpanIt, Northern Flints vs SpanIt within the European continent. In addition, we performed longitudinal comparisons, American Northern Flints vs European Northern Flints, Corn Belt Dents vs European Flints, Tropicals vs SpanIt. We computed D within each of the 5 groups.We considered the 5 groups defined by FastStructure as indicated in D and Fst as follows: (1) we determined D and Fst values for non-genic regions of similar range size as the genic regions; (2) we computed the P-value of each candidate gene as the proportion of non-genic regions with equal or higher Fst  than the value observed for this candidate gene. The same rationale was employed for XP-CLR. Non-genic windows were located at least 50kb away from genes.We attributed P-values for D  within one of the 2 groups under comparison. These putatively selected genes are named Differentially-Selected (DS_) candidates thereafter.We considered as putatively selected, genes passing the significance thresholds for all 3 statistics in at least one of the pairwise comparison that is, genes displaying: (1) a significant Fst  a significant XP-CLR  in at least one of the two reciprocal comparisons; and (3) a significant negative Y is the vector of phenotypes (latitude or longitude), Xs is the structure matrix accounting for the membership in one of the 5 groups  analysis using all 67 lines and either latitude or longitude as phenotypes with FaST-LMM . We consribed in . For eacrocedure  to contrhttp://mapman.gabipd.org/web/guest/mapman). Considering only the subset of genes with a MapMan annotation, we tested for categories’ enrichment in candidate genes using a unilateral exact Fisher test. We performed this analysis on our set of DS_ candidates and GWA_candidates seperately.Finally, we conducted a functional annotation of candidate genes using the MapMan Ontology  and an additional comparison between American and European Northern Flints. P-values were further used to determine a normalized Z-score [To analyze selection at the network level, we employed the method of  with the Z-score  followin Z-score . Because Z-score ) as the S1 Fig(EPS)Click here for additional data file.S2 Fig(EPS)Click here for additional data file.S3 FigThe former was computed on 33,046 positions (blue) and the latter on 10,813,686 positions (red). Only sites with less than 8 missing genotypes (≥60 genotyped lines) were conserved and the sample was projected down to 60 individuals.(EPS)Click here for additional data file.S4 FigT. dactyloides which defines the allele ancestral state. The sample was projected down to 60 individuals.The SFS is based on positions aligned to (EPS)Click here for additional data file.S5 FigK groups. Classification of individuals according to the groups defined in the structuring analyzes  are partitioned into colored segments whose length represents the admixture proportions from analyzes  and furtanalyzes  are indi(EPS)Click here for additional data file.S6 FigNucleotide diversity is estimated on non-overlapping windows of 10kb along the genome (with less than 50% missing positions). N = Northern Flints from America and Europe, E = European Flints, C = Corn Belt Dents, T = Tropicals, K = SpanIt.(EPS)Click here for additional data file.S7 FigEach color represents a group. Vertical lines  are partitioned into colored segments whose length represents the admixture proportions from 2 parental groups. Classification of individuals are indicated below each graph: N = American Northern Flints, C = Corn Belt Dents, T = Tropicals and K = SpanIt, E = European Flints, N = Northern Flints.(EPS)Click here for additional data file.S8 Figπ/bp was computed along chromosomes 2 to 10 from 50kb sliding windows.Variation of (EPS)Click here for additional data file.S9 FigZCN8 gene.Status of biallelic SNPs per line within each genetic group as inferred from Structure in a region encompassing the GWA_candidate, GRMZM2G095955, and the (EPS)Click here for additional data file.S10 Fig2 from positions 113,529,804 to 114,530,887 bp. ZCN5 position is indicated with a red box. (C) Zoom within the ZCN5 genomic region with XP-CLR (dots) and D values (lines with the same colour code as in A) presented along with the genome-wide 5% quantile .(A) Nei’s genetic diversity (H) averaged on overlapping sliding windows of 1,000 SNPs (1 SNP step) along chromosome 10 with positions indicated in kb . (B) Status of biallelic SNPs per line within each genetic group as inferred from Structure is shown as well as pairwise LD computed from r(EPS)Click here for additional data file.S11 Fig(A) Rate of heterozygosity (%) among 57 lines. Positions along the chromosome are indicated in kb. Shaded area corresponds to centromere position. Horizontal bars designate the rate of heterozygosity per segment with grey and red corresponding to non-significant and significant segment respectively. (B) zoom within a specific region. Black crosses indicate rate of heterozygosity at each SNP. As in (A) non-significant and significant segments are indicated in grey and red respectively. Blue segments encompass less than 5 SNPs. Location of 4 genes within the region is shown by boxes underneath.(EPS)Click here for additional data file.S12 Fig(EPS)Click here for additional data file.S13 FigA: segments significant in our sample of 57 lines (first-cycle inbreds and SSD lines)– 6,978 segments. B: subset of segments also significant in Doubled Haploids– 1,088 segments.(EPS)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLS)Click here for additional data file.


Gossypium hirsutum L). To dissect the genetic architecture of this agronomically important trait, a population consisting of 355 upland cotton germplasm accessions was genotyped using the specific-locus amplified fragment sequencing (SLAF-seq) approach, of which a subset of 185 lines representative of the diversity among the accessions was phenotypically characterized for six early maturity traits in four environments. A genome-wide association study (GWAS) was conducted using the generalized linear model (GLM) and mixed linear model (MLM).Early maturity is one of the most important and complex agronomic traits in upland cotton (rs13562854) on chromosome Dt3 and a potential candidate gene (CotAD_01947) for early maturity were detected.A total of 81,675 SNPs in 355 upland cotton accessions were discovered using SLAF-seq and were subsequently used in GWAS. Thirteen significant associations between eight SNP loci and five early maturity traits were successfully identified using the GLM and MLM; two of the 13 associations were common between the models. By computing phenotypic effect values for the associations detected at each locus, 11 highly favorable SNP alleles were identified for five early maturity traits. Moreover, dosage pyramiding effects of the highly favorable SNP alleles and significant linear correlations between the numbers of highly favorable alleles and the phenotypic values of the target traits were identified. Most importantly, a major locus  contains supplementary material, which is available to authorized users. Gossypium hirsutum L. , also referred to as ‘upland cotton’, accounts for 95 % of the world’s cotton production. Early fiber production is one of the most important traits in cotton, and the selection and popularization of early-maturing cotton varieties are of significant value in reducing the dilemma of whether to plant farmlands with cotton or cereals during cropping system optimization in China [Cotton is the most important natural textile fiber source worldwide. The tetraploid species in China , 2. Earlin China , 2. Thesin China . Early min China . It is dG. hirsutum with different target traits, such as fiber quality traits [Over the last two decades, many QTLs related to target traits have been identified using QTL-mapping methods by constructing intraspecific segregating populations of y traits –6, yieldy traits , resistay traits –10, early traits , 11, 12 y traits . In a stArabidopsis thaliana [G. hirsutum accessions using microsatellite markers. Further, Kantartzi and Stewart [G. arboreum accessions using association mapping with simple sequence repeat (SSR) markers. Recently, Association mapping was performed to assess QTL alleles during three cotton breeding periods, revealing that some alleles could be detected in nearly all of the Chinese cotton cultivars studied [Association mapping is another effective approach for connecting phenotypes and genotypes in plants when information on population structure and linkage disequilibrium (LD) is available . This methaliana , 22, ricthaliana , 23, maithaliana  and soybthaliana  for the thaliana , 26–30. thaliana  performe studied . Favorab studied . Some QT studied . HoweverTo better understand the genetic architecture of early maturity traits in upland cotton, genome-wide SNP discovery based on the specific-locus amplified fragment sequencing (SLAF-seq) method and a GWAS strategy were used to identify the SNP loci associated with early maturity traits. We successfully identified several significant associations between SNP loci and early maturity traits using the generalized linear model (GLM) and mixed linear model (MLM). The highly favorable SNP alleles for early maturity traits were mined by computing the phenotypic effect of each SNP locus identified, and the pyramiding effects of the highly favorable SNP alleles for these traits were assessed. Moreover, major SNP loci and potential candidate genes for early maturity were detected. The results of this important study serve as a foundation for analyses of the genetic mechanisms underlying cotton earliness and for MAS for early maturity in cotton.SLAF-seq was performed with an Illumina HiSeq 2500  at Biomarker Technologies Corporation in Beijing to genotype 355 cotton varieties/accessions. The sequencing run generated 96.10 Gb of data, including 874.44 million paired-end reads with an length of ~80 bp. The Q30 ratio and guanine-cytosine (GC) content, which are indicators of sequencing quality, were 89.75 and 39.11 %, respectively, indicative of good quality. A total of 678,397 high-quality SLAF tags were obtained for each of the 355 genotypes, and 505,823 polymorphic SLAFs were identified from these reads by performing sequence alignments with the TM-1 reference genome . The SLAt1 (5882 SNPs), and the smallest was identified on chromosome At7 (1006 SNPs). The average marker density was approximately one SNP per 24.85 kb. The highest marker density was detected on chromosome Dt8 (one SNP per 15.76 kb), and the smallest was identified on chromosome At3 (one SNP per 36.24 kb)  durations were 116.61, 117.92, 118.03 and 120.39 d in the four experiments, respectively. The minimum WGP was 96.67 d in SU-2013, and the maximum WGP was 147.00 d in SP-2014. Analogously, the FT and FBP exhibited wide ranges of 53.00–80.67 d and 38.00–73.67 d, with means of 66.59 and 51.64 d, respectively. The NFFB ranged from 3.00 to 12.00, with a mean of 6.50. The mean HNFFB values exhibited continuous variation, ranging from 15.45 to 34.03 cm. The YPBF exhibited the largest range of variation, ranging from 1.55 to 100 %. The mean coefficients of variance (CVs) for the WGP, FT, FBP, NFFB, HNFFB and YPBF were 6.88, 5.91, 8.79, 15.79 16.92 and 18.11 %, respectively. These data indicated a high degree of diversity in early maturity phenotypic traits in the natural population. Based on the WGP, the number of early-maturing accessions (106 d < WGP ≤112 d), early-middle-maturing accessions (114 d < WGP ≤120 d) and middle-late-maturing accessions (122 d < WGP ≤128 d) were 62 (33.51 %), 20 (10.81 %) and 59 (31.89 %), respectively. The early-middle-maturing accessions accounted for a very small percentage, thus these traits were typically bimodally distributed  for all six traits   indicated that the genotype (G) and interactions between the genotype and environmental factors (G × E) were both significant P < 0.0 for all t3, and 25 % were distributed on chromosome At3. Among these associations, five associations each with the WGP and FT were identified, as well as one association each with the FBP, NFFB and YPBF; the corresponding SNP loci were distributed on chromosome Dt3. The SNP loci for various early maturity traits identified through GWAS explained 5.36–15.56 % of the phenotypic variance  was simultaneously associated with the WGP, FT, NFFB and YPBF  on chromosome Dt3 was found to be simultaneously associated with the WGP and FT according to BLUPs and in two of the four environments, explaining 9.23–16.46 % of the phenotypic variance  according to the best linear unbiased predictions (BLUPs) and in at least two of the four environments (-lg(p) ≥6.21). Of these SNP loci, 50 % were distributed on chromosome Drs26538646 (tightly linked with rs26538688), rs13562854, rs8917898 and rs13153008 had the strongest positive phenotypic effects on the WGP, shortening it by 6.70 d, 7.53 d, 7.58 d and 7.76 d, respectively; in addition, rs22465987, rs48627288, rs13562854, rs8917898 and rs37255056 shortened the FT by 0.07 d, 0.55 d, 3.88 d, 3.69 d and 3.40 d, respectively; rs13153008 shortened the FBP by 4.09 d; and rs13562854 shortened the NFFB by 0.91, whereas it increased the YPBF by 10.45 %. These findings indicated that the phenotypic characteristics of the genotypes with favorable SNP alleles were significantly enhanced compared with those of the genotypes with unfavorable SNP alleles, with the exception of rs22465987 and rs48627288 . The highly favorable SNP alleles exhibited significantly different traits compared with the unfavorable alleles (P < 0.01). Finally, the eleven highly favorable SNP alleles were mined by ANOVA. The numbers of highly favorable SNP alleles for the WGP, FT, FBP, NFFB and YPBF were 5, 3, 1, 1 and 1, respectively  and between the FT and number of highly favorable SNP alleles (R2 = 0.6988), further confirming the pyramiding effects of the highly favorable alleles  associated with both the WGP and FT in the GLM and MLM was used to compare the differences between the accessions that carried favorable alleles and those that carried unfavorable alleles in six traits related to early maturity. The mean phenotypic value of 66 accessions that contained a favorable allele (A) was significantly better  compared with the remaining accessions that contained unfavorable alleles (G)  Fig. . This firs13562854)  had potential involvement in the early maturity response in plants. Two early-maturing cotton varieties and two late-maturing varieties were selected. The WGPs of the early-maturing varieties zhongmiansuo50 and zhongmiansuo74 were 107.92 d and 102.75 d, respectively, and those of the late-maturing varieties lumianyan28 and zhongmiansuo41 were 124.17 d and 126.67 d, respectively    Fig. . The tra01) Fig. . However2 populations and recombinant inbred lines (RILs)) [t3.Both linkage mapping and association analysis provide tools for interpreting the genes that underlie complex traits. To date, linkage mapping is a major method for the mining of QTLs for early maturity traits in cotton. Based on the findings of previous studies, it can be concluded that only preliminary progress has been achieved toward localization of QTLs for cotton early maturity traits with desirable effects in the segregation population (F (RILs)) , 32, and (RILs)) , 34, thers13562854) for the WGP and FT was positioned between DPL0200 and CIR347. This finding validates the GWAS results and increases confidence in the identity of the main SNP locus (rs13562854).In a previous study, one significant QTL for the GP, BP and YPBF was found to be located close to the bridge markers DPL0041 and CIR347 on Chr17 (D3) in two biparental populations, explaining 20.00 % of the phenotypic variation [Obtaining satisfactory yield and quality during a short growing season is complicated due to conflict between early maturity and yield, as well as between early maturity and fiber quality; thus, it is increasingly difficult to simultaneously improve upon these agriculturally desirable traits in early-maturing cotton using traditional breeding methods. Therefore, the mining of favorable SNP (or QTL) alleles is necessary for improving important agronomic traits in upland cotton cultivars via MAS. Association mapping is one of the most effective approaches for the mining of favorable alleles. Elite alleles for fiber-quality traits  and yielrs13562854 was located on chromosome Dt3 and that the 32 candidate genes in the nearby region were the most highly associated with the WGP and FT. Specifically, four candidates  related to plant floral development were annotated. CotAD_01947 and CotAD_01914 were located -452.83 kb (backward) and 495.80 kb (forward), respectively, from the peak SNP (rs13562854), with MADS-box genes that encode transcription factors involved in plant developmental control and signal transduction [CotAD_01936) was identified 154.53 kb from the rs13562854 locus. Plant WDR proteins are intimately involved in various cellular and organismal processes, including cell division and cytokinesis, apoptosis, light signaling and vision, cell motility, flowering, floral development and meristem organization [CotAD_01947 expression in the early-maturing varieties zhongmiansuo50 and zhongmiansuo74 was significantly higher than that in the late-maturing varieties lumianyan28 and zhongmiansuo41. However, expression of the other genes did not significantly differ between the early-maturing and late-maturing varieties  in Arabidopsis is another MADS-box family member that regulates the transcription of genes required for cellular differentiation and floral determination [CotAD_01947 with the Arabidopsis AGL8 gene was as high as 47.50 %  on chromosome Dt3 and a potential candidate gene (CotAD_01947) for early maturity were detected. The beneficial alleles and candidate gene should be useful for improving early maturity in upland cotton breeding via a molecular design approach.A substantial number of SNP markers in upland cotton were developed through SLAF-seq technology and were used in a GWAS. Thirteen significant associations were identified among eight SNP loci and five traits related to early maturity using the GLM and MLM, and two of the 13 associations were observed in both models. Eleven highly favorable SNP alleles for the WGP, FT, FBP, NFFB and YPBF were identified. Moreover, dosage pyramiding effects of the highly favorable SNP alleles and significant linear correlations between the number of highly favorable alleles and the phenotypic values of target traits were detected. Most importantly, a major locus  were used for genome sequencing. Seeds from the 355 upland cotton accessions were obtained from the cotton germplasm collection in our laboratory and from the low-temperature germplasm genebank of the Cotton Research Institute, Chinese Academy of Agricultural Sciences (CRI-CAAS). All accessions had been self-pollinated for more than three generations.Young leaves of ten plants from each of the 355 varieties/accessions were collected, mixed, frozen in liquid nitrogen, and used for DNA extraction. Genomic DNA was isolated from samples from each cotton variety/accession using the cetyltrimethylammonium bromide (CTAB) method, as described by Paterson et al. ; RNase ARsaI and HaeIII, New England Biolabs, NEB, USA) were used to digest the genomic DNA. A single nucleotide (A) overhang was subsequently added to the digested fragments using Klenow Fragment (3′ → 5′ exo−) (NEB) and dATP at 37 °C. Duplex tag-labeled sequencing adapters  were then ligated to the A-tailed fragments using T4 DNA ligase. PCR was performed using diluted restriction-ligation DNA samples, dNTP, Q5® High-Fidelity DNA Polymerase and PCR primers  . Next, the PCR products were purified using Agencourt AMPure XP beads  and pooled. The pooled samples were separated by 2 % agarose gel electrophoresis. Fragments that ranged in size from 314 to 364 bp (with indexes and adaptors) were excised and purified using a QIAquick gel extraction kit . The gel-purified products were subsequently diluted. Paired-end sequencing (125 bp at each end) was performed using an Illumina HiSeq 2500 system  according to the manufacturer’s recommendations.The SLAF library was constructed as described by Sun et al.  with sevGossypium hirsutum v 1.0) [The raw reads (100 bp in length) were filtered and trimmed as follows: reads with ≥10 % unknown nucleotides were removed; reads with ≥30 % low-quality bases  were removed; reads with clear index information were trimmed; and low-quality bases at the 3′ ends of reads were removed. Read quality was considered acceptable if the Q30 ratio was ≥80 % after trimming and a paired sequence length of 80 bp was retained at each end. To evaluate sequence quality, real-time monitoring was performed in each cycle during sequencing, and the ratio of the number of high-quality reads with quality scores > Q30  to the total number of raw reads and the GC content were calculated. BWA software was used to map the raw paired-end reads to the reference genome (m v 1.0) . SLAF gr2) of alleles using GAPIT software [The ADMIXTURE  program software .A subset of 185 lines was selected from the 355 upland cotton accessions from the cotton germplasm collection in our laboratory and from the low-temperature germplasm genebank of the CRI-CAAS. Selection was based on analyses of population structure and maturity, with the genotypes from the nine subpopulations characterized into two main groups according to maturity traits. The first group (103 genotypes) contained the early-maturing genotypes, including 76 varieties/accessions that originated from the Yellow River region, 15 varieties/accessions that originated from the northern specific early-maturing region, ten varieties/accessions that originated from the northwestern inland early-maturing region and two varieties introduced from the United States. The second group (82 genotypes) contained the late-maturing genotypes, including 69 varieties/accessions that originated from the Yellow River region, five varieties/accessions that originated from the Yangtze River region and 8 varieties introduced from the United States . All cotton lines were sown at two time points, including late April and late May , in 2013 and 2014. The different cotton varieties/accessions were each grown in a single-row plot (5.0 m long and 0.8 m row wide), with three replicates and a random complete block design. The field management conformed to local practices.The following six traits related to early maturity were investigated in this study: WGP (the period from sowing to the first boll opening), FT (the period from sowing to the first flower blooming), FBP (the period from the first flower blooming to the first boll opening), NFFB (the number of nodes from the cotyledon node to the first fruiting branch node), and HNFFB (the distance between the cotyledon node and the NFFB) and YPBF (the seed yield percentage before October 25th). Ten consecutive plants in the middle of each row were tagged for trait measurements. These plants were observed, and the average value of three replicates was recorded. The phenotypic data were analyzed using SAS 9.3 statistical software . To reduce environmental error, BLUPs for six early maturity traits per genotype were obtained using the PROC MIXED procedure of SAS9.3. ANOVA was performed using PROC ANOVA. Linear regression analysis was conducted using the GLM procedure in SAS.P-values of ≤0.01 and 0.05 (-lg(p) ≥ 6.91 and -lg(p) ≥ 6.21, respectively) were used as thresholds to determine whether significant associations existed [i) was estimated through comparison of the average phenotypic value for each accession for the specific locus with that of all accessions. The favorable alleles were subsequently identified according to the breeding objective of each target trait. For the WGP, FT, FBP, NFFB and HNFFB, ai < 0 indicates a favorable allele, and for the YPBF, ai > 0 indicates a favorable allele.For all SNP loci and phenotypic data, we applied the GLM and MLM. In addition, to minimize the effects of environmental variation, BLUPs were computed for GWAS . The BLU existed . SNP loc existed , the pheactin was used as an endogenous control, and the gene expression levels were calculated using the 2−ΔΔCT method [Total RNA was isolated from the samples using a Plant RNA Purification Kit . Reverse transcription was conducted using a SuperScript III First-Stand Synthesis System to obtain cDNA for qRT-PCR . Transcript levels were subsequently determined by qRT-PCR using a 7500 Real-Time PCR System  and SYBR PremixEx Taq (2×) (TaKaRa). The gene-specific primer pairs used for PCR amplification are listed in Additional file T method .ANOVA, analysis of variance; BLUP, best linear unbiased prediction; CV, coefficients of variance; FBP, flowering and boll-setting period; FT, flowering time; GLM, generalized linear model; GWAS, genome-wide association study; HNFFB, height of the node of the first fruiting branch; LD, linkage disequilibrium; MAF, minor allele frequency; MAS, marker-assisted selection; MLM, mixed linear model; NFFB, node of the first fruiting branch; SLAF-seq, specific-locus amplified fragment sequencing; SNP, single nucleotide polymorphism; SSR, simple sequence repeat; WGP, whole growth period; YPBF, yield percentage before frost


The present study was thus undertaken to analyze the genetic diversity of Kilakarsal and Vembur sheep breeds using single-nucleotide polymorphism (SNP) markers within Toll-like receptor (TLR) 3, 5, 6, 9, and 10 genes.IS), and testing for Hardy–Weinberg equilibrium (HWE) were estimated using package for elementary analysis of SNP data software program.Competitive allele-specific polymerase chain reaction (PCR)-based end-point genotyping was performed using real-time PCR to type the SNPs. Allele discrimination module implemented in real-time PCR was utilized to call the genotypes based on fluorescence intensity recorded for each of the two alleles. Basic diversity indices, namely, gene frequencies, observed heterozygosity, expected heterozygosity, and inbreeding coefficient  due to heterozygosity deficit. In Vembur sheep, TLR10_82_CT and TLR10_292_CG loci showed significant deviation (p<0.05) due to heterozygosity excess. Other SNP loci did not deviate from HWE (p>0.05) revealing that the population was in HWE proportions.Of the 25 SNPs, 22 were found to be polymorphic, whereas two SNPs, namely, TLR3_1081_AC and TLR9_2036_CT, were monomorphic in both Kilakarsal and Vembur sheep populations. The SNP TLR10_1180_AG was monomorphic in Kilakarsal but polymorphic in Vembur sheep. The observed heterozygosities were estimated as 0.289 and 0.309 in Kilakarsal and Vembur sheep, respectively, whereas the expected heterozygosity values were 0.305 and 0.309 in the two breeds, respectively. The overall mean FThe SNP markers within five TLR genes  utilized for genotyping in this study were highly polymorphic in Kilakarsal and Vembur breeds of sheep. This study on the genetic diversity analysis of the Kilakarsal and Vembur sheep breeds revealed considerable genetic variation within the breeds and it can be utilized to improve desirable traits. In India, sheep breeds are well-known for ability to withstand harsh climatic conditions, disease resistance, and lack of fodder. There has been a rapid decline in population and uniqueness of most of the indigenous sheep populations through breed substitution, indiscriminate crossbreeding, and the absence of conservation programs. At present, to meet the consumer demand and global climate change and emerging diseases, there is an urgent need to maintain the biodiversity and conservation of valuable native germplasm. Several studies have been carried out to characterize the sheep breeds based on morphometric characters, production, and reproduction performances . Breed cHowever, at present, single-nucleotide polymorphism (SNP) is becoming the standard marker for diversity analysis and genome-wide studies. The usefulness of SNPs in analyses of population diversity and structure has been demonstrated in several studies . SNP is There are many SNP techniques that can be used depending on the purpose of the research considering throughput, data turnaround time, ease of use, performance, flexibility, requirements, and cost. For a small number of SNPs, a uniplex assay like KBioscience competitive allele-specific polymerase chain reaction (PCR) genotyping system (KASP) is used. The KASP method is more cost-effective than multiplex methods. There is also a much shorter turnaround time to receive the results with the KASP method than other multiplex methods. In addition, there is a lower genotyping error rate of 0.7-1.6%. The KASP method is more flexible than other methods in that it can be used when there are many SNPs in a few samples or when there are few SNPs in many samples .The present study was thus undertaken to analyze the genetic diversity of Kilakarsal and Vembur sheep breeds using SNP markers within Toll-like receptor (TLR) 3, 5, 6, 9, and 10 genes through competitive allele-specific PCR. Many studies have reported that genetic variation in the TLR genes modifies cellular immune response and alters susceptibility to disease. Recent studies have indicated that there were plenty of polymorphisms in the TLR genes in humans and livestock [This research was conducted in sheep breeds with natural infection only. Hence the ethical approval is not required.This study was carried out in Kilakarsal sheep  maintainThe volume of 5 ml of blood was collected routinely from all the animals from external jugular vein using vacutainer. All the chemicals and reagents used in this experiment were of molecular biology grade and obtained from Hi-Media and Sigma-Aldrich. DNA was isolated from blood samples using a modified high salt method . The purCompetitive allele-specific PCR-based end-point genotyping was performed using real-time PCR to type the SNPs. Allele discrimination module implemented in real-time PCR was utilized to call the genotypes based on fluorescence intensity recorded for each of the two alleles. The genotyping system KASP is a homogeneous, fluorescent, end-point genotyping technology. KASP genotyping assays are based on competitive allele-specific PCR and enable bi-allelic scoring of SNPs and insertions and deletions at a specific locus. Details of SNP name of the gene, chromosome location, alleles at each locus, genic region, synonyms/non-D synonyms, and amino acid change are presented in The quantitative real-time PCR machine  was utilized for KASP genotyping assay.IS), and testing for Hardy–Weinberg equilibrium (HWE) were estimated using package for elementary analysis of SNP data software program [Basic diversity indices, namely, gene frequencies, observed heterozygosity, expected heterozygosity, and inbreeding coefficient  in Vembur sheep. The size and sign of FIS reflect the deviation from HWE of the genotypes such that when FIS is zero the locus is in HWE, and when FIS is positive, there is a deficiency in heterozygotes. Negative FIS values indicate that the level of heterozygosity is higher than its expectation from HWE [IS in each sheep population of small size was very low. Studies have found that small populations exhibit high inbreeding. Some FIS values were high, for example, in Kilakarsal SNP locus, TLR10_292_CG and TLR10_82_CT showed a value of 0.241, and in Vembur SNP locus, the TLR3_340_CT and TLR3_370_AG showed a value of 0.255. This reflects a high frequency of a particular allele among homozygotes.The Fent loci . The ovefrom HWE . The aveIS values across 20 SNP loci were found to be positive in Kilakarsal sheep, except for TLR5_1578_CT and TLR9_1769_CT, whereas, in Vembur sheep population, the estimated FIS values were found to be positive only in TLR3_340_CT, TLR3_370_AG, TLR5_1578_CT, TLR9_1769_CT, and TLR9_2099_CT loci. Positive values for FIS indicate decrease of heterozygous in the population and negative FIS values reveal an increase in heterozygosity.The estimated FThe numbers of SNP loci deviating from HWE at different TLR gene SNP loci in Kilakarsal and Vembur sheep are presented in In this study, the SNP markers within five TLR genes  were utilized for genotyping of Kilakarsal and Vembur sheep through KASP-based end point genotyping. In the similar previous studies, a total of 30 SNPs in sheep major histocompatibility complex Class II and Class III regions described using KASP PCR, all SNPs exhibited Hardy–Weinberg proportions in the sheep population studied .Among 25 SNPs studied, 92% were found to be polymorphic and 8% were monomorphic in the Kilakarsal and Vembur sheep populations. The SNP TLR10_1180_AG was monomorphic (in one form) in Kilakarsal but polymorphic (more than one form) in Vembur sheep populations. A monomorphic site is one site in which all the individuals have the same form of genotype and these markers should be excluded from analysis because it gives no information. In a similar studies, out of 14 non-synonymous SNPs studied in 22 sheep breeds of different countries including Madras Red, Mecheri, and Pattanam sheep breeds of Tamil Nadu, only three of these SNPs  were found to be polymorphic, whereas the remaining 11 were monomorphic in those populations . ExaminaHeterozygosity is a measure of genetic variation within a population. High heterozygosity values for a breed may be due to long-term natural selection for adaptation to the mixed nature of the breeds or historic mixing of strains of different populations. A low level of heterozygosity may be due to isolation with the subsequent loss of unexploited genetic potential. Locus heterozygosity is related to the polymorphic nature of each locus. A high level of average heterozygosity at a locus could be expected to correlate with high levels of genetic variation at loci with critical importance for adaptive response to environmental changes . In thisIS) was 0.107 ranging from −0.005 to 0.241 in Kilakarsal sheep breed and −0.047 ranging from −0.005 to 0.255 in Vembur sheep breed. A positive low FIS values confirming that a high deficit of heterozygotes in these breeds, and if the FIS is zero, then the locus is considered to be in HWE, and when FIS is positive, there is a deficiency in heterozygotes and the amount of heterozygous offspring in the population will decrease, usually due to inbreeding and the mating is non-random. A number of factors such as null alleles, nature of locus, and inbreeding may lead to deficiency of heterozygotes. A negative FIS value reveals an increase in heterozygosity which in turn reflects outbreeding and wide genetic variability as a result of admixture of population [IS values across 23 SNP loci were found to be positive in Kilakarsal sheep except for TLR5_1578_CT and TLR9_1769_CT, whereas, in Vembur sheep population, estimated FIS values found to be positive in TLR3_340_CT, TLR3_370_AG, TLR5_1578_CT, TLR9_1769_CT, and TLR9_2099_CT.The overall mean estimated inbreeding coefficient  [IS value of 0.024 has been reported in indigenous sheep breeds of China [Similar result was reported with the global Fmil Nadu  and the ectively . The glo (0.123) , Nigeria (0.123) , and Bul (0.123) . In contIS values, namely, TLR10_292_CG and TLR10_82_CT, showed a value of 0.241 in Kilakarsal sheep, and in Vembur SNP locus, TLR3_340_CT and TLR3_370_AG showed a value of 0.255 and this reflects a high frequency of a particular allele of homozygotes  due to heterozygosity deficit. However, TLR10_82_CT and TLR10_292_CT loci in Vembur sheep populations showed a significant deviation (p<0.05) due to heterozygosity excess. Other SNP loci did not deviate from HWE (p>0.05) .This observation is in accordance with the previous reports where the mean number of loci deviating from HWE was 7.6, 5, 4, 7.4, and 6.3 in South Asian, Southeast Asian, Southwest Asian, European, and South American sheep, respectively, and 6, 4, and 7 in Mecheri, Madras Red, and Pattanam sheep breeds, respectively, and Hamdani sheep breed was found to be in equilibrium at panel of 41 SNP loci studied including TLR SNPs . In a stIS was 0.107 in Kilakarsal sheep which revealed shortage of heterozygotes (11%) and excess of homozygotes (89%), and a number of factors such as null alleles and nature of locus may lead to deficiency of heterozygotes. In Vembur sheep, 18 loci revealed negative FIS values indicating the absence of inbreeding at these loci. The majority of the loci under investigation showed significant departure from HWE which might be due to both the systematic and dispersive forces operating in the population. This study on the genetic diversity analysis of the Kilakarsal and Vembur sheep breeds revealed considerable genetic variation within the breeds and it can be utilized to improve desirable traits. The genetic information of the markers has been used as a criterion of indirect selection for genetic improvement of a quantitative trait and therefore could be useful for future breeding and in situ and ex situ conservation programs.The SNP markers within five TLR genes  utilized for genotyping in this study were highly polymorphic in Kilakarsal and Vembur breeds of sheep which indicated the high utility of TLR SNP loci used in this study for genetic diversity studies. This high level of average heterozygosity indicated the high levels of genetic variation at the examined loci. The overall mean FThe work was carried out by RS as part of Ph.D., Research Program, and NM, AKT, RS, GP, TPJ are the members of advisory committee. All authors read and approved the final manuscript.


Synthesis of thyroid hormones and regulation of their metabolism involve free radicals that may affect redox balance in the body. Thyroid disorders causing variations in the levels of thyroid hormones may alter cellular oxidative stress. The aim of this study was to measure the antioxidant activities and biomarkers of oxidative stress in serum and red blood cells (RBC) of patients with benign and malignant thyroid disorders and to investigate if changes in the antioxidant activities in these patients were linked to alterations in genes encoding the antioxidant enzymes.n = 18), follicular thyroid adenoma (FTA) (n = 7), papillary thyroid cancer (PTC) (n = 10), and follicular thyroid cancer (FTC) (n = 6). Serum and RBC of patients were analysed for antioxidant activities, antioxidant enzymes, and biomarkers of oxidative stress. Alterations in genes encoding the antioxidant enzymes were analysed using whole exome sequencing and PCR–DNA sequencing.Forty-one patients with thyroid disorders from University of Malaya Medical Centre were recruited. They were categorised into four groups: multinodular goitre (MNG) (Patients with thyroid disorders had significantly higher serum superoxide dismutase (SOD) and catalase (CAT) activities compared to control, but had lower activities in RBC. There were no significant changes in serum glutathione peroxidase (GPx) activity. Meanwhile, GPx activity in RBC was reduced in PTC and FTC, compared to control and the respective benign groups. Antioxidant activities in serum were decreased in the thyroid disorder groups when compared to the control group. The levels of malondialdehyde (MDA) were elevated in the serum of FTA group when compared to controls, while in the RBC, only the MNG and PTC groups showed higher MDA equivalents than control. Serum reactive oxygen species (ROS) levels in PTC group of both serum and RBC were significantly higher than control group. Whole exome sequencing has resulted in identification of 49 single nucleotide polymorphisms (SNPs) in MNG and PTC patients and their genotypic and allelic frequencies were calculated. Analyses of the relationship between serum enzyme activities and the total SNPs identified in both groups revealed no correlation.Different forms of thyroid disorders influence the levels of antioxidant status in the serum and RBC of these patients, implying varying capability of preventing oxidative stress. A more comprehensive study with a larger target population should be done in order to further evaluate the relationships between antioxidant enzymes gene polymorphisms and thyroid disorders, as well as strengthening the minor evidences provided in literatures. However, under normal redox balance, the ROS are removed by antioxidant systems in the body, hence limiting oxidative damage. On the other hand, certain conditions such as inflammation of the thyroid gland as well as tumour cell proliferation could alter the balance between ROS and antioxidant levels  in favouoduction .Several studies have shown the link between oxidative stress and cancer, including thyroid cancer . AlthougAs the metabolic effects of thyroid hormones are linked to oxidative stress, genetic variations in antioxidant enzymes may also have an impact on the endogenous antioxidant defence system . There in = 9) and PTC (n = 9) groups as they are the most common form of thyroid disorders. To minimise cost, gene alterations detected in the MNG and PTC patients were reanalysed in patients with FTC (n = 5); a less common form of thyroid cancer, by PCR–DNA sequencing analysis.Therefore, the aim of this study was to analyse the antioxidant status in patients with thyroid disorders by measuring antioxidant activities and parameters of oxidative stress including antioxidant enzyme activities, lipid peroxidation and levels of ROS. In addition, nucleotide alterations in genes encoding the antioxidant enzymes, superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT), were screened in order to assess the underlying genetic cause that may be linked to thyroid disorders. For the screening of gene alterations, whole exome sequencing (WES) analysis were performed in both MNG . The patients were categorised into four groups according to their diagnosis of thyroid gland disorders; MNG, FTA, PTC, and FTC. Their blood samples were taken prior to surgery, for antioxidant analyses and thyroid function tests. In addition, a total of 14 healthy subjects served as control group. Blood samples of the patients and control group were sent to Clinical Diagnostic Laboratory (CDL) of UMMC for the analyses of TFT using ADVIA Centaur CP Immunoassay System  .n = 9) and PTC (n = 9) groups were included. For comparison, genes alterations detected in the MNG and PTC patients were also analysed in the FTC patients, using PCR–DNA sequencing. Tissue samples were extracted from the nodule and their clinical diagnoses were confirmed by histopathological examination via H&E stain. The tissue samples were kept fresh-frozen prior to DNA extraction.Meanwhile tissue samples of thyroid glands taken from patients with thyroid disorders were used for analyses of genes encoding the antioxidant enzymes. For the WES analysis, selected patients from the MNG . Plasma was isolated and the RBC was washed with phosphate buffered saline (PBS) three times, followed by centrifugation at 1,000g for 5 min at 4 °C. Double distilled water was added into the tube, and the RBC was allowed to lyse for 2 h at 4 °C. After the incubation, RBC was centrifuged at 2,000g for 15 min at 4 °C. The supernatant, containing lysed RBC, and the serum samples were stored at −80 °C until further analysis.Blood (3 mL) was drawn by venepuncture from the subjects. Serum was collected into BD Vacutainer® SST tube and was centrifuged at 2,236Superoxide dismutase enzyme activity was measured using commercially available SOD Assay Kit  according to the manufacturer’s protocol. Serum and RBC samples were diluted to 1:5 and 1:100 with sample buffer, respectively. The measurement was based on the activity of SOD in the sample to cause dismutation of the superoxide radicals generated by xanthine oxidase and hypoxanthine. The absorbance was read at 450 nm. One unit of SOD is defined as the amount of enzyme needed to exhibit 50% dismutation of the superoxide radicals. The analyses were performed in triplicate.2O2. The assay is based on the reactions of CAT with methanol in the presence of H2O2. The absorbance was read at 550 nm using a plate reader. CAT activity was expressed as unit per millilitre whereby one unit is defined as the amount of enzyme that will cause the formation of 1.0 nmol of formaldehyde per minute at 25 °C. The assays were done in triplicate.Catalase enzyme activity was measured using commercially available CAT Assay Kit  according to the manufacturer’s protocol. The assay utilised the peroxidatic function of the enzyme. Formaldehyde served as the standard, and samples were diluted with sample buffer in a ratio of 1:5 for serum, and 1:1,000 for RBC prior to the assay. The reactions were initiated with the addition of H+ per minute at 25 °C. All analyses were performed in triplicate.Glutathione peroxidase enzyme activity was measured using commercially available GPx Assay Kit , according to the manufacturer’s protocol. Serum and RBC samples were diluted into 1:10 ratio with sample buffer. GPx activity was measured indirectly by a coupled reaction with glutathione reductase, and the reactions were initiated by the addition of cumene hydroperoxide. The absorbance was read every minute at 340 nm for 5 min using a plate reader to obtain five time points. GPx activity was expressed as unit per millilitre whereby one unit is defined as the amount of enzyme that will cause the oxidation of 1.0 nmol of NADPH to NADP+ radical cation by incubating the solution in the dark at room temperature for 16 h. The solution was then diluted with methanol to obtain an absorbance of 0.70 ± 0.02 at 415 nm. Four microliters of samples (serum or RBC) were added to 400 μL of ABTS reagent and the mixture was incubated in the dark for 10 min. After incubation, the sample was centrifuged at 1,643g for 1 min, and the absorbance of the supernatant was read at 415 nm. Trolox was used as standard and was similarly analysed as above. Trolox standard curve was plotted with a concentration range of 0, 0.5, 1, 1.5, and 2 mM. The final results were expressed as Trolox equivalent antioxidant capacity (TEAC).The 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) (ABTS) radical scavenging activity in the blood samples was measured based on the method of Dichlorofluorescein diacetate (DCF-DA) was used as the fluorescence-based probe for the detection of ROS in the blood samples. In the assay, 5 μL of sample and 100 μL of DCF-DA reagent (20 μM) were added into a black 96-well plate and was incubated for 30 min at room temperature. Fluorescence reading was taken with the excitation and emission wavelengths set at 485 and 530 nm, respectively (Bioplex 200 Systems) . All reactions were done in triplicate. Results were expressed as relative fluorescence unit.4) in 80 mL of double-distilled water. Sample (50 μL) was mixed with 250 μL of TBARS reagent and boiled at 90 °C for 20 min. After cooling on ice, the mixture was centrifuged at 309g for 10 min at 25 °C. Absorbance of the supernatant was read at 532 nm . A standard curve was constructed using 1,1,3,3-tetraethoxypropane (TEP) (0–100 ng/μL). Protein content of the sample was measured using Bradford protein assay, with bovine serum albumin (BSA) as the standard. The levels of lipid peroxidation were expressed as nanomole MDA equivalents per milligram of protein. All analyses were performed in triplicate.Thiobarbituric acid reactive substances (TBARS) assay was used in estimating lipid peroxidation in the blood samples. Malondialdehyde (MDA) as the by-product of lipid peroxidation, reacted with thiobarbituric acid (TBA) to form TBARS. TBARS reagent was prepared by mixing 0.3 g of TBA, 12 g of trichloroacetic acid (TCA) and 1.04 mL of 70% perchloric acid (HClOn = 9) and PTC (n = 9), were sent to BGI Tech Solutions, Shenzhen, China for WES analysis. The protocols were based on the Agilent SureSelect XT Target Enrichment System for Illumina Paired-End Sequencing which involved the following steps; library preparation, hybridisation and capture, and sequencing. The raw image files obtained from the sequencing were processed using the Illumina Software 1.7 via base-calling, and the raw data was saved in the FASTQ format. The quality control (QC) step was performed using FastQC (www.bioinformatics.babraham.ac.uk). Data filtering was applied to the FASTQ data and the sequencing reads were aligned to the Reference Genome of human genome build37 (hg19) using BWA software (http://bio-bwa.sourceforge.net). The SOAPsnp software was used for SNP calling (http://soap.genomics.org.cn) and following identification of SNPs, an in-house AnnoDB software (AnnotationDB) was used for RefGene annotation.DNA samples of the patients; MNG (n = 5) patients using real-time polymerase chain reaction (qRT-PCR). The list of genes with their respective SNP primers used for the qRT-PCR are listed in TaqMan SNP Genotyping Assays  were used to amplify specific SNP alleles in purified DNA samples of FTC  version 24.0  was used to perform the following statistical analyses. One-way analysis of variance (ANOVA) was used to compare the mean differences of serum and RBC antioxidant analyses between the groups of subjects for all normally distributed dataset whilst the non-parametric Kruskal–Wallis test was used, if otherwise. The Tukey post-hoc test was used for multiple comparisons of specific sample pairs when equal variances were assumed whilst Dunnett’s T3 post-hoc test was used when unequal variances were assumed. Levene’s test of homogeneity of variances was used to verify the assumption. Spearman’s correlation coefficient test was used to compare the association between SNPs and antioxidant enzyme activities among MNG and PTC groups of subjects.p-values less than 0.05 were considered significant.Hardy–Weinberg (HW) exact test was used to test the genetic equilibrium of the SNPs using Genepop (version 4.2) population genetics software package . The disp > 0.05).p < 0.05)  . Amongst < 0.05) . Another < 0.05) . ROS levp < 0.05). On the contrary, there were no significant changes in the MNG and FTA groups when compared to control (p > 0.05) ( > 0.05) .Analysis of ABTS radical scavenging activities indicated no significant changes among the thyroid disorder groups with that of control . Howevern = 9) and PTC (n = 9) revealed 11 SNPs in SOD gene, 28 SNPs in GPX gene, and 10 SNPs in CAT gene , eight PTC patients and in all FTC patients (n = 5). Meanwhile rs2536512 was detected in six PTC patients and only in three of MNG and FTC patients. For the GPX gene, four SNPs are found to be common; rs1970951, rs381852, rs3763011, and rs8177447. Eight MNG and all PTC patients appeared to have rs1970951 and rs381852, whereas the former and the latter SNPs was detected in four and three of the FTC patients, respectively. Four FTC and seven patients from each of the MNG and PTC groups have rs3763011. The same FTC and MNG patients as well as all PTC patients have rs8177447. The CAT SNPs; rs769217 and rs769218 were shared by the same patients of MNG (n = 4) and PTC (n = 7). Meanwhile all FTC patients (n = 5) have rs769217, and only four FTC patients have rs769218 . The allelic frequency distribution of the eight SNPs (HW—p > 0.05) in our studied populations was then compared with the healthy subjects of South Asian (SAS) and East Asian (EAS) countries, taken from the database of 1000 Genomes Project Phase 3 allele frequencies  and rs1970951 (p = 0.05) among the MNG, PTC, and FTC groups  assay measures the overall antioxidant capacity in the samples. The assay relies on the ability of antioxidants in the serum sample to scavenge ABTS radicals and inhibit oxidation in comparison to Trolox as the standard . Serum m2−), hydroxyl radicals (OH−), nitric oxide (NO), and peroxyl radical (ROO−). Their production may be influenced by antioxidant enzymes including SOD, CAT, and GPx. SOD enzyme causes dismutation of superoxide anion radical, leading to production of another radical, hydrogen peroxide (H2O2). CAT and GPx convert H2O2 into oxygen and H2O , while SOD3 is found in the extracellular elements (EC-SOD). SOD2 has manganese as a cofactor and is localised in the mitochondria (Mn-SOD) (GPX1–GPX8) which are localised mostly in the cytosol, nucleus, and extracellular space. CAT enzyme, encoded by CAT gene, is localised in peroxisomes  . GPx enzoxisomes . Whole eSOD2 and WTAP genes of chromosome 6q25.3. To date, there is no reported study on the association of this SNP with disease development. rs2536512 is the SNP of SOD3 gene that is located on chromosome 4p15.2. It is a missense variant identified in exon 2 of SOD3 with the base change of G to A, and amino acid substitution of Alanine to Threonine (A58T). This SNP was reported to be associated with type 2 diabetes and hypertension . rs752779 was the most common SNP detected in the patients in this study. This SNP is located at the intergenic region of rtension . A previrtension . Althougrtension .GPX7 gene with a base change of T to C, maintaining the amino acid phenylalanine (F79F). There is no known link between this SNP with any diseases. rs381852 is a missense variant identified in exon 3 of GPX8 gene with the base change of A to G, and amino acid substitution of Lysine to Arginine (K182R). Meanwhile, both rs3763011 and rs8177447 are from GPX3 gene located at intron 1 and intron 4, respectively. CAT is the only enzyme without isoforms, and is encoded by the CAT gene. rs769217 is a synonymous variant in exon 9 of CAT gene with base change of C to T without the substitution of Aspartic acid, while C6 rs769218 is located at intron 1. There is also no known link between this SNP with any diseases.The rs1970951 SNP is a synonymous variant identified in exon 2 of n = 49) compared to the benign group (n = 55)  . However(n = 55)  and 5C, This study demonstrates that patients with thyroid disorders have lower antioxidant defence system, potentially predisposing them to oxidative stress. Additionally, different forms of thyroid disorders could influence the levels of antioxidant status in the serum and RBC of these patients, implying varying capability of preventing oxidative stress. No significant association was observed between serum enzyme activities and the total number of SNPs identified in this study. SNP-disease association study however, demonstrated that the genotypic  distributions of rs752779 and rs1970951 are markedly different among subjects with malignant thyroid disorders. The lack of association between other SNPs and thyroid disorders need to be validated in a larger sample size using appropriate controls subjects.10.7717/peerj.3365/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.3365/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.3365/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.3365/supp-4Supplemental Information 4Click here for additional data file.


Arabidopsis halleri individuals from nine populations using 20 microsatellite markers. Twelve of these markers were originally developed for Arabidopsis thaliana (cross-species markers) and eight for A. halleri (species-specific markers). We further characterized 2 million SNPs across the genome with a pooled whole-genome re-sequencing approach (Pool-Seq).Microsatellite markers are widely used for estimating genetic diversity within and differentiation among populations. However, it has rarely been tested whether such estimates are useful proxies for genome-wide patterns of variation and differentiation. Here, we compared microsatellite variation with genome-wide single nucleotide polymorphisms (SNPs) to assess and quantify potential marker-specific biases and derive recommendations for future studies. Overall, we genotyped 180 He) was not significantly correlated with genome-wide SNP diversity estimates (SNP-He and θWatterson) in A. halleri. Instead, microsatellite allelic richness (Ar) was a better proxy for genome-wide SNP diversity. Estimates of genetic differentiation among populations (FST) based on both marker types were correlated, but microsatellite-based estimates were significantly larger than those from SNPs. Possible causes include the limited number of microsatellite markers used, marker ascertainment bias, as well as the high variance in microsatellite-derived estimates. In contrast, genome-wide SNP data provided unbiased estimates of genetic diversity independent of whether genome- or only exome-wide SNPs were used. Further, we inferred that a few thousand random SNPs are sufficient to reliably estimate genome-wide diversity and to distinguish among populations differing in genetic variation.Our analyses revealed that estimates of genetic diversity and differentiation derived from cross-species and species-specific microsatellites differed substantially and that expected microsatellite heterozygosity  contains supplementary material, which is available to authorized users. Genetic diversity is essential for organisms to adapt to changing environmental conditions and is recognised as a key component of biodiversity e.g. , 2). Mic. Mic2]).−6 and 10−2 per locus and generation , thus vview see . In contview see . Knowledneration . Microsaneration . This maneration , 27. Addneration  and fragneration  potentiaDespite these potential caveats, a large number of studies has relied on microsatellite markers to estimate genetic diversity and genetic differentiation, not only within and among populations, but also among species e.g. , 10, 30), 3010, 3He) and SNP diversity in nuclear gene sequences have been reported at the population level in salmon [To date, only few studies have explored in detail to what degree microsatellite variation reflects genetic variation at other nuclear loci, and which genetic diversity estimator for microsatellites provides the most accurate prediction of genome-wide diversity. Positive but sometimes weak correlations between expected microsatellite heterozygosity , to compare estimates of genetic diversity and differentiation. Overall, we genotyped 180 individuals of A. halleri from nine natural populations using 20 microsatellite markers, which is above the average number of microsatellites typically used in population and conservation genetic studies [We used two types of microsatellite markers (markers developed for the same species and cross-species markers) as well as genome-wide SNP variation in the meadow rock cress,  studies . We comp studies , 51) andArabidopsis halleri is a perennial, insect-pollinated, strictly outcrossing and functionally self-incompatible herb [ble herb  with a wble herb . It growble herb , 55 and ble herb ). The spble herb  and has ble herb .A. halleri was sampled in south-eastern Switzerland and northern Italy  and mean number of alleles  per locus were quantified with Genetix 4.05 [Ar did not have to be corrected with a rarefaction approach. All population parameters were computed for three different marker sets including (i) all, (ii) the cross-species, and (iii) the species-specific microsatellite markers  in R 3.2 [He derived from cross-species and species-specific markers, we used a Pearson’s correlation test (function ‘cor.test’) in R. To test whether population-specific estimates of SSR-He obtained from the different microsatellite types differ [He medians and quantiles for each population, we inferred whether non-significant differences were caused by high variance. When the absence of significant differences between populations was obviously caused by overly high variances in the genetic diversity estimates computed per microsatellite marker and population, we interpreted this as variance bias. P-values were adjusted for multiple testing using Bonferroni correction.For every marker and population, we assessed the following population genetic parameters. The inbreeding coefficient e 2.0b23  using thh FreeNA . Pairwisop 4.2.2 , 66, whetix 4.05 . We consin R 3.2 . For thes differ , we usedhttp://hannonlab.cshl.edu/fastx_toolkit). The separately trimmed forward and reverse reads were then re-synchronized to pairs with an in-house perl script. Only paired sequences were used for further analysis [Pooled next-generation sequencing (Pool-Seq) has been shown to produce accurate population-specific allele frequencies , 51, 70.analysis .A. halleri, reads were mapped to the A. thaliana reference genome  usinexcluded . All ambexcluded . SNPs weexcluded , 76).θWatterson), an estimator that takes into account the number of segregating sites to estimate the population mutation rate. θWatterson was calculated on a gene-by-gene basis (only exons) for each population using the gene and exon annotation of TAIR10 . The penes.gff;  was usedHe), taking all SNPs into account, not only those located in exons. Mpileup files were synchronized and filtered for base quality (Q20) with the perl script ‘mpileup2sync.pl’ of PoPoolation2 [He asn is the number of SNPs, and pi is the minor allele frequency of the ith allele. This approach assumes Hardy–Weinberg equilibrium within populations.Second, we calculated genome-wide SNP-based expected heterozygosity  with an average of zero and the same standard deviation as observed in the real data of each population.To infer potential demographic events that could strongly influence genetic diversity within populations, we calculated exome-wide Tajima’s oolation . It is soolation , which iion size , 81. To FST) were calculated with ‘fst-sliding.pl’ in PoPoolation2 [FST were calculated using the same parameters as mentioned above for the estimates of SNP-He as explained in detail in Fischer et al. [Estimates of pairwise population genetic differentiation  and expected heterozygosity (SSR-He). Estimates of genome-wide SNP diversity were derived from exon sequences (θWatterson) and genome-wide SNP expected heterozygosity (SNP-He). Further, to account for possible confounding effects due to linkage, associations were also tested for a subset of SNPs, consisting of every 50th SNP.To explore associations between estimates of genetic diversity derived from microsatellites and SNPs, we performed Pearson’s correlations (‘cor.test’ in R) of microsatellite-based allelic richness (FST derived from genome-wide SNPs and (i) all, (ii) cross-species, (iii) and species-specific microsatellite markers. The same analysis was used to assess correlations between values of FST derived from species-specific and cross-species microsatellite markers. All analyses were performed with 1001 permutations using Ecodist 1.2.7 [t-tests implemented in R to quantitatively evaluate whether He and FST derived from microsatellite markers and SNPs significantly differ.We performed Mantel tests to check for correlations between values of st 1.2.7  in R. FiHe with the aim to infer the required number of randomly selected and unlinked SNP markers to obtain accurate estimates of genetic diversity and to reliably rank populations according to their genetic diversity (e.g. for CU identification). Thus, each population was resampled for the same k random SNP markers drawn from the pool of more than 2 million SNPs. For each value of k varying between 100 and 400,000, we created 1000 random subsamples of k SNP markers . We then computed the mean expected heterozygosity (He) and 95% confidence intervals for each value of k observed in each of the 1000 subsamples. Obtained results were used to draw curves representing the variation of the estimated He as a function of genotyping effort in each population. We then identified the number of SNPs for which the upper and lower confidence intervals for expected heterozygosity (SNP-He) fell below ±0.01, ±0.005, and ±0.001.We used a down-sampling procedure to estimate SNP-A. halleri individuals from nine populations. We excluded marker ah59 from further analyses, because it deviated significantly from Hardy–Weinberg equilibrium and exhibited an estimated null allele frequency of 10%  ranged between 2.2 and 3.1 per population, with an average of 2.71 (±0.29 SD), and expected heterozygosity (SSR-He) ranged from 0.025 to 0.717 per microsatellite marker. Further details are given in Table Twenty microsatellite loci were initially used to characterise 180 A. thaliana, successfully amplified in A. halleri. Ar at cross-species microsatellite markers was 2.2 alleles per marker and population and thus significantly lower than Ar at species-specific markers . Without Bonferroni correction, only populations Aha11 and AhaN3 differed significantly in their estimates of He ; species-specific  and cross-species microsatellites markers  did not deviate significantly from each other .Average pairwise A. thaliana reference genome (TAIR10), from which organellar DNA was excluded. The average coverage per site, after filtering with the defined thresholds, was 60.7× with a range of population-wise coverage of 52.7 to 69.3 × .The Illumina sequencing yielded a total of 1,247,939,483 100-bp paired-end reads corresponding to 249,587,896,600 nucleotides. After quality filtering and trimming, 1,197,105,373 paired-end reads were mapped to the FST, and 2,064,681 bi-allelic SNPs, which were used for the calculation of population-specific SNP-He. All populations had even population-specific allele frequency distributions , but lower when only cross-species markers were used , but slightly weaker than non-ranked comparisons.In marked contrast to heterozygosity, θWatterson and SNP-He, were highly correlated , and estimates of SNP-He were calculated from more than two million SNPs across the entire genome. In fact, θWatterson estimates inferred from introns and intergenic regions were highly correlated to estimates of exon-based θWatterson  fell below ±0.01, ±0.005, and ±0.001 with 1000, 4000 and 93,000 random SNPs, respectively. To accurately and consistently rank all A. halleri populations according to their genome-wide diversity , 300,000 SNPs were required , an estimator of genetic diversity that is widely used and reported in microsatellite studies, and genome-wide SNP diversity  was a much better proxy for genome-wide diversity  might not be good surrogates for genome-wide diversity in outbreeding species. Further, we found that the marker-specific variation of SSR-He is too high to distinguish populations based on their levels of genetic diversity , one of the simplest estimators of genetic diversity available. Ar was significantly correlated with genome-wide SNP diversity in our study  should cause lower FST values than low-diversity markers like SNPs [FST [FST continue to be frequently used to infer indirect estimates of gene flow and migration, even though estimates of gene-flow should not be derived from FST [FST estimates has a major impact on comparative studies of the divergence of quantitative traits, known as QST–FST comparisons, because the inference of the role of natural selection and genetic drift as causes of population genetic differentiation in complex polygenic traits is biased [A different pattern emerges for estimates of population genetic differentiation in terms of pairwise NPs Fig. . Similarike SNPs . HoweverPs [FST . Overallrom FST . Furthers biased .While our results suggest that microsatellites should not be used for estimating genome-wide heterozygosity, we emphasize that microsatellites remain useful molecular markers for other applications. For example, microsatellites perform very well in genetic stock identification or paternity analysis owing to their high variability , 93–96 aθWatterson, intronic and intergenic θWatterson, or genome-wide SNP-He, were highly correlated with each other and led to the same ranking of populations  than on SNP-He or SNP nucleotide diversity estimates [A. halleri, most likely because the small-scale linkage effects are compensated by the large numbers of unlinked SNPs. Thus, our SNP-He estimates based on a subset of putatively unlinked SNPs were nearly identical to the estimates inferred for all SNPs, see Fig. In contrast to microsatellite-derived data, estimates of genome-wide diversity inferred from whole-genome re-sequencing data, e.g. exome-wide He Fig.  might bestimates , as rareHe) to 93,000 SNPs (±0.001 SNP-He). This number is in the range of SNPs that can be inferred with standard RADseq protocols also in non-model organisms [de novo through NGS approaches (to prevent ascertainment bias) are highly suitable to distinguish, for example, between populations differing in genetic diversity and may therefore support decision-making in conservation management. A further advantage of genome-wide SNP data is that they not only allow one to estimate neutral genetic diversity, but also to identify adaptive genetic variation ..He) to A. halleri reveals that genetic diversity estimated from microsatellite markers, notably expected heterozygosity, may not adequately reflect genome-wide genetic diversity estimated from single-nucleotide polymorphisms and may therefore be a poor proxy for genome-wide estimates of genetic diversity. Possible causes include the limited number of microsatellite markers used, marker ascertainment bias, as well as the high variance in microsatellite-derived diversity estimates. Interestingly, microsatellite allelic richness (Ar) was found to be a reasonable proxy for genome-wide diversity, but the absolute ranking of populations was still inconsistent. Estimates of genetic differentiation (FST) among populations derived from microsatellites were consistently higher than SNP-based estimates but were significantly correlated with the latter.This case study in the perennial and outcrossing plant Our results do not question the usefulness of microsatellites per se, but point to research questions for which NGS-derived SNPs may be better suited than microsatellite markers, given the availability of robust and cost-effective SNP genotyping approaches based on high-throughput sequencing. As a consequence, we recommend using genome-wide analyses of SNP diversity when the inference and comparison of genetic diversity within and among populations and species is the goal of a study. A few thousand NGS-derived SNPs are sufficient for this purpose and this number of unbiased SNPs can nowadays easily been obtained also for non-model species, for example by using a reduced representation sequencing approach such as RADseq .