Medicago sativa L.), a species with high economic value but limited genomic resources.Single nucleotide polymorphisms (SNPs) are the most common type of sequence variation among plants and are often functionally important. We describe the use of 454 technology and high resolution melting analysis (HRM) for high throughput SNP discovery in tetraploid alfalfa  sequences and 30,072 singletons, ranging from 100 bp to 6,662 bp in length, with an average length of 541 bp. We identified 40,661 candidate SNPs distributed throughout the genome. A sample of candidate SNPs were evaluated and validated using high resolution melting (HRM) analysis. A total of 3,491 TCs harboring 20,270 candidate SNPs were located on the M. truncatula (MT 3.5.1) chromosomes. Gene Ontology assignments indicate that sequences obtained cover a broad range of GO categories.The alfalfa genotypes selected from We describe an efficient method to identify thousands of SNPs distributed throughout the alfalfa genome covering a broad range of GO categories. Validated SNPs represent valuable molecular marker resources that can be used to enhance marker density in linkage maps, identify potential factors involved in heterosis and genetic variation, and as tools for association mapping and genomic selection in alfalfa. Zea mays L.) , [Genebank: JL881210-JL898333], [Genbank: SRX040822], and [GenBank: SRX040823], respectively for Wisfal short reads, Chilean short reads, Chilean assembly, and Wisfal assembly. For the purposes of annotation, the combined alfalfa transcript assemblies were located on the Medicago truncatula Gene Index (MtGI) Release 10.0 maintained by the Dana Farber Cancer Institute (http://compbio.dfci.harvard.edu/cgi-bin/tgi/gimain.pl?gudb=medicago). A total of 41,350 alfalfa unique sequences (76%) had a blastn hit to 24,358 MtGI sequences . For the 41,350 sequences with hits in the MtGI database, Gene Ontology (GO) categories of their best match were assigned to each sequence. The proportion of alfalfa 454 sequences in each category follows the same general trend as the MtGI  155,120 reads with a total length of 57.5 Mb from genotype Chilean and 177,871 reads with a total length of 67.6 Mb from genotype Wisfal  of the alfalfa 454 sequences had a blastn  hit with 15,932 M. truncatula CDS. We essentially obtained the same result at the amino acid level, in which 35,791 (66%) of the alfalfa 454 sequences had a blastx hit  to 15,870 M. truncatula predicted proteins.The available e Figure , 20,067 -5) with 763 proteins in the soybean Glyma1.0 high confidence gene protein database [-5) with 572 L. japonicus proteins [-5). The remaining 7,045 sequences represent a total length of 2.3 Mb , did not have a significant match when blasted against the NCBI non-redundant nucleotide database suggesting they may be miss-assembled or novel sequences.When combined with the MtGI results, 46,086 (85%) alfalfa sequences had a blastn or blastx hit in MtGI and/or MT3.5 databases. Of the remaining 8,130 alfalfa sequences, 992 of them had a blastx hit  identified in the alfalfa transcriptome were used to design primer pairs  and M. sativa subsp. falcata , which have contrasting phenotypes for water stress tolerance. Sequencing and SNP discovery based on transcriptome sequencing of water stressed plants increased the likelihood of having sequence coverage in genes relevant to water-stress and identification of allelic differences in these genes between the two alfalfa genotypes evaluated in this study.SNP markers have a broad range of applications and a large number of markers are needed to implement association genetics approaches and genomic selection in plants with low levels of LD ,29. LargM. truncatula and alfalfa, same basic chromosome number (x = 8), high degree of sequence similarity and marker co-linearity [M. truncatula genome sequence as a scaffold to align alfalfa transcriptome sequences. Despite the size and complexity of the alfalfa genome, alignments of alfalfa 454 sequences throughout the M. truncatula genome were apparent  using fy Chips . The appM. truncatula and alfalfa, and potentially to other forage legume species including white and red clover. Evaluations of SNP variability, frequency, and distribution, as well as estimates of linkage disequilibrium in alfalfa are now attainable research targets. Applications of crop-related SNP discovery efforts include genetic diversity analysis, increasing the marker density of genetic maps to support high-resolution association and linkage mapping of target traits, facilitation of future genome assembly of this highly complex genome, integration of genetic and physical maps, and increasing the feasibility of integrating marker-dense molecular breeding approaches such as genomic selection in this important forage legume.The large number of candidate SNPs with potential functional relevance identified in this study provides a valuable tool for comparative genomic studies between In order to enhance crop productivity, we need to improve our understanding of the genes involved in plant growth, development, and adaptation to a changing environment including suboptimal soil conditions and biotic and abiotic stress conditions. The 454 EST-based SNP discovery and validation pipeline described here focused on two tetraploid alfalfa subspecies with contrasting water stress tolerance provides a platform for the discovery of agriculturally-relevant genes and corresponding SNPs. The availability of a large number of validated SNPs is a valuable molecular marker resource for genetics, high-resolution linkage and association mapping, and molecular breeding approaches using genomic selection, for practical and agriculturally-relevant improvements in alfalfa which are not feasible using model systems.M. sativa subsp. sativa var. 'Chilean' and M. sativa subsp. falcata var. 'Wisfal' [Two tetraploid alfalfa genotypes  following the manufacturer's recommendations, digested with DNAseI , column purified with RNeasy MinElute Cleanup kit  and quantified using a NanoDrop Spectrophotometer ND-100 . Total RNA integrity was assessed using a Bioanalyzer 2100 . Messenger RNA (mRNA) isolation was performed using Poly(A)Purist(tm) MAG . For the first strand cDNA synthesis, three microgram of mRNA was used with Oligo (dT)12-18 and Superscript II Reverse Transcriptase followed by a second cDNA synthesis (dscDNA) using Superscript First and Double Stranded cDNA synthesis Kit . Double stranded cDNA from roots and shoots from the same genotype was pooled and deep sequenced using Titanium 454 technology at the J. Craig Venter Institute (JCVI) with each genotype occupying half of a 454 sequencing plate.Total RNA was extracted using TRIzol-10 as a cut-off expected value. The TCs and singletons were assigned to the GO categories based on their top hit.The raw sequences were assembled for each genotype separately and then combined Table  using TGM of forward and reverse primer, 1X LightScanner High Sensitivity Master Mix  and 10 mL mineral oil. After an initial denaturation step of 2 min at 95°C, 45 PCR cycles were performed with 30 s of denaturation at 94°C and 30 s at the target annealing temperature, followed by a final hold at 4°C. Samples were then transferred to a LightScanner 384-well system  and a melting cycle was performed by increasing the temperature at 0.1°C s-1 from 56 to 95°C. Melting data was analyzed and visualized with the LightScanner Software with CALL-IT 2.0  using the small amplicon module.Candidate SNP calls were predicted using Perl scripts to parse the transcript assembly files and requiring a minimum coverage of at least two 454 reads for each form of the SNP at the variant position. A confidence score was generated based on the percent of nucleotides matching the surrounding regions of the location of the SNP. Low confidence regions with less than 90% identity where a SNP was called were not included in the final SNP predictions. SNPs were quality controlled by remapping the 454 reads back onto the assembly and checked against the ACE alignment files. Indels were ignored and the process was biased towards getting good quality SNPs vs. getting all the SNPs at the expense of false SNP calls. Primer3 was used to design primers targeting candidate SNPs. Criteria for primer design include a predicted annealing temperature (Tm) of 59°C to 61°C, primer length ranging between 18-24 bp and PCR amplicon lengths of 40 to 200 bp. All PCR reactions were performed in 384-well plates using a 9700 Thermal Cycler  using a total volume of 5 mL per well. The PCR reaction mixture consisted of 5 ng of genomic DNA, 0.25 μThe PCR amplicons from twenty candidate SNP primers were cleaned using Exo/SAP-IT PCR product cleanup kit following the manufacturer's instructions  and directly sequenced using a BigDye Terminator v3.1 Cycle Sequencing Kit  using both forward and reverse primers. The amplicons were analyzed using an ABI 3730 automated sequencer . The sequence data was analyzed and aligned for SNP discovery with Sequencher 4.8 (Gene Codes).M. truncatula genome. PXZ assisted in the bioinformatics analysis of sequence data and SNP identification. MU supervised sample preparation and sequencing. MM managed the overall project and drafted the manuscript. All authors have read and approved the final manuscript.YH identified candidate SNPs, performed SNP validation, and drafted the manuscript. YK and ITJ collected the samples, extracted RNA and prepared cDNA for sequencing. FC and CDT performed 454 sequencing, data assembly, SNP predictions and alignment of reads to the Characteristics of 192 SNP primer pairs developed using alfalfa 454 sequences. The file contains the primer name, SNP category, amplicon size, Tm, and primer sequences.Click here for fileSummary of validation results of 192 SNP in alfalfa using HRM analysis. The file contains the SNP category, number of SNP, and the SNP validation status.Click here for file


The information provided by dense genome-wide markers using high throughput technology is of considerable potential in human disease studies and livestock breeding programs. Genome-wide association studies relate individual single nucleotide polymorphisms (SNP) from dense SNP panels to individual measurements of complex traits, with the underlying assumption being that any association is caused by linkage disequilibrium (LD) between SNP and quantitative trait loci (QTL) affecting the trait. Often SNP are in genomic regions of no trait variation. Whole genome Bayesian models are an effective way of incorporating this and other important prior information into modelling. However a full Bayesian analysis is often not feasible due to the large computational time involved.This article proposes an expectation-maximization (EM) algorithm called emBayesB which allows only a proportion of SNP to be in LD with QTL and incorporates prior information about the distribution of SNP effects. The posterior probability of being in LD with at least one QTL is calculated for each SNP along with estimates of the hyperparameters for the mixture prior. A simulated example of genomic selection from an international workshop is used to demonstrate the features of the EM algorithm. The accuracy of prediction is comparable to a full Bayesian analysis but the EM algorithm is considerably faster. The EM algorithm was accurate in locating QTL which explained more than 1% of the total genetic variation. A computational algorithm for very large SNP panels is described.emBayesB is a fast and accurate EM algorithm for implementing genomic selection and predicting complex traits by mapping QTL in genome-wide dense SNP marker data. Its accuracy is similar to Bayesian methods but it takes only a fraction of the time. Genome-wide association (GWA) studies are being used more often for risk prediction in humans and trait prediction in livestock. Such studies associate individual single nucleotide polymorphisms (SNP) from a dense genome-wide panel with between-individual variation in traits. The GWA provides measures of strength of association and estimates of the size of the effect of each SNP even though SNP identified as being of predictive value are unlikely to be causative. These GWA studies have had limited success as the individual effects of loci are often small and relatively few loci pass the very stringent statistical testing criteria imposed. The detected variants can be used to construct genetic profiles ,2 but joResearchers in other fields, in particular animal and plant breeding, have developed methods of prediction of genetic value that use all available marker information simultaneously and do not apply such stringent tests of statistical significance ,7. Thus,Different statistical approaches to genomic selection have been attempted. One approach is to use the markers to construct the realised relationship matrix, rather than an expected one based upon pedigree, followed by use of this realised relationship matrix in established BLUP procedures . When BLt-distribution for BayesA. As the DE places more density at zero than a t-distribution this suggests that more shrinkage will occur for small effects with BayesL than with BayesA. In fact the original LASSO  with respect to the parameters gj, γ, λ and To maximize the log posterior, we use lgorithm . In the a posteriori (MAP) estimate of gj isIn Additional file Gj is the maximum likelihood (cML) estimate of gj conditional on all other SNP estimates. Hence Gj) after shrinking it toward 0 due to the DE prior for gj. If γ = 1 then gj as the posterior mode for a DE prior is the LASSO [2DEmode) and the other is the posterior mode when the Dirac Delta is the only prior (DDmode). That is,where he LASSO ,11. HoweDDmode is always zero (i.e. DDmode = 0) reflecting the posterior certainty due to the Dirac Delta prior certainty about the SNP effect.as the γ, λ and It is also shown in equation (B9) of Additional file γk is the vector of posterior probabilities at iteration k.where The steps in the EM algorithm using Gauss-Seidel (GS) iteration are as follows:Step 1. Start with an initial set of values for h2), and 2/λ2 and so the total genetic variance is Step 2. For SNP j , calculate Gj values to calculate the posterior probabilities k as shown in equation (A4) of Additional file NP j j = ,…,m, calStep 3. Use the current estimates of gj as shown in equation (7). Then update the ML estimates of γ, λ and Step 4. Repeat Steps 2 and 3 until convergence which is assessed at iteration k using the criterion If needed, fixed effects can be fitted in the model simultaneously with the SNP effects as explained in .Step 2 of the EM algorithm using GS we calculate all possible combinations of b'jbl (i.e. B'B) each iteration. It is more computationally efficient to store the symmetric matrix B'B at the start. However this matrix is of order m × m which will be huge for large SNP panels. To avoid the calculation of B'B we use Gauss-Seidel iteration with residual update (GSRU) as described in [B'B in a heuristic BLUP approach to genomic selection. Basically GSRU avoids the calculation of B'B by using the identity ek+1,j is the vector of estimated residuals at iteration k + 1 for the calculation of Steps 1 and 4 of the EM algorithm need no modification but Steps 2 and 3 need to be changed. The new Steps 2 and 3 are:In ribed in  where itStep 2GSRU. For SNP j , calculate Gj value to calculate the posterior probability k + 1 as shown in equation (A4) of Additional file e using Gj+1. The update of e results from the identity gj.NP j j = ,…, m, caStep 3GSRU. Now update the ML estimates of γ, λ and e should be recalculated periodically (e.g. at each iteration) using e.As mentioned in , e shoulTo benchmark the capabilities of emBayesB the SNP data distributed to participants of the QTLMAS XII workshop was analysed. A summary of the data simulation is given here, with full details available in . An initThere were 6000 biallelic marker loci on six 100 cM chromosomes with a 0.1 cM spacing between marker loci which gave 1000 markers per chromosome. Marker alleles were sampled with equal probability in the founders. QTL effects were sampled from a gamma distribution. The genomic location and allele substitution effects of the 48 simulated biallelic and additive QTL are shown in Figure 1 × 10-8. The prediction equation was used to calculate the GEBV of the 1200 individuals in the validation data set using only the genotype of their 5726 SNP. The accuracy of the prediction equation was determined by correlating GEBV and TBV separately for each of the 3 generations  of the validation data and combined over all 3 generations. The linear regression of TBV on GEBV was also calculated as a slope of one indicates that the GEBV are unbiased. Spearman's rank correlation was calculated for the top 10% of individuals ranked on TBV in the validation data.The prediction equation γ = 1 in each analysis, and also by fixing λ and γ, λ and GEBV were also calculated for GS-BLUP, LASSO and the ICE algorithm. The estimated SNP effects for GS-BLUP were solutions to given in . ICE useλ for some heritabilties. This is probably a reflection of the flat likelihood surface for estimating λ particularly when combined with estimating γ. Hence an upper bound was placed on λ in each analysis with the upper bound being the corresponding λ used as the initial value for the LASSO. If the bound was reached then the current estimate of λ was reset to its initial value. This procedure seemed to produce a good searching algorithm for parameter estimation with emBayesB given the complexity of the likelihood surface.The emBayesB algorithm had difficulty with estimation of The emBayesB algorithm, and indeed all methods in Table emBayesB was the most accurate method of predicting TBV in the validation data over all heritabilities Table . The emBγ = 0.01 produced a correlation of 0.87 when heritability was 0.1  of Additional file n = 500 plus we have used a DE with λs = 1000 (i.e. a Spike at 0) to replace the Dirac Delta function as done in Additional file h in Figure In this section we graphically explore features of emBayesB in order to assist with understanding how the algorithm works. Figure quation A of AdditGj) of gj is far from 0 the conditional posterior resembles the conditional likelihood, but is slightly shifted (or shrunk) toward 0. The mode of the shrunk likelihood is Gj-λσ2(=Gj-0.02) when Gj is much greater than 0. This is the LASSO estimate as the Spike has little influence when Gj is far from 0. However as Gj approaches 0, the conditional posterior becomes bimodal, with the height of the mode at 0 increasing the closer Gj is to 0  it can be shown that the area under the DE part of the conditional posterior is 0.99, 0.67 and 0.18 for Gj values of 0.19, 0.15 and 0.11 as shown in Figure j is in LD with at least one QTL given the data and all other current estimates at iteration k.When the cML estimate ) are 0.1677, 0.0868 and 0.0163 for the same values of Gj. So the MAP estimate of gj is an accurate estimate of the conditional posterior mean. Hence at convergence, it is reasonable to expect that the MAP estimate will be an accurate estimate of the marginal posterior mean of gj. Bayesian MCMC methods use the marginal posterior mean of each SNP in the prediction equation Using numerical integration it can also be shown that the mean of the conditional posterior is 0.1677, 0.0868 and 0.0165 for E and the MAP estimate of gj is explored further in Figure E is given in Appendix 1 of [gj is calculated using equation (7) with γj given by equation (A4). Plots of the E versus Gj are given in Figures Gj are given in Figures λ = 1.0 and the same γ), or when comparing Figures λ and γ = 0.1). Once again it seems that the MAP estimate of gj is an accurate estimate of the conditional posterior mean as found earlier. The difference between the paired curves is largest when γ = 1 and Gj is close to 0 as can be seen in Figures The analytical relationship between the conditional posterior mean dix 1 of , while tγ = 1 in Figure λ. The LASSO's λσ2  when Gj is past the break in the stick. As the value of γ decreases in Figure Gj±λσ2(=DEmode) is shrunk even more, and in a non-linear manner, as Gj approaches the origin, with greater shrinkage for smaller γ values. This is due to the a priori belief that a proportion (1 -γ) of the SNP are 0 and so small values of Gj are more probably 0, and so shrunk more, as γ decreases. In fact the shrinkage is proportional to γj as shown in equation (7).When γ = 0.1, the MAP curves show strong shrinkage to 0 for any Gj values between -2σ and 2σ for all values of λ  as shown in Figure λ increases in Figure m and γ. Hence we need more shrinkage (±λσ2) of large |Gj| values as shown by the different asymptotes in Figure When λ Figure . Even moThis study has developed a fast EM algorithm for genome wide prediction in which there is a joint prediction of breeding value from accumulated SNP data. The benefits of the algorithm are its fast performance, its verity in relation to the proposed model, and the optimality properties it brings from application of the EM algorithm. The time advantage of emBayesB over a full Bayesian analysis is expected to be even greater with dense 500 k SNP panels currently being used in GWA studies. A disadvantage of emBayesB is that no standard errors are routinely available from an EM algorithm. However there are methods of obtaining standard errors with an EM algorithm  and evena priori that an unknown proportion of SNP will not be in LD with QTL through the use of the Dirac Delta function in the prior mixture distribution for the SNP effects. This SNP prior mixture is quite different to that used in the EM algorithm wBSR in [γl (the SNP weight) into the EM modelling process. Using a Dirac Delta function in the prior mixture seems a more theoretically attractive way of modelling the LD between SNP and QTL and produces some appealing analytical results like the posterior probability formula in equation (A4) and the result that the best estimate of a SNP effect can be viewed as a regressed DE mode as shown in equation (7).The predictive power of emBayesB comes from the use of the information-rich prior mixture distribution which is of particular value when the number of QTL is small relative to the number of independent genomic segments . In fact wBSR in  where th wBSR in  is only y data and assuming fixed values for γ, λ and γ, λ and h2 = 0.1 in Table γ, λ and emBayesB is an EM algorithm which has similarities with the fast heuristic algorithm called ICE . ICE useThe simulated example of  used an emBayesB is similar to the empirical Bayes method suggested by  where Baγ is small and λ is small . However emBayesB will estimate values for γ and λ, and so, unlike the Bayesian sigmoid model, emBayesB can adapt its shrinkage such that it is appropriate for the prevailing nature of the data like in Figure The simulated example used in this paper did not show any advantage for emBayesB over the LASSO. However in a simulated example of wavelet denoising,  demonstrThis paper reports an EM algorithm called emBayesB for genome wide prediction in which there is a joint prediction of breeding value from dense SNP marker data. A formulation of the emBayesB algorithm using GSRU is developed to handle large SNP panels. Using a simulated and widely available dataset it was found that the accuracy of emBayesB was similar to Bayesian approaches, but emBayesB took only a fraction of the computational time. Using emBayesB may be a promising solution to the problem found in GWA studies with the use of stringent statistical thresholds. The emBayesB calculation of posterior probabilities of SNP being in LD with QTL may also be useful in the area of SNP subset selection. Due to the fast computational speed, opportunities exist with emBayesB to explore fitting innovative models which could include non-additive genetic variation or even simultaneous fitting of multiple traits. More research is needed to explore the opportunities which emBayesB offers and to benchmark its capabilities.th QTLMAS workshop website http://www.computationalgenetics.se/QTLMAS08/QTLMAS/DATA.html. The program emBayesB is available both as Fortran 90 source code and as a Windows executable in Additional file The simulated data analysed in the paper is available on the 12RKS developed the emBayesB theory, wrote the emBayesB software and the paper. JAW developed aspects of the emBayesB theory and wrote sections of the paper. THEM formulated the basic Gauss Seidel algorithm, and helped formulate the research and writing the paper. All authors read and approved the final version of the paper.Appendix A. A pdf file giving the E-step of the EM algorithm: Derivation of j is in LD with at least one QTL.Click here for fileAppendix B. A pdf file giving the derivation of the estimators Click here for fileemBayesB.zip. The zip file "emBayesB.zip" contains the Fortran 90 source code "emBayesB_gs.f90" and the Windows executable "emBayesB_gs.exe" for the emBayesB program. A readme file gives instructions on using the program and the input/output files. The two input data files "emBayesB_input*.txt" are also in the zip file.Click here for file


Medicago truncatula. We used these data to identify candidate genes and the genetic architecture underlying phenotypic variation in plant height, trichome density, flowering time, and nodulation. The characteristics of candidate SNPs differed among traits, with candidates for flowering time and trichome density in distinct clusters of high linkage disequilibrium (LD) and the minor allele frequencies (MAF) of candidates underlying variation in flowering time and height significantly greater than MAF of candidates underlying variation in other traits. Candidate SNPs tagged several characterized genes including nodulation related genes SERK2, MtnodGRP3, MtMMPL1, NFP, CaML3, MtnodGRP3A and flowering time gene MtFD as well as uncharacterized genes that become candidates for further molecular characterization. By comparing sequence-based candidates to candidates identified by in silico 250K SNP arrays, we provide an empirical example of how reliance on even high-density reduced representation genomic makers can bias GWAS results. Depending on the trait, only 30–70% of the top 20 in silico array candidates were within 1 kb of sequence-based candidates. Moreover, the sequence-based candidates tagged by array candidates were heavily biased towards common variants; these comparisons underscore the need for caution when interpreting results from GWAS conducted with sparsely covered genomes.Genome-wide association study (GWAS) has revolutionized the search for the genetic basis of complex traits. To date, GWAS have generally relied on relatively sparse sampling of nucleotide diversity, which is likely to bias results by preferentially sampling high-frequency SNPs not in complete linkage disequilibrium (LD) with causative SNPs. To avoid these limitations we conducted GWAS with >6 million SNPs identified by sequencing the genomes of 226 accessions of the model legume  Medicago truncatula, a diploid self-fertilizing species with a sequenced reference genome Medicago truncatula have also made this a valuable model system for investigating the genetic basis of agronomic traits in legumes and other crop species M. truncatula as a model for investigating the genetics of complex traits, including legume-rhizobia symbiosis, we developed resources for conducting genome-wide association study (GWAS).Legumes are a highly diverse plant family that contains many economically important species including soybean, peanuts, and alfalfa. Legumes are especially important because they host rhizobial symbionts, which when growing in symbiosis inside of root organs called nodules, convert atmospheric nitrogen (N) into plant usable forms. This symbiosis annually contributes >50 million metric tons of N to managed ecosystems and twice this amount to natural ecosystems M. truncatula accessions to ∼8X mean coverage  mapping, aims to identify genes responsible for naturally occurring phenotypic variation but allows for screening a much larger panel of accessions and, because of ancestral recombination, mapping candidate genes to a much finer scale than is possible in traditional QTL mapping coverage . After ai) explore the genetic architecture of complex traits by investigating the proportion of among accession variance that can be explained by candidate SNPs and the relationships between minor allele frequency and effect size, ii) identify candidate genes underlying important developmental and symbiosis traits, and iii) empirically explore bias associated with conducting GWAS with reduced-representation SNP arrays relative to sequence data.Our primary goals in this work were to Sinorhizobium meliloti. The sample of 226 accessions is much smaller than used in human GWAS studies, where sample sizes often exceed 10,000 individuals, but is larger or similar to the number of accessions used in GWAS in A. thalianaD. melanogastorOryza sativaZ. maysM. truncatula genotypes differ in the number of nodules they form with different rhizobia strains Phenotypic data on three developmental  and five nodulation traits  were collected from each of 226 plant accessions grown in replicate and co-inoculated with two strains of To identify candidate genes responsible for the among-accession variation we conducted GWAS with >6 million SNPs present at a MAF >0.02 using the mixed linear model P-values as candidates underlying phenotypic variation in each trait (2) are >0.3. By contrast, the strongest candidates for flowering time and trichome density are in clusters of high LD. The clustering of flowering time SNPs is particularly distinct, with >75% of candidate SNPs within ∼800 kb on chromosome 7. Interestingly GWAS conducted in rice and A. thalianaFor exploring genetic architecture we considered genes containing or adjacent to the 200 SNPs with the smallest ch trait . Althougch trait . For mosch trait . For exadf = 7,1590, P<0.0001, Mean MAF of candidate SNPs also differed significantly among traits (F 2) in phenotypes .If the candidate SNPs identified through GWAS act additively and capture the majority of genomic variation for that trait, than we would expect a high proportion of the phenotypic variation in a trait be explained in a linear regression in which the candidate SNPs are used as explanatory variables. For our data, linear regression using the top 50 candidate SNPs as potential explanatory variables (17–33 SNPs retained after model simplification by AIC depending on the trait) explained 41–75% of among-genotype variance  , similarP = 3×10−9, P<0.05 after a conservative Bonferroni correction for multiple tests) as well as 7 other candidate SNPs are adjacent to MtFD, an uncharacterized gene in Medicago but with high sequence similarity to A. thaliana FD which controls expression of floral identity genes MtFD, lies the third highest ranking SNP  within a FAR1 homolog, a gene family containing members involved in light signaling and flowering time CONSTANS and FT homologs −9, P<0.05 after a conservative Bonferroni correction for multiple tests) is centered at a MADS-box transcription factor, a family of genes with roles in plant development. The potential for MADS-box genes to affect trichome production has been shown in Petunia where constitutive expression of the MADS-box gene UNSHAVEN causes ectopic trichome production Many of the genes tagged by candidate SNPs have annotated functions that support a role in contributing to variation in the corresponding phenotype. For flowering time, the highest ranking  resistance] and uncharacterized B, F genes) may contribute to differences in strain occupancy, or the GWAS candidates may tag distant regions involved in gene regulation.We find that many candidate SNPs responsible for variation in nodule traits, considering the 200 SNPs with smallest P values as candidates, are located within or near genes that forward genetics previously identified as involved in nodule formation and symbiosis . In addi−7, 1×10−6) tag uncharacterized genes with evidence for expression in the nodules and roots only. Candidates underlying variation in nodule number are also overrepresented among genes with nodule- or root-specific expression; 8 of the top 20 SNPs (40%) associated with the number of nodules in lower roots that tag expressed genes show complete nodule- or root-specific expression, by comparison only 850 of the 21,000 genes (4%) located on chromosomes 1–8 for which expression was assayed show nodule or root specific expression (P<0.001). One of these eight genes is annotated as a nodule-specific glycine rich gene, a member of a small gene family involved in nodule development Also noteworthy is that the top two candidate SNPs underlying variation in the strain occupancy in the lower roots  including the majority of lines contained in the INRA core collection www1.montpellier.inra.fr/BRC-MTR/). Prior to GWAS 62 accessions were excluded; 18 because they are highly diverged from others (We sequenced 288 m others  and 44 bM. truncatula reference genome v.3.5 www.medicagohapmap.org) using GSNAP i) a position was covered by ≥2 unique reads for the 26 accessions sequenced to ∼15X mapped coverage www.medicagohapmap.org), and ii) reads that called a non-reference allele had a quality score ≥10 and variant nucleotides were called by >70% of reads. The >70% of reads calling a variant means that there are no heterozygous sites within individuals. This should have minor effects given high selfing rates in natural populations (>95%) www.medicagohapmap.org/downloads/mt35. Because of the very high SNP density, an average of 1 SNP 50 bp−1 in a species in which previous analyses of genome-wide SNPs indicated LD to extend an average of 3,000 bp Reads that passed initial quality control filtering were aligned to the During February 2011, seeds from each of 226 genotypes were planted into bleach-sterilized 650 ml conetainers filled with an equal mixture of steam-sterilized Sunshine Mix LP5 (low nutrient potting soil) and Turface. Prior to planting, seeds were scarified in sulfuric acid for 5 minutes, rinsed, sterilized in 10% bleach for 90 seconds, rinsed, and cold-stratified (4°C) on sterile filter paper for 4 days. Seeds were then placed in the dark at room temperature for ∼16 hours prior to planting. After planting, one replicate from each genotype was placed in each of eight randomized complete blocks in a single greenhouse room . Pots were adjacent to one another and plants were top-watered with a fine mist sprayer as necessary. Seeds for all accessions are available by submitting an on-line seed request form at medicagohapmap.org or by contacting INRA-Montpellier (Jean-Marie Prosperie) or the Western Regional Plant Introduction Station (WRPIS) at Washington State University directly.7 cells) of a nearly equal mixture of two strains of S. meliloti, M249 and KH46c , that preliminary experiments revealed to differ in nodulation phenotypes. Innocula was grown in TY medium  then diluted 1∶200 (KH46c) or 1∶400 (M249) in 0.85% saline solution.Plants were inoculated two days after planting with 1 ml  and roots below the top 5 cm (lower roots), as well as total nodule number. Plant height (length from cotyledons to tip of the farthest branch) was measured 10 weeks after planting, 1 week before plants were harvested. Time to first flower was assayed every 3–4 days starting 6 weeks after emergence. Plants that did not flower at the time of harvest (11 weeks after planting) were treated as having not flowered in analyses. For GWAS, flowering date was treated as a continuous variable with 9 flowering dates and a 10n-normal , the q-qn-normal  and was GWAS and other analyses were conducted using the least-squares means values for each accession after removing among-block block differences. We used the efficient mixed-linear model approach expedited EMMAX  as impleK) to lessen confounding effects of population structure. The K matrix was calculated in TASSEL using 5,000 randomly sampled SNPs from each chromosome. Correcting for multiple testing in association analyses is problematic because of the large number of tests and dependency of P-values, as well as false discovery rates (FDR) on the distribution of the data M. truncatula nuclear genes found in GenBank. Candidate SNPs that were within 10 kb of a named gene were considered as tagging that gene.In all analyses, we included a kinship matrix  were randomly assigned to accessions (leaving genotype data intact). For each randomized dataset, we repeated the TASSEL analysis and fit a multiple linear model using the 50 SNPs with lowest P values as explanatory variables. We calculated the mean and standard deviation of the adjusted proportion of variance explained from the 20 randomized datasets for each trait. For each randomized data set we also calculated the correlation between the effect size and MAF of the top 50 and 200 SNPs, and the number of SNPs found with MAF 2–5%, 5–10% and >10%. We caution that randomized data are approximate null expectations because, to the extent that the K matrix is used in the mixed-linear model analyses, the data are not fully exchangeable. Nevertheless, the q-q plots of actual to expected P-values , S4 revein silico platforms of 250 K regularly spaced SNPs. Each platform was designed using SNP data from a 26-accession, deeply-sequenced ascertainment panel In silico GWAS was conducted for three traits  by extracting genotype and P- values for SNPs on the in silico platform using the same methods used for the sequence data.To compare performance of GWAS with sequence data to reduced representation genotyping platforms, we generated 100 Figure S1Mean coverage for each of the 226 accessions included in the GWAS.(TIF)Click here for additional data file.Figure S2Histograms of accession means for each trait . Above diagonal are bivariate scatterplots for the 226 accessions means, the line in each plot is the linear correlation between traits. Below diagonal are correlation values between each pair of traits.(TIF)Click here for additional data file.Figure S3Quantile-quantile (Q-Q) plots with and without K for height and nodules in upper roots.(TIF)Click here for additional data file.Figure S4Manhattan, quantile-quantile, and LD (top 50 SNPs) plots for all traits.(PDF)Click here for additional data file.Figure S5Minor allele frequency (MAF) distribution of all SNPs with MAF >0.02 and the top 200 candidates for each of the eight phenotypic traits.(TIF)Click here for additional data file.Figure S6Histograms of minor allele frequency . a) Sequence-based candidate SNPs, b) in silico candidate SNPs, and c) sequence-based candidate SNPs within 1 kb of in silico candidates for nodules in lower roots and strain occupancy in lower roots.(TIFF)Click here for additional data file.Figure S7Neighbor-joining tree based on 5,000 randomly selected SNPs showing relatedness of all 288 sequenced accessions. Trees constructed with other 5,000 SNP samples were qualitatively similar. The distinct clade shown in the middle of the tree represents the 18 accessions that were removed prior to analyses.(PDF)Click here for additional data file.Table S1Results of GWAS conducted on 20 sets of randomized data for each of three traits .(DOCX)Click here for additional data file.Data File S1List of genomic location, annotation, p values, and expression for the top 200 candidates SNPs  for each of the eight phenotypes.(CSV)Click here for additional data file.


Sir,We read with interest the recent review by Santiago Munné entitled, “Preimplantation Genetic Diagnosis for Aneuploidy and Translocations Using Array Comparative Genomic Hybridization” (1). As part of the review of array comparative genome hybridization (aCGH), the author provides additional information on other 24-chromosome preimplantation genetic diagnosis/screening (PGD/PGS) techniques. As a commercial lab that offers single nucleotide polymorphism (SNP) microarray analysis for PGD/PGS, we would like to comment on a few claims that were made within this article regarding SNP microarrays. 1) in the article summarizes the differences between 24-chromosome PGD/PGS techniques. This table contains a number of errors regarding SNP microarray detection capabilities as it groups the different approaches of SNP microarray analysis under a single heading of “SNPs”. SNP technologies that employ a combination of qualitative and quantitative data analysis detect far more abnormalities than those that use just one type of analysis. First, it is not accurate to say that “SNPs” cannot detect tetraploidy. SNP microarrays using qualitative/quantitative analysis can detect some forms of tetraploidy; this method will not detect 2:2 tetraploidy, though is indeed capable of detecting 3:1 tetraploidy. Second, SNP microarray technologies that use a qualitative/quantitative approach can detect meiotic and mitotic duplications without recombination (3); the table incorrectly states that SNP microarray approaches cannot detect these abnormalities. Lastly, it is an exaggeration to state that aCGH approaches are able to detect all unbalanced translocations and SNP microarray approaches can only detect some; both approaches are equally limited in their inability to detect very small deletions and duplications .  had additional detectable abnormalities; however, these additional abnormalities are not named. It is our experience that other abnormalities are not typically found with 69,XXX. Next, the author credits SNP microarray with the ability to detect uniparental disomy (UPD) but then goes on to use the incidence of UPD of chromosome 15 (UPD-15) to say that UPD in general is a very rare event. Chromosome15 is only one of six imprinted chromosomes , which if UPD is present, could lead to the birth of a baby with a severe genetic syndrome (4). We feel that detection of UPD prior to embryo transfer decisions is highly beneficial to, and desired by, couples undergoing IVF with PGD/PGS. Furthermore, in regards to PGD/PGS for reciprocal and Robertsonian translocations, we would like to clarify that not all SNP microarray approaches can differentiate between normal and balanced (carrier) embryos. Last, the author correctly points out that SNP microarray approaches require parental DNA analysis prior to the embryo sample analysis. However, our lab does not charge a cancellation fee for this parental analysis when IVF cycles are cancelled, thus patients do not pay for unnecessary parental testing.et al. , 4[14], 2[15], 2[16], 4[17], 6[18], 4[19], 4[20], 4[21], 4[22]. Others were a combination of polyploidy and aneuploidy,and the vast majority of haploid embryos were hapohaploid i.e. XO, 0[13], 1[14], 2[15], 0[16], 0[17], 1[18], 1[19],0[20], 0[21], 1[22]. The theory advanced already in the 1994 paper is that monospermic polyploid embryos are usually slowly developing ones with some cytoplasmic but not karyoplasmic arrest, with those cells arrested still continuing to duplicate or more its genome, creating complex mosaics. The haploid embryos usually are partegenetic lacking a well-formed spindle and thus divide a single chromosome set at random. About polyploidy, it is well known from the times of FISH , which are diploid instead of haploid, and thus are also selected against by embryologist. The few that escape embryologist detection, might indeed by purely triploid. In our database that is 0.2% of embryos .et al. (2011) in which we argued that the finding of UPD is being exaggerated by the fact that most embryos reported with UPD had other chromosome abnormalities, indicative of complex abnormalities. The finding of UPD is relevant in the absence of another deadly chromosome abnormality; otherwise the classification of the embryo is of complex abnormal, mosaic, or other, not UPD. We refer the authors to our previous paper by Bisignano et al. 2007), all explained by mosaicism. The authors group has yet to present data on cell by cell reanalysis of non-replaced embryos avoiding the question of mosaicism. Even the best diagnosis cannot get around day-3 mosaicism and achieve an error rate below 2-3%, measured by full reanalysis of not replaced embryos. Using FISH to analyze all the other cells of previously analyzed abnormal embryos has a very low error rate since many cells are analyzed and compared to each other, and is a cheap way to validate a technique, but any other technique can be used to validate another one, provided that all the cells of the non-replaced embryo are analyzed to differentiate mosaicism from technical error, something not published yet by the corresponding authors of the letter.Error rates by FISH can be high or low depending on the lab. In our hands was 7% (Colls Sincerely,


FST, significant genetic differentiation appeared between Suffolk and Rambouillet (FST = 0.1621), while Rambouillet and Targhee had the closest relationship (FST = 0.0681). A scan of the genome revealed 45 and 41 differentially selected regions (DSRs) between Suffolk and Rambouillet and among Rambouillet-related breed populations, respectively. Our data indicated that regions 13 and 24 between Suffolk and Rambouillet might be good candidates for evaluating breed differences. Furthermore, ovine genome v3.1 assembly was used as reference to link functionally known homologous genes to economically important traits covered by these differentially selected regions. In brief, our present study provides a comprehensive genome-wide view on within- and between-breed genetic differentiation, biodiversity, and evolution among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds. These results may provide new guidance for the synthesis of new breeds with different breeding objectives.Sheep are among the major economically important livestock species worldwide because the animals produce milk, wool, skin, and meat. In the present study, the Illumina OvineSNP50 BeadChip was used to investigate genetic diversity and genome selection among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds from the United States. After quality-control filtering of SNPs (single nucleotide polymorphisms), we used 48,026 SNPs, including 46,850 SNPs on autosomes that were in Hardy-Weinberg equilibrium and 1,176 SNPs on chromosome × for analysis. Phylogenetic analysis based on all 46,850 SNPs clearly separated Suffolk from Rambouillet, Columbia, Polypay, and Targhee, which was not surprising as Rambouillet contributed to the synthesis of the later three breeds. Based on pair-wise estimates of  Ovis aries) using both Sanger sequencing and the next-generation sequencing platforms http://www.ncbi.nlm.nih.gov/SNP/). In collaboration with the ISGC, Illumina developed the OvineSNP50 Genotyping BeadChip that contains a total of 54,241 SNPs with a marker placed approximately every 46 Kb along the sheep genome (www.illumina.com).During the last five years, the animal genome community has made significant progress in mapping, sequencing, assembly, and annotation of the ovine genome. Based on BAC  end sequences, Dalrymple and colleagues PITX3 gene is responsible for microphthalmia DMP1) as responsible for inherited rickets in Corriedale sheep SLC13A1) gene for chondrodysplasia in Texel sheep Haemonchus contortus in sheep The Illumina OvineSNP50 Genotyping BeadChip has been successfully used in sheep and goat genome research. For example, BeadChip analysis revealed that the http://www.ustargheesheep.org/).It is well known that Suffolk and Rambouillet were developed in England and France, respectively, but the breeding history of American synthetic breeds may be unfamiliar to readers. In brief, Columbia was one of the first breeds of sheep developed in the United States. In 1912, rams of the long wool breeds were crossed with high quality Rambouillet ewes to produce large ewes yielding more pounds of wool and more pounds of lamb. The original cross was made at Laramie, Wyoming, and then moved to the Sheep Experiment Station, Dubois, Idaho, in 1918. Subsequently, Columbia sheep were released to the public http://u-s-s-a.org/). On the other hand, Rambouillet sheep represent a fine wool breed with a well-developed flocking instinct, an extended breeding season, and high-quality fleece (http://www.countrylovin.com/ARSBA/facts.htm). Columbia, Targhee, and Polypay are, however, considered as dual-purpose breeds, because they are fast-growing, high-quality market lambs that also yield heavy, medium-wool fleeces with good staple length http://www.sheepusa.org/).Generally speaking, sheep breeds can be classified into three groups: meat, wool, or dual-purpose breeds based on their breeding objectives. For example, Suffolk is a typical meat breed as the animals possess large body size, rapid growth rate, and high cutability carcasses  deviations from the Hardy-Weinberg equilibrium (HWE) test due to the small number of samples. We did not test chromosome X because the SNPs on chromosome X in males carry only one copy. As a consequence, 46,850 autosomal SNPs were included in linkage disequilibrium, genetic diversity and DSR analyses, while the 1,176 SNPs with non-heterozygous males on chromosome X were used for DSRs analysis only. As shown in Among the 54,241 SNPs on the Illumina OvineSNP50 BeadChip that were genotyped on the 94 sheep DNA samples, we observed that 695 SNPs had no calls, 1,019 SNPs were not genotyped for at least 95% of all the individuals, 1,235 SNPs were monomorphic in all breeds, 350 SNPs could not be assigned to chromosome locations and 2,057 SNPs had MAF ≤0.05 for the whole dataset. By excluding the SNPs described above, the remaining 48,885 SNPs, including 47,597 autosomal SNPs and 1,288 SNPs on chromosome X, were used for further analysis. Of the 1,288 SNPs on chromosome X, 1,176 SNPs identified non-heterozygous males, while 112 SNPs were heterozygous in some rams. This might be related to a homologous region between chromosomes X and Y because our data do not show that the heterozygous regions are random. As suggested by Gautier et al 2 values for pairs of loci were measured along with the physical distance separating the loci and averaged within each breed. As the sheep genome is currently estimated to be 2.86 Gb in size (http://genome.ucsc.edu/), the 46,850 SNPs used in linkage disequilibrium (LD) analysis would have an average inter-marker distance of approximately 60 Kb. As shown in 2 dropped quickly toward its asymptotic value when physical distances reached 200 Kb. More interestingly, the decreasing trends of the average r2 values remained similar among these five breeds, but the Suffolk breed had the highest r2, followed by Columbia, Rambouillet, Targhee, and Polypay, respectively.The rAs shown in FST, significant genetic differentiation appeared between Suffolk (meat breed) and Rambouillet (fine wool breed) (FST = 0.1621). In comparison, Rambouillet-related breeds were not significantly separated (FST = 0.0681–0.0952). In particular, Rambouillet and Targhee had the closest relationship (FST = 0.0681)  .2 = 204.1084–1510.9140, P = 0.0000), indicating that each sheep breed had different numbers of SNPs in each MAF range.Based on minor allele frequency of SNPs, different levels of variation across breeds were observed. As shown in FST estimates, a total of 45 DSRs were identified in genomes between Suffolk and Rambouillet, which contained the top 0.1% of markers  (FRY), which included four of the top 0.1% SNPs of this region, is an evolutionarily conserved protein implicated in cell division and morphology MITF and GHR.Based on the SNP gion 13)  was locagion 13) . Furry hFST among Rambouillet, Columbia, Polypay, and Targhee are shown in FST  (KCTD14) gene ranked highest, but unfortunately, little is known about this gene. Our data also show that both sheep and cattle may share eight DSRs identified among Rambouillet, Columbia, Polypay, and Targhee (Genome-wide distribution of gion 37)  that bel Targhee .FST results showed significant genetic difference between Suffolk and Rambouillet (FST = 0.1621). Between these two distinct breeds, 45 DSRs and 608 candidate genes were identified using the Ovine Genome v3.1 Assembly as a reference. On the other hand, 41 DSRs and 526 genes were also determined among the four Rambouillet-related breeds.In the present study, we used the Illumina OvineSNP50 Genotyping BeadChip to analyze the genetic diversity and genome selection among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds from the USDA, ARS, U.S. Sheep Experiment Station. Our present study determined that close genetic relationships exist within Rambouillet-related breeds: Rambouillet, Columbia, Polypay, and Targhee, while Suffolk sheep are well separated from the Rambouillet-related breeds. The FST averaged 0.1140 but Rambouillet-related breeds were not significantly separated, suggesting that the genetic differentiation is mainly between Suffolk and Rambouillet-related breeds. Not unexpectedly, cluster analysis also clearly showed that Suffolk is genetically distant from the other four sheep breeds  p breeds . These rbouillet . These rhttp://u-s-s-a.org/). In comparison, mature Rambouillet rams, that have been bred to produce high quality wool, are smaller and weigh between 113 to 135 kg while the fleece staple length varies from 5 to10 cm and fiber diameter ranges from 18.5 to 24.5 microns (http://www.countrylovin.com/ARSBA/facts.htm).Meat, wool, and dual-purpose breeds of sheep were developed because these are highly valued traits in sheep production. Based on genetic distance, our results indicated that the meat breed (Suffolk) is very distant from the fine wool breed (Rambouillet), which is very much in line with the functional purpose of the different breeds. For example, sheep selected for meat production generally have greater body weights. Mature weights of Suffolk rams, which have been historically selected for meat production, range from 113 to 159 kg and the fleece is considered a medium wool type with a staple length of 5 to 8.75 cm . It is well-known that this gene affects body growth and decreases fatness FST>0.90) were located in glutamate receptor interacting protein 1 (GRIP1) and ankyrin repeat and sterile alpha motif domain containing 1B (ANKS1B). Many studies have found GRIP1 plays an important role in receptor trafficking, synaptic organization, transmission in glutamatergic and GABAergic synapses and modulating autistic phenotype GRIP1 in livestock. Here our results might provide a new clue for its role in sheep production. Recently, ANKS1B gene had been shown to be associated with body weight index and waist circumference in human GWAS studies ANKS1B gene might be a good growth trait candidate. However, additional studies are required to confirm this speculation. Interestingly, we discovered MITF gene in DSRs (region 37 on OAR 19). This gene accounts for pigmentation phenotypes in cattle ASIP, which controls a series of alleles of black and white coat color FRY, a gene involved in growing wing hairs FRY resulted in the formation of a strong multiple hair cell phenotypes that consisted of clusters of epidermal hairs and branched hairs FRY in livestock. In the present study, Rambouillet is often considered a fine wool breed while Suffolk has rather poor quality wool. Therefore, our results provide strong evidence for the role of FRY in sheep wool development. Additionally, 13 of the 45 DSRs identified in sheep represent those in cattle, suggesting that these genes are targets for selection across multiple species.In the present study, a genome-wide scan or differentiation analysis using CNTNAP5, ADAM23, and PCDHB4 in cell adhesion, ME3, RIMS2, and TDRD7 in cellular respiration, cellular macromolecule/protein localization, and multicellular organism reproduction, respectively. These might result from long-term selection for improved reproduction and wool traits in these four sheep breeds As described above, Columbia, Polypay, and Targhee are related to Rambouillet sheep. Among these four sheep breeds, a total of 41 DSRs were identified . InteresIn summary, we revealed the genetic diversity among Suffolk, Rambouillet, Columbia, Polypay, and Targhee sheep breeds of the United States using the Illumina OvineSNP50 BeadChip. Meanwhile, DSRs between Suffolk and Rambouillet and among Rambouillet-related sheep breeds were also identified with production of a list of candidate genes in these regions based on Ovine Genome v3.1 Assembly. Estimation of genome-wide diversity and identification of DSRs regions provide a powerful method to identify economically important trait-related genes that have been enriched during a long-term selection for different breeding objectives. Furthermore, our results also provide a foundation to further investigate sheep evolution and gene functions in the near future.The U.S. Sheep Experiment Station Animal Institutional Care and Use Committee specifically approved this study (Protocol number: 11–01). All efforts were made to minimize any discomfort during blood collection.In the present study, blood samples were collected from 19 Columbia, 19 Polypay, 16 Rambouillet, 18 Suffolk, and 22 Targhee rams at the U.S. Sheep Experiment Station in Dubois, Idaho. Rams were produced from unique dams and 12, 12, 9, 10, and 17 unique sires of the Columbia, Polypay, Rambouillet, Suffolk, and Targhee breeds, respectively. The number of sheep per breed in this study is similar to the average number of sheep per breed Kijas and coworkers 2 measure between each marker pair within each breed separately using Haploview 4.1 Analysis of minor gene allele frequencies (MAF) was conducted with the chi-squared test using SAS Software for Windows v9.2 . An exact test for Hardy-Weinberg Equilibrium (HWE) FST. An IBS matrix of distance (D) was constructed by Plink v1.07 www.r-project.org).Gene diversity, heterozygosity, polymorphism information content (PIC), and classical F-statistics P value <0.05 was considered to be a statistically significant SNP after Bonferroni correction. Then, estimation of SNP and population-specific FST were based on the model proposed by Nicholson et al FST values were selected as the top significant SNPs; 3) centered on the top significant SNP (0.1%), neighboring markers were included until markers were encountered more than three consecutive SNPs ranking outside of the top significant 5%. We only considered the range between the upstream and downstream 1.5 Mb of the top SNP (0.1%) if the length of candidate regions were more than 3 Mb and combined any two regions as one region if they overlapped. SNP-specific Fst values were smoothed over each chromosome with a local variable bandwidth kernel estimator Fisher’s exact test was performed by R 2.14.0 to compare the allele frequencies between Suffolk and Rambouillet and among Rambouillet-related breed populations first. A SNP with a http://www.livestockgenomics.csiro.au/cgi-bin/gbrowse/oarv3.1/). The functional annotation of target genes for the gene ontology was performed using DAVID bioinformatics resources www.animalgenome.org/repository/pub/USDA2013.0411/.Genes in these DSRs were examined for potential involvement in phenotypes using the Ovine Genome v3.1 Assembly Click here for additional data file.Figure S22 with inter-marker distance for the different sheep breeds.Decay of average pairwise r(TIF)Click here for additional data file.Figure S3Genetic diversity analysis in different sheep breeds.(TIF)Click here for additional data file.Figure S4Minor allele frequencies (MAF) with 46,850 SNPs for different sheep breeds.(TIF)Click here for additional data file.Table S1Classical F-statistics in different sheep breeds.(XLS)Click here for additional data file.Table S2List of ovine positional candidate genes based on the predicted protein coding genes from DSRs between Suffolk and Rambouillet.(XLS)Click here for additional data file.Table S3Gene ontology analysis related to the ovine positional candidate genes from DSRs between Suffolk and Rambouillet.(XLS)Click here for additional data file.Table S4Selection signals identified in both sheep and cattle using the ovine positional candidate genes from DSRs between Suffolk and Rambouillet.(XLS)Click here for additional data file.Table S5List of ovine positional candidate genes based on the predicted protein coding genes from DSRs among Rambouillet, Columbia, Polypay, and Targee.(XLS)Click here for additional data file.Table S6Gene ontology analysis related to the ovine positional candidate genes from DSRs among Rambouillet, Columbia, Polypay, and Targhee.(XLS)Click here for additional data file.Table S7Selection signals identified in both sheep and cattle using the ovine positional candidate genes from DSRs among Rambouillet, Columbia, Polypay, and Targhee.(XLS)Click here for additional data file.


Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs.SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA.We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative  SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples. The power of genome-wide association studies (GWAS) to discover common genetic variants associated with complex traits has been empirically demonstrated -6. The set al  is a complex brain disorder characterized by disturbances in multiple domains of brain function, including cognitive, emotional, and perceptual processes . Evidencbp content, and average content. We found these factors are uncorrelated with pathway significance . The full list of 17 shared pathways is shown in Table The traditional strategy for GWAS studies tests one SNP at a time. Although widely used, single-SNP GWAS analysis does not have adequate power to detect SNPs that have marginally weak, but jointly strong genetic effects. Jointly analyzing SNPs within the same biological pathway simultaneously complements the single-SNP analysis and can reveal new insights to the understanding of complex human traits. Our SNP set enrichment analysis operates on representative SNPs of genes and then combines the effects of SNPs within the same pathway by a weighted Kolmogorov-Smirnov running sum statistic test . This stCompared to gene set enrichment analysis, the SNP set enrichment analysis is a much larger scale and is more computationally challenging. Several pathway-based methods have recently been developed to analyse GWAS ,20,41-44It is also worthy to point out that the number of selected SNPs varies between genes. This is because we used permutations to decide both the number and the set of SNPs to represent each gene. The permutation of phenotypes and recalculation of statistical values for about half a million SNPs and thousands of subjects is computationally expensive. To seek a balance between the computational complexity and not losing too much information from SNPs, we set a nominal significance threshold chose only SNPs with smaller P-value for pathway analysis. To further reduce computation, we recommend using an upper limit for the number of representative SNPs for each gene.et al. [Our method has a critical assumption. In combing P-values of SNPs in a gene we assume that the P-values are independent, although in reality some SNPs in a gene are in linkage disequilibrium (LD). When comparing the results with and without removing SNPs in strong LD, we found there is no big difference between them. However, a future direction is to relax this assumption and develop a SNP selection method that explicitly takes the LD patterns into account rather than remove SNPs in LD. It is interesting to note that Peng et al.  also fouA critical component for the success of the pathway-based analysis is the availability of a comprehensive collection of relevant gene sets related to the disease/genetic trait of interest. Current understanding of gene functions and pathways is still very limited. This is especially the case for neuropsychiatric diseases, as most of the gene sets currently available were generated based on experiments done on tumor cell lines. As a consequence, we have only limited knowledge regarding the pathways involved in brain development, and normal and pathological activities. In this regard, the pathways discovered by SSEA for schizophrenia are likely to be substantially incomplete. We expect the performance would improve as better and more comprehensive disease-related pathways become available. A future challenge is to curate pathways and gene sets in a disease specific way, possibly by taking advantage of the high-throughput functional genomics tools.In summary, we have developed a new SNP-based method, called SNP Set Enrichment Analysis (SSEA), for pathway analysis of GWAS data. SSEA selects a multiple and varying number of SNPs to represent each gene using an adaptive truncated product statistic. The selected SNPs are then ranked and enrichment of pathways is tested using a weighted Kolmogorov-Smirnov test. We tested SSEA in two genetically distinct GWAS studies of schizophrenia with large samples, and discovered 22 significant pathways in the European-American sample and 11 significant pathways in the African-American sample. Eight important pathways were found in both distinct samples providing support for our method.http://cbcl.ics.uci.edu/SSEA/.The SSEA method is coded in a JAVA software package with a user-friendly interface. The software is freely available at Designed the experiments: WL, SGP, YZ and XX; Performed the experiments: WL; Wrote the paper: WL, FM, YZ, and XX; All authors contributed to the analysis, and approved the paper.Supplementary tables and figures on genes and pathways. Supplementary tables and figures on genes and pathways discovered by SSEA.Click here for fileDetailed information on replicated pathways. A excel file containing detailed information on replicated pathways.Click here for file


MUC4 gene, we surveyed the nucleotide variability and linkage disequilibrium (LD) within this gene in Chinese indigenous breeds and Western commercial breeds.MUC4 is a type of membrane anchored glycoprotein and serves as the major constituent of mucus that covers epithelial surfaces of many tissues such as trachea, colon and cervix. MUC4 plays important roles in the lubrication and protection of the surface epithelium, cell proliferation and differentiation, immune response, cell adhesion and cancer development. To gain insights into the evolution of the porcine MUC4 gene were genotyped on 5 wild boars and 307 domestic pigs representing 11 Chinese breeds and 3 Western breeds. The nucleotide variability, haplotype phylogeny and LD extent of MUC4 were analyzed in these breeds. Both Chinese and Western breeds had considerable nucleotide diversity at the MUC4 locus. Western pig breeds like Duroc and Large White have comparable nucleotide diversity as many of Chinese breeds, thus artificial selection for lean pork production have not reduced the genetic variability of MUC4 in Western commercial breeds. Haplotype phylogeny analyses indicated that MUC4 had evolved divergently in Chinese and Western pigs. The dendrogram of genetic differentiation between breeds generally reflected demographic history and geographical distribution of these breeds. LD patterns were unexpectedly similar between Chinese and Western breeds, in which LD usually extended less than 20 kb. This is different from the presumed high LD extent (more than 100 kb) in Western commercial breeds. The significant positive Tajima’D, and Fu and Li’s D statistics in a few Chinese and Western breeds implied that MUC4 might undergo balancing selection in domestic breeds. Nevertheless, we cautioned that the significant statistics could be upward biased by SNP ascertainment process.A total of 53 SNPs covering the MUC4 region. Western breeds exhibited unusual low LD extent at the MUC4 locus, reflecting the complexity of nucleotide variability of pig genome. The finding suggests that high density (e.g. 1SNP/10 kb) markers are required to capture the underlying causal variants at such regions.Chinese and Western breeds have similar nucleotide diversity but evolve divergently in the  The genetic variability of pig genome has been shaped by many evolutionary forces such as domestication and selection. Studying the genetic variability has at least two implications: revealing the evolutionary history of certain breeds; and finding out genomic regions or genes that have been subject to natural or artificial selection, thus helps to dissect the genetic basis of fitness traits.el al. (2008) conducted a worldwide survey of haplotype variability around IGF2, a well-characterized causal gene affecting lean content [FABP4[SERPINA6[PPARD[Analyzing natural and artificial selection pressure on genes of interest can deepen our understanding on these genes. The existing pig breeds have undergone intensive artificial selection during the past decades or centuries, which provide valuable resources for such analysis. For instance, Ojeda content . They shel al. 008 condu[SERPINA6 and PPARMUC4 gene comprises of 26 exons spanning from 65 bp to 22 kb. Exon 2 of human MUC4 is particularly important as it contains a highly variable tandem repeated sequence that encodes a heavily O-glycosylated protein domain forming functional extracellular structures [MUC4 is related to progression of several carcinomas [Mucins are a family of large membrane-bound or secretory glycoproteins normally produced by epithelial cells of tissues . Mucins uctures . Abnormacinomas ,10. Morecinomas .MUC4 expression on uterine epithelial changes during oestrous cycle and early pregnancy, suggesting the relevance of MUC4 in protecting and maintaining the endometrium conditions [S.typhimurium-infected pigs show reduced MUC4 expression on the surface of colonic epithelium compared with non-infected pigs, indicated that MUC4 is important in inflammatory response in colonic tissues [MUC4 polymorphisms that were strongly associated with susceptibility to enterotoxigenic Escherichia coli (ETEC) F4ab/ac in pigs [MUC13 gene proximal to MUC4 (unpublished data). More recently, a variant in MUC4 has been evidenced to be significantly associated with prolificacy traits; MUC4 expression levels in the uterine of high prolific sows are two-fold greater than those in low prolific sows [The functional importance of MUC4 in pigs has also been implicated in several studies. ditions . S.typhitissues . Previouin pigs ,14, althic sows .MUC4 gene in 312 pigs pertaining to 11 Chinese indigenous breeds, 3 Western commercial breeds and 5 wild boars. The findings provide novel insights into evolutionary history and functional importance of the porcine MUC4 gene.In this study, we investigated the nucleotide variability, haplotype phylogeny and linkage disequilibrium within the MUC4 gene from 4 White Duroc and 4 Chinese Erhualin pigs. We selected 58 SNPs for further genotyping according to their uniformly genomic distribution, reliability and informativness. A final set of 53 SNPs passing the filtering criteria with call rates >90% were used for further analyses. The primer sequences and chromosomal positions of the 53 SNPs are shown in Additional file MUC4 gene with an average interval of 1.7 kb. All SNPs were intronic variants except one synonymous mutation on exon 25.We identified a total of 90 SNPs by sequencing 32 amplicons  of the porcine N), Tajimas’s D, and Fu and Li’s D statistics in the tested breeds. In Chinese breeds, Tongcheng pigs were the most variable breeds (πN = 0.29), followed by Jiangquhai and Laiwu (πN = 0.26). In comparison, Jinhua (πN = 0.13) and Hang (πN = 0.16) pigs were the least polymorphic breeds. For three Western commercial breeds, White Duroc is the most variable breed (πN = 0.23) while Landrace has the least nucleotide diversity (πN = 0.16). The overall nucleotide variability of Chinese breeds (πN = 0.27) were larger than that in Chinese Wild boars (πN = 0.19). This could be caused by an underrepresented ancestral genetic pool by the 4 Chinese wild boars used in this study. Overall, nucleotide heterozygosities in Chinese breeds (πN = 0.27) was greater than that in Western breeds (πN = 0.23). This is conceivable as the genetic variability of Chinese breeds has been repeatedly evidenced to be greater than that of Western commercial breeds [IGF2 gene at which intensive selection for growth and lean production has not wiped out genetic variability in Western commercial breeds especially in Duroc and Pietrain [MUC4 in Duroc was comparable to those Chinese obese breeds like Erhualian . In a recent genome-wide scan of nucleotide diversity in Western wild boars and commercial breeds, Amaral et al. found high nucleotide diversity in MHC and olfactory receptor genes, indicating an effect of balancing selection [Table a πN = 0.3 and Hang πN = 0. pigs wera πN = 0.3 and Hana πN = 0.3 and Hand above -10,17,18s πN = 0.. This coP > 0.8) were retained for subsequent analyses of haplotype frequency and phylogeny. We focused on 14 major haplotypes with frequencies higher than 0.02 in the 139 pigs. As haplotypes of wild boars were not reliably inferred, ancestral and derived haplotypes can not be firmly determined for the 14 haplotypes. Of these haplotypes, 12 were found in Chinese breeds while only 5 were evidenced in Western breeds. Chinese breeds thus had higher haplotype diversity than Western breeds, which was in agreement with the previous reports [NR6A1 haplotype, which suggest direct or indirect introgression of Western breeds to Laiwu pigs [A total of 80 haplotypes were reconstructed using the genotypes of 53 SNPs from all 312 pigs, out of which 139 pigs with 278 reliably inferred haplotypes  were inferred  in these breeds [MUC4 region. Our observations suggested that higher density markers e.g., 1SNP/10 kb, are required to capture potential functional DNA variants of genes like MUC4 in the pig genome. In this sense, the current available porcine 60 SNP chip with a marker density of 1SNP/50 kb could lead to false negative result in genome-wide association studies due to insufficient capturing ability of LD markers. Of note, despite having similar haplotype block sizes, the locations of haplotype blocks differ across Chinese and Western breeds , which varied from 0.1 to 0.33 in different breeds. With a threshold of r2 = 0.3, we observed that the LD extended less than 20 kb in all Western breeds. Large White pigs have the smallest LD extent (~5 kb) followed by Landrace (~10 kb) and Duroc (~20 kb). These results are in stark contrast to those reported by Amaral et al. (2008) [We further examined the LD decline against genomic distance in breeds with sample size greater than 10 Figure d. We useMUC4 evolved neutrally or undergo directional or balancing selection. Directional selection usually results in negative values of these statistics while balancing selection causes positive values. We detected significant positive Tajima’ D, or Fu and Li’s D statistics in both Chinese and Western breeds. Especially, all Chinese breeds except Erhualian and Jinhua and all Western breeds except Landrace had significant positive Fu and Li’s D statistics in the MUC4 region . Template DNA was extracted from four White Duroc pigs and four Chinese Erhualian pigs. Amplifications were performed in a 25-μl final volume containing 1.5 mM MgCl2, 0.5 mM dNTPs, 0.5 μM of each primer, 40 ng DNA, and 2.5 unit Taq DNA polymerase . Thermocycling parameters were 95°for 5 min, 35 cycles of 95°for 30 s, optimal annealing temperatures . SNPs were identified through manual checking and verification after aligning the obtained sequences by using the DNAstar software (http://www.dnastar.com/). Highly polymorphic SNPs were chosen for further genotyping the above-mentioned 312 animals by using the MassARRAY SNP genotyping system  or the ABI Snapshot protocol . The MassARRAY assay combined a reproducible primer extension reaction chemistry with MALDI-TOF mass spectrometry to quickly characterize genotypes. The SNaPshot technology allowed for the detection of up to 10 know SNPs in a single run on 3130XL Genetic Anayzer  by using varied length of primer and incorporating a fluorescent labeled dideoxynucleotide at desired SNP site. SNPs passing the filtering criteria with call rates >90% were used for further analyses.To identify N), Tajima’s D, Fu and Li’s D, and the measure of population differentiation (FST) between breed pairs. Only those haplotypes with probabilities of more than 0.8 (P > 0.8) were used for phylogenic and molecular evolutionary analyses. Neighbor-Joining haplotype tree was constructed using MEGA v5 [r2) were created with Haploview V4.2 [ST was drawn using hclust function in R. The LD decay plot were created using the approach described in [r2) were calculated using R package genetics[nls to estimate coefficients that describe the decline of r2 values with distance. Predicted values r2 were plotted against the distances based on the estimates.Haplotype phases were inferred with Phase v2.1.1 . The proMEGA v5 . The hapew V4.2 . The denibed in . In brie genetics. Then, e genetics was fittThe authors declare that they have no competing interests.Conceived and designed the experiments: JR and LH. Performed the experiments: MY, XY, JO and WZ. Analyzed the data: BY, MY, HA and JR. Wrote the paper: BY, MY and JR. Provided comments for the manuscript: LH. All authors read and approved the final manuscript. Primers for identification of SNP markers in the region of MUC4 gene that were genotyped in outbred populations.Click here for file The genomic structure of the porcine MUC4 gene (lower panel) and locations of 53 SNPs covering a 92-kb region around MUC4 (upper and lower panels). Blue boxes indicate exons and thin lines indicate introns. Untranslated regions at 5’ and 3’ end are highlighted in yellow. Exon 2 of MUC4 is a tandem repetitive region in which no SNP was genotyped in this study.Click here for file Minor allele frequencies and heterozygosities of 90 SNPs identified by sequencing 4 Duroc and 4 Erhualian pigs. The 53 SNPs that were genotyped in the 312 pigs were highlighted in red colors.Click here for file The measures of fixation index (lower triangle) and average number of pairwise nucleotide differences (upper triangle) between pairs of breeds.Click here for file


Alfalfa, a perennial, outcrossing species, is a widely planted forage legume producing highly nutritious biomass. Currently, improvement of cultivated alfalfa mainly relies on recurrent phenotypic selection. Marker assisted breeding strategies can enhance alfalfa improvement efforts, particularly if many genome-wide markers are available. Transcriptome sequencing enables efficient high-throughput discovery of single nucleotide polymorphism (SNP) markers for a complex polyploid species.De novo assembly of quality-filtered 72-bp reads generated 25,183 contigs with a total length of 26.8 Mbp and an average length of 1,065 bp, with an average read depth of 55.9-fold for each genotype. Overall, 21,954 (87.2%) of the 25,183 contigs represented 14,878 unique protein accessions. Gene ontology (GO) analysis suggested that a broad diversity of genes was represented in the resulting sequences. The realignment of individual reads to the contigs enabled the detection of 872,384 SNPs and 31,760 InDels. High resolution melting (HRM) analysis was used to validate 91% of 192 putative SNPs identified by sequencing. Both allelic variants at about 95% of SNP sites identified among five wild, unimproved genotypes are still present in cultivated alfalfa, and all four US breeding programs also contain a high proportion of these SNPs. Thus, little evidence exists among this dataset for loss of significant DNA sequence diversity from either domestication or breeding of alfalfa. Structure analysis indicated that individuals from the subspecies falcata, the diploid subspecies caerulea, and the tetraploid subspecies sativa  were clearly separated.The transcriptomes of 27 alfalfa genotypes, including elite breeding genotypes, parents of mapping populations, and unimproved wild genotypes, were sequenced using an Illumina Genome Analyzer IIx. http://medsa.comparative-legumes.org/) and can contribute to future alfalfa research and breeding applications.We used transcriptome sequencing to discover large numbers of SNPs segregating in elite breeding populations of alfalfa. Little loss of SNP diversity was evident between unimproved and elite alfalfa germplasm. The EST and SNP markers generated from this study are publicly available at the Legume Information System ( Alfalfa has also been proposed as a dual-purpose biofuel crop, in which stems could be separated from leaves and used as biofuel feedstocks, with the leaves used for feeding animals.-10 E-value cut-off against individual databases comprising gene models from model plant species A. thaliana, G. max, M. truncatula, O. sativa; BLASTx was performed against the NCBI nr protein database; and InterProScan[M. truncatula 3.5 genome were performed using GMAP[Reads from each alfalfa genotype were quality-trimmed and assembled separately into “synthetic ESTs” using Abyss performirProScan analysising GMAP, and posp value of genotypic contingency test less than 0.01. Genotypic contingency test was conducted for testing independence of reference and variant alleles (rows) across 27 genotypes (columns) in a contingency table of read counts, as described by Myles et al.[The filters we used to call a variant a true SNP are as follows: in at least one of the 27 samples, the allele of the variant was called with at least 2 reads, at bases whose average quality is at least 20 and with 20% frequency within the reads covering the position in that sample. To remove the potential false-positive SNP identifications due to sequencing errors, we added two more filters: a total unique aligned read number greater than 20 across all samples and s et al..http://probes.pw.usda.gov/batchprimer3/) with the following criteria: predicted annealing temperature (Tm) of 59°C to 61°C ; primer length ranging from 18 to 24 bp ; PCR amplicon length of 40 to 130 bp ; PCR amplicon Tm between 70°C and 87°C . In total 192 SNPs were evaluated for SNP validation using the HRM assay  was performed using R package ion 3.67. The resion 3.67.The authors declare that they have no competing interests.XL coordinated the study. XL, ADF, and ECB wrote the manuscript. XL, AA, ADF, JAC, AKB, RSK, YW, YH, and JG analyzed data. GDM oversaw bioinformatics aspects of the experiment and MJM coordinated comparisons of the study to previous SNP work. ECB designed and supervised the entire study. All authors read, contributed to, and approved the final manuscript.Gene ontology assignments for M. sativa, compared to A. thaliana. Gene ontology assignment was found for the 11,222 Arabidopsis best-hit gene models (1× 10-10). Numbers of best hits in each GO category were plotted and compared to that of all of the gene models in the Arabidopsis genome annotation.Click here for fileSNP distribution along the M. truncatula chromosomes. The X-axis represents the genome location (Mbp) for each chromosome. The number of SNP per 1000 bp was calculated for each 0.5 million base pairs interval and plotted on the Y-axis.Click here for fileThe numbers of SNP homozygous and heterozygous for each genotype based on 173,947 SNPs for which sequence information was available for all genotypes.Click here for filePotential candidate genes involved with domestication and/or selection.Click here for filePotential candidate genes differentiating subsp. falcata from subsp. sativa.Click here for fileSNP primer sequences used for validation using high resolution melting (HRM).Click here for file


Eucalyptus breeding programs.Brazil stands on the international scenario by having one of the largest natural forest heritage and extensive sustainable planted forests. Brazilian planted forestry is based mainly on fast growing eucalyptus and pines, with the pulp, paper and steel industries as the major consumers. Introduced commercially in Brazil in the early twentieth century, the eucalypts have experienced increasing levels of genetic improvement over the years. Along with the classical breeding, the use of genetic markers has positively influenced breeding programs, contributing to quality control processes of clonal forestry and advanced breeding. Microsatellite markers have been the main tool used to date. They are multiallelic, highly polymorphic and thus very efficient for several applications that require identification and discrimination of elite clones and determination of parentage. While di and tri-nucleotide repeat microsatellites tend to be more polymorphic, tetra and higher order repeats allow a more robust allele calling . SNP marE. grandis, E. urophylla, E. globulus, E. nitens, E. camaldulensis and E. dunnii) were genotyped with three different sets of markers: (1) 24 di- and trinucleotides microsatellites (2) 17 tetra-, penta- and hexanucleotides microsatellites and (3) selected subsets of SNPs from the 768 developed earlier [Samples belonging to six species of Eucalyptus .The panel of di- and trinucleotide microsatellites showed the fastest increase in the combined paternity exclusion with the addition of new markers. For example, a PE > 99% was achieved with only five markers in Eucalyptus. We are now extending this analysis to controlled and spontaneous hybrids to compare the estimates of genomic ancestry provided by the three marker panels. Additionally we are developing much larger sets of SNPs by re-sequencing reduced genomic representations of pooled individuals from all six species. This should allow a targeted selection of more ancestral SNPs that are homogeneously polymorphic across species to be used for fingerprinting and parentage or historically more recent SNPs that display contrasting allele frequencies among species and thus be more useful for genomic ancestry determination. We anticipate that despite the lower individual genetic information content, careful selection of SNPs panels for targeted applications coupled to their accurate genotype calling and high throughput will become an increasingly attractive alternative for operational application in breeding programs.Results show that di- and trinucleotide microsatellites tested are around three times more informative than the tetra-, penta- and hexanucleotide microsatellites used and about seven times more informative than the specific SNPs employed for assessment of parentage and individual identification. Although more informative, the di- and trinucleotide microsatellites are subject to a higher inaccuracy in allele calling, which can result in inconsistencies across analyses carried out in different labs.All three marker panels showed high resolution power to detect genetic structure and carry out assignment tests in species of