Genomic prediction of breeding values from dense single nucleotide polymorphisms (SNP) genotypes is used for livestock and crop breeding, and can also be used to predict disease risk in humans. For some traits, the most accurate genomic predictions are achieved with non-linear estimates of SNP effects from Bayesian methods that treat SNP effects as random effects from a heavy tailed prior distribution. These Bayesian methods are usually implemented via Markov chain Monte Carlo (MCMC) schemes to sample from the posterior distribution of SNP effects, which is computationally expensive. Our aim was to develop an efficient expectation–maximisation algorithm (emBayesR) that gives similar estimates of SNP effects and accuracies of genomic prediction than the MCMC implementation of BayesR (a Bayesian method for genomic prediction), but with greatly reduced computation time.emBayesR is an approximate EM algorithm that retains the BayesR model assumption with SNP effects sampled from a mixture of normal distributions with increasing variance. emBayesR differs from other proposed non-MCMC implementations of Bayesian methods for genomic prediction in that it estimates the effect of each SNP while allowing for the error associated with estimation of all other SNP effects. emBayesR was compared to BayesR using simulated data, and real dairy cattle data with 632 003 SNPs genotyped, to determine if the MCMC and the expectation-maximisation approaches give similar accuracies of genomic prediction.We were able to demonstrate that allowing for the error associated with estimation of other SNP effects when estimating the effect of each SNP in emBayesR improved the accuracy of genomic prediction over emBayesR without including this error correction, with both simulated and real data. When averaged over nine dairy traits, the accuracy of genomic prediction with emBayesR was only 0.5% lower than that from BayesR. However, emBayesR reduced computing time up to 8-fold compared to BayesR.The emBayesR algorithm described here achieved similar accuracies of genomic prediction to BayesR for a range of simulated and real 630 K dairy SNP data. emBayesR needs less computing time than BayesR, which will allow it to be applied to larger datasets.The online version of this article (doi:10.1186/s12711-014-0082-4) contains supplementary material, which is available to authorized users. Genomic prediction uses information from high-density genetic polymorphisms, such as single nucleotide polymorphisms (SNP) panels, to predict the genetic merit of individuals for quantitative traits. Selection based on these estimated breeding values could substantially increase the rates of genetic improvement for quantitative traits in animal and plant species . ImplemeSeveral methods, which differ in the assumed prior distribution of SNP effects, have been proposed to estimate SNP effects for genomic prediction. The prior assumption that SNP effects are all drawn from the same normal distribution results in the statistical method called best linear unbiased prediction (BLUP). BLUP for genomic prediction can be implemented using two equivalent models . Either In dairy cattle routine genomic evaluations, different genomic prediction methods have been implemented by different countries and organisations . AccordiThere have been a number of proposals to reduce the computing time required to arrive at satisfactory estimates of the SNP effects from Bayesian methods e.g. -30). The. The30])Our aim in this paper was to develop a fast EM counterpart to MCMC BayesR (emBayesR). BayesR assumes that SNP effects are drawn from a mixture of normal distributions, one with zero variance (and hence zero effects). BayesR shares some of the advantages of BayesB, in that SNP effects can be zero, moderate, or large, but is more computationally efficient since it can be implemented with Gibbs sampling . In BayeOur EM algorithm retains the BayesR model assumption that SNP effects are assumed to be derived from four different normal distributions, but requires much less computing time than BayesR. It also differs from other EM methods by estimating the effect of each SNP while accounting for the errors in the estimates of all other SNPs. It does this by treating the combined effect of the other SNPs as a residual breeding value, and approximating its prediction error variance from a GBLUP prediction. To compare speed and accuracy of prediction of emBayesR with that from BayesR, we used both a simulated dataset and a real dataset on 630 K SNPs for dairy cattle.In this section, we first describe the model of BayesR  for genomic prediction and second, an EM algorithm named emBayesR. Finally, the 10 K simulated data and 630 K real dairy data that were used to evaluate the performance of emBayesR, are described.The linear model for phenotypes is:y is a n × 1 vector of phenotypic records ; 1n is a n x 1 vector of 1 s, μ is the population mean; Z is a n × m design matrix with elements xi is the n × 1 vector of genotypes for the ith SNP , and pi is the allele frequency of each SNP i (m is the number of SNPs); e is a n × 1vector of random normal deviates, g is a m × 1 vector of SNP effects.where, For convenience, polygenic effects were not included in the model but they can be readily added .g) are drawn from a mixture of four normal distributions Pr = {Prk|k = 1, 2, 3, 4}. Variances used were α = T, which is a 4 × 1 vector of the pseudo-counts of the number of SNPs in each distribution. Therefore, the BayesR model has two fixed parameters as input: α (the prior for Pr).BayesR  assumes i, there is a latent binary variable bik (bik = 0 or 1) that indicates whether or not the effect of SNP i follows the normal distribution with variance For each SNP gi) conditional on variable bik is:Then, the prior distribution of each SNP effect (δ(gi) denotes the Dirac delta function with all probability mass at gi = 0.where p conditional on Pr is:Then, the joint distribution ĝ) and the proportion of SNP effects in each distribution Zg by Zĝ as accurately as possible. The best predictor for gi would be gi = E(gi|y), but we approximated this by estimating ĝi by the value of gi that maximises the posterior probability ,Pr, μ, and y. In the following, we first deal with estimating ĝi and then return to An EM algorithm is applied to BayesR to obtain estimates of parameters, including SNP effects , and use the EM algorithm to integrate them out of the posterior distributions. Here, u is the combined effects of all other SNPs except the current SNP, i.e. u = Zg − Zigi, and the other vector bi = {bik|k = 1, 2, 3, 4} is for indicator variables that determine which normal distribution each SNP effect is derived from, as described above. Then Equation (1) can be re-written as:For estimation of The full posterior distribution with the missing data, Wherey* and u, and is the likelihood of the data given This can be re-written as:u, b). Only the second term (6b) in the equation bi. Therefore:In the E-step of emBayesR, we will take expectation of the log posterior function of Equation (6) over the missing data  ' (y* − u − Zigi) in the first term of Equation (6a) is related to u. To calculate the expectation of Equation (6a) over u, we only need to take the expectation of Q over u. Applying Searle’s expectation rule /h2, where σ2TBV is the variance of TBV in the population. Thus, we generated four datasets named HD_Mix_45 (five replicates following the mixture data model with heritability 0.45), HD_Mix_10 (five replicates following the mixture data model with heritability 0.10), HD_One_45  and HD_One_10 . Each replicate entailed sampling new SNP effects and generating new phenotypes.Simulated data were used to determine how close the genomic prediction accuracy of emBayesR was to that of BayesR. The simulated dataset described in  was usedneration , based oh2 of 0.45, there were 2500 individuals in the reference set and 2500 in the validation set. With an h2 of 0.1, there were 3750 individuals in the reference set and 1250 in the validation set. Accuracies were the correlations between GEBV and TBV.To compare prediction accuracies and computing efficiencies of emBayesR, BayesR, GBLUP and fastBayesB, 5000 individuals were randomly separated into reference sets and validation sets. With an DGAT1 gene affects fat % , , Pr, mayh2 = 0.45, and 5% when h2 = 0.1  versus the mode (Equation 8a) in the simulated data Table . As showIn the simulation data, the accuracy of genomic prediction with emBayesR was the same as with BayesR when heritability was 0.10, but 1% lower when heritability was 0.45 Table . HoweverAccuracies of genomic prediction with BayesR, GBLUP, FastBayesB, and emBayesR on the 630 K dairy data are in Table We compared the speed of emBayesR with BayesR and fastBayesB using three criteria: the time complexity of each iteration , the number of iterations to convergence , and total computing time required with the 630 K real data.O(nm), which is the same as with the MCMC method for BayesR and with ICE iterations for fastBayesB, and with the nonlinear A method of VanRaden [First, as mentioned in the method section, the time complexity for emBayesR is VanRaden  and SNP_VanRaden .Second, for BayesR, the accuracy of prediction exceeded 0.61 at 20 000 iterations, and did not improve with a larger number of iterations, as shown in Figure Finally, the overall computing times for emBayesR, BayesR and fastBayesB with the same implementation (each trait on one processor) were compared Figure . The algPr with emBayesR and BayesR to the underlying data model. Three underlying models for QTL effects were investigated: (1) an equal mixture of three non-zero normal distributions in HD_Mix; (2) all QTL effects follow a normal distribution in HD_One; and (3) an unknown model of QTL effects in the 630 K real data.In this final Results section, we investigate the sensitivity of the accuracy of genomic prediction and estimates of Pr, emBayesR generally had somewhat poorer agreement with the underlying data model than BayesR (Table emBayesR and BayesR gave higher accuracies than GBLUP for the HD_Mix model data (M45_2), while for the HD_One data, the advantage of emBayesR and BayesR was smaller than that of GBLUP Table , as mighsR Table , especiaPr and accuracy of genomic prediction than BayesR and GBLUP (accuracy only for the later comparison)  Tables . One conkth distribution .Genomic prediction with non-linear Bayesian methods, including BayesR, can be more accurate than GBLUP in some situations, such as when QTL with moderate to large effects segregate ,3, but aPr with emBayesR tended to have more SNP effect estimates in the smallest non-zero distribution than BayesR; emBayesR shrunk small SNP effects towards 0 somewhat more than BayesR; and the accuracy of emBayesR predictions was approximately 0.5% lower than the accuracy of BayesR. Our EM algorithm differed from the MCMC BayesR in several respects, which may explain these results. The EM algorithm estimates the SNP effect (gi) by the mode of the posterior distribution when the mixing proportions (Pr) and the error variance gi by the mean of the posterior distribution while Pr and gi as an estimate of gi, we found that it makes no discernible difference in prediction accuracy, as shown in Table Pr and gi.Results from BayesR and emBayesR differed in three ways, albeit to a small degree. Estimates of Pr with both BayesR and emBayesR were affected by the prior α but not to a large degree, considering that the simulated data contained only 50 causal mutations and the prior had little effect on the accuracy of genomic predictions. Results from using emBayesR with the simulated data indicate the algorithm was unable to consistently distinguish a SNP with no effect from a SNP with a very small effect. We would expect that, in data in which more causal mutations are segregating and with many more animals, estimates of Pr would be less sensitive to the prior.Bayesian estimates are sensitive to the prior if the data does not contain enough information to overwhelm the prior. Estimates of t-distributions or exponential distributions as priors for the SNP effects. These include EM-BSR [Other EM algorithms for genomic prediction have been described using thick-tailed e EM-BSR  and Faste EM-BSR , which ae EM-BSR , emBayese EM-BSR , em_BSR e EM-BSR , and Mixe EM-BSR , ignore e EM-BSR  could beOur results demonstrated the computing speed of emBayesR over the MCMC implementation of BayesR. The time complexity for emBayesR at each iteration is proportional to the number of markers and the number of records, as it is in the MCMC methods. However, much fewer iterations were required for the emBayesR SNP effects to converge than for BayesR to sample sufficiently from the posterior distributions of SNP effects to achieve maximum accuracy of genomic prediction. Specifically, compared with 20 000 iterations of MCMC sampling Figure , emBayesO(nm), with reported times similar to emBayesR. One difference between BayesR and the nonlinear A method is that SNP effects can actually be 0 with BayesR, whereas in nonlinear A, SNPs will always have a non-zero effect, although it may be very small. This difference between the algorithms apparently does not affect accuracies of prediction with the 630 K real data, although it may become more important with whole-genome sequence data, for which the number of variants is much larger. However, this is yet to be demonstrated.With high-density SNP data (630 K), the prediction accuracy of emBayesR and BayesR was greater than GBLUP only for yield traits. Similar results (an advantage of a Bayesian approach over GBLUP for yield traits only) were obtained using the nonlinear iterative A method with imputed high-density data from 15 842 reference animals and 28 traits . ComputiIt should also be noted that some reduction in computing time can be achieved by “pruning” SNPs that are in very high linkage disequilibrium from the dataset, since these SNPs carry redundant information. For example, Su et al.  reduced Our aim is to eventually integrate emBayesR into genetic evaluations for Australian dairy cattle. Currently, the Australian National DNA reference population has more than 20 000 cattle, including 3719 Holstein bulls, 9630 Holstein cows, 1017 Jersey bulls and 4249 Jersey cows. For the evaluation of these national reference populations, GBLUP is currently used to calculate the Australia Genomic Breeding Value on 50 K SNP genotypes. However, even with the current data, prediction accuracy is higher with Bayes R than with GBLUP for some traits and GBLUP is unable to take advantage of the extra information that would be contained in whole-genome sequence data. Therefore, we anticipate moving to a Bayesian method to take advantage of whole-genome sequence data and increase prediction accuracies, and we expect that an EM algorithm will be part of this methodology in order to limit computing time.In this paper, we used only bulls in the reference and validation sets, to avoid the added complexity of weighting bull and cow trait deviations differently. However, further development of the method described in this paper is needed to include appropriate weighting of phenotypes, multi-breed effects, polygenic effects in the model (as implemented in the MCMC version ) and to emBayesR uses an EM-based method to estimate the posterior mode of SNP effects, rather than the MCMC sampling used in BayesR. emBayesR can reduce computing time up to 8-fold compared to BayesR. Results with simulated data and real 630 K SNP dairy cattle data show that genomic prediction accuracy of emBayesR is similar to that of BayesR (0.5% accuracy loss averaged over traits). The computing advantages of emBayesR make it attractive for implementation of genomic prediction in very large datasets.


Aedes aegypti), we used double digest Restriction-site Associated DNA (ddRAD) sequencing for the discovery of genome-wide single nucleotide polymorphisms (SNPs). We aimed to characterize the new SNP set and to test the resolution against previously described microsatellite markers in detecting broad and fine-scale genetic patterns in Ae. aegypti.Genetic markers are widely used to understand the biology and population dynamics of disease vectors, but often markers are limited in the resolution they provide. In particular, the delineation of population structure, fine scale movement and patterns of relatedness are often obscured unless numerous markers are available. To address this issue in the major arbovirus vector, the yellow fever mosquito (Ae. aegypti samples from three continents we identified more than 18,000 putative SNPs. They were widely distributed across the three Ae. aegypti chromosomes, with 47.9% found in intergenic regions and 17.8% in exons of over 2,300 genes. Pattern of their imputed effects in ORFs and UTRs were consistent with those found in a recent transcriptome study. We demonstrated that individual mosquitoes from Indonesia, Australia, Vietnam and Brazil can be assigned with a very high degree of confidence to their region of origin using a large SNP panel. We also showed that familial relatedness of samples from a 0.4 km2 area could be confidently established with a subset of SNPs.We developed bioinformatics tools that support the customization of restriction enzyme-based protocols for SNP discovery. We showed that our approach for RAD library construction achieves unbiased genome representation that reflects true evolutionary processes. In Ae. aegypti. The variants were annotated and positioned onto the three Ae. aegypti chromosomes. The new SNP set provided much greater resolution in detecting population structure and estimating fine-scale relatedness than a set of polymorphic microsatellites. RAD-based markers demonstrate great potential to advance our understanding of mosquito population processes, critical for implementing new control measures against this major disease vector.Using a cost-effective customized RAD sequencing approach supported by our bioinformatics tools, we characterized over 18,000 SNPs in field samples of the dengue fever mosquito  Aedes aegypti , is the major vector of human arboviruses. Because of its tight connection to humans and its distribution in most sub-tropical and tropical regions, this insect causes a substantial burden on global public health [Ae. aegypti populations [Wolbachia infection [The yellow fever mosquito, c health . The devulations . Understnfection ,4 or insnfection .Ae. aegypti. In the last decade, mitochondrial DNA (mtDNA) sequences have commonly been used for Ae. aegypti population genetic studies [Ae. aegypti limits the reliability of inferences from mtDNA [Ae. aegypti until the complete genome sequence became available [Ecological processes can often be inferred indirectly from observed genetic patterns  and this studies . However studies . Furtherom mtDNA . Fast-muom mtDNA . Close available -12. Popuvailable , that wevailable  or Exon-vailable -17.Ae. aegypti movement and gene flow patterns have been inferred. Studies have investigated genetic structure across different spatial scales [Based on these marker systems, some details of l scales ,13, and l scales -17. Despl scales , a high l scales ,19. Unexl scales . Howeverl scales ,8 remainAe. aegypti.Next generation sequencing provides an opportunity to generate SNP markers at a genome-wide level even for non-model species. A mere 50 bi-allelic SNPs can provide the same resolution as 20 highly variable microsatellites for distinguishing closely related individuals . A largevia whole genome sequencing is still prohibitively expensive for Ae. aegypti, given its large genome size . A cost1.3 Gbp; ,25. RAD 1.3 Gbp; ,27. RAD 1.3 Gbp; . Choice Aedes aegypti. Here, we developed bioinformatics tools that support the customization of restriction-enzyme-based protocols and used them to discover over 18,000 SNPs in Ae. aegypti collected on three continents. We have positioned these SNPs onto the newly assembled Ae. aegypti reference genome [Recently, Brown et al.  used 1,5e genome  and provDDsilico as a memory efficient program written in C language for in silico genome digestion with a single restriction enzyme or a combination of enzymes. This program calculates the number of potential RAD loci from the available genome sequence RADseq protocol [DDsilico by comparing results with the Bioanalyzer profiles of empirical digestions. DNA of the transformation vector Stinger GFP was digested with restriction enzymes NlaIII and MluCI (NEB). Observed and expected fragment profiles were highly concordant for sizes over 150 bp , but only a small proportion constitutes potential ddRAD loci  for our ddRAD library construction. The ratio between the number of loci in the Ae. aegypti catalog  and the number of loci predicted with DDsilico  was 89.6%.To estimate the number of potential ddRAD tags in the DDsilico can be used for any method that utilizes restriction enzyme(s) to create libraries with a reduced genome representation, such as Genotyping-by-sequencing (GBS) [ng (GBS) ,32, Redung (GBS) , ezRAD [ng (GBS)  or RESTsng (GBS) . Simulatng (GBS) , leadingng (GBS) .DDemux as a demultiplexer of fastq files under various barcoding schemes . In our library, each sample was defined by a combination of variable length barcodes at the 5’ and 3’ ends (found in P1 and P2 reads). Such a combinatorial scheme reduces the cost for generating adapters with unique barcodes, while the varying length of barcodes increases library diversity at the 5’ and 3’ ends. This is particularly useful for ddRAD and other non-shearing-based libraries, where paired end (P2) reads can be used for SNP discovery but lack any diversity in the initial sequencing cycles when barcodes are absent.We also developed http://hannonlab.cshl.edu/fastx_toolkit/). P1 and P2 reads are then matched, leaving the unpaired reads as orphans. Paired reads are first aligned to the reference genome using Bowtie [We developed a customized pipeline that takes individually sorted raw sequences and outputs files ready for SNP calling Figure . It autog Bowtie  accordinAe. aegypti reference genome. With a minimum depth of five reads per individual, aligned sequences formed 574,715 RAD loci in the catalogue created with the Stacks pipeline for SNP discovery [Prior to any quality filtering, the average number of reads per individual was 3.31 million, ranging between 1.67 and 5.99 million reads. Quality filtering removed on average 5.5% of reads, retaining sequences with a mean quality score of 38 and a GC content of 41%. On average, 72.7% high quality reads aligned uniquely to the iscovery .We further filtered loci that were present in at least 75% of individuals and retained 13,591 loci with an average 12× read depth Table . When weMost common sequencing errors of the Illumina machines are A↔C and G↔T transversions . In our Ae. aegypti genome. Their number was significantly correlated with the size of supercontigs . Thanks to the recently improved assembly of the A. aegypti genome [A. aegypti chromosomes: 2,423 SNPs on chromosome 1, 5,313 on chromosome 2 and 4,320 on chromosome 3  and untranslated regions (UTRs) of 4,492 genes. In our catalog, 19% of all SNPs were found within ORFs and UTRs of 2,374 genes. As in Bonizzoni et al., the most common SNPs were synonymous, 3’UTRs contained 4 times more SNPs than 5’UTRs, and SNPs that affect splice site donors, start and stop codons were very rare . We uncovered more non-synonymous changes than the mentioned study, likely because we analyzed pantropical Ae. aegypti populations that are very divergent from the laboratory reference strain LVP [Recently, Bonizzoni et al.  used RNArain LVP .FIS values ranging between 0.079 and 0.120  than the expected heterozygosity 0.27-0.28) in all samples, with overall -0.28 in μ = 10-8 per site per generation) and 230,000 (for μ = 10-9 per site per generation) across four collections  are concordant with the estimates of census population sizes at these collection sites. For example, Jeffery et al. [Ae. aegypti adult females in Tri Nguyen village (Vietnam) could be as high as 26,431 individuals , while we found the lower range of Ne to be 18,401 individuals , a recent estimate for adult females within the release area was 7,261 individuals [Ne estimate was 19,479 individuals. Hence, we consider our SNP panel to be reflecting true evolutionary processes in Ae. aegypti populations.Nucleotide diversity averaged over all SNP loci (π) was lower and comparable among samples  with 18,147 SNPs revealed a clear separation of distinct genetic clusters, while the eight polymorphic microsatellites showed much less resolution in delineating Australian, Indonesian and Vietnamese samples tropical New World and from there, relatively recently, invaded Southeast Asia and the Pacific. Their set of SNPs enabled clear separation of African and neotropical populations, but provided no resolution within the Southeast Asia/Pacific group [Using 1,504 RAD-generated SNPs, Brown et al.  recentlyic group . Here weFST values were larger for SNP markers than for microsatellites  and maximum likelihood relatedness (r) were highly correlated  and all imputed relationships had a strong support (likelihood ratio test p < 0.001). Six full-sib pairs and one half-sib pair were detected in the same trap  was found in the same trap , 80 SNPs outperformed 11 highly variable microsatellites in parentage and kinship assignment [The advantage of SNPs over microsatellites is increasingly reported in kinship and parentage analyses. Biallelic SNPs provide less information per locus, but this can be offset by their larger numbers. Unlike 17 microsatellites with low variability, a set of 960 SNPs ensured successful paternity and identity analysis in the European bison, n bonasus. In the signment . On the signment , but selAe. aegypti can shift from traditional ‘deme-based’ methods to new individual-based methods. Assignment tests and similar approaches provide more direct inferences of dispersal and contemporary gene flow, avoiding a number of problems associated with traditional indirect inferences [via individual-based landscape genetic analyses [With high-resolution genetic data, analyses of ecological processes in ferences ,8. Fine analyses .We have demonstrated that RAD sequencing provides insight into various population patterns and processes, going from broad-scale structuring, effective population size to fine scale relatedness. RAD-based markers therefore have the great potential to advance our understanding of mosquito ecology and assist in developing appropriate measures of vector control at local and regional scales.Ae. aegypti. Our approach of using frequent cutting enzymes enabled unbiased sampling of genomic regions; we found 48% of variants in intergenic regions and 35% in exons and introns of over 2,300 genes. Their imputed effects in ORFs and UTRs were highly concordant with the effects found in the recent Ae. aegypti transcriptome sequencing study [Ae. aegypti populations even within a recently invaded neotropical region. Familial relatedness of samples collected from a small area could be confidently established with a subset of SNPs. RAD-based genetic data enables studies of basic mosquito ecology and evolution in a versatile manner and at an extremely fine resolution, facilitating targeted control measures designed for a particular local mosquito population.Next-generation sequencing techniques such as RADseq provide an increasingly-affordable approach for generating numerous genetic markers to study disease vector populations. Here we developed a new set of bioinformatics tools that support the customization of restriction enzyme-based protocols for SNP discovery. We identified more than 18,000 putative SNPs in field samples of the major arbovirus vector ng study . Our SNPin silico genome digestion and calculation of potential ddRAD loci numbers. DDsilico is written in C language and is compiled for execution on Windows and Linux operating systems  contig or chromosome sequences. It outputs a text file with a distribution of fragments for a given bin size (1 - n bp), distinguishing between sequenceable fragments (different overhang on each end) and non-sequenceable fragments (the same overhang on both ends). Also, DDsilico outputs the sum of nucleotides within each bin, corresponding to the fluorescence intensity in the Bioanalyzer assay, for comparison with the empirical digestion.We developed a memory-efficient program for DDemux sorts reads for samples labelled with a single barcode or a combination of two barcodes that can vary in length , Yogyakarta (Indonesia), Hon Mieu island (Vietnam) and Gordonvale . No specific field ethics approval is needed for the collection of wild mosquitoes in these areas. Verbal consent was obtained from residents at each location where collections occurred on private property. These locations were not on protected land and the field collections did not involve endangered or protected species. Thirteen to 17 individuals were analyzed from each location , NEB Buffer 4, BSA and water for 3 hrs at 37°C, without a heat kill step. The digestion products were cleaned with 1.5× volume of Ampure XP™ paramagnetic beads  and ligated to the modified Illumina P1 and P2 adapters. We used a combinatorial indexing scheme, labelling each individual with a unique combination of P1 and P2 barcodes containing variable length barcodes to increase library diversity at 5’ and 3’ ends , expected heterozygosity (HE) and nucleotide diversity (π). We also estimated the long-term effective population sizes (Ne) using the nucleotide diversity averaged over all loci, where π = 4* Ne*μ[μ) for SNPs is low, ranging between 10-8 and 10-9 per nucleotide site per generation [A catalogue of RAD loci used for SNP discovery was created using the ref_map.pl pipeline in sion 1.0 . First, sion 1.0 . A catal= 4* Ne*μ. The mutneration .Ae. aegypti gene set AaegL1.4 (http://www.vectorbase.org/organisms/aedes-aegypti/liverpool-lvp/AaegL1.4).To annotate and predict effects of filtered SNPs, we used SNPEff ver. 3.3 h  with defWe also screened all individuals at eight microsatellite loci  described previously . Primersadegenet ver. 1.3-9.2 [FST[We used Discriminant Analysis of Principal Components (DAPC) to identify and describe clusters of genetically related individuals implemented in the R package  1.3-9.2 ,59. This 1.3-9.2 .We there-9.2 [FST in Genep-9.2 [FST.k[k to be between 0.25 and 0.375 for full-sibs and between 0.125 and 0.25 for half-sibs. A negative kinship coefficient indicates that a pair of individuals is less related than random pairs. We also calculated maximum likelihood relationship and used the likelihood ratio test with 1000 randomly simulated genotypes in ML-Relate [To explore the power of SNP and microsatellite markers to confidently assign relationships to pairs of individuals at a small spatial scale, we calculated Loiselle’s kinship coefficients k for sampk. First, k, we consL-Relate .http://www.ncbi.nlm.nih.gov/sra/?term=SRP040064). All other supporting data, including programs DDsilico and DDemux, are included as Additional files Sequencing data are deposited at NCBI’s Sequence Read Archive (SRA) under the project accession number SRP040064  and Linux (ddsilico), the complete Stinger GFP sequence in fasta format (pstinger.fa) as a test input file, and README file (README.txt).Click here for fileDDsilico and Bioanalyzer results for Stinger GFP. DNA of the transformation vector Stinger GFP was digested with restriction enzymes (NlaIII and MluCI) and compared with DDsilico results. Please note that the Bioanalyzer profile has two additional peaks at 35 bp and 10,380 bp that are the internal size standards for the High Sensitivity DNA chip  (i.e. the two peaks are NOT part of the digested vector DNA). Also, Bioanalyzer peaks bellow 150 bp were only partially retained with the paramagnetic bead solution during the required purification step. Table S2. Fragment size distribution from the Bioanalyzer and DDsilico runs. Concordance between the two results is high, with only two very low intensity peaks (88 bp and 323 bp) present in the Bioanalyzer but absent in DDsilico.Comparison of Click here for fileDDsilico results for Aedes aegypti genome digested with various combinations of restriction enzymes. The x-axis represents fragment sizes (in base pairs), and the y-axis represent the number of fragments for a given size. Blue line depicts fragments that are not sequenceable (created by the same enzyme), while a red line depicts potential ddRAD loci. Distinguishing between amplifiable fragments is a useful DDsilico feature, as some double digestions produce numerous fragments in the desirable size range (100–500 bp), but only a small proportion constitutes potential ddRAD loci.Click here for fileThe compressed archive contains DDemux executables for Widows (ddemux.exe) and Linux (ddemux), small Illumina fastq sequencing files (20000_R1.fq and 20000_R2.fq) as test input files, configuration file (config.txt) and README file (README.txt).Click here for fileCompressed bash scripts that take individually sorted raw sequences and output files ready for SNP calling. The compressed archive contains two scripts: preprocess_paired_reads.sh that also invokes sort_paired_reads.sh. preprocess_paired_reads.sh performs conversion of sequence quality score into the Sanger format, ensuring standardization of sequence data generated by various Illumina machines with different Q score set-ups. Sequences are then filtered with FASTX-Toolkit, trimmed to a desired reads length and filtered based on the Phred score. After these processes, reads are sorted and matched as pairs, while orphans are kept in separate files (for P1 and P2 reads). Reads are then uniquely aligned in Bowtie, as described in Materials and Methods. The final output file contains concatenated uniquely aligned reads for each sample ready for the SNP calling pipeline.Click here for fileSource SNP and genotyping data in VCF. Compressed Variant calling format (VCF) file for a set of 18,147 SNPs filtered from the Stacks catalogue. Information field contains predicted SNP effects. Imputed individual genotypes from this file were used for the population genetic analyses. Additional file Click here for fileAedes aegypti microsatellites. N – number of individuals screened for a particular microsatellite locus, Na – number of alleles per locus, Ho – observed heterozygosity, He – expected heterozygosity, FIS – fixation index.Descriptive statistic for eight Click here for fileAdapter and PCR primer sequences. We have modified the adapter sequences from [ces from  by incorClick here for file


Gastrointestinal nematodes are one of the most serious causes of disease in domestic ruminants worldwide. There is considerable variation in resistance to gastrointestinal nematodes within and between sheep breeds, which appears to be due to underlying genetic diversity. Through selection of resistant animals, rapid genetic progress has been demonstrated in both research and commercial flocks. Recent advances in genome sequencing and genomic technologies provide new opportunities to understand the ovine host response to gastrointestinal nematodes at the molecular level, and to identify polymorphisms conferring nematode resistance.ST and Peddrift revealed sixteen regions, which included candidate genes involved in chitinase activity and the cytokine response. Two of the sixteen regions identified were contained within previously identified QTLs associated with nematode resistance.Divergent lines of Romney and Perendale sheep, selectively bred for high and low faecal nematode egg count, were genotyped using the Illumina® Ovine SNP50 BeadChip. The resulting genome-wide SNP data were analysed for selective sweeps on loci associated with resistance or susceptibility to gastrointestinal nematode infection. Population differentiation using FIn this study we identified fourteen novel regions associated with resistance or susceptibility to gastrointestinal nematodes. Results from this study support the hypothesis that host resistance to internal nematode parasites is likely to be controlled by a number of loci of moderate to small effects.The online version of this article (doi:10.1186/1471-2164-15-637) contains supplementary material, which is available to authorized users. Gastrointestinal nematodes are one of the most serious causes of disease in domestic ruminants worldwide , 2. ProdBreeding for host resistance has been shown to be a viable method of nematode control . Host reST) is a single marker test that detects highly differentiated alleles, where positive selection in one area causes larger frequency differences between populations as compared to neutrally evolving alleles. Peddrift [ST and Peddrift, tests based on linkage disequilibrium, such as the extended haplotype homozygosity (EHH) statistic and its derivatives, are dependent on SNP spacing and frequency, as they are multi-marker tests. The integrated haplotype score (iHS) [With sheep it is possible to manipulate breeding lines to produce strong phenotypic differences, in well-defined pedigrees, in a relatively short space of time. Reduction of variation in genomic regions surrounding a beneficial mutation due to strong and recent selection is known as a “selective sweep”; identification of regions that have undergone selective sweeps can help to reveal genes underlying phenotypic differences. Different statistics pick up different patterns of variation left by selection of a beneficial mutation. Wright’s fixation index (FPeddrift  is a prore (iHS)  and crosre (iHS)  tests arre (iHS) , 14, as re (iHS) , 16.Recent advances in genomic technologies have provided new opportunities to detect regions in the sheep genome that have undergone selection. The advent of the SNP50 BeadChip provided 54,241 evenly spaced Single Nucleotide Polymorphisms (SNP) across the sheep genome for association analysis. The chip has already been utilised to map causal mutations for traits controlled by a single locus –21 and dThe SNP50 BeadChip provides a rapid way to detect regions under selection, which can be further fine-mapped using Sequenom® or other technologies. To this end, lines of sheep that have been selected for resistance, resilience, or susceptibility coupled with high-density genetic maps are a key resource that would enable future marker assisted selection of animals without the need for parasite challenge. Here we utilise data from Romney and Perendale parasite selection lines to conduct whole genomic screens for selection, in the hope of identifying loci, within and between the two breeds, that affect individual host resistance or susceptibility to nematode parasites.After quality control (see methods) the final data set consisted of 46,736 SNP for the Romney data set and 48,436 SNP for the Perendale data set. In total 177 Romney (82 high FEC and 95 low FEC) and 146 Perendale (72 high FEC and 74 low FEC) animals passed the quality control. The average MAF of the remaining SNP over all samples was 0.24 (SD = 0.16) in the Romney data set and 0.26 (SD = 0.15) in the Perendale data set.ST[ST is a generic population differentiation statistic, Peddrift is specific to this example in that it was designed to account for structure within the population surveyed.Two analytical methods, FST and PeddST, were usST value in the Romney selection lines . A total of 16 genomic regions contained the top 0.1% of markers  , chitinase 3-like 2 (CHI3L2) and oviduct-specific glycoprotein (OVGP1). Selection was also observed on chromosome 4 (region 5), chromosome 16 (region 14) and chromosome 19 (region 15), containing genes previously implicated in resistance to gastrointestinal nematodes.As individual SNP may not show a strong signal, a 5-SNP moving average (WIN5) was used to identify regions with strong signatures of selection over multiple SNP, which also reduces noise . The aves Figure A was 0.0s Figure B with anST value in the region increased slightly from 0.3475  within the haplotype block, consistent with selection being imposed on the locus [The LD correlation coefficient rhe locus .Table 2Analysis using Sweep (v1.1) showed that 11 of the 12 above SNP created a haplotype block. In the Romney lines two contrasting haplotypes were observed, denoted 4 and 8  was chosen for exon sequencing. CHIA has previously been associated with the control of helminth infection [CD53, CHI3L2, CHIA, and DENND2D. Sequencing the CHIA exons of animals homozygous for both haplotypes showed the presence of several SNP  for NZ industry Romneys 5 generations ago was estimated as 226 and for Perendale 109, derived from extensive analysis of more than 10,000 New Zealand animals genotyped with the SNP50 BeadChip [e) [The Perendale lines have not been selected for as long (23 versus 31 years) and the genetic divergence in the selected trait is only half as large (Table BeadChip . As an iBeadChip  the PereChip [e) , 35 thanIn the regions showing signatures of selection, candidate genes were defined as those with a previously reported role in immunity. We recognise that by examining in detail only those genes with obvious functional links to immunity we have eliminated some genes that could have novel and unexpected effects on the trait concerned, or may contain as yet unidentified features that have an effect separate from the gene itself. However, we believe our approach is a tractable solution, with the data and annotation currently available, and there will be potential to extend this analysis in the future. For example, several regions that appeared to be under selection from our analyses appear to contain no underlying genes Table . The curCHIA exons of animals homozygous for both haplotypes showed the presence of several SNP; however none were responsible for the observed haplotype. The observed effect could also be driven by a regulatory element, such as a transcription factor, that could be interacting with a locus or loci in other parts of the genome [The discovery that the same core haplotype (haplotype 4) in region 2  and the major histocompatibility complex (MHC) region respectively. The SNP50 BeadChip contains four SNP within the IFNG locus ; the maximum WIN5 –log10  in the region was 0.62, which was only slightly higher than the genome-wide average of 0.42. The Romney and Perendale FST peaks were 0.0505 and 0.0377 respectively, which when compared to the genome wide distributions  peak of 1.56. While this value is reasonably high when compared to the genome-wide distribution , was 167th (OAR20_1876702).Previously many studies have focussed specifically on chromosomes 3 and 20, which contain interferon gamma , OAR 4 (RELN), OAR 16 (NSUN2) and OAR 19 (HRH1).Four regions Table  containein vivo mouse model of allergic inflammation [TNF-α has been observed in the abomasal lymph node of sheep challenged with T. circumcincta 5 days after infection [DRB1*1101 carrier lambs at 3 days post infection [The leukocyte surface antigen CD53 contributes to the transduction of CD2-generated signals in T cells and natural killer (NK) cells . NK cellammation , and mayammation . The CD5ammation , and hasammation . In humaammation , and polammation ; up-regunfection , and abonfection .H2 induced inflammatory responses, including nematode infection [Chitinases are a group of digestive enzymes that break down glycosidic bonds in chitin, which is present in fungi and the exoskeletal elements of some animals, including nematodes and arthropods . Mammalinfection  suggestiCHIA[CHIA is via a TH2 specific, IL-13 mediated pathway, and has been implicated in TH2 dominated disorders such as asthma [CHIA has previously been implicated in increased resistance or susceptibility to gastrointestinal parasites in ungulates.CHIA has prevCHIA. Inductis asthma . In mices asthma . DespiteCHI3L1, has been shown to be up-regulated in the abomasum of sheep in response to T. circumcincta challenge of previously infected animals [CHIA expression levels were also examined in the same study, and while expression was observed up-regulation of transcripts was not significant. Expression of CHI3L2  has been observed in the abomasum of 18 and 21 week old steers exposed to Ostertagia ostertagi[T. circumcincta in comparison to sham-infected controls [CHI3L2 has been found to be upregulated by IL-4 and TGF-β [Chitinase-like proteins can bind chitin, however, due to mutations in their active domains they do not have chitinolytic enzyme activity . The chi animals . CHIA exstertagi. Expresscontrols . In humand TGF-β .H1/TH2 dichotomy has not been proven in sheep, the components involved in response to gastrointestinal parasite infection are typical of a TH2 pathway; immunity is associated with the production of TH2-associated cytokines, increased numbers of mast cells, peripheral and tissue eosinophilia, and elevated production of multiple antibody isotypes [HRH1 is predominantly expressed on TH1 cells, in an IL3-upregulatable manner [HRH1 had lower percentages of Interferon-γ (IFNG)-producing cells, and produced higher levels of antigen-specific IgG1 and IgE. Mice lacking either HRH1 or HRH2 tended to have a higher frequency of IL4-producing cells. Jutel et al. [H1 and TH2 responses, as well as antibody isotypes, as a regulatory loop in inflammatory reactions. In Blackface lambs challenged over a period of three months with T. circumcincta, significantly increased levels of HRH1 expression in the abomasal lymph node were observed [While the Tisotypes –62. HRH1e manner . Mice lal et al.  concludeobserved .DENND2D, RELN and NSUN2 do not have obvious roles in immunity, they have previously been reported as being upregulated in susceptible animals. The DENND2D protein was found to be more abundant in genetically susceptible sheep following infection by H. contortus[RELN was upregulated in Suffolk (susceptible) compared to Texel (resistant) animals three days post infection with T. circumcincta[NSUN2 was found to be more highly expressed in susceptible animals [While the genes ontortus. RELN waumcincta. Finally animals .T. colubriformis or H. contortus respectively [ST difference between adjacent loci, that 150,000 evenly spaced SNP would be required to study selective signatures across the bovine genome [For complex traits, where many loci of small to moderate effect are likely to influence phenotype, the 50,000 SNP available for ovine analysis, which are also biased to high MAF SNP, may not provide enough information. In sheep, single markers were estimated to explain a maximum of 0.48% or 0.08% of the phenotypic variance in FEC following challenge with either ectively . It has e genome .In humans, the search for selective sweeps is aided by a large number of densely spaced SNP, with over 3.1 million SNP available from Phase II of the HapMap project (approximately one marker per 1 kb) . DenselyAs previously discussed the SNP50 BeadChip has already been used to map causal mutations for traits controlled by a single locus, and furthermore used to validate and detect selection sweeps in sheep , 23. WhiGenome wide analysis of selection signatures revealed 16 regions, which included genes involved in chitinase activity and the cytokine response. Many of the signals of selection found in this study are novel observations; further knowledge of the genes involved in gastrointestinal parasite resistance or susceptibility can only increase our understanding of the mechanisms involved.This study was carried out in strict accordance of the guidelines of the 1999 New Zealand Animal Welfare Act and was approved by the AgResearch’s Invermay Animal Ethics committees .Trichostrongylus and Teladorsagia genera, however Cooperia, Haemonchus, and Nematodirus species were also present, with other genera being less abundant [H. contortus larvae.Divergent lines of Romney , 31 and abundant , 7, 31. Animals were genotyped using the SNP50 BeadChip . Minor allele frequency (MAF) and call rate was calculated for each SNP. Quality control checks [-6) within selection line were also excluded. The Bonferroni correction was used to address the problem of multiple comparisons [-6 as the test-wise significance level for HWE. This is conservative as the Bonferroni correction factor is based on independent tests. While departure from HWE may be caused by selection, it is more likely that extreme violations indicate a poorly performing SNP [SNP locations for version 3.1 of the sheep genome were obtained from CSIRO  to 1 .Where the variance of p is computed across sub-populations, and p(1-p) is the expected frequency of heterozygotyes. The value of F2) distribution. Peddrift was run for both Romney and Perendale lines together using known pedigrees  to estimate the distribution of X2 under the null hypothesis of no selection. Results are expressed for each breed as a P-value for each marker.Allele frequency differences at each SNP were also compared using Peddrift . PeddrifTo explore regions under selection across both breeds, the Peddrift P-values for each SNP were combined; if they have the same overall hypothesis, results from two independent tests can be combined using Fisher’s method , using ti is the P-value for the ith hypothesis test. The combined P-value was found by comparing X2 to a χ22k distribution. To reduce noise a 5 SNP moving average (WIN5) of –log10 of the combined P-values was used; signatures of selection are shown by SNP in a region showing low P-values. The concordance between Peddrift p-values for each SNP in Romney and Perendale was investigated by setting a p-value upper threshold of 0.01. There were 21 SNP under this threshold in both breeds, more than would be expected if there was no association in the two breeds by chance (14), suggesting that some regions had been selected in common which supports using the combined approach.where p10 , and the top 0.1% of markers (n = 44) were used to determine regions under selection. The method of Kijas et al. [SNP were ranked using WIN5 –logs et al.  was usedCandidate regions were annotated using Ensembl release 74 (as of 1/2014), and gene function determined using Online Mendelian Inheritance in Man (OMIM) and a literature search. Candidate genes were defined as those with a known role in the immune response. Sheep QTL were obtained from the Sheep QTLdb .Fine-mapping allows further analysis of LD in areas that appear to be under selection; with the information obtained from more SNP, definition of LD in the area increases, improving the ability to be able to localise causal variants. One region on chromosome 1 (region 2) was chosen for fine-mapping with a denser set of SNP . In total 41 extra SNP were genotyped.All known SNP in region 2 were examined for suitability for sequencing on the Sequenom® MassARRAY® platform; these included SNP discovered on both the Solexa and 454 platforms (2 within each selection line using Haploview [Linkage disequilibrium (LD) between two loci was visualised using the correlation coefficient raploview , with arhttp://hgdp.uchicago.edu/Software/). Standardized iHS (|iHS|) was calculated using the genome-wide empirical distributions, following the method of Voight et al. [Bos taurus, Sus scrofa, Canis familiaris, Equus caballus and Homo sapiens was used to discover ancestral alleles for the remaining SNP.Haplotype phase estimation was performed using fastPHASE . Haplotyt et al. . AncestrOvis aries CHIA mRNA (XM_004002314.1) to search for SNP.Four animals were chosen for sequencing using standard amplicon sequencing .Linkage disequilibrium (LD), as shown by r2, for region 2 (OAR1:87384757–88132568) SNP in the Romney resistant selection line animals.(PNG 162 KB)Additional file 1:Linkage disequilibrium (LD), as shown by r2, for region 2 (OAR1:87384757–88132568) SNP in the Romney susceptible selection line animals.(PNG 152 KB)Additional file 2:Linkage disequilibrium (LD), as shown by r2, for region 2 (OAR1:87384757–88132568) SNP in the Perendale resistant selection line animals.(PNG 159 KB)Additional file 3:Linkage disequilibrium (LD), as shown by r2, for region 2 (OAR1:87384757–88132568) SNP in the Perendale susceptible selection line animals.(PNG 164 KB)Additional file 4:SNP inCHIAdiscovered using sequencing. Haplotype: Sweep (v1.1) haplotype, avBV: Average FEC breeding value, Positions refer to base in CHIA (XM_004002314.1). (CSV 307 bytes)Additional file 5:All known genes in regions of interest as of 01/2014.(CSV 4 KB)Additional file 6:Pedigree information and Illumina® Ovine SNP50 BeadChip data for Romney and Perendale selection line animals.(ZIP 6 MB)Additional file 7:SNP used on iPLEX™ genotyping assay for the Sequenom® MassARRAY® platform. Multiplex number; SNP ID; Amplification primer sequence; Extension primer sequence; Ancestral allele (AA). (CSV 4 KB)Additional file 8:PCR primers used for direct sequencing ofCHIAexons.(CSV 911 bytes)Additional file 9:


Drosophila Genome Reference Panel with minor allele count ≥5. We used 2r ≥ 0.5 as the cutoff for a highly correlated SNP. We make available the list of all highly correlated SNPs for use in association studies. Seventy-six percent of variant SNPs are highly correlated with at least one other SNP, and the mean number of highly correlated SNPs per variant over the whole genome is 83.9. Disequilibrium between distant SNPs is also common when minor allele frequency (MAF) is low: 37% of SNPs with MAF < 0.1 are highly correlated with SNPs more than 100 kb distant. Although SNPs within regions with polymorphic inversions are highly correlated with somewhat larger numbers of SNPs, and these correlated SNPs are on average farther away, the probability that a SNP in such regions is highly correlated with at least one other SNP is very similar to SNPs outside inversions. Previous karyotyping of the DGRP lines has been inconsistent, and we used LD and genotype to investigate these discrepancies. When previous studies agreed on inversion karyotype, our analysis was almost perfectly concordant with those assignments. In discordant cases, and for inversion heterozygotes, our results suggest errors in two previous analyses or discordance between genotype and karyotype. Heterozygosities of chromosome arms are, in many cases, surprisingly highly correlated, suggesting strong epsistatic selection during the inbreeding and maintenance of the DGRP lines.We calculated the linkage disequilibrium between all pairs of variants in the  Drosophila Genome Reference Panel (DGRP) (Drosophila melanogaster. The DGRP has been used for a series of genome-wide association (GWA) studies on a wide variety of phenotypes . Linkage (gametic-phase) disequilibrium (LD) is a challenge to all GWA studies, because it confounds the signal from variant sites  that cause phenotypic variation with those that are genetically correlated with the causal variant but that do not have effects on the phenotype. The nature of the DGRP in many ways minimizes the presence of LD relative to vertebrates or to line-cross-derived mapping populations . The DGRP lines are drawn from a natural population in Raleigh, North Carolina, with large effective size, as shown by the relatively low level of structure within the population. r2 < 0.2 at just 10 base pairs on the autosomes. This result might suggest that the overall impact of LD on GWAS results will be low.The D. melanogaster from these two source populations  of five or more, although we calculated correlations of these focal sites with those where MAC was three or more. We excluded sites where the number of missing calls was >85. There are 2,659,276 focal sites with MAC ≥5, and 3,159,155 sites with MAC ≥3. For focal sites, the median minor allele frequency (MAF) was 0.13, and the median number of lines scored was 195 out of 205 possible.We used Freeze 2 genotype calls for the DGRP lines obtained from r2 2. This process was repeated for each bin with larger MAF. We retained a list of all pairs of SNPs with 2r > 0.5. SAS programs to calculate the MAF limits for a given 2r cutoff and for calculating and storing the identity of SNP pairs with high 2r are included in Supporting Information, File S1. This algorithm will miss a small number of highly correlated SNPs that have missing genotype information in many lines.To calculate LD between all pairs of SNPs within this limit, we started with the lowest frequency bin, whose rounded frequency is ftp://ftp.hgsc.bcm.edu/DGRP/freeze1_July_2010/snp_calls/). We first identified all SNP pairs correlated at r2 > 0.25 using an algorithm similar to that outlined above for the Freeze 2 data. We then drew 100 random focal SNPs that were each correlated at r2 > 0.25 with at least one other SNP, which we call a SNP family. Ten focal SNPs were chosen from each MAF decile. In cases in which the focal SNP was correlated at r2 > 0.25 with more than 100 SNPs , we retained a random sample of 100 correlated SNPs. We simulated SNP phenotypic effects that explained 1% of the total phenotypic variance in a multivariate trait. Simulated data were analyzed using MANOVA, with SNP genotype as the sole predictor variable. MANOVA P-values were calculated using a chi-square approximation of Wilk’s Lambda.Prior to performing the above calculation, we characterized the probability that a SNP correlated with a causal SNP will result in a false positive using simulated data. We simulated phenotypic effects on a set of correlated SNP genotypes drawn from the Freeze 1 genotype calls for 165 DGRP lines  of the genotypes of a randomly chosen subset of 5000 SNPs for each inversion and used the scores on PC1 to diagnose which genotypes are characteristic of each karyotype. We also calculated the proportion of SNPs that were scored as heterozygous for chromosomal regions defined by the inversion breakpoints.The African ancestry of each inverted region was calculated using the results of Calculations were performed in SAS version 9.3 for Windows and Unix .r2 as our measure of disequilibrium  by chromosome arm are available in File S2.We refer to pairs of SNPs correlated at Figure S1 suggests that inversions may compound the effects of linkage and rarity disequilibrium, because the mean number of highly correlated SNPs inside inversions is substantially higher when MAF is less than 0.2. This is particularly so for SNPs distant from the focal variant.5 bp away that are in high LD is 1 or more when MAF is 0.04 or less, but 0 for all higher frequencies. The median number of variants correlated at 2r > 0.5 is virtually identical between SNPs inside and outside of inversions, regardless of MAF (not shown).The variance in the number of correlated SNPs is high and skewed toward smaller numbers, so that the mean number of correlated SNPs is quite a bit higher than the median, as shown in The mean number of highly correlated distant sites across the genome is shown in The cytogenetic analyses of Given the pattern of distant LD shown in File S3. We plot the PC1 scores vs. the average heterozygosity (H) of each inversion region in The full list of previous karyotype inferences, scores on the first PC for inversion-diagnostic genotypes for each chromosome, and heterozygosities of all SNPs within regions defined by inversion breakpoints are given in A number of inversion regions have highly heterozygous sequence data (H >0.15) but no evidence of similarity to common inversion genotypes. In four cases t and In(3R)Mo. For In(2R)NS, however, the two mismatches between our predictions and previous studies both have African ancestry more consistent with the opposite type. The highest African ancestry in the 187 consensus Standard karyotypes is 920 kb, whereas line 83 has African identity for 5840 kb. The lowest African ancestry in the 7 consensus In(2R)NS lines is 3286 kb, whereas line 69 has African identity for just 874 kb. These are both cases where the sequence-based typing of Inversions In(2L)t and In(2R)NS both have African origin, whereas In(3R)Mo has a non-African origin . Pool  pairs in 205 lines in the Freeze 2 data set that have LD above Drosophila Genome Reference Project are in strong LD (LD of 2r ≥ 0.5) with at least one other variant. More strikingly, many SNPs in the full DGRP with minor allele frequency less than 0.2 are highly correlated with at least one SNP more than 100 kbp distant. Thus, although it is true that the DGRP population has low LD relative to other eukaryotes, disequilibrium is still a major element of these data, and careful consideration should be given to its impact at all stages of an association analysis.Most SNPs in the D. melanogaster from which the DGRP lines were sampled was founded by admixture of African and European genotypes . These results show that differences in the genotypes chosen can substantially change the inferred LD structure for rarer SNPs. Nevertheless, the correlations that we have calculated will be useful as the basis for analyses of multi-SNP associations. For example, after identifying a set of SNPs with significant associations, one could reanalyze those in multi-SNP analyses that include the most highly correlated SNPs to diagnose which SNPs are most likely to represent the variants that cause phenotypic differences, and which have their signal confounded with those from other SNPs. If such SNPs are all nearby, then the inference of that genomic region as causal can be strong, even if the precise nucleotide responsible remains unknown. Follow-up studies of such regions are likely to be worthwhile. In contrast, SNPs for which the addition of distant SNPs renders effects ambiguous would be poor candidates for follow-up studies.


Citrus species originated from interspecific hybridisation between four ancestral taxa  with limited further interspecific recombination due to vegetative propagation. This evolution resulted in admixture genomes with frequent interspecific heterozygosity. Moreover, a major part of the phenotypic diversity of edible citrus results from the initial differentiation between these taxa. Deciphering the phylogenomic structure of citrus germplasm is therefore essential for an efficient utilization of citrus biodiversity in breeding schemes. The objective of this work was to develop a set of species-diagnostic single nucleotide polymorphism (SNP) markers for the four Citrus ancestral taxa covering the nine chromosomes, and to use these markers to infer the phylogenomic structure of secondary species and modern cultivars. Species-diagnostic SNPs were mined from 454 amplicon sequencing of 57 gene fragments from 26 genotypes of the four basic taxa. Of the 1,053 SNPs mined from 28,507 kb sequence, 273 were found to be highly diagnostic for a single basic taxon. Species-diagnostic SNP markers (105) were used to analyse the admixture structure of varieties and rootstocks. This revealed C. maxima introgressions in most of the old and in all recent selections of mandarins, and suggested that C. reticulata × C. maxima reticulation and introgression processes were important in edible mandarin domestication. The large range of phylogenomic constitutions between C. reticulata and C. maxima revealed in mandarins, tangelos, tangors, sweet oranges, sour oranges, grapefruits, and orangelos is favourable for genetic association studies based on phylogenomic structures of the germplasm. Inferred admixture structures were in agreement with previous hypotheses regarding the origin of several secondary species and also revealed the probable origin of several acid citrus varieties. The developed species-diagnostic SNP marker set will be useful for systematic estimation of admixture structure of citrus germplasm and for diverse genetic studies.Most cultivated  Citrus L. includes commercially important cultivars grown in tropical to temperate parts of the world over several thousands of years. Two major systems are widely used to classify Citrus species: the Swingle and Reece classification  as the ancestors of all cultivated Citrus. The differentiation between these sexually compatible taxa may be explained by the foundation effect in different geographic zones and initial allopatric evolution. Citrus maxima originated in the Malay Archipelago and Indonesia, C. medica evolved in north-eastern India and the nearby region of Myanmar and China, C. reticulata diversification occurred over a region including Vietnam, southern China, and Japan  arose from hybridisations between the four basic taxa [Citrus are mosaics of large chromosome fragments from the basic taxa in frequent interspecific heterozygosity. Another consequence of apomixis and horticultural vegetative propagation practices is that most citrus horticultural groups  have minimal intragroup genetic diversity resulting from clonal variation/selection [C. aurantium, C. sinensis, and C. clementina (clementine) resulted from reticulation events between the C. reticulata and C. maxima gene pools and enabled to decipher the phylogenic origin of genomic fragments over the whole genome. However, the genomic structures of other secondary species and most modern varieties resulting from sexual crosses remain to be studied. For such objective it is essential to identify diagnostic molecular polymorphisms of the four citrus basic species throughout the genomes, and to develop molecular markers for routine phylogenetic genotyping of large germplasm collections. Moreover, diagnostic markers for ancestral taxa will aid the management of interspecific introgression in sexual breeding schemes and, more widely, will enable studies of sexual recombination at the diploid or polyploid levels and to analyse the mechanisms of 2n gamete formation for genotypes of interspecific origin.In this paper, we will refer to the Swingle and Reece  classifiCitrus –10. Thesnd Japan ,12 and Chipelago ,14, primhipelago , and sechipelago  proved tsic taxa ,7,8,10. election . These helection . The effelection ,20 confiet al. [et al. [et al. [C. maxima and C. reticulata were also identified from SNP markers developed from clementine Bac-end sequencing [et al. [C. maxima and C. reticulata through analysis of whole genome sequencing (WGS) data from several mandarins and pummelos. However, the currently available validated species diagnostic markers [C. medica and C. micrantha.Simple sequence repeat markers (SSRs) were widely developed in citrus during the last 15 years –25 SSRs et al.  showed t [et al.  and Olli [et al.  showed t [et al. ,30 revea [et al. . Severalquencing . In addi [et al.  revealed markers  are stilCitrus ancestral taxa with coverage of the nine chromosomes of the citrus haploid genome, and (ii) to estimate the interspecific admixture genomic structure of the secondary cultivated species and several modern cultivars from 20th century breeding programs. Specific diagnostic markers were mined from 454 amplicon sequencing of 57 gene fragments from 26 representative genotypes of the four basic taxa . Eighty-five SNP marker analyses based on competitive allele-specific PCR were developed. Effectiveness of marker development and transferability to related genera of the Aurantioideae subfamily is described in addition to the identification of ancestral alleles and validation of specific mutation occurrences in the four phylogenetic branches. Admixture analysis was performed using 73 markers successfully developed from the 454 SNP mining and 32 specific diagnostic SNP markers from previous research [The objective of the present work was (i) to develop a set of species-diagnostic markers for the four research ,9.Citrus genus and related genera were collected from pathogen-free plants from the IVIA Citrus Germplasm Bank  and the Inra/Cirad citrus collection of San Giuliano  were used for SNP mining by 454 amplicon sequencing , three sour oranges (C. aurantium), three sweet oranges (C. sinensis), three grapefruits (C. paradisi), one ‘Combava’ (C. hystrix), and one ‘Nasnaran’ mandarin (C. amblycarpa). Sixteen recent hybrid varieties from international breeding programs or supposed natural interspecific hybridisation were also used .Aurantioideae subfamily was studied by the analysis of 14 accessions representative of the two tribes of the Aurantioideae (Clausenae and Citreae). In Clausenae, the subtribe Clauseniae was represented by two accessions (Clausena excavata Burm. f. and Murraya koenigii (L.) Spreng). Within the Citreae, two subtribes were represented: Triphasilinae (Triphasia trifolia (Burm. F.) P.Wils.; one accession), and Citrinae (eleven accessions representing nine genera including the Citrus genus). Analysis of the Citrinae was conducted according to the subdivision into three groups proposed by Swingle and Reece [Severinia buxifolia), two accessions of two genera of the “near citrus fruit” group  Oliv and Citropsis gilletiana Swingle & M. Kellerm), and eight accessions of the “true citrus fruit trees” group that included six genera  in addition to the Citrus species  according to the manufacturer’s instructions.ftp://ftp.jgi-psf.org/pub/compgen/phytozome/v9.0/Cclementina/annotation/ at the Phytozome webpage (“Cclementina_182_gene.gff3” file).The reference citrus whole genome sequence, released in Phytozome  by the IDuplicated and overlapping genes were discarded. Then, from a specific annotation of the whole sequence for SSR (up to tetranucleotidic motifs and at least 11 bp sequences), all genes presenting microsatellite motifs were eliminated. For this study, 57 gene fragments covering the nine chromosomes were selected for SNP mining in genomic areas complementary to previously identified SNP marker sets ,9.The 454 sequencing technique requires amplicon primers containing a directional GS FLX Titanium primer sequence (which includes a four base library “key” sequence) at the 5′ portion of the oligonucleotide in addition to the gene-specific sequence at the 3′ end. Multiplex Genotype Identifier (MID) sequences defined by Roche  S2 Tabl were addFor the 57 selected gene fragments , 57 primRaw reads obtained from 454 pyrosequencing were pre-processed by removing low quality reads and adapter/primer sequences using PRINSEQ . We consFor each sequenced gene of each variety, 454 pyrosequencing reads were aligned independently using SeqMan NGen software version 7.0  with theRaw data are available in EMBL-EBI (European Nucleotide Archive), under the study accessions number: PRJEB8550http://www.kbioscience.co.uk/). The KASPar Genotyping System is a competitive, allele-specific dual Förster resonance energy transfer (FRET)-based assay for SNP genotyping. Primers were designed by LGC Genomics based on the SNP locus-flanking sequence (approximately 50 nucleotides either side of the SNP). Detailed information for all SNP markers can be found in SNP genotyping was performed using KASPar technology  and genotype calling was made with KlusterCaller software (LGC Genomics).SNP numbers and locations were identified from sequence data using SniPlay online software ,40.w [ST) were calculated using Weir & Cockerham [Unbiased expected heterozygosity (He), observed heterozygosity (Ho), and F Stat parameters (Fw  and FST)ockerham  estimatoockerham .ST parameter [ST estimations were computed using Excel software:2, where pi is the frequency of a given allele in the considered population or subpopulation). Values of GST range from 0 to 1. Low values indicate that little variation is proportioned among subpopulations, while high values denote that a large amount of variation is found among subpopulations. In this study, GST Taxoni = 1 indicated that the taxon i was totally differentiated from the three other basic taxa and probably fixed for a mutant allele that most likely occurred in the taxon i after its separation. SNPs presenting a GST value >0.9 where considered as diagnostic SNP for the corresponding taxon. For GST values <0.5, a SNP was considered as specific for a taxon when it was polymorphic within the taxon but fixed for the same allele in the other three taxa. The same criteria were used to select 32 additional diagnostic markers from SNP markers previously developed by our group [The search for SNPs diagnostic for each taxon was based on Garameter  estimatiur group ,9.Factorial Analyses (FA) from fragment sequences were performed using DARwin software .Principal Component Analysis (PCA) was performed using XLSTAT software from allelic frequencies.Genotypic genetic relationships were studied by Neighbour-joining analysis (NJA) based on the SNP data using DARwin software  with thea priori population structure was defined. The linkage model option was used, with allele frequencies correlated and computed probability of the data for K estimating. Analyses were made with K value (number of subpopulations) varying from 1–10. The statistics used to select the correct K value was ΔK as proposed in Evanno et al. [Population structure was inferred using the Structure program version 2.3.4 , which io et al. . Ten runo et al.  or infero et al. .Fluidigm amplification followed by 454 sequencing produced 295,169 useful reads. The reads were classified according to their MID, and then Titanium sequences and MID sequences were removed using 454 software tools. All reads were attributed to one of the 1,482 (57 × 26) amplicons according to the fragment gene sequence. Forty-six gene fragment/variety amplicons did not have a corresponding read (3.1%), and eight had insufficient read numbers for genotype calling. The average of reads per amplicon was 205.55 . However, the distribution of the number of reads per amplicon was highly heterogeneous , and 414C. micrantha (represented by a single accession), gene fragments with missing data were Sanger sequenced to complete the data set.For ST value of 0.445±0.020. The SNP/Kb rate varied between gene fragments  suggest an important structuration of the analysed varietal sample, as confirmed by the NJA  and average F the NJA .C. micrantha was available, and its observed heterozygosity value (0.07) lay between the pummelo and mandarin values . Citron displayed low heterozygosity (Ho = 0.04 ± 0.02) and polymorphism (He = 0.04 ± 0.003) compared with mandarins and pummelos. Only one representative of n values .The analysis of the average number of SNPs/kb between two varieties within and between the four supposed basic taxa revealed values of 1.62–3.49 SNPs/kb within groups and 9.96–13.19 SNPs/kb at the inter-group level .ST parameter as described in the Materials and Methods. The distribution of the highest GST value of each SNP for the four basic taxa  and C. micrantha (91) displayed many more diagnostic SNPs (GST >0.9) than C. reticulata (42) or C. maxima (26).An initial analysis of taxa differentiation at each SNP position was performed directly from genotype calls from the representative of each of the four basic taxa. Diagnostic SNP assignment was based on the Gsic taxa  Table shC. micrantha SNPs with GST values of 0.3–0.4 corresponded with heterozygous SNPs in C. micrantha fixed for a same allele in mandarin, pummelo, and citron. No specific allele was observed for 92 SNPs with the highest taxon GST value ≤0.5  and the estimation of heterozygosity of each genotype was combined for each gene fragment. Genotypes having interspecific phylogeny for the considered fragment were expected to be in intermediate positions between basic taxa clusters and to display much higher heterozygosity than genotypes without interspecific heterozygosity. The C2P27 fragment is provided as an example in C. reticulata and C. maxima clusters resulted from heterozygosity for SNPs differentially fixed in the C. maxima and C. reticulata clusters.Previous WGS  and amplC. micrantha, which had a relatively central position in the 1/2 plan, had very low heterozygosity and was totally differentiated from the three other basic taxa in the third axis.It should be noted that ST parameters were then re-estimated, with only gene fragments that did not exhibit interspecific heterozygosity for the considered variety.Basic taxa allelic frequencies, population diversity parameters  and GST ST value of each SNP for the four basic taxa  and C. micrantha (92 SNPs) continued to have a larger number of diagnostic SNPs (GST >0.9) than C. reticulata or C. maxima, but the number of strong diagnostic markers for the latter two taxa improved, respectively reaching 72 for C. reticulata and 43 for C. maxima . Ten markers characterised intraspecific polymorphisms .SNPs were selected from those with high differentiation values between basic taxa and those displaying intraspecific variability within each basic taxon . To limit the risk of PCR drift between alleles, SNPs were rejected for KASPar marker development if further SNPs or indels were in close proximity. Eighty-five SNP markers were developed from the 454 SNP mining data. Amplification failed for four of these SNP markers, and eight further SNP regions produced inconsistent results: these twelve markers were thus discarded. The remaining 73 markers were successfully amplified, and according to the 454 genotyping data, 63 of these SNPs were diagnostic for one of the four taxa  and low in the “true citrus fruit trees” group . The missing data rate increased, respectively, to 9.6% and 12.3% in the “near citrus” and “primitive citrus” groups of the Citrinae subtribe. The missing data rate reached a level of 24.7% for the other subtribe of the Citreae tribe (Triphasilinae) and 38.4% for the two representatives of the Clauseniae tribe. These results indicate an increasing loss of transferability with increasing taxonomic distance. As expected due to the discovery panel, the Citrus genus was the most polymorphic (73 polymorphic loci), followed by the “true citrus fruit trees” group without the Citrus genus (18 polymorphic loci). For some markers (46/73), all accessions except those in the Citrus genus displayed the same homozygous genotypes for the non-diagnostic allele. For these 46 markers, the alternative allele fixed in all citrus and relative taxa, with the exception of one of the four basic citrus taxa, was clearly the ancestral allele. The alternative allele therefore resulted from a mutation that occurred during the separation of the differentiated basic taxa.The missing data rate was very low in C. reticulata, C. maxima, C. medica, or C. micrantha.In addition to the 73 new KASPar SNP markers, we selected 32 of our previously developed SNP markers ,9 to comThe distribution in the clementine reference sequence  and allew, FST, and specific GST) values for each marker as well as average values are in C. micrantha, which was totally homozygous and displayed no polymorphisms between the two accessions, the intraspecific parameters Ho and He were not significantly different between the whole set of SNPs identified by 454 and the results of the KASPar analysis.Diversity parameter (Ho and He) and genetic organization parameter  were found to share similar polymorphisms in mandarins and pummelo and were therefore discarded from the specific allele homozygosity/heterozygosity analyses. We therefore concluded that 29, 22, 30, and 22 of the selected markers displayed specific alleles for C. reticulata, C. maxima, C. medica, and C. micrantha, respectively. Nine of these markers, with lower specific GST values (<0.6), were fixed for the same allele in three horticultural groups but displayed an intraspecific polymorphism in the fourth group .However, the structuration of the population was higher with the selected KASPar markers, with average Frameters  confirmeC. aurantium (three accessions), C. sinensis (three accessions), or C. paradisi, while four different genotypes were observed in C. aurantifolia (four accessions) and five in C. limon (six accessions). All secondary species displayed high heterozygosity values (Ho) compared with the four horticultural groups. Ho ranged from 0.35 in C. paradisi to 0.48 in C. limon. The mandarin hybrid, tangor, and tangelo groups displayed increasing average Ho values . However, substantial heterozygosity variations were observed between varieties within the tangor group (0.13–0.30), and the differences between the groups were thus not statistically significant.When secondary species accessions were considered , no inteCitrus cultivars genotyped with the 105 KASPar SNPs. The population displayed a very strong structuration, with 85.6% of the total diversity encompassed by the three first axes . The fourth axis supported only 1.7% of the diversity. The first axis mainly differentiated citrons from other species  and the mandarin cluster (average Ho = 0.07) to the central region (Ho = 0.42 for the three accessions of the C. aurantium sour orange group). ‘Mexican’ lime, ‘Alemow’, and ‘Excelsa’ lime were clustered in intermediary positions between the citron cluster and C. micrantha, with heterozygosity values of 0.43–0.45. ‘Rangpur’ lime and ‘Volkamer’ lemon also displayed high heterozygosity values  and were located between the citron and the mandarin clusters. The ‘Palestinian’ sweet lime, ‘Meyer’ lemon, and ‘Eureka’ lemon were slightly displaced, having higher values for the F2 axis and heterozygosity values of 0.44–0.49. C. amblycarpa had a similar heterozygosity level but was located between C. micrantha and the mandarin cluster.PCA was performed with the data from the 70  species . The secC. reticulata, C. maxima, C. medica, and C. micrantha).The relative contributions of the ancestral taxa to the modern cultivars suggested by PCA and average heterozygosity levels were confirmed by a deeper analysis of cultivar homozygosity and heterozygosity for the four sets of diagnostic markers .Among the four basic horticultural groups, no evidence of interspecific introgression was found for the analysed accessions of pummelos, citrons, and icrantha . InterspC. aurantifolia accessions  displayed very similar phylogenomic patterns, with close to half contributions from C. medica and C. micrantha with most of C. micrantha and C. medica diagnostic markers in heterozygosity  had a distinct pattern and appeared to have a similar phylogenetic structure to four of the six accessions of C. limon . These contained close to 50% C. medica (most C. medica diagnostic markers in heterozygosity) and displayed a three ancestral taxa admixture pattern  with a greater contribution from C. reticulata than C. maxima. Diagnostic alleles of C. medica, C. maxima, and C. reticulata were found systematically in heterozygosity in these five accessions. The two last C. limon accessions (‘Rangpur’ lime and ‘Volkamer’ lemon) had a very similar phylogenetic structure with a complete heterozygosity for their C. reticulata and C. medica diagnostic alleles and no diagnostic alleles for the other taxa  with variable contributions. The three C. aurantium accessions displayed around half contribution of each taxon and appeared totally heterozygous for their C. reticulata and C. maxima diagnostic alleles. C. sinensis had a more complex genomic structure and part of the diagnostic alleles for C. reticulata and for C. maxima, were in homozygosity. C. paradisi accessions had higher contributions of C. maxima than C. reticulata with homozygous diagnostic alleles of C. maxima in addition to heterozygous alleles from C. maxima and C. reticulata. All mandarin hybrids, tangors, and tangelos had a greater contribution from C. reticulata than C. maxima. All displayed homozygous diagnostic alleles of mandarin in relatively high proportion (34% in ‘Kiyomi’ to 77% in ‘‘Nadorcott’) as well as heterozygous diagnostic alleles of C. reticulata and C. maxima. In addition, seven accessions presented one or two C. maxima diagnostic alleles in homozygosity. The ‘Triumph’ orangelo displayed a similar pattern to C. paradisi but with a higher proportion of homozygosity for C. maxima diagnostic alleles .All accessions of C. amblycarpa displayed around 50% contributions from C. reticulata and C. micrantha and appeared totally heterozygous for its C. reticulata and C. micrantha diagnostic alleles.C. hystrix) with basic diagnostic SNPs only testified to C. micrantha specific alleles in homozygosity and heterozygosity without any specific alleles for the three other basic taxa.The direct analysis of ‘Combava’ , and the average number of SNPs between varieties of two horticultural groups was 10.0–13.19 SNP/kb. As might be expected, this global SNP rate was lower than the 52.9 SNP/kb, reported by Garcia-Lor et al. [Poncirus, Fortunella, Microcitrus, and Eremocitrus) and Citrus. However, the authors noted values similar to those reported here for the average number of SNPs between varieties of pummelos, citrons, and mandarins. At the intraspecific level, the higher diversity in C. reticulata and C. maxima than in C. medica is in agreement with previous studies [C. maxima, C. medica, C. reticulata, and C. micrantha agreed with previous molecular studies [Citrus species. As a consequence of the high genetic differentiation resulting from the allopatric evolution of the four basic taxa, 271 of the 1,053 SNPs were found to be highly diagnostic for one of the four basic taxa (specific GST >0.9).The  studies –6,10,66 et al. [C. maxima introgression in two mandarin varieties (‘Ponkan’ and ‘Willowleaf’) that were generally considered as true representatives of C. reticulata. In addition, the proportion of the C. maxima genome was quantified in these two cultivars, one mandarin hybrid (‘W. Murcott’ = ‘Nadorcott’), clementine, sweet orange, and sour orange. Interestingly, the values found in our study from Structure analysis based on a limited set of markers (103) were well correlated with the previous observations from WGS data [et al. [C. maxima from sour orange to ‘Willowleaf’ as follows: sour orange (0.49), sweet orange (0.44), clementine (0.21), ‘W. Murcott’ (0.15), ‘Ponkan’ (0.077), and ‘Willowleaf’ (0.045). Our structure analysis inferred values of 0.509, 0.391, 0.236, 0.125, 0.035, and 0.040, respectively, for the same varieties. For the seven cultivars the correlation coefficients for estimations of C. maxima and C. reticulata contributions in the two studies were very high (0.993). Moreover, the analysis of the proportions of the diagnostic alleles of C. reticulata and C. maxima in heterozygosity and homozygosity agreed with the Wu et al. [C. maxima alleles in heterozygosity in ‘Willowleaf’, ‘Nadorcott’, and clementine. Therefore, we consider that the estimations of interspecific genomic structure provided by the set of species-diagnostic markers constitute a first approximation of the true phylogenomic structures of the analysed genotypes. As a result of their selection process, these markers minimised intraspecific variability. The SNP markers should therefore be combined with a set of SSR markers to retain intraspecific variability data when analysing germplasm diversity. Indeed, in citrus, comparative studies of SSRs, indels, and SNP markers [Recently, Wu et al.  analysedWGS data . Indeed, [et al.  found deu et al.  analysisnkan’ 0.07, and ‘W markers  showed tC. maxima and C. medica without identified interspecific introgressions. Similarly, no evidence of introgression was found in the only two C. micrantha observed. As mentioned above, based on WGS analysis, Wu et al. [C. maxima in two mandarin varieties considered as true representatives of C. reticulata. A 454 amplicon haplotype study for chromosome 2 [C. maxima in nine of the thirteen mandarins studied. In the present work, species-diagnostic marker analysis revealed such C. maxima introgression in 13 of the 17 mandarin analysed. Only three mandarins were found without any indication of interspecific introgression . C. daoxianensis is considered as an ancestral mandarin [C. maxima share the mitotype of edible mandarins [C. reticulata and C. maxima and further introgression processes appear to be important components of the mandarin domestication. Only deep genomic analysis will determine whether all edible mandarins result from one or several reticulation events and how introgression produced the modern mandarin displaying only a limited part of the C. maxima genome. During the 20th century, mandarin breeding was based on sexual hybridisations between mandarins but also between mandarins and sweet oranges (tangors), and mandarins and grapefruits (tangelos). All these recent hybrids, as well as supposed natural tangors such as ‘Ortanique’, ‘Murcott’, ‘Temple’, ‘Nadorcott’, and clementine, displayed admixture structure genomes between C. reticulata and C. maxima with predominant contribution of C. reticulata. It appears that mandarins, tangelos, tangors, sweet oranges, sour oranges, grapefruits, and the orangelo (grapefruit × sweet orange hybrid) provide a large range of phylogenomic constitutions between the C. reticulata and C. maxima clusters. This is favourable for genetic association studies based on phylogenomic structures of the germplasm.The representative genotypes of the pummelo and citron horticultural groups appeared, respectively, as pure u et al.  evidencemosome 2  also revmandarin , and themandarin , while aandarins  and are C. sinensis, C. aurantium, and C. paradisi, although they were highly heterozygous . This confirms that the intraspecific phenotypic polymorphisms in these secondary species arose from punctual mutation, transposable element movement [C. limon and C. aurantifolia accessions with the exception of ‘Eureka’ and ‘Lisbon’ lemons.The partial apomixis of most of the secondary species explained that, in agreement with previous molecular studies –8,22, nomovement , or epigC. sinensis and C. aurantium are believed to derive from hybridisations between the C. maxima and C. reticulata gene pools [C. reticulata gene pool. The present study concurred with the conclusions obtained from WGS data [C. maxima and C. reticulata specific alleles and structure analysis inferred close to 50% contribution for each of the two species. This is in agreement with a direct hybridisation between C. maxima and C. reticulata as proposed by Garcia-Lor et al. [et al. [C. reticulata and C. maxima, as testified by the presence of specific C. maxima and C. reticulata alleles in homozygosity.ne pools ,10,20,70ne pools  and a SNne pools  suggest C. paradisi: the origin of grapefruit is attributed to a natural hybridisation between pummelo (C. maxima) and sweet orange (C. sinensis). This hybridisation may have occurred in the Caribbean more than 200 years ago [C. reticulata specific alleles were displayed in heterozygosity and C. maxima allele were displayed in heterozygosity or in homozygosity. Our results are therefore consistent with the pummelo (C. maxima) x sweet orange (derived from hybridisations between the C. maxima and C. reticulata gene pools) hybridisation hypothesis.ears ago ,72,73. IC. aurantifolia: Tanaka [C. aurantifolia, C. excelsa, C. macrophylla, and C. limettioïdes, respectively. However, Swingle and Reece [C. aurantifolia. In all of the analyses reported here, the three first genotypes displayed limited differences, whereas ‘Palestinian’ sweet lime appeared much more related to several C. limon cultivars. In PCA, the ‘Mexican’ lime, ‘Excelsa’ lime, and ‘Alemow’ displayed an intermediary position between the citron cluster and C. micrantha. These were highly heterozygous for C. micrantha and C. medica specific alleles, and structure analysis inferred close to 50% contribution for each of the two species. Therefore, our results suggest a similar origin by direct hybridisation between C. micrantha and C. medica for these three varieties. For ‘Mexican’ lime, this agrees with the hypothesis proposed by Nicolosi et al. [et al. [C. micrantha mitotype. Recent analysis with mitochondrial indels and chloroplatic SSRs [C. medica origins. An enhanced study of papeda germplasm will be necessary to definitively conclude C. micrantha or another papeda as the female parent of these three varieties. ‘Palestinian’ sweet lime structure will be discussed with C. limon.: Tanaka  considernd Reece  combined [et al.  showed ttic SSRs  leads toC. limon: ‘Eureka’/’Lisbon’ lemon, ‘Marrakech’ limonette, ‘Meyer’ lemon, ‘Rangpur’ lime, ‘Volkamer’ lemon were considered by Tanaka [C. limon, C. limetta, C. meyeri, and C. limonia. These four species were joined in C. limon by Swingle and Reece [C. limon, C. limetta, and C. meyeri species as defined by Tanaka. All displayed a three species admixture structure  with specific alleles of these three taxa in heterozygosity. The ‘Palestinian’ sweet lime (C. limettioïdes) displayed a very similar pattern. The contribution of C. medica, as revealed by structure analysis, was close to 50% for all these varieties. Therefore, they are probably direct hybrids between C. medica and varieties with admixture structure between C. maxima and C. reticulata. Based on RFLP, RAPD, and CAPS data, Nicolosi et al. [C. aurantium and C. medica. This hypothesis was supported by nuclear SSR [C. limonia accessions (‘Volkamer’ lemon and ‘Rangpur’ lime) shared similar characteristics that differed from the previous lemon and lime patterns. In PCA, the C. limonia accessions displayed an intermediary position between C. medica and the mandarin group. The accessions were heterozygous for most C. medica and C. reticulata specific alleles, and structure analysis inferred close to 50% contributions from each of the two species. Therefore, C. limonia accessions results probably from direct hybridizations between C. reticulata and C. medica. Previous mitochondrial marker analyses [C. limonia accessions.y Tanaka  as four nd Reece . Our analear SSR ,74 and Slear SSR  analyseslear SSR , and is analyses  showed tC. amblycarpa is native to Indonesia, where it is called Djerook leemo [et al. [C. micrantha and C. hystrix. In PCA, C. amblycarpa displayed an intermediary position between C. micrantha and the mandarin group. It was highly heterozygous for C. micrantha and C. reticulata specific alleles, and structure analysis inferred close to 50% contribution from each of the two species. Therefore, the hypothesis of papeda × acidic mandarin proposed for C. amblycarpa by Ollitrault et al. [ok leemo . It is g [et al.  showed tt et al.  was confST values >0.9 for one of the basic taxa. Seventy-three KASPar markers were successfully developed and used with 32 previously developed SNP markers for analysis of the admixture structure of actual varieties and rootstock. Good correlations were observed between the contribution of the four basic taxa inferred with the set of species-diagnostic markers and recent published data from WGS of seven citrus varieties. The analysis of admixture genomic structures of cultivated citrus species and cultivars with 105 species-SNP diagnostic markers revealed C. maxima introgressions in most modern mandarin cultivars and in all recent selections of small citrus issued from 20th century breeding programs. This suggests that C. reticulata × C. maxima reticulation events and introgression processes were important elements of sweet mandarin domestication. The large range of phylogenomic constitutions between C. reticulata and C. maxima, revealed in modern mandarin, tangelo, tangor, sweet orange, sour orange, grapefruit, and orangelo germplasm, appears to be favourable for genetic association studies based on phylogenomic structures of the germplasm. Inferred admixture structures of several secondary citrus species were in agreement with previous hypotheses regarding their origin. Admixture structures also revealed the genomic structure and probable origin of several acid citrus varieties  and the incorrect assignation of ‘Palestinian’ sweet lime to C. aurantifolia by Swingle and Reece [The 454 sequencing of 57 gene fragments covering the nine chromosomes of the haploid citrus set for 26 genotypes revealed that the length of 454 reads and the level of differentiation between the ancestral taxa of modern citrus allowed efficient selection of ancestral species-diagnostic markers. A large number 271) of the 1,053 SNPs mined from the 28,507 kb of amplicon sequence displayed specific G of the 1S1 FigN.D.: non-diagnostic.(TIF)Click here for additional data file.S2 Figx axis: discrepancy rate; y axis: number of markers.(TIF)Click here for additional data file.S1 FileCitrus cultivars from genotyping data with 103 SNP markers. Red, blue, yellow, and green correspond to the inferred contributions of C. reticulata, C. maxima, C. medica, and C. micrantha, respectively.Table A, Estimation of the best number of subpopulation in Structure software analysis based on 103 SNP markers. Table B, Means and confidence interval of the contribution of the four basic taxa estimated from 103 SNP markers . Figure A, Structure analysis of 70 (PDF)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(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.