Single nucleotide polymorphisms (SNPs) have been associated with many aspects of human development and disease, and many non-coding SNPs associated with disease risk are presumed to affect gene regulation. We have previously shown that SNPs within transcription factor binding sites can affect transcription factor binding in an allele-specific and heritable manner. However, such analysis has relied on prior whole-genome genotypes provided by large external projects such as HapMap and the 1000 Genomes Project. This requirement limits the study of allele-specific effects of SNPs in primary patient samples from diseases of interest, where complete genotypes are not readily available.In this study, we show that we are able to identify SNPs de novo and accurately from ChIP-seq data generated in the ENCODE Project. Our de novo identified SNPs from ChIP-seq data are highly concordant with published genotypes. Independent experimental verification of more than 100 sites estimates our false discovery rate at less than 5%. Analysis of transcription factor binding at de novo identified SNPs revealed widespread heritable allele-specific binding, confirming previous observations. SNPs identified from ChIP-seq datasets were significantly enriched for disease-associated variants, and we identified dozens of allele-specific binding events in non-coding regions that could distinguish between disease and normal haplotypes.Our approach combines SNP discovery, genotyping and allele-specific analysis, but is selectively focused on functional regulatory elements occupied by transcription factors or epigenetic marks, and will therefore be valuable for identifying the functional regulatory consequences of non-coding SNPs in primary disease samples. However, despite the availability of these massive datasets, our understanding of the functional importance of most genomic variants is still very limited. Genome-wide association (GWA) studies have been the main method to associate genomic variants with a variety of phenotypes including diseases. SNP genotyping is performed in large numbers of disease individuals and controls to find statistically significant associations between SNPs and diseases . In . In 8]. We have previously shown that the transcription factor CTCF exhibits allele-specific binding bias at hundreds of heterozygous SNP sites in the human genome . This stWe carried out SNP discovery from ChIP-seq data that we generated for the transcription factor CTCF in 10 human cell lines, including six lymphoblastoid cell lines that had been previously sequenced and genotyped by the 1000 Genomes Project, embryonic stem cells (H1 ESCs), vascular endothelial cells (HUVEC), and normal and disease fibroblasts  were sequenced to an average depth of 42X coverage, and complete genotype information for these six individuals was published [More than 90% of the SNPs we identified de novo using CTCF ChIP-seq data in the previously resequenced cell lines overlapped Pilot 2 SNP calls. Because DNA recovered after ChIP represents only a small portion of the genome, we discovered only 3-7% of all Pilot 2 SNPs. However, if we considered only Pilot 2 SNPs within regions where we had at least 5X coverage in the CTCF ChIP, more than 70% of such Pilot 2 SNPs were recovered in our discovery  and those that were not found in Pilot 2 and were therefore novel (novel SNPs). First, we established by genomic sequencing that our novel SNPs were as accurate as G1000 SNPs Table . We thenWe examined the genomic distribution of the SNPs we identified from ChIP-seq data. Compared with Pilot 2 SNPs, a larger proportion of the SNPs we discovered from RNAPII and H3K4me3 ChIP-seq data were localized to the 5’ UTR, reflecting RNAPII and H3K4me3 localization over the transcription start site (TSS) region Figure . In contWhen SNP discovery is carried out using ChIP-seq data, it might be expected that the highest SNP density occurs around significant ChIP peaks which are identified on the basis of normalized read coverage. We therefore investigated the distance between identified SNPs and the corresponding ChIP peak positions. Most SNPs from CTCF ChIP-seq data were indeed localized around −200 to +200 bp from CTCF peak binding positions, but interestingly, the number of CTCF SNPs dropped at the very center of CTCF peaks Figure . Since CIn contrast to CTCF SNPs, SNPs discovered from RNAPII ChIP-seq data showed only a modest enrichment around RNAPII binding peaks Figure . PhylogeAnalysis of the chromosomal distribution of SNPs discovered from CTCF ChIP-seq data showed that the relative SNP density on the X chromosome was markedly lower than that of the autosomes Figure . This waFrom SNP discovery with CTCF ChIP-seq data in multiple cell lines, we found that the number of SNPs discovered varied considerably between cell lines Table . BecauseSince SNPs within transcription factor binding regions are of the greatest interest from the perspective of understanding how genetic variation affects transcription factor binding, we were interested in understanding how increased sequencing depth affected SNP discovery in those ChIP-ed regions. For this purpose, we used CTCF ChIP-seq data in Progeria fibroblasts where we had the greatest sequencing depth. We defined ChIP-ed regions as regions covered by 5 or more reads and analyzed the total number of SNPs, and SNPs discovered within ChIP-ed regions as a function of sequencing depth. While the total number of discovered SNPs increased linearly with increasing sequencing depth, the number of SNPs within ChIP-ed regions increased more slowly and almost plateaued after ~32 million reads. Furthermore, with increased sequencing depth, the percent of total SNPs within ChIP-ed regions decreased, suggesting that the additional SNPs discovered with deeper sequencing occur outside of the ChIP-ed regions and are therefore less likely to be relevant for transcription factor binding Figure .We have previously found that at a subset of assayable transcription factor binding sites, CTCF displays an allelic binding bias in that there is significant difference in its occupancy of the two alleles . This alA pairwise comparison of binding bias between two individuals, at shared heterozygous SNPs where the bias was statistically significant in at least one of the two individuals, showed that binding biases were largely concordant across individuals Figure . The SpeWe then extended this bias analysis to normal and disease fibroblasts that had not been previously genotyped, and found CTCF allelic binding bias between these two lines was similarly well correlated  and assessment of allele-specific effects directly from the ChIP or other functional experimental data, without requiring a separate genotyping dataset. We found that SNPs at transcription factor binding sites discovered from ChIP-seq data show an overrepresentation of disease-associated SNPs. Interestingly, previous studies have also shown an enrichment of GWAS SNPs in active intergenic chromatin regions that are defined partly by high CTCF binding . AdditioWhile genotyping using arrays or deep sequencing might appear to be straightforward, arrays generally cover only a small subset of all possible polymorphisms, and whole genome resequencing requires far deeper sequencing coverage than required for typical ChIP-seq or RNA-seq assays. The fact that we could identify and verify SNPs that were missed in the 1000 Genomes Project genotypes suggests that the selective focus of our method on biochemically functional elements offers an advantage even over whole-genome sequencing, likely because our sequencing coverage over these loci is higher at these enriched loci. While it is possible that a subset of phenotypic effects are due to rare "private" variants, distinguishing whether a rare non-coding variant has a functional consequence or is neutral is challenging. The approach outlined here can not only identify such rare variants but also immediately establish whether it significantly affects the experimental assay that was used to identify it in the first place. We propose that by analogy to exome sequencing which is aimed at identifying variants in coding regions, the approach we have used here can be considered to be "targetome" sequencing, in which variants in functional non-coding regions are identified along with information on how these variants affect enrichment in the functional assay. Despite its potential, technical issues are likely to arise in certain domains. For example, one application might be to evaluate the allelic effects of non-coding somatic mutations and germline polymorphisms in cancer, a disease where the underlying affected cells are readily identifiable (as tumors) and can be isolated for experimental analysis. However, cancer cells are often aneuploid and tumor samples from patients are often genetically heterogeneous, so bias analysis for transcription factor binding or chromatin must account for the background allele composition. SNP arrays have been used to determine copy number differences in cancer cells , so in pAlthough our study focused on ChIP-seq data, combining ChIP-seq and RNA-seq allele-specific analysis in the same samples will clarify the relationship between non-coding regulatory polymorphisms and allele-specific gene expression. Since transcription factor and chromatin changes are likely some distance away from the transcribed region along the linear chromosome, such an understanding will also require knowledge of SNP phasing along individual chromosomes, but haplotype inference based on statistical approaches or direct haplotype resolved sequencing methods can fill this gap ,34. AlleOur approach demonstrates that it is possible to discover SNPs with very high accuracy and simultaneously perform transcription factor binding bias analysis directly from ChIP-seq data. While the overwhelming majority of SNPs discovered from ChIP-seq data coincide with published genotype data, the small number of novel SNPs that we found appear to be indistinguishable from previously published variants based on several criteria. Using SNPs discovered de novo from ChIP-seq data, we confirmed and extended previous observations that allele-specific biases tend to occur towards the same allele across individuals and are therefore consistent with an underlying genetic basis for these binding differences. SNPs discovered from ChIP-seq data are strongly enriched for disease-associated variants, and allele-specific binding at many of these SNPs could allow a functional distinction to be made between normal and disease haplotypes.http://genome.ucsc.edu/ENCODE/). H3K4me3 and H3K27me3 ChIP-seq data was also downloaded from the ENCODE website. RNA-seq data for GM12891 was from a previously published study [http://www.genome.gov/gwastudies) was downloaded and the data as accessed on Feb 23 2012 was used for our analysis.All CTCF and RNAPII ChIP-seq and peak finding was performed by our group as described earlier  and the ed study . The staRaw ChIP-seq sequencing reads were aligned to the human reference genome NCBI36 (hg18) with BWA . To ensuBias analysis was performed as described previously . To avoiThe authors declare no competing interests.VRI and YN conceived of and designed the study. YN and AB carried out the computational analysis. AWH performed the experimental validation. VRI and YN wrote the manuscript. All authors read and approved the final manuscript.Figure S1. Diagram of SNP discovery pipeline. Figure S2. Numbers of Pilot 2 SNPs rediscovered correlate with ChIP-seq coverage. For each trio cell line, the percentage of Pilot 2 SNPs rediscovered with ChIP-seq data is plotted together with percent of the genome with at least 5X coverage from ChIP-seq. Figure S3. Validation of de novo discovered SNPs by genomic sequencing. The top row shows examples of SNPs discovered de novo from ChIP-seq data that were also genotyped in that individual by the 1000 Genomes Pilot 2 Project. The remainder are examples of SNPs discovered de novo from ChIP-seq data but missed in the 1000 Genomes Pilot 2 set in that individual (GM cell lines) or found in ungenotyped lines . The top of each panel shows the genomic DNA sequence, with the SNP at the center in bold. Chromosomal coordinates, transcription factor/histone modification, and cell line are listed below the chromatogram. Figure S4. SNP calling in low coverage regions. (A) Location overlap and genotype overlap between CTCF ChIP-seq SNPs and Pilot 2 SNPs. Location overlap is when the SNP location and alleles match, but sometimes only one allele of a heterozygous genotype is observed in the other set. Genotype overlap refers to an exact genotype match. (B) Percent heterozygosity for CTCF ChIP-seq discovery SNPs and Pilot 2 SNPs. (C) Read number filtering increases discovery SNP heterozygosity and genotype overlap with Pilot 2 SNPs. SNPs covered by less than the indicated number of reads were filtered out. Blue bars represent the number of SNPs passing the filter. Red squares represent SNP heterozygosity and green triangles represent the percent genotype overlap with Pilot 2 SNPs, both on the secondary Y axis on the right. Figure S5. Individual distribution of SNPs. G1000 SNPs and novel SNPs discovered in the indicated GM cell lines. CTCF ChIP-seq samples were categorized according to their individual distribution. ‘1’ represents SNPs found in only one of the six individuals, ‘2’ represents SNPs found in two people and so on. Figure S6. Pilot 2 SNP distribution around (A) CTCF and (B) RNAPII ChIP peak centers and (C) transcription start sites. (D) Conservation scores around transcription start sites. All distances are in bp. Figure S7. CTCF allelic binding bias at Pilot 2 SNPs was plotted similarly as in Fig. 5. The inset tables show the Spearman correlation coefficients (top) and Spearman P values (bottom). Figure S8. CTCF allelic binding bias at discovered SNPs in Progeria and FB8470  fibroblast cells. Table S1. Description of apparent errors. This table lists all 6 discrepancies that we observed between genotypes called from ChIP-seq data and our genomic Sanger sequencing validation (127 out of 133 were exactly correct). For errors 1 and 3, the ChIP-seq data recovered the alternate allele and called it homozygous, but the reference allele was apparently not observed at sufficient coverage. Errors 2 and 4 are discrepant between the ChIP-seq and Sanger genotyping, but our ChIP-seq call matched the 1000 Genomes Pilot 2 genotype. For errors 5 and 6, the ChIP-seq data called it heterozygous and Sanger sequencing reported homozygous (similar to errors 2and 4), but the two alleles reported by ChIP-seq correspond to the two alleles known to occur at that position  according to dbSNP 129. Table S2. Indels called from ChIP-seq data overlap with 1000 Genomes Project indel calls. Table S3. Novel SNPs found by ChIP-seq overlap with SNPs found in other individuals in the same population in the 1000 Genomes Project low coverage data. Table S4. Overlap between biased  SNPs discovered from ChIP-seq data and biased Pilot 2 SNPs.Table S5. Significantly biased allele-specific CTCF binding sites within 500 bp of a GWAS SNP locus. P-val refers to the significance of the allele-specificity binding bias at a heterozygous SNP. Table S6. SNP calling from H3K4me3 and/or H3K27me3 ChIP-seq data in 17 additional cell lines (ENCODE data) as well as from RNA-seq data in GM12891  21:991-8).Click here for fileP value of less than 0.05 are included. Each tab contains information for one individual. CTCF allele-specific binding at SNPs discovered de novo from CTCF ChIP-seq. SNPs with an FDR corrected bias Click here for fileP value of less than 0.05 are included. Each tab contains information for one individual. CTCF allele-specific binding at Pilot 2 SNPs. SNPs with an FDR corrected bias Click here for file


Automated Luminex Genotyping (ALG) algorithm for SNP calling. ALG genotypes were called twice to test for reproducibility and were compared to sequencing data to test for accuracy. Globally, this analysis demonstrates the accuracy (99.6%) of the method, its reproducibility (99.8%) and the low level of no genotyping calls (3.4%). The high efficiency of the method proves that ALG is a suitable alternative to the current commercial software. ALG is semi-automated, and provides numerical measures of confidence for each SNP called, as well as an effective graphical plot. Moreover ALG can be used either through a graphical user interface, requiring no specific informatics knowledge, or through command line with access to the open source code. The ALG software has been implemented in R and is freely available for non-commercial use either at http://alg.sourceforge.net or by request to mathieu.bourgey@umontreal.caSingle nucleotide polymorphisms (SNPs) are the most commonly used polymorphic markers in genetics studies. Among the different platforms for SNP genotyping, Luminex is one of the less exploited mainly due to the lack of a robust (semi-automated and replicable) freely available genotype calling software. Here we describe a clustering algorithm that provides automated SNP calls for Luminex genotyping assays. We genotyped 3 SNPs in a cohort of 330 childhood leukemia patients, 200 parents of patient and 325 healthy individuals and used the  A single nucleotide polymorphism (SNP) is a DNA sequence variation that occurs at a single nucleotide position in the genome. As genotyping has become less expensive, it has become common to attempt to map disease genes via genome-wide scans On the other hand, an important limitation of the Luminex genotyping platform is the lack of a freely available automated genotype calling software. The commercial STarStation/STarBase SNP or MasterPlex GT V2.3 analysis softwares can be purchased respectively from Applied Cytometry®  and MiraiBio® ; otherwise, genotypes must be called manually, which could incur substantial increases in time and in genotype errors due to user subjectivity and human error. In response to the need for additional Luminex genotype calling software, we have developed the Automated Luminex Genotyping (ALG) software package that allows for extensive genotype calling from Luminex assays using either a friendly graphical user interface (GUI) or a command line interface in R. As we describe here, the ALG software is efficient and provides internal quality controls, and is an ideal alternative to the current commercial software. These properties have been confirmed by the blind analysis of a childhood leukemia dataset.ALG was used to genotyped a set of 95 SNPs in a cohort consisting of 300 childhood acute lymphoblastic leukemia patients and 329 healthy controls from the province of Quebec. Of these, 84 SNPs yielded distinct genotype clusters that were subsequently validated by manual inspection, providing a 88% SNP to assay conversion rate. We selected 3 SNPs based on the presence of independent sequence analysis (Sanger sequencing) in order to allow comparing genotypes obtained by ALG methods to those coming from the sequencing experiment considered as true genotypes. These 3 SNPs, rs2267437, rs828907 and rs11685387 were analyzed at blind. Genotypes were called twice, firstly in a process totally automated by ALG using defaults setting and secondly genotype calls were done manually. Manual calls can be easily made by adjusting settings of the software using the GUI  based onhttp://www.miraibio.com/masterplex-gt/gt-overview.html). MasterPlex software gave an overall accuracy of 89% whereas ALG reached 98% when the software is used with a manual management of parameters. Thus when a genotype is called by ALG, this call is highly accurate (99.6%) and reproducible (99.8%). These high levels of reproducibility prove the robustness of the method to experimental variations. Moreover they emphasized an important point where experimental variation that can not be caught by the method will lead to no calls and not to genotyping errors. Despite that manual setting gives more accurate results than the use of default settings, approximately 90% of results show similarity between the two settings. Globally default settings developed based on our experiments are sufficient to provide efficient calls (86.7% of reproducibility and 93.9% of accuracy). However, we recommend users to manually manage parameters: confidence level, threshold value and cut-off value. At the end of the call procedure, it is also possible to manually call unknown genotypes . But once manual adjustments are defined for a SNP, they can be automatically re-used for the genotype call of other samples/plates for the same SNP. The relevance of genotypes called by ALG has been shown in a recent replication study Actually no freely available software has been proposed to automate the genotype calling on the Luminex platform. In order to overcome this situation, we propose the accurate ALG clustering tool. ALG is semi-automated, requiring no prior manual inspection of the microassay Luminex data, and provides numerical measures of confidence for each SNP called, as well as an effective graphical plot  of the dWe investigated ALG performance by genotyping the Quebec childhood acute lymphoblastic leukemia (ALL) cohort. The study population has been previously described Luminex assay analysis gives quantitative values that measure the mean intensities of fluorescence of each allele. So for a SNP marker with alleles A and B, which will give three possible genotypes: AA, AB and BB, the mean intensities are vA and vB. Individuals with genotype AA are expected to have high vA value and low vB value. By contrast, individuals with genotype BB are expected to have high vB value and low vA value. Individuals with genotype AB are expected to have similar vA and vB values. vA and vB values obtained for the 3 SNPs on a subset of the cohort are given in the Individuals with genotype AA will have an ϕ close to 1; an ϕ close to 0 will correspond to individuals with BB genotype and individuals with AB genotype will have an ϕ of approximately 0.5. Following genotyping of a large amount of individuals, this approach will provide three clusters corresponding to pools of individuals with the same genotype.In such case, genotyping will consist of defining the boundaries of each cluster and determining the individuals that belong to them. An accurate definition of clusters will allow unbiased genetic analyses. Ambiguous individuals located outside of clusters will be automatically considered as no call. Four main features could influence the clustering: (1) the cut-off definition allows groups to be created with similar ϕ values; (2) the definition of the group boundaries using the mean value and confidence interval method to determine call accuracy; (3) each group of calls must undergo quality controls to verify that there are no experimental or mathematical aberrations that have biased the calling procedure; (4) this approach must be applicable also to multi-allelic SNPs.The cut-offs are two numerical values (CoD and CoU) that are used to separate the possible range of normalized values in three intervals: low values . High values will allow assignment of AA genotypes, intermediate values for AB genotypes and low values for BB genotypes. The ALG software provides two methods to compute cut-off values. The first method is an arbitrary definition of the cut-offs where CoD is equal to 0.3 and CoU to 0.7. The principle behind this definition is to strictly follow the mathematical definition of genotypes and do not consider experimental variations. In this scenario one expects ϕAA values to be close to 1, ϕBB values to be close to 0 and ϕAB values to be around 0.5 in order for the intervals created by the arbitrary CoD and CoU values to correctly discriminate between the three groups.However, if experimental variation occurs, then the position of three clusters can be skewed. For example a distribution of clusters, where ϕAA values are close to 1, ϕBB values are around 0.3 and ϕAB values are around 0.75, is possible if the probe of the A allele is more luminescent. In that case the theoretical definition of cut-offs (0.3 and 0.7) is not accurate. So we develop in ALG a second cut-off computation method that takes into account experimental variability. For each SNP, the cut-off computation is based on the maximum and the mean of the fluorescence intensities measured for the two alleles as such:Max(vA) is the maximal vA value. The min term represents the minimal value of either the maximal vA value, the maximal vB value or the mean value computed on all vA and vB values ). For the usual situation in which all three genotypes are present, we expect that a sufficient proportion of vA values are higher than vB values (for AA genotypes) and a sufficient proportion of vB value are higher than vA values (for BB genotypes). In that case, the range of vA and vB is large and the mean of the overall value should be lower than the maximum of both vA and vB. So the min value will correspond to mean term. Thus cut-off values will be correlated to the ratio of the global mean reported on the maximum of vA (mean-max ratio). If measures are normally distributed and if probes has equivalent fluorescent level, this ratio will be close to 1/2  and cut-off values close to 2/3 and 1/3 for CoU and CoD, respectively. In that case, the experimental based cut-off definition will approximate the arbitrary values (0.7 and 0.3). However, if experimental specificities provide variation in the mean-max ratio, the cut-off will follow this variation. For example, if the mean-max ratios are equal to 1/3, 2/3 or 1 the CoU values are 3/4, 3/5 and 1/2, respectively, showing the limitation of arbitrary values. Note that the most important factor that can influence the cut-off definition is the probe intensity balance, a variation in the strength of intensity between the two probes of a same SNP. In that case the default cut-off definition will provide the most accurate estimates. On the other hand this method will provide skewed cut-off values when genotyping monoallelic SNPs and in that case the theoretical definition of cut-off (0.7 vs 0.3) should be more appropriate. Finally, as experimental variation can be unpredictable, the software also includes the option of manually specifying the cut-off values by inputting a cut-off value file.Using the two cut-off values (CoU and CoD), normalized mean of intensity values ϕ are separated into three groups: lower, median and higher. To ensure that clusters are accurately defined, we computed confidence intervals under a standard normal distribution using the following formula:1-α/2 is the standard normal value for the quantile 1-α/2. As the normalization step shown in equation A induces upper and lower limits of 0 to 1 for the range of ϕ values, confidence intervals used to define the clusters in each group have the same natural limits. Taking into account these limits, the lower boundary of the lower group (BB genotypes) is limited at 0 and the upper boundary of higher group (AA genotypes) is limited at 1. Therefore confidence intervals are measured only for one boundary of the AA group and for one boundary of the BB group, whereas both upper and lower confidence interval boundaries are computed for the median BA group. Thus we can define the cluster boundaries for each genotype using the confidence interval formula and removing the sample size term:X represents the sample of intensities in each of the three groups formed by the two cut-off values, By default the type I error rate is 0.05, corresponding to the commonly used threshold of significance. However, users are allowed to modify α to reduce or increase the cluster stringency. Note that normalized intensities were approximated with a normal distribution. This implies that for analysis to be performed, certain conditions  should be met. We considered that the conditions are met when the normalization was done on sample of a size higher or equal than 30 individuals for each SNP . For smaller sample size, normalized intensities will not follow a normal distribution implying that both cut-offs and confidence intervals could not be applicable. In that case, ALG software will still provide genotyping calls but they will come with warning messages that inform users of the possible non-normality of the data.We further developed the software to control for quality of genotype calls. ALG currently offers six quality control tools. First, ALG controls the minimum threshold of fluorescence, which represents the minimum measure for each individual to be considered as a potential call. For a given SNP, if the sum of the mean intensity for each probe is lower than a minimum threshold of fluorescence, then the genotype calling of these individuals will not be considered in the analysis and the value THR (for minimum threshold) is returned as call. Based on experimental tests the minimal threshold has been fixed to 300 by default; however users are allowed to adjust it in function of their specific experimental conditions.The next quality control tool of ALG is used only when more than 80% of individuals are found in the same cluster. Using this tool, ALG has the power to investigate whether experimental artefact has biased the analyses. To do so, we perform a second round of computations of the cut-off values and confidence interval boundaries using only the individuals found in this cluster. Clustering of these individuals validates the first round of computation, which indicates that the initial clustering was correct. Grouping of the individuals in different clusters shows that individuals are not homogeneous and need to be divided into different groups. This cancels the first analysis and we continue the analysis using the second cut-off values. New confidence interval boundaries are computed using these new cut-off values and all the individuals used for the first analysis.ALG also contains mathematical quality control tools. During the analysis, it is important to avoid overlap in different clusters. Overlapping cluster will result in two different genotypes for an individual and thus lead to a no call genotype. In case of overlap, the boundaries of the two clusters are moved to create flanking confidence intervals. The break point between the two clusters is determined as the point located at equidistance from the old overlapping boundaries. Limiting the cut-off deviation from the expected 0.3 and 0.7 values is crucial for the analysis. To consider the presence of three possible clusters, the CoU is limited to values higher than 0.6 and CoD to values lower than 0.4. These mathematical controls are essential in order to both limit the accumulation of no calls and to avoid extreme values created by experimental variation that can bias the analysis.Finally, the software has quality control tools that are post calling controls that verify the robustness of the genotype calls. These controls correspond to the computation of both the Silhouette scores An important limitation in most genotype calling algorithms is dealing with multi-allelic SNPs. Ignoring multi-allelic SNPs could leads to bias genetic association studies http://sourceforge.net/projects/alg.Additional improvement should be done to both increase the efficiency of software and reduce the manual management of the software parameters. The high efficiency of the ALG algorithm, proven by the analysis of real data, makes also conceivable to adapt it to other genotyping platforms. The ALG package provides an implementation of this tool in the open source R programming environment that will promote additional development either by actual developers or by external users. Finally the ALG GUI allows a user friendly interface to input data and run analyses without specific informatics knowledge. ALG is actually available for both windows and Linux/Unix operating systems at the project home page: Table S1PCR amplification primers. The PCR amplification primers and the size of the fragment generated using these primers are given for the 3 SNPs included in the performance analysis.(DOCX)Click here for additional data file.Table S2ASPE hybridization primers. The ASPE hybridization primers and the corresponding bead number on the assay are given for the 3 SNPs included in the performance analysis. The lowercase sequence represents the part of the primer sequence specific to the bead whereas the uppercase sequence characterises the SNP locus. The SNP allele corresponds to the last base of the primer sequence represented here in bold. * indicates uppercase sequences designed from the reverse strand.(DOCX)Click here for additional data file.Table S3Median FMI values and genotype call. The median MFI values obtain from the Lunminex analysis with standard sensitivity are provided for the 3 SNPs included in the performance analysis. The values are available for a subset of 38 control individuals. For each SNP and each individual the median MFI values are given for both the reference and the variant probes. The corresponding individual's genotype determined by the ALG software is represented through the background colour of the cells. Blue cells indicate homozygote reference calls, pink cells represents homozygote variant calls and orange cells correspond to heterozygote calls. White cells are used to identify no-calls .(DOCX)Click here for additional data file.


Genotyping-by-Sequencing (GBS) is an excellent tool for characterising genetic variation between plant genomes. To date, its use has been reported only for genotyping of single individuals. However, there are many applications where resolving allele frequencies within populations on a genome-wide scale would be very powerful, examples include the breeding of outbreeding species, varietal protection in outbreeding species, monitoring changes in population allele frequencies. This motivated us to test the potential to use GBS to evaluate allele frequencies within populations. Perennial ryegrass is an outbreeding species, and breeding programs are based upon selection on populations. We tested two restriction enzymes for their efficiency in complexity reduction of the perennial ryegrass genome. The resulting profiles have been termed Genome Wide Allele Frequency Fingerprints (GWAFFs), and we have shown how these fingerprints can be used to distinguish between plant populations. Even at current costs and throughput, using sequencing to directly evaluate populations on a genome-wide scale is viable. GWAFFs should find many applications, from varietal development in outbreeding species right through to playing a role in protecting plant breeders’ rights. To put this into context, it would provide enough data for greater than 100 times coverage of the perennial ryegrass genome  developed by To date, GBS has only been reported for studies aimed at characterizing genetic variation in single genotypes. There are many applications where characterizing genetic variation in populations will be necessary, for example breeding of outbreeding species. Some previous approaches to characterizing genetic variation in populations have used multi-allelic marker systems, such as Simple Sequence Repeats (SSR) that were typed on pooled DNA For each perennial ryegrass variety 0.5 g of seed was weighed out and sprinkled onto wet soil packed in to an eight by eight cm square pot. Seeds were covered with perlite and allowed to germinate in growth cabinets. After eight days, the resulting lawn was cut back to 3 cm using a cutting guide, and the cuttings discarded. The bottom 3 cm was then sampled and placed in envelopes, flash frozen and stored at −80°C. This was performed in four replicates for each of the eight perennial ryegrass varieties Sponsor, Stolon, Glenveagh, Mongita, Bronzyn, Beatrice, Chardin, and Greenway. These will be referred to as ‘sampling replicates’. DNA was isolated from ground tissue.Libraries for GBS were generated according to the protocol developed for maize https://github.com/najoshi/sabre), allowing no mismatches within the barcode. All reads were trimmed back to 64 bp in response to an observed deterioration of quality towards the end of the reads in some sequencing lanes. Command line tools from the FASTX-Toolkit were used to trim adaptors and quality filter reads . Any reads with less than 64 bp after adaptor trimming were eliminated. We used the program ustacks Samples were demultiplexed using sabre   = 29.89, p<0.001 on read numbers. This effect of barcode length limits our ability to cluster barcodes into groups of equal efficiency, due to the need to modulate barcode length. The sequencing of the eight ApeKI GBS libraries on the Illumina GAII instrument yielded between 24 and 28 million sequences per lane, prior to any processing. In a first analysis we demultiplexed all libraries and counted the number of reads assigned to each sample. The Coefficient of Variation (CV) for reads per sample varied between 0.27 and 0.33 for the eight libraries. The design of the ApeKI experiment, using the same 32 barcodes in each of the 8 lanes, allowed us to evaluate the effect of barcode length on the number of reads recovered. There was a significant effect of both barcode, F  = 20.93, The sequencing of the library generated by PstI digestion on the Illumina HiSeq 2000 instrument yielded over 130 million sequences from the single lane. The CV for reads per sample after demultiplexing was 0.62 see . Howeverin silico digestions. ApeKI generated over 2.2 million fragments between 100 and 1000 bp, while PstI generated over 136 thousand fragments between 100 and 1000 bp . The two enzymes tested in this study differ in the frequency of their recognition site within the genome. We used the sequenced genome of maize, which is a similar size to perennial ryegrass, to perform  1000 bp . AlthougIn order to identify a non-redundant set of loci we used the ustacks algorithm on the complete data set for both enzymes. The number of loci generated was 1,955,745 for ApeKI, and 252,879 for PstI. Therefore, based on our data and ustacks settings, there is nearly eight times as much loci available for genotyping when using ApeKI compared to PstI. If the likelihood of sequencing each locus was uniform, ApeKI libraries would require much greater levels of sequencing to ensure genotyping of a common set of loci across all samples with sufficient coverage, which will be particularly important for accurate determination of allele frequencies. However, all loci are not sequenced uniformly, leading to some loci being sequenced far more frequently than others. This is due to enrichment of shorter fragments during the PCR amplification step, and the more efficient bridge amplification of shorter fragments during sequencing on an Illumina flow cell.In order to establish the number of loci we can expect to genotype given different sequencing budgets, we randomly sampled reads (between 1 and 60 million) and mapped them back to the set of consensus loci generated by ustacks. We then determined the number of loci achieving a 5, 10, and 20 X coverage with the different sequencing budgets. The results are plotted in Ensuring that a common set of loci is genotyped across samples requires a strong correlation in the coverage levels of individual loci. Pearson correlation analysis of coverage at loci containing the selected SNP sites  for PstI and ApeKI libraries was performed between samples and clearly demonstrates a high correlation in the coverage at each locus . This coEach perennial ryegrass variety is generated from a polycross of multiple parents, and the resulting populations will possess numerous haplotypes (in excess of 20 in many cases). In order to get an idea of the potential SNP harvest across the eight varieties for each restriction enzyme, we pooled together an equal number of reads from each variety. In the case of the ApeKI libraries, 12 million quality filtered reads were pooled from each variety, and in the case of the PstI libraries, 4 million quality filtered reads were pooled for each variety. Ensuring an equal contribution from each variety was done to obtain more accurate estimates of allele frequencies across the eight varieties. The pool of reads was mapped against the consensus loci, and the Minor Allele Frequency (MAF) was estimated from the read coverage. MAF thresholds of either 1 or 5%, and a minimum of 5 reads supporting each allele was required to call a bi-allelic SNP. Distribution of SNPs within reads showed that there was no bias towards SNP identification in the 3′ end of reads for PstI libraries, and only a slight increase in the number of SNPs found in the last 10–15 bp for ApeKI libraries . Using aAccurate determination of allele frequencies in a population will be important for many applications and will depend on both good sampling procedures and sequence coverage. We performed four ‘sampling replicates’ for each variety to allow testing of the reproducibility of our sampling protocol. This also enabled us to compare allele frequency calls between replicates using various coverage thresholds. If we can determine allele frequencies accurately, we should see a high level of reproducibility in our allele frequency calls between the ‘sampling replicates’. To investigate this we used data from the ApeKI libraries and calculated the frequency of the variant allele in all samples at the SNP positions identified with a MAF threshold of 5% . We lookPrinciple Component Analysis (PCA) was performed to test our ability to distinguish populations, in this case perennial ryegrass varieties, on the basis of GWAFFs. Allele frequencies of the variant allele were determined at reference SNP sites identified with a minimum MAF threshold of 5 percent. For PCA, we only included SNP positions where we were able to call allele frequencies in all samples . At a minimum coverage threshold of 5, allele frequencies were determined at 21,942 and 10,958 SNP positions for ApeKI and PstI libraries, respectively. It is important to note that although we applied a minimum coverage threshold of 5, the actual coverage at many SNP positions will be much higher, particularly for PstI which is sampling a much smaller portion of the genome. In both cases the ‘sample replicates’ clustered together, and populations can be distinguished from each other  method described by Controlling the extent of genome complexity reduction is achieved by choosing different restriction enzymes. We tested two restriction enzymes that differ in the length of their recognition sites. ApeKI and PstI have a 5 bp and 6 bp recognition site, respectively. Furthermore, the ApeKI recognition site includes a 1 bp wobble. The utility of ApeKI for genome complexity reduction has already been reported for maize, where it was found to preferentially cut in the low-copy fraction of the genome GBS offers an excellent tool to generate Genome Wide Allele Frequency Fingerprints (GWAFFs) for a given population. We have demonstrated this by generating GWAFFs for perennial ryegrass varieties, which are synthetic populations derived from a poly-cross of multiple genotypes. We generated GWAFFs in four replicates of each of eight populations to evaluate reproducibility. PCA showed that the ‘sample replicates’ of each variety were indistinguishable based on GWAFFs, which highlights that our sampling protocol is reproducible. Sampling will be an important consideration for generating accurate GWAFFs of a population. In our case it involved germinating a large quantity of seed from each variety and taking consideration to pool approximately equal amounts of tissue from each germinated seedling. However, different applications and biological systems will require their own suitable sampling approach. The ‘sample replicates’ also allowed us to evaluate the reproducibility of our allele frequency calls. We performed pearson correlation analysis between the frequency of the variant allele in each sample. Not surprisingly, reproducibility improves as we increase coverage. At coverage levels greater than 20, we see very strong correlations , but even at relatively low coverage levels of between 5 and 10, we also see evidence of strong correlations .Using a minimum coverage threshold of 5 we were able to distinguish varieties from one another with GWAFFs based on allele frequency calls at 20,942 and 10,958 SNP positions for ApeKI and PstI libraries, respectively. We evaluated our power to distinguish varieties with different SNP numbers, and it was clear that as the number of SNP positions for which we have allele frequency calls increases, our ability to distinguish between varieties improves correspondingly . GWAFFs In addition to distinguishing between varieties, GWAFFs also enable the partitioning of varieties into forages and turf types on the first principle component. This demonstrates the utility of using GWAFFs for analyzing variation on a population level. The usefulness of GWAFFs lies in their ability to measure the frequency of alleles in a population on a genome-wide scale, without the need to genotype a representative number of individuals from a population. Furthermore, like using GBS on single individuals, it incorporates discovery and characterization of genetic variation in a single step. This avoids any problems with SNP ascertainment bias. In this study we performed multiplexing of 32 barcoded samples in all our libraries, however, it is possible to increase the level of multiplexing. The degree of multiplexing that can be achieved will depend on (1) the level of throughput of the sequencing instrument, (2) the degree of genome complexity reduction, and (3) the level of coverage required to accurately call allele frequencies at the required number of SNP positions, given the complexity of the population. Current throughput on the Illumina HiSeq 2000 instrument is up to 1.5 billion single end reads per flow cell or up to 200 million reads per flow cell lane. However, throughput has been continually rising over the years so there is potential that this will increase further in the future. The two enzymes we tested in the current study differed greatly in the number of loci they generated for sequencing, and particularly in the case of PstI, there will be an opportunity to increase the number of populations that can be multiplexed. The variation in coverage across loci means that a percentage will have a relatively high coverage and can be used to generate a high quality GWAFF of the population. As more sequencing is done, more positions will be added to this high quality set. A useful approach for library creation may be to generate a library containing all populations to be multiplexed and sequencing this library on multiple lanes progressively. Using this strategy, the number of reads per sample can be monitored and additional ‘top-up’ libraries can be prepared to boost populations with low output. This will help to keep the variation in coverage between samples low, resulting in less missing values.http://www.forageselect.com/?lang=en). GWAFFs may also have application in breeding and genetic studies of allotetraploid species, where allele dosage is an important consideration. Approaches to determine allele dosage from array based SNP assays, which rely on signal intensity being proportional to allele dosage have already been evaluated GAWFFs have numerous potential applications, including in the breeding of outbreeding species reliant on population-based breeding strategies. These species include forage legumes, forage grasses, oil palm and sugar beet. Many perennial ryegrass breeding programs use full-sib progeny selection in cultivar development, and accurate evaluation of many traits, including yield, must be done in swards Another very interesting application of GWAFFs is in the monitoring of population structure over time. A good example of this is in the case of grasslands, which can undergo re-seeding infrequently. Over the years, it is likely that changes will occur in population allele frequencies, to a point where the population becomes significantly different from the original variety sown. These shifts in allele frequency may be the result of selection pressures not necessarily associated with improved productivity, and therefore the agricultural fitness of the grassland may decrease over time. Breeding varieties that can persist and produce stable grasslands for longer periods would be of significant benefit. GWAFFs could serve as an excellent tool to evaluate varietal persistence and relate changes in GWAFFs to changes in productivity. Other areas where monitoring shifts in genome-wide population allele frequencies may have a role include; monitoring plant pathogen populations over time or in response to different hosts, landscape genomics, and monitoring changes in genebank populations after multiplication. It may also be of assistance in reducing redundancy in under resourced genebanks, where duplicate accessions could be merged or one of the duplicates “archived” to reduce maintenance costs GWAFFs may also have a function in varietal protection of outbreeding populations and allotetraploid species. Variety registration is based on the DUS system, which specifies that a variety must be distinct, stable, and uniform This study highlights the ease at which current genotyping-by-sequencing strategies can be utilized to evaluate allele frequencies in population’s at large numbers of SNP positions across the genome. The resulting profiles have been termed Genome Wide Allele Frequency Fingerprints (GWAFFs), and we have shown that these fingerprints are reproducible and can be used to distinguish between plant populations. Even at current costs and throughput, using sequencing to directly evaluate populations on a genome wide scale is viable. GWAFFs should find many applications, from varietal development in outbreeding species right through to protection of plant breeders’ rights.Figure S1Heatmaps of pearson correlation scores (between samples) of the number of reads mapping to the consensus loci for (A) PstI library, and (B) ApeKI library. The correlations are based on coverage of loci containing SNP positions identified with a Minor Allele Frequency (MAF) of 5%. In the case of multiple SNPs per locus, only the first SNP in the locus was included, leaving over 24,000 data points for PstI, and over 350,000 data points for ApeKI.(TIFF)Click here for additional data file.Figure S2Histogram showing the distribution of SNPs within reads for PstI and ApeKI libraries. SNPs identified with a MAF threshold of 1% are shown on the left and those identified with a MAF threshold of 5% on the right.(TIF)Click here for additional data file.Figure S3Barcharts showing the number of SNPs per loci for ApeKI (top) and PstI (bottom) libraries.(TIFF)Click here for additional data file.Methods S1Evaluation of alternative restriction enzymes tested for their potential to generate suitable GBS libraries for perennial ryegrass.(DOCX)Click here for additional data file.Results S1Tables showing the number of sequences per barcode and samples after demultiplexing both PstI and ApeKI GBS libraries . Also shown is a scatter plot matrix of the number of sequence per barcode across the eight lanes for the ApeKI GBS libraries.(DOCX)Click here for additional data file.Results S2Scatterplots between ‘sampling replicates’ of allele frequencies calculated with different coverage thresholds.(DOCX)Click here for additional data file.


The river red gum or Eucalyptus camaldulensis (Ec) is a fast growing, hardy and highly adaptable eucalypt species acclimatized to Indian climatic conditions and these new advancements would aid in developing new tools and techniques for its improvement. In our knowledge, limited efforts have been undertaken to identify SNP markers in eucalypts either by employing RNA sequencing . A total of 100.5 kb genomic sequence from Ec which has shed important light on quantitative and qualitative distribution of SNPs. In addition, the analysis of EF blocks also provided important guidelines for selection of SNPs for genotyping.Herein, NGS (Illumina) platform was successfully used for identifying ~1,200 SNPs in 41 targeted genes in


The decreasing cost along with rapid progress in next-generation sequencing and related bioinformatics computing resources has facilitated large-scale discovery of SNPs in various model and nonmodel plant species. Large numbers and genome-wide availability of SNPs make them the marker of choice in partially or completely sequenced genomes. Although excellent reviews have been published on next-generation sequencing, its associated bioinformatics challenges, and the applications of SNPs in genetic studies, a comprehensive review connecting these three intertwined research areas is needed. This paper touches upon various aspects of SNP discovery, highlighting key points in availability and selection of appropriate sequencing platforms, bioinformatics pipelines, SNP filtering criteria, and applications of SNPs in genetic analyses. The use of next-generation sequencing methodologies in many non-model crops leading to discovery and implementation of SNPs in various genetic studies is discussed. Development and improvement of bioinformatics software that are open source and freely available have accelerated the SNP discovery while reducing the associated cost. Key considerations for SNP filtering and associated pipelines are discussed in specific topics. A list of commonly used software and their sources is compiled for easy access and reference. Arabidopsis, and rice , Sde novo assembly of a plant genome is achieved using sequence information obtained through a combination of Sanger and/or NGS of bacterial artificial chromosome (BAC) clones, or by whole genome shotgun (WGS) with NGS [De novo assemblies are time consuming and require much greater computing power than read mapping onto a reference genome. The assembly accuracy depends in part on the read length and depth as well as the nature of the sequenced genome. The genomes of Arabidopsis thaliana [In the absence of a reference genome, with NGS . De novothaliana , rice [6thaliana , and maithaliana  were genthaliana , grape [thaliana , and sorthaliana  genomic thaliana .de novo assembly. MIRA is well documented and can be readily customized, but it requires substantial computing memory and is not suited for large complex genomes. Of the freely available software, SOAPdenovo is one of the fastest read assembly programs and it uses a comparatively moderate amount of computing memory. The assembly generated by SOAPdenovo can be used for SNP discovery using SOAPsnp as implemented for the apple genome [Software programs such as Mira , SOAPdene genome . ABySS cDrosophila [Arabidopsis [Aegilops tauschii [The most common application of NGS is SNP discovery, whose downstream usefulness in linkage map construction, genetic diversity analyses, association mapping, and marker-assisted selection has been demonstrated in several species . NGS-derosophila , wheat [osophila , 82, eggosophila , rice [8osophila –86, Arabbidopsis , 88, barbidopsis , 89, sorbidopsis , cotton bidopsis , common bidopsis , soybeanbidopsis , potato bidopsis , flax [9bidopsis , Aegiloptauschii , alfalfatauschii , oat [97tauschii  to name Arabidopsis [SNP discovery using NGS is readily accomplished in small plant genomes for which good reference genomes are available such as rice and bidopsis , 99. Altbidopsis , 82, barbidopsis , 89, oatbidopsis , and beabidopsis  can be abidopsis . NGS reade novo assembly strategies [In theory, a SNP is identified when a nucleotide from an accession read differs from the reference genome at the same nucleotide position. In the absence of a reference genome, this is achieved by comparing reads from different genotypes using rategies . Read asrategies . Threshorategies   and STAMPY [Read assembly algorithms such as Bowtie and SOAP as well as variant calling/genotyping softwares such as GATK  are rapid STAMPY , althougd STAMPY . SNP calls can be significantly improved using filtering criteria that are specific to the genome characteristics and the dataset. For instance, projects aimed at resequencing can compare different datasets from the same genotype and thus eliminate data with large discrepancies. This strategy identifies the most common sources of error and is applied in the 1000 genome project . ReducedSpecific-PCR SNP genotyping system (KASPar) (http://www.lgcgenomics.com/) or the High Resolution Melting (HRM) curve analysis. Validation can serve as an iterative and informative process to modify and optimize the SNP filtering criteria to improve SNP calling. For example, a subset of 144 SNPs from a total of 2,113,120 SNPs were validated using the Goldengate assay on 160 accessions in apple [Prior to any SNP applications, the discovered SNPs must be validated to identify the true SNPs and get an idea of the percentage of potentially false SNPs resulting from an SNP discovery exercise. The need for validation arises because a proportion of the discovered SNPs could have been wrongly called for various reasons including those outlined above. SNP validation can be accomplished using a variety of material such as a biparental segregating population or a diverse panel of genotypes. Usually a small subset of the SNPs is used for validation through assays such as the Illumina Goldengate , KBiosciin apple . Anotherin apple . With thThe two major factors affecting the SNP validation rate are sequencing and read mapping errors as discussed above. NGS platforms have different levels of sequencing accuracies, and this may be the most important factor determining the variation in the validation, from 88.2% for SOLiD followed by Illumina at 85.4% and Roche 454 at 71% . The SNPSNP genotyping is the downstream application of SNP discovery to identify genetic variations. SNP applications include phylogenic analysis, marker-assisted selection, genetic mapping of quantitative trait loci (QTL), bulked segregant analysis, genome selection, and genome-wide association studies (GWAS). The number of SNPs and individuals to screen are of primary importance in choosing an SNP genotyping assay, though cost of the assay and/or equipment and the level of accuracy are also important considerations. http://www.illumina.com/). HRM analysis is suitable for a few to an intermediate number of SNPs and can be performed within a typical laboratory setting. KASPar and SNPline genotyping systems (http://www.lgcgenomics.com/) can be used for genotyping a few to thousands of SNPs in a laboratory setting. The SNPline system is available in SNPlite or SNPline XL versions to allow flexibility in sample number and SNP assays. The iPLEX Gold technology developed by Sequenom (http://www.sequenom.com/) is based on the MassARRAY system which uses primer extension chemistry and matrix-assisted laser desorption/ionisation-time of flight (MALDI-TOF) mass spectrometry for genotyping. Illumina Goldengate is a commonly used genotyping assay because of its flexibility in interrogating 96 to 3,072 SNP loci simultaneously (http://www.affymetrix.com/estore/) and the Illumina BeadChips (http://www.illumina.com/) are capable of validating up to a million SNPs per reaction across an entire genome. Detailed analyses of SNP genotyping assays and their features are reviewed in Tsuchihashi and Dracopoli [The iPLEX Gold system has gained acceptance due to its high precision and cost-effective implementation. High throughput chip-based genotyping assays such as the Affymetrix GeneChip arrays  are limited by their low SNP throughput but can be useful to assay a large number of genotypes with few SNPs. NGS technologies have become viable for genotyping studies and may offer advantages over other genotyping methods in cost and efficiency. Array-based technologies such as Infinium and Goldengate substantially improved SNP genotyping efficiency, but they are species-specific, expensive to design and require specific equipment and chemistry. PCR and primer extension technologies like KASPar and Taqman , restriction-site-associated DNA sequencing (RAD-Seq), and GBS . Of thesde novo genome assembly. We predict tremendous advances in functional genomics and plant breeding from the implementation of GBS because it is truly a democratizing application for NGS in nonmodel plant systems.GBS has the potential to be a truly revolutionary technology in the arena of plant genomics. It brings high density genotyping to the vast majority of plant species that, until now, have had almost no investment in genomics resources. With little capital investment requirement and an affordable per sample cost, all plant researchers now have powerful genomic and genetic methodologies available to them. Uses of GBS include applications in marker discovery, phylogenetics, bulked segregant analysis, QTL mapping in biparental lines, GWAS, and genome selection. GBS can also be applied to fine mapping in candidate gene discovery and be used to generate high-density SNP genetic maps to assist in Arabidopsis [NGS and SNP genotyping technologies have made SNPs the most widely used marker for genetic studies in plant species such as bidopsis  and ricebidopsis . SNPs cabidopsis . The easBrassica [Brassica [A genetic map refers to the arrangement of traits, genes, and markers relative to each other as measured by their recombination frequency. Genetic maps are essential tools in molecular breeding for plant genetic improvement as they enable gene localization, map-based cloning, and the identification of QTL . SNPs haBrassica . The ideBrassica  and maizBrassica  are pracAssociation mapping (AM) panels provide a better resolution, consider numerous alleles, and may provide faster marker-trait association than biparental populations . AM, ofthttp://www.bing.com/search?q=www.genome.gov%2Fgwastudies%2F&src=ie9tr). In plants, such a study was first reported in Arabidopsis for flowering time and pathogen-resistance genes [In the past few years, NGS technologies have led to the discovery of thousands, even millions of SNPs, and novel application platforms have made it possible to produce genome-wide haplotypes of large numbers of genotypes, making SNPs the ideal marker for GWASs. So far, 951 GWASs have been reported in humans  and the second (next) generation sequencing technologies have enabled researchers to characterize DNA sequence variation, sequence entire genomes, quantify transcript abundance, and understand mechanisms such as alternative splicing and epigenetic regulation . Numerous plant genomes are now sequenced at various levels of completion and many more are underway . The NGS


Finding the most parsimonious phylogeny forMost Parsimonious Phylogeny Estimation Problem (MPPEP), which isPhylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove that these constraints can often lead to significant reductions in the gap between the optimal solution and its non-integral linear programming bound relative to the prior art as well as often substantially faster processing of moderately hard problem instances.We provide an indication of the conditions under which such an optimal enumeration approach is likely to be feasible, suggesting that these strategies are usable for relatively large numbers of taxa, although with stricter limits on numbers of variable sites. The work thus provides methodology suitable for provably optimal solution of some harder instances that resist all prior approaches. These relationships are usually described through a weighted tree, called a phylogeny, whose leaves represent the observed taxa, internal vertices represent the intermediate ancestors, edges represent the estimated evolutionary relationships, and edge weights represent measures of the similarity between pairs of taxa.Molecular phylogenetics studies the hierarchical evolutionary relationships among species, or Accurately characterizing evolutionary relationships between organisms and their genomes is the basis of comparative genomic methods for interpreting meaning in sequence data, and for this reason the use of molecular phylogenetics has become widely used (and sometimes indispensable) in a multitude of research fields such as systematics, medical research, drug discovery, epidemiology, and population dynamics. For exan taxa from among plausible alternatives. The criteria can usually be quantified and expressed in terms of objective functions, giving rise to families of optimization problems whose general paradigm can be stated as follows[The literature on molecular phylogenetics proposes a number of criteria for selecting the phylogeny of a set follows:The Phylogeny Estimation Problem (PEP)– T a phylogeny ofT is compatible (according to the selected criterion) for the setf and g, and the phylogeny T∗ that optimizes f and satisfies g is referred to as optimal.where parsimony criterion, which assumes that one taxon evolves from another by means of small changes and that the most plausible phylogeny will be that requiring the smallest number of changes. That evolution proceeds by small rather than smallest changes is due to the fact that the neighborhood of possible alleles that are selected at each instant of the life of a taxon is finite and, perhaps more important, that the selective forces acting on the taxon may not be constant throughout its evolution[hi to hj in T∗ is not smaller than the observed number of changes between hi and hj[One of the classic criteria for phylogeny estimation is the volution,13. Overvolution. Such anhi and hj. The firThe parsimony assumption can be considered accurate in the limit of low mutation rates or short time scales, making it a reasonable model for situations such as analysis of intraspecies variation where little time is presumed to have elapsed since the existence of a common ancestor of all observed taxa. Maximum parsimony also remains valuable as a model for novel methodology development in phylogenetics because of its relative simplicity and amenity to theoretical analysis. Novel computational strategies, such as those developed in this paper, might therefore productively be developed and analyzed in the context of maximum parsimony before being extended to more complicated models of phylogenetics.Most Parsimonious Phylogeny Estimation Problem (MPPEP). Some of the variants of the MPPEP, see e.g.,[Finding the phylogeny that satisfies the parsimony criterion involves solving a specific version of the PEP, called the ee e.g.,,16, can [NP-hard,17 and t[NP-hard,17,18. S[NP-hard or recon[NP-hard,21. In tribed in.In this article, we explore the prospects for improving on the implicit enumeration strategy of,2 using Single Nucleotide Polymorphism (SNP). Specifically, a site is considered a SNP if for a minority of the population a certain nucleotide is observed  while for the rest of the population a different nucleotide is observed . The minor allele, or mutant type[wild type[m over an alphabet Σ = {0,1}[Before proceeding, we shall introduce some notation and definitions that will prove useful throughout the article. The human genome is divided in 23 pairs of chromosomes, i.e., organized structures of DNA that contain many genes, regulatory elements and other nucleotide sequences. When a nucleotide site of a specific chromosome region shows a variability within a population of individuals then it is called a tant type, is genewild type, general = {0,1}.n haplotypes, denotehi(s),s-th allele of haplotypehi and hj,distance between hi and hj, and we say that hi and hj are adjacent ifGiven a setphylogeny T ofG, i.e., an acyclic subgraph of G in which a pair of verticesT ifE, in general a phylogeny ofConsider a graphlength of the phylogenyDenoten haplotypes having m alleles each, find the minimum cardinality haplotype setThe Most Parsimonious Phylogeny Estimation Problem from SNP haplotypes (MPPEP-SNP).Given a setperfect phylogeny condition i.e., the requirement that each allele changes only once throughout the optimal phylogeny , then teny . In . In H′ i problem.Finding the optimal solutions to the MPPEP-SNP is fundamental to validating the parsimony criterion, i.e., to verify whether, for a given instance of MPPEP-SNP, the results predicted by the criterion match the experimental ones. Unfortunately, then haplotypes inn vertices in V. The first task in designing an integer programming model for the MPPEP-SNP that uses a polynomial-size number of variables consists of characterizing V, i.e., determining an upper and a lower bound on the cardinality of the sethi,hj) ofLetSimilarly, denoteained as,31: ui, i ∈ V, as a decision variable equal to 1 if the i-th vertex of V is considered in the optimal solution to the MPEPP-SNP and 0 otherwise;s-th coordinate of the vertex ui, i ∈ V, is 1 and 0 otherwise;i,j ∈ V has a change at s-th coordinate, and 0 otherwise; and yij as a decision variable equal to 1 if the pair of distinct vertices i,j∈Vis adjacent in the optimal solution to the MPPEP-SNP and 0 otherwise. Finally, letQ = {1,2,…,n + LB}. Then, a valid formulation for the MPPEP-SNP is the following:Denote Basic Modeln vertices in V are exactly the ones of the input haplotype sets-th coordinate of vertex ui, i ∈ V, can assume value 1 only if vertex ui is considered in the optimal solution to the problem. Constraints (1d)-(1e) force variablesij ∈ V having a different value at the s-th coordinate. Constraints (1f) impose that in an optimal solution to the problem two distinct vertices ij ∈ V can be adjacent only ifyij may assume value 1 only if both vertices i and j are considered. Vice versa, constraints (1i) impose that if in the optimal solution to the problem a vertex ui, i ∈ V, is considered then at least one variable yij must assume value 1. Constraints (1j) and (1k) impose the Generalized Subtour Elimination Constraints (GSEC)[n + LBvertices in V have to be considered in the optimal solution to the problem.The objective function (1a) aims at minimizing the length of the optimal phylogeny. Constraints (1b) impose that the coordinates of the first s (GSEC). FinallyΣ = {0,1,2,…,γ}. In fact, in such a case it is sufficient to replace each character c in the haplotype by a binary γ -vector ν such that the s-th coordinate of ν is equal to 1 if the character c is equal to s, s ∈ Σ, and 0 otherwise. For example, if the generic haplotype were, for example, the string 〈AACGT〉, then it could be represented as 〈1000 1000 0100 0010 0001〉.Note that Formulation 1 can be easily extended to the case in which the haplotypes are represented by multi-character data, i.e., sequences over an alphabet Formulation 1 is characterized by a polynomial number of variables and an exponential number of constraints. Its implementation can be performed by means of standard branch-and-cut approaches that use GSEC separation oracles such as those described in.yij can be removed from Formulation 1 as it is easy to realize that they are redundant. Similarly, all variablesdij > 1 do not need to be defined as their value will be always zero for anyui, i ∈ Q, do not need to be declared as their value will be always 1 any feasible solution to the problem. Finally, variablesxiŝ = 1. A similar situation occurs when there exists an alleleequal alleles, i.e., by the existence of two alleles, sayIt is worth noting that variablesws as the number of alleles ins-th,Z as the set for which variablesR = {n + LB + 1,n + LB + 2,…,n + UB}, andC ⊂ V, the following reduced formulation for the MPPEP-SNP can be obtained:By applying the previously cited reduction strategies to Formulation 1 and denotingReduced ModelNote that in Formulation 2 variablesui,By exploiting the integrality of variables Constraintsare valid for Formulation 2.ui, i ∈ V∖(Q ∪ {n + UB}), can assume only value 0 or 1. If ui = 0, constraint (3) reduces to ui + 1 ≤ 0 which is trivially valid for Formulation 2. If ui = 1, constraint (3) reduces to ui + 1 ≤ 1 which is again valid. □In a feasible solution to the problem variable ui, i ∈ R, so that vertex ui + 1 can be considered in the optimal solution to the problem only if vertex ui has been already considered.Constraints (3) impose an ordering on the variables Constraintsare valid for Formulation 2.ui,ui fromui ∈ {0,1}. If ui = 0, constraint (4) reduces to In a feasible solution to the problem a vertex ui = 1, constraint (4) reduces to  which is trivially valid. Vice versa, if  which is again valid for the above arguments. □Constraints are valid for Formulation 2.i,j∈Z, can assume only value 0 or 1. IfAs observed in the previous proposition, in a feasible solution to the problemSimilar arguments can be used to prove the following proposition:Constraintsare valid for Formulation 2.hi and hj, there exit multiple equivalent paths that may connect hi and hj in the unary hypercube. This characteristic constitutes the principal class of symmetries for the MPPEP-SNP and may lead to poor relaxations for the problem. For example, suppose that the input haplotype seth1 = 〈00〉 and h2 = 〈11〉. Then a possible solution to the instance may consist either of a star centered in haplotype h3 = 〈10〉 or a star centered in haplotype h3 = 〈01〉-(10) impose an ordering on the coordinates of the vertices inConstraintsare valid for Formulation 2.i,j∈Z, can assume only value 0 or 1. IfIn a feasible solution to the problem, the sumProposition 6 forces vertices ink ∈ V, k ∉ Q3. Then the following proposition holds:LetConstraintsare valid for Formulation 2.hiand hjis greater or equal to three, vertices i and j cannot be adjacent to a same vertex k, i.e., only one of the two sumsIn a feasible solution to the problem the path between two distinct haplotypesk ∈ R then (12) can be strengthened by replacing the right-hand-side by uk. Moreover, Proposition 7 can be generalized as follows. Consider the setsd ∈ {3,4,…,m}, C ⊂ V such that 2 ≤ |C| ≤ d − 1 and C ∩ Qd = ∅, and a path p that involves only vertices in C. Denote pk the k-th vertex in p. Then the following proposition holds:Note that if The family of constraintscalled forbidden path constraints, are valid for Formulation 2.p between two distinct haplotypeshi and hj is greater or equal to d, at most |C| vertices can belong to p. □Similarly to Proposition 7, in a feasible solution to the problem the path In this section we analyze the performance of our model to solve the MPPEP-SNP. Our experiments were motivated by a twofold reason, namely: to evaluate, with respect to Formulation 1, the benefits obtained by the removal of the redundant variables and by the inclusion of the valid inequalities previously described; and to allow the analysis of larger datasets with respect to the ones analyzable by means of[Similar to, we emphWe implemented Formulations 1 and 2 by means of Mosel 64 bit 3.2.0 of Xpress-MP, Optimizer version 22, running on a Pentium 4, 3.2 GHz, equipped with 2 GByte RAM and operating system Gentoo release 7 (kernel linux 2.6.17). In both formulations, we computed the overall solution time to solve a generic instance of the problem as the sum of the preprocessing time due to the implementation of[We considered two different implementations of Formulation2, namely: the GESC-based Reduced Model(GSEC-RM) and the Flow-based Reduced Model (Flow-RM). The GESC-RM consists of Formulation 2 strengthenedby the valid inequalities previously described. TheFlow-RM consists of Formulation 2 strengthened by thevalid inequalities and such that the GSEC are replacedby a multi-commodity flows. Specifically, by denotingIn preliminary experiments we observed that the Flow-RM outperforms the GESC-RM in terms of solution time. This fact is mainly due to the computational overhead introduced by the GSEC separation oracle which seems to be not compensated by the size of the analyzed instances. Hence, we did not consider the GESC-RM any further in our experiments.http://homepages.ulb.ac.be/~dacatanz/Site/Software_files/iMPPEP.zip.During the runtime, we enabled the Xpress-MP automatic cuts and the Xpress-MP pre-solving strategy. Moreover, we also tested a number of generic primal heuristics for the Steiner tree problem to generate a first primal bound to the MPPEP-SNP . UnfortQd, for all d ∈ {3,4,…,m}. Letd ∈ {3,4,…,m}, consider a pair of vertices i,j∈Qd. Then, the forbidden path constraints (13) are violated if there exists a path p having internal vertices in C ⊂ V, 2 ≤ |C| ≤ d − 1, C ∩ Qd = ∅, and such that When using the Flow-RM, the valid inequalities (3)-(12) are loaded together with the reduced model. On the contrary, the valid inequalities (13) are dynamically generated by means of a separation oracle working as follows. Before loading the reduced model, we precompute the sets V∖Qd) ∪ {i,j}. In order to have a fast separation oracle for the forbidden path constraints we do not solve exactly (19) but we use a heuristic approach instead. Specifically, we first sort edges ofv1,v2 ∈ V∖Qd such that edge  has the largest weight. Subsequently, we set C = {v1,v2}, mark v1 and v2 as visited, and build a simple path from vertex i to vertex j passing by v1 and v2. If p is such that (19) is satisfied then we add the constraint Note that searching for the most violated constraint (19) is in generalV∖Qd and iterate this procedure until either 10 distinct paths have been generated or all possible pairs of vertices in V∖Qd have been selected. If all vertices have been selected but less than 10 distinct paths have been generated, then we select a larger subset of V∖Qd  and we iterate again the previous steps. It is easy to realize that this procedure may potentially generate all the possible paths violating (13). However, we stop the procedure after generating 10 paths or after considering subset C containing more than 5 vertices as we observed in preliminary experiments that this strategy provides the best trade-off between speed and tightness of the cut.to the formulation; otherwise, we select a different pair of vertices in ws in the objective function. This inconveniently leads to formulations characterized by slow solution times. To improve this aspect we implemented a different strategy consisting of branching on the following constraints: In preliminary experiments we observed that the standard branching strategy implemented in the Xpress-MP Optimizer is not appropriate for the problem as it is not able to exploit the dissimilarity of the weights or α ∈ {1,2,…,q} andws are very dissimilar among them. This branching strategy can be implemented by introducing a decision variable where α ∈ {1,2,…,q}, and by adding the following constraints for allws and by branching first on variablesui, and subsequently on variablesWe observed that even better runtime performance can be obtained by sorting the coordinates of the input haplotypes in decreasing way according to the weights http://homepages.ulb.ac.be/~dacatanz/Site/Software_files/iMPPEP.zip.In order to obtain a measure of the performance of the Flow-RM, we compared’s polynoTablen∈{100,150,200,250,300}, we created an instance of the problem by generating at random n strings of length 10 over the alphabet Σ={0,1}. During the generation process, we randomly selected the number of SNPs equal to 1 in a given haplotype, and subsequently we randomly chose the sites of the haplotype to be set to 1. We iterated the instance generation process 10 times for a fixed value of n, obtaining eventually an overall number of 50 random instances of the MPPEP-SNP downloadable athttp://homepages.ulb.ac.be/~dacatanz/Site/Software_files/iMPPEP.zip.Interestingly, sometimes in real applications the number of haplotypes can be much bigger than the number of SNPs. Hence, it is important to test the ability of an exact algorithm to tackle instances of the MPPEP-SNP containing e.g., hundreds haplotypes. observedThe results obtained in our experiments are shown in TableThe results showed that the integrality gaps are usually very low, ranging from 0% to 4.63% and assuming in average a value about 1%, confirming once again the tightness of the Flow-RM and the efficiency of the strengthening valid inequalities.Finally, we also tested the performance of the Flow-RM on a set of 5 HapMap Human mitochondrial DNA instances of the MPPEP-SNP that were not solvable by using’s brute-ws,k contiguous SNPs (or k-block) ink ∈ {10,13,15}. We did not consider greater values for k as we observed that k = 15 was already a threshold after which the haplotype submatrix gave rise to too large formulations. For each k-blockk-block maximizing the sumA part from m4, all the remaining instances gave rise to too large formulations  to be handled by the Xpress Optimizer. Hence, instead of analyzing entirely each instance we decomposed it into contiguous SNP blocks and analyzed the most difficult block separately. In more in detail, we defineation of’s reductTablek ∈ {13,15}. Specifically, The Flow-RM exactly solved in less than a minute the instance f1 when considering values of k ∈ {10,13} ; in 20 minutes the instance i2 when considering k = 10 ; in less than 3 minutes the instance k3 when considering k = 10; and the instance m4 in 5 seconds. In contrast, the Flow-RM was unable to solve the instance p5, regardless of the value of k considered. In fact, already when considering k = 10, the Xpress Optimizer took more than 12 hours to exactly solve the instance p5 and explored over 10 million nodes in the search tree. A more detailed analysis of the instance showed that, despite the presence of the strengthening valid inequalities, p5 is characterized by highly fractional relaxations. This fact implies the existence of equivalent optimal solutions to the instance that, on the one hand, delay the finding of a primal bound and, on the other hand, force the Optimizer to explore many more nodes in the tree search. This situation in more pronounced in p5 but also occurs in the instances i2 and k3. To improve the tightness of the formulation we tried to include in the Flow-RM also classical facets and strengthening valid inequalities for the Steiner tree problem in a graph . -38. k ∈ In order to measure the performance of the model on multi-state character data we also considered set of iIn this article we investigated the Most Parsimonious Phylogeny Estimation Problem from Single Nucleotide Polymorphism (SNP) haplotypes (MPPEP-SNP), a recent version of the phylogeny estimation problem that arises when input data consist of SNPs extracted from a given population of individuals. The MPPEP-SNP isribed in,22,26-28ribed in,2 using ribed in’s mixed The authors declare that they have no competing interests.The authors equally contributed to conceive the work and write the article. DC implemented the algorithms and performed computations. All authors read and approved the final manuscript.


Medicago sativa L.) genotypes from 120 accessions to infer chromosome-wide patterns of LD. We also sequenced four lignin biosynthesis candidate genes (caffeoyl-CoA 3-O-methyltransferase (CCoAoMT), ferulate-5-hydroxylase (F5H), caffeic acid-O-methyltransferase (COMT), and phenylalanine amonialyase (PAL 1)) to identify single nucleotide polymorphisms (SNPs) and infer within gene estimates of LD. As the second objective of this study, we conducted association mapping for cell wall components and agronomic traits using the SSR markers and SNPs from the four candidate genes. We found very little LD among SSR markers implying limited value for genomewide association studies. In contrast, within gene LD decayed within 300 bp below an r2 of 0.2 in three of four candidate genes. We identified one SSR and two highly significant SNPs associated with biomass yield. Based on our results, focusing association mapping on candidate gene sequences will be necessary until a dense set of genome-wide markers is available for alfalfa.Association mapping enables the detection of marker-trait associations in unstructured populations by taking advantage of historical linkage disequilibrium (LD) that exists between a marker and the true causative polymorphism of the trait phenotype. Our first objective was to understand the pattern of LD decay in the diploid alfalfa genome. We used 89 highly polymorphic SSR loci in 374 unimproved diploid alfalfa (The online version of this article (doi:10.1007/s00122-012-1854-2) contains supplementary material, which is available to authorized users. Linking DNA polymorphism to trait phenotypic variation is an increasingly important tool for plant breeding programs  to short chromosomal intervals, enabling potentially statistically strong and robust marker-trait associations to be detected  domesticated from the Medicago sativa–falcata complex. Autotetraploidy complicates genetic mapping, but diploid (2n = 2x = 16) relatives of alfalfa exist that share the same karyotype, have highly syntenic genetic linkage groups, and can be hybridized with tetraploid individuals   , ferulate-5-hydroxylase (F5H), caffeic acid-O-methyltransferase (COMT), and phenylalanine ammonialyase (PAL 1). Primers were designed to amplify fragments from these genes ranging in length from 289 to 645 bp. Contiguous fragments were overlapped by 77–327 bp to facilitate haplotype assignments . Three introns were not amplified presumably due to their excessive length  on half of a picotiter plate.We initially targeted to sample one individual from each of 72 accessions from the original 120 accessions for 454 amplicon resequencing  file for use with an in-house Perl script. The script was written to determine single nucleotide polymorphisms (SNPs) and insertion–deletion polymorphisms (indels) within each contig by tallying the number of reads containing the same series of SNPs. Both SeqMan and our Perl script eliminated bases at low frequency (<0.05), variation likely attributable to sequencing errors such as homopolymer regions.Sequences were sorted based on their tags using software provided at r et al. , resultir et al. . M. trunCCoAoMT and F5H. These manually determined haplotypes from CCoAoMT and F5H had similar LD plots as the LD analyses based on unphased SNP genotypes. Therefore, we used unphased SNP genotype data to estimate within gene LD as well as to conduct association tests. Manually determined haplotypes had slightly elevated diversity statistics for CCoAoMT and slightly lower diversity statistics for F5H  compared to haplotypes determined by PHASE  was five, with each of the groups corresponding to biologically meaningful divisions. The three subspecies clearly separated into distinct clusters, and the subspecies falcata and caerulea were each further divided into two subgroups. However, among the subset of 72 genotypes selected for candidate gene association, the optimum number of subpopulations (K) was found to be three , Tajima’s stic Nei , and Watstic Nei  using thstic Nei . DnaSP dP values obtained from MLM using the positive FDR method  and one from subspecies hemicycla (PI 631814) were missing eight of the 16 amplicons while one individual from subspecies falcata (PI 577558) was missing seven of 16 amplicons. COMT contig 2 had the highest level of missing data. Three individuals lacked suitable read coverage for COMT contig 2 to be included in any analyses and 27 individuals were missing one of the three amplicons.A total of 370,779 reads with an average length of 453 bp was generated from the tagged amplicon library of four candidate genes. One hundred and sixteen of 1,152 total amplicons were missing or lacked sufficient coverage for further analysis. Thus, the range of read coverage for each contig was from 0 to over 2,000 (Table F5H exon 2 had the greatest number of SNPs per bp (1 SNP for every 15 bp) as well as the highest number of SNPs resulting in nonsynonymous substitutions (5 SNPs). CCoAoMT and COMT contig 2 are the only contigs that include introns and indels. CCoAoMT had nine indels. COMT contig 2 had only one indel.Alignments of genotype sequences across the seven contigs yielded 194 SNPs with minimum allele frequencies (MAF) above 0.05 (Table PAL1 exon 2) to 0.0257 (F5H exon 2) with an average of 0.0122. The number of polymorphic sites per gene corrected for sample size, θ, ranged from 0.0097  to 0.0197 (F5H exon 2) with an average of 0.0142  ranged from 0.008 (M. truncatula genome sequence build (version 3.5.1), which covers about 66 % of the gene space . We could estimate LD from 199 locus pairs between markers known to be located on the same chromosome   ≥4] to indicate true LD, then we conclude that, in general, LD decays very quickly. Disregarding the five groups previously detected  compared to upland falcata (1.1 %)  Fig. . If we c%) Table .Table 3Nr2 = 0.1 within 750 bp in three of the seven contigs. In CCoAoMT and PAL 1 exon 2, LD decayed rapidly to below r2 = 0.1 within 500 bp. In PAL 1 exon 1, LD decayed to an r2 = 0.14 in the 377 bp length of the sequence. For F5H exon 1, LD did not decay below r2 = 0.2 within the 723 bp length, although the trend showed a decrease in LD with increased distance. COMT contig 2 had an average r2 = 0.17 and LD did not decay across the 1,203 bp region. LD was highest in F5H exon 2 with an average r2 = 0.33 and did not decay. The pattern of linkage disequilibrium in F5H exon 2 for each subspecies indicates that linkage disequilibrium was strongest in subspecies hemicycla . COMT contig 2 and F5H exon 2 had significant values for Tajima’s D in subspecies falcata and hemicycla, respectively  with total biomass yield in 2007  graphs were plotted . One marker, SNP 111 in CCoAoMT, was associated with total biomass yield in both 2007 and 2008, stem proportion in 2007, and stem thickness in 2008. CCoAoMT SNP 111 is located in the first intron  >0.05 from the four candidate genes with 23 phenotypic traits resulted in seven significant associations with FDR For sequencing in this project, we multiplexed four genes consisting of seven contigs derived from 16 overlapping tagged amplicons and aimed for 600× coverage using Titanium chemistry on the 454 FLX sequencer. Our amplicon lengths were approximately 500 bp, but we were not certain about the read lengths we could expect from the Titanium chemistry when we started the experiment. We were also concerned that our estimation of the concentrations of the 1,200 amplicon reactions was accurate. Taken together, this uncertainty supported our conservative approach. Our experience from this study indicates that accurate haplotype deduction can be achieved with as few as 35 454 FLX reads because, at this coverage level, sequencing errors can still be easily distinguished from true SNPs. The advantages of 454 sequencing over Sanger and Illumina methods are that read lengths allow for the determination of phase over longer distances and insertion-deletion mutations can be easily deduced, both of which are useful for distinguishing haplotypes. Template concentration for PCR, amplicon quantification for pooling prior to sequencing, and methods to avoid PCR recombination which we encountered and that has been seen by other groups  and sunflower (Helianthus annuus L.), which are also outcrossing populations and could be assumed to be more similar to alfalfa than populations from autogamous species. The average θ value estimated in this study (0.0142) was comparable to the values from 21 genes in 15 maize landraces  and the nature of genetic material used . Alfalfa has a predominantly outcrossing breeding system and the plant material used in this experiment is unimproved germplasm collected from broad-based populations. Although we observed higher sequence diversity in the four candidate genes compared to other crop species and model plants  implying extensive LD  in our candidate gene analysis, this lack of association is not surprising. The CCoAoMT and F5H directly impact the lignin content of alfalfa Below is the link to the electronic supplementary material.