Nevertheless, EST sequencing errors and assembly of EST paralogous sequences can lead to the identification of false positive SNPs which renders the reliability of EST-derived SNPs relatively low. Further validation of EST-derived SNPs is therefore required. The objective of this work was to assess the quality of and to validate a large number of rainbow trout EST-derived SNPs.There is considerable interest in developing high-throughput genotyping with single nucleotide polymorphisms (SNPs) for the identification of genes affecting important ecological or economical traits. SNPs are evenly distributed throughout the genome and are likely to be functionally relevant. In rainbow trout, A panel of 1,152 EST-derived SNPs was selected from the INRA Sigenae SNP database and was genotyped in standard and double haploid individuals from several populations using the Illumina GoldenGate BeadXpress assay. High-quality genotyping data were obtained for 958 SNPs representing a genotyping success rate of 83.2 %, out of which, 350 SNPs (36.5 %) were polymorphic in at least one population and were designated as true SNPs. They also proved to be a potential tool to investigate genetic diversity of the species, as the set of SNP successfully sorted individuals into three main groups using STRUCTURE software. Functional annotations revealed 28 non-synonymous SNPs, out of which four substitutions were predicted to affect protein functions. A subset of 223 true SNPs were polymorphic in the two INRA mapping reference families and were integrated into the INRA microsatellite-based linkage map.Our results represent the first study of EST-derived SNPs validation in rainbow trout, a species whose genome sequences is not yet available. We designed several specific filters in order to improve the genotyping yield. Nevertheless, our selection criteria should be further improved in order to reduce the observed high rate of false positive SNPs which results from the occurrence of whole genome duplications. Danio rerio), medaka (Oryzias latipes), stickleback (Gasterosteus aculeatus), takifugu (Takifugu rubripes), and tetraodon (Tetraodon nigroviridis)). Whole genome sequencing are currently underway for a number of aquaculture species: rainbow trout (Oncorhynchus mykiss), Atlantic salmon , Nile tilapia (Oreochromis niloticus), Asian seabass , European seabass (Dicentrarchus labrax), channel catfish  and common carp (Cyprinus carpio). At the same time, high-throughput genomic tools have been developed, improving the description of genomic structure and function.International genome initiatives have resulted in draft sequences of the genome of several farm animals  and of model fish species  or short insertion/deletion mutations (indels). The bi-allelic nature of SNPs makes them less informative than microsatellites. Nevertheless, SNPs are considered as a highly reliable and valuable molecular marker system for genotyping and selective breeding because of their omnipresence throughout the entire genome, both within gene coding and non-coding regions.SNPs in gene coding sequences can be either synonymous (silent polymorphism) or non-synonymous (replacement polymorphism). They are of particular interest to study the genetics of expressed genes and to map functional traits. Synonymous SNPs may alter RNA secondary structures and can affect protein conformation and function. Non-synSNPs in non-coding regions can occur in introns, promoters, intergenic sequences, and in 5'- or 3'-untranslated regions. They may alter gene expression by affecting gene splicing, transcription factor binding, mRNA degradation, or non-coding RNA sequences.Over the last decades, large-scale SNP production initiatives have been associated with the development of high-throughput genotyping technologies that facilitate the simultaneous analysis of hundreds of thousands of SNPs. These low-cost but highly reliable assays have permitted fine-scale gene mapping and candidate gene association studies for complex traits in several species such as humans, mouse chickenin silico strategy for de novo SNP identification. This approach does not require any additional bench work, offers a low cost source of SNPs, and has been recently used in a few aquaculture species such as blue and channel catfish species[In species whose complete genome sequences are not yet accessible, the increasing availability of expressed sequence tags (ESTs) represents an alternative  species, and sal species-13. More species.However, the usefulness of EST-derived SNPs remains putative until their true informativity (sequence polymorphism) and duplication status have been checked with genomic DNA in the populations of interest. Although it is possible to use base quality values to discern true allelic variations from sequencing errors, validation is a key step for detection of true SNPs. This isRainbow trout is the most widely cultivated cold freshwater fish in the world. It has great potential for aquaculture and recreational sport fisheries. In addition to its commercial interest, rainbow trout is also a model species for a wide range of genome-related research activities.The rainbow trout haploid genome size was estimated to be between 2.4 and 3.0 × 109 bp,18. A coBoth the interest brought into rainbow trout as a research model and the need for its genetic improvement for aquaculture production efficiency and product quality led to the development of several genomic resources for this species. Meanwhile, great efforts have been and are still devoted to the development of SNP genetic markers-23.Previous efforts using reduced representation libraries and refeFinally, these putative SNPs were not yet publicly available. Therefore, EST-derived SNPs could represent an alternative and complementary in silico approach to assess the quality and to validate larger numbers of SNPs. These resources will add to the already available 184 SNPs validated from the reduced representation libraries study.Miller and co-authors have alsin silico SNP identification in rainbow trout. Up to 31,121 in silico EST-derived SNPs are currently available at the INRA Sigenae database (http://www.sigenae.org/). However, they do not provide any information neither on their true informativity nor on their duplication status. Therefore, it is necessary to validate the status of these markers. Validation of rainbow trout EST-derived SNPs in a large number of populations will not only allow to identify fully informative true SNPs but also will highlight the proportion of informative SNPs shared across different populations, a crucial information to efficiently design future rainbow trout specific SNP chips. These new tools will contribute to studies on population genetics and will facilitate quantitative trait loci (QTL) identification, and marker assisted selection.Mining EST datasets remains an attractive alternative approach for In the present study, a panel of 1,152 EST-derived SNPs was selected from the Sigenae SNP database and were subsequently assayed for allelic variation in several rainbow trout population samples using the Illumina GoldenGate assays. Successfully validated EST-derived SNPs were used to analyse the genetic diversity in three bisexually reproducing experimental stocks and a collection of doubled haploid (DH) clones and to update the INRA linkage map by integrating 223 new markers.http://www.sigenae.org/; restricted access) was used to select a validation SNP panel. A public version of this release will be available in the near future. Almost 31,121 SNPs were produced by assembling EST sequences. Briefly, several stringent filters were used to improve the quality of predicted SNPs: (1) the value of the local depth at the polymorphic position must be at least equal to 7; (2) the 4 bases flanking regions around the SNP position need to be exactly conserved within the aligned sequences; (3) the minimal number of sequences with the lowest represented base must be at least equal to 3; (4) gaps on consensus sequences were ignored; and (5) N or gaps on sequences were ignored.The INRA Sigenae rainbow trout EST-derived SNP database  individuals, DNA from 10 DH individuals from various origins provided by Dr Gary Thoorgard (ARS), 20 individuals from the INRA synthetic reference strain (INRA-SY), 20 individuals from the INRA spring spawning strain (INRA-SP), and DNA from 44 individuals from five NCCCWA mapping families providedINRA rainbow trout fin clips were collected from euthanized and/or anesthetized fish elevated at the INRA fish farm facilities. Under French regulation, the INRA facilities are authorized for experimental activities and both the staff of the facilities and scientists have personal authorization to conduct animal experimentations. All animal manipulations were done according to the good animal practice as defined by the French Direction of Veterinary Services.High-throughput genotyping reactions were performed at the INRA genomics GET PlaGe core facility, using the Illumina GoldenGate BeadXpress systems, according to the manufacturer's protocol. SNPs wiGenotype clustering was performed using the GenomeStudio software (Illumina Inc.). GenCall and GenTrain quality scores for each genotype were generated. A GenCall score cutoff of 0.25 was used to determine valid genotypes at each SNP and the retained SNPs had to have a minimum GenTrain score of 0.25 (a stringent criterion that is used in human genetic studies). ClusterThe STRUCTURE software was usedBoth contig and SNP allele sequences were analysed for gene content by blastx using the ENSEMBL non redundant protein databases for zebrafish .Blastx searches were carried out using an e-value cut off of 1e-5. The blastx search results were filtered to remove non specific homologies using the following filtration steps: (1) the Ensembl protein ID in the blastx results were renamed by their corresponding Ensembl gene ID , (2) for each sequence read (query ID) with a gene hit (subject ID), results were filtered to keep only the hits with the minimal e-value score; and (3) sequence reads with several hits having the same minimal e-value were further filtered to keep the hits with the highest HSP . Only SNP sequences and their corresponding contig sequences having a gene hit were used for further analysis.For each contig read, query start and query end positions were used to retrieve corresponding contig sequences between these two values. DNA sequences were then translated and the translation product was used to construct a specific RT peptide database. Both SNP allele sequences were then analyzed for synonymous/non-synonymous SNPs by blastx using the produced RT peptide database. For synonymous SNPs, both allele sequences should result in a perfect match with a given peptide sequence (100 % identity). For non-synonymous SNPs, one allele sequence should result in a perfect match and the other should present only one amino acid mismatch.Finally, we assessed the deleterious effect of non-synonymous SNPs using SIFT (Sorting Intolerant From Tolerant) program (sift.jcvi.org/). Prediction was carried out using the SIFT sequence tool through PSI-Blast searches against UniProt - SwissProt databases . Median conservation of sequences was fixed to 3.0 and hits showing more than 90 % identity to the query sequence were removed.Linkage groups were constructed with CARTHAGENE and optihttp://www.sigenae.org) contains 31,121 putative SNPs identified in 13,374 EST contigs  and standard individuals from three different domestic populations.Assays were developed for 1,152 putative EST-derived SNPs, out of which 958 83 %) were successfully genotyped . These were incorrectly identified as SNPs since EST sequencing presents a high rate of sequencing errors resulting in the identification of pseudo-SNPs. Some of these SNPs may also correspond to rare polymorphisms that were not present in the population samples genotyped in this study. ESTs are issued from a wide variety of tissues usually collected from a limited number of individuals and genetically constrained populations and may represent a bias in true allelic variations. The effectiveness of EST resources to detect fication,38.Two hundred and sixty two SNPs (27 %) revealed paralogous sequences as all samples, including DH individuals, were heterozygous. Out of these, 63 % were identified in contigs containing at least six SNPs/contig (Table) across loci was 0.35 ± 0.14, and the mean PIC  was 0.28 ± 0.1  of the successfully genotyped SNPs were polymorphic and reliably scored, and thus were considered as true SNPs Table. They we2Three domestic populations of different origins (INRA SP and SY strains and NCCCWA population) were used in this study. This offers the opportunity to determine whether a Bayesian clustering software such as STRUCTURE could detect the underlying genetic populations among all analysed samples using the observed SNP genotypes only.We first used the non parametric approach to inferEven though microsatellites have higher allele diversity, the frequent occurrence of mutations following a stepwise mutation model within these markers may lead to homoplastic alleles which may represent a significant problem in population genetics. SNPs arTo assign putative functions to the 350 true SNPs, we performed blastx searches of both SNPs and corresponding EST contig sequences against the ENSEMBL zebrafish non redundant peptide database.Blastx search results made it possible to assign putative functions to 321 contig sequences  were successfully genotyped with the 350 true SNPs. Out of these, 120 SNPs were not polymorphic in the two reference families and were therefore excluded from the final panel for linkage mapping. Segregation pattern analyses of the remaining 230 SNPs revealed seven SNPs showing non Mendelian inheritance. These were excluded from the final panel for linkage mapping. The remaining 223 SNPs were successfully positioned on the microsatellite-based linkage map  and the other one polymorphic. MSVs can be used for both linkage and association studies and they can provide information on two paralogous loci resulting from the latest WGD event.One hundred markers 29 %) were identified within duplicated regions or adjacent to duplicated markers delimiting known duplicated regions of rainbow trout  rainbow trout EST-derived SNPs.In the lack of a rainbow trout reference genome, EST resources represent an attractive approach for Almost two thirds of the successfully genotyped SNPs were considered as either false SNP  or duplicated heterozygous in double haploid individuals, expected to be all homozygous. This higher failure rate is most probably due to EST sequencing errors or the assembly of duplicated gene sequences, which can lead to the production of MSVs (true but duplicated SNPs) and PSVs . Although MSVs and PSVs can provide information on paralogous loci resulting from the latest WGD event in rainbow trout, their presence can hinder the use of SNP arrays in genome-wide association and should be avoided in population genetics studies. MSVs are of particular interest since they can be used for both mapping, linkage, and for association studies. However, since these markers are not easily interpreted, they were either discarded or their genotypes were incorrectly assigned. Since up to half of the rainbow trout genome still retains duplicated regions, calling and assigning MSVs to the correct paralogous loci become difficult and hinder genotype calling and genetic mapping. Moreover, currently available standard genotyping software programs have not been designed to automatically deal with this type of polymorphisms. It is noteworthy that nearly all the SNPs which were mapped in the duplicated regions were assigned to the same linkage group suggesting that, in most cases, only one of the two duplicated sites could be genotyped. This is particularly important for most of populations genetics studies where the same set of markers must be analysed.The Illumina GenomeStudio software was recently updated and provides the GT module v2010/v1.8 which allows assigning genotypes in tetraploid genomes. However, users need to perform manual clustering of genotype calls for polyploid loci. This is unrealistic to accomplish when thousands of genotyped markers are analysed simultaneously. Consequently, an accurate and automated genotyping system for calling polyploid genotypes is highly desirable. A recent genotyping software, beadarrayMSV, has been designed and was used for genotype calling and mapping of MSVs in Atlantic salmon. NeverthCombining the identification and validation of MSV markers with regular SNP polymorphisms in rainbow trout will allow the identification of fully informative genetic markers in this species and will provide valuable tools to efficiently design a future rainbow trout specific SNP chip.de novo SNP identification in species whose genome sequences are not yet available.Results reported here represent the first study of EST-derived SNP validation in rainbow trout and demonstrate the utility of EST databases as an alternative approach for The authors declare that they have no competing interests.MB conceived and supervised the project, and drafted the manuscript. RG carried out the linkage maps and genetic diversity analysis, and revised the manuscript. CC performed EST-derived SNP identification and Sigenae SNP database management. DE carried out genotyping work. EQ contributed in designing the study, helped with interpretation of data analyses and revised the manuscript. All authors read and approved the final manuscript.2q2, where p and q are the sequence frequencies of the two alternate alleles.Minor sequence frequencies, heterozygosity and PIC values were indicated for all SNPs in the INRA Sigenae database (worksheet 1) and for the validation panel (worksheet 2). Allele frequencies were calculated by direct count of sequence depth for each allele, expected heterozygosities per locus (He) were estimated by He = 2pq and PIC values were estimated by PIC = H-2pClick here for fileIllumina quality design scores for the validation panel.Click here for file2q2, where p and q are the allele frequencies of the two alternate alleles.SNP allele frequencies. Allele frequencies for both alleles and minor allele frequencies (MAF) were shown for DH clonal lines (worksheet 1), INRA-SY (worksheet 2), INRA-SP (worksheet 3), NCCCWA (worksheet 4), all 3 populations (worksheet 5) and for true SNPs (worksheet 6). Allele frequencies were calculated by direct count in the raw genotype data file, expected heterozygosities per locus (He) were estimated by He = 2pq and PIC values were estimated by PIC = H-2pClick here for fileBest k analysis. Results were shown for the five independent runs using a bur-in of 50,000 and 200,000 iterations. The best K value was determined using the non parametric test as previously described.Click here for fileSNP functional annotations. BlastX search results using EST contig (workseet 1) and SNP (worksheet 2) sequences as well as synonymous/non-synonymous SNP prediction results (workseet 3) were summerized.Click here for fileINRA rainbow trout linkage maps. Newly integrated SNPs were highlighted in red.Click here for fileList of duplicated markers and markers assigned in duplicated region.Click here for file


A vast amount of SNPs derived from genome-wide association studies are represented by non-coding ones, therefore exacerbating the need for effective identification of regulatory SNPs (rSNPs) among them. However, this task remains challenging since the regulatory part of the human genome is annotated much poorly as opposed to coding regions. Here we describe an approach aggregating the whole set of ENCODE ChIP-seq data in order to search for rSNPs, and provide the experimental evidence of its efficiency. Its algorithm is based on the assumption that the enrichment of a genomic region with transcription factor binding loci (ChIP-seq peaks) indicates its regulatory function, and thereby SNPs located in this region are more likely to influence transcription regulation. To ensure that the approach preferably selects functionally meaningful SNPs, we performed enrichment analysis of several human SNP datasets associated with phenotypic manifestations. It was shown that all samples are significantly enriched with SNPs falling into the regions of multiple ChIP-seq peaks as compared with the randomly selected SNPs. For experimental verification, 40 SNPs falling into overlapping regions of at least 7 TF binding loci were selected from OMIM. The effect of SNPs on the binding of the DNA fragments containing them to the nuclear proteins from four human cell lines  has been tested by EMSA. A radical change in the binding pattern has been observed for 29 SNPs, besides, 6 more SNPs also demonstrated less pronounced changes. Taken together, the results demonstrate the effective way to search for potential rSNPs with the aid of ChIP-seq data provided by ENCODE project. Single nucleotide polymorphisms (SNPs) represent the most common type of sequence variation. Recently, the advance in high-throughput DNA sequencing methods has provided a rapid growth in the volume of information about the saturation of genomes with SNPs. For example, the NCBI dbSNP in 2005 contained slightly over ten million SNPs in the human genome Of special interest among the rSNPs are the polymorphisms localized to the binding sites of various transcription factors . Such rSNPs can exert a functional effect by altering the regulation of gene transcription. This is explainable with a corresponding increase or decrease in the binding of a given TF, leading to allele-specific gene expression. In some cases, rSNPs may eliminate an existing binding site and/or generate a binding site for another TF, which can have a dramatic effect on the gene expression pattern. There are numerous examples of such rSNPs associated with various diseases. In particular, the substitution of −30 T>A in the TATA box of human beta-globin gene (HBB) promoter leads to a fourfold decrease in the TBP/TATA affinity Among rSNPs, currently registered in the different databases, the most are polymorphisms localized to promoter regions of genes in silico by comparison of samples of clinically associated and randomly selected human SNPs and experimentally confirmed on a sample of SNPs from OMIM catalog.Chromatin immunoprecipitation (ChIP) technique with subsequent microarray (ChIP-chip) or massively parallel sequencing (ChIP-seq) is a powerful approach, which enables a genome-scale mapping of transcription factor occupancy pattern in a given cell type and state Two major sources of data were used in this study:http://www.ncbi.nlm.nih.gov/snp/. Sample Sclinic consisting of 34,373 clinically associated SNPs was selected from dbSNP NCBI (http://www.ncbi.nlm.nih.gov/snp/limits) by limits criteria “organism: Homo sapiens” and “annotation: Clinical/LSDB Submissions”. Sample Somim composed of 18,291 SNPs was also selected from database dbSNP NCBI in criteria “organism: Homo sapiens” and “annotation: OMIM”. Sample SR contains 1,000,000 randomly selected SNPs from 30,249,489 SNPs founded in the 1000-Genomes Project . Sample Sgwas comprising 10,345 SNPs which were phenotypically associated with the p-values below 1 e−5 was selected from the NHGRI GWAS catalog (http://www.genome.gov/gwastudies/).Genomic data on the locations of SNPs in the human genome were downloaded from database dbSNP NCBI http://genome.ucsc.edu/) page “ENCODE Txn Factor ChIP track” . The ENCODE TF binding data were utilized to extract genomic regions containing 2 or more (i) overlapping TF binding loci, referred to as OTFR(i) (E(i) of sample by such SNPs was calculated for each value of i>2.Chromosome coordinates of loci where TFs bind to DNA as assayed by ChIP-seq were downloaded from the UCSC website ( OTFR(i) . Gaussiaomim, Sclinic, Sgwas, Sr, etc.) 500 random samples were generated by resampling (In house Perl script is available as E(i) was calculated for each pair of resulting SNP and OTFR(i) samples. To test the hypothesis of normal distribution of the data (random error) Pearson's chi-squared test (χ2) was used. It was found that for all samples the distribution was close to normal .Estimates of standard deviations and confidence intervals for enrichment analysis were derived by bootstrapping procedure . Briefly2 at 37°C.Human hepatoma cell line HepG2, human cervical adenocarcinoma cell line HelaS3, human colorectal cancer cells HCT-116, human erythromyeloblastoid leukemia cell line K562 were grown in DMEM/F12 (GIBCO) containing 10% fetal calf serum (Thermo Scientific HYCLONE), 100 units/ml penicillin, and 100 mg/ml streptomycin (Sigma) under 5% CO7) were washed with ice-cold PBS and then pelleted by centrifugation at 300 g for 2 min. The cell pellet was resuspended in 1 ml of Buffer 1  and placed on ice to swell for 15 min. After addition of 62 µl of 10% (w/v) Nonidet P-40, the sample was gently vortexed for 10 s and then centrifuged at 400 g for 5 min at 4°C. The nuclear pellet was resuspended in 100 µl of Buffer 2  and incubated for 20 min on ice followed by a centrifugation at 10,000 g for 10 min at 4°C. The supernatant containing the nuclear proteins was stored at - 70 C.Cells , 2 active units of Klenov fragment, and 10 µCi (α-32P)dATP.3BO3, 2 mM EDTA at 4°C). The gel was exposed with X-ray film. Two to three independent replicates were performed for each EMSA experiment.The protein nuclear extract was incubated with sheared salmon sperm DNA  for 10 min at 0°C. After that 4 µg of extract was added to the probes which contain 50 pM of radioactive labeled oligonucleotide. After incubation at room temperature for 15 min the mixture was subjected to electrophoresis in 4.5% PAAG in 0.5×TBE  requiring experimental verification. Electrophoretic mobility shift assay (EMSA) was used for this purpose as an inexpensive and appropriate method, which allows for estimation of the SNP effect on both the efficiency of TF binding and the range of the bound proteins.clinic comprised 34,373 clinically associated SNPs, 2) sample Somim included 18,291 SNPs cataloged in OMIM, and 3) sample Sgwas consisting of 10,345 SNPs from the NHGRI GWAS catalog containing SNP with trait association p-values below 1 e–5 r consisting of 1,000,000 SNPs randomly selected from over thirty millions SNPs founded in the 1000-Genomes Project.To test the operation efficiency of the proposed approach we analyzed the enrichment of different phenotype-associated SNP samples with putative rSNPs. For this purpose, three samples were constructed: 1) sample SP* is the observed probability of finding a putative rSNP in the analyzed sample; P, probability of finding an rSNP in the case of uniform genomic distribution.The enrichment of samples with putative rSNPs was calculated as follows: P* was calculated as ratio of the number of SNPs falling into OTFRs to the total number of SNPs in the analyzed sample: The probability 9 bp): The probability P was determined as ratio of total length of OTFRs to the genome length (3*10i) samples, where i>2 is the minimum allowed number of overlapping TF binding loci for OTFR. i) and proportion of the genome falling into OTFRs, depending on the i values.The enrichment was calculated for a series of OTFR and SNP samples  was 4±0.07  means) for Somim and 3.6±0.07 for Sclinic, corresponding to 4.9- and 4.4-fold enrichment relative to control sample Sr at i = 30. In contrast, there is no enrichment of random sample Sr with putative rSNPs, rather 1.2-fold depletion is seen at i = 30 (E(30) = 0.84±0.003). Thus, with increasing allowed number of overlapping TF binding loci in OTFRs, the fractions of putative rSNPs shows increasing difference between phenotype-associated and control samples.It is evident from the plot in gwas consisting of 10,345 SNPs. It is evident from E(i) value for Sgwas is almost independent of the i value in contrast to Sclinic and Somim, displaying 2.6–3.3-fold enrichment for rSNPs relative to Sr.Intriguing results have been also obtained when applying our approach to the samples of SNPs from the NHGRI GWAS catalog gwas sample by p-value and OR (odds ratio) parameters reflecting the significance of SNP-trait association could improve the observed enrichment. Since under the terms of NHGRI GWAS catalog all SNPs from Sgwas already have trait association p-values below 1 e–5, we constructed sample SpV consisting of 5,115 SNPs with the p-values below 1 e−7, and sample SOR, which comprises 3,084 SNPs associated with a certain disease with OR>3 and OR<0.33. As could be seen from the gwas sample. The highest enrichment was obtained for Sint sample . These lines are most frequently used in the ChIP-seq experiments of the ENCODE project and have different tissue origins, which expanded the range of nuclear proteins able to bind to specific DNA sites. Two to three independent replicates were performed for each EMSA experiment.For experimental verification, 30 non-coding SNPs falling in OTFRs with i≥7  were ranselected . A totalselected . These oA, B and C). For other 10 SNPs of the found 35, the corresponding allelic variants V1 and V2 form the retardation bands with equal mobility (example in D) but different intensities, which may suggest impairment/weakening of the binding sites for certain TFs. The remaining five SNPs of the 40 tested in the experiment either had no effect on binding or the corresponding oligonucleotides did not bind to any proteins from the nuclear extracts of the selected cell lines (example in E). However, it cannot be excluded that these SNPs will behave as rSNPs when using the nuclear extract of another human cell line or, even better, a biopsy specimen. The fact that the dependence of binding pattern not only on the SNP allelic variant, but also on the used cell line has been demonstrated for several of the tested SNPs also confirms this assumption. Thus, the EMSA tests has allowed us to validate a regulatory potential for 29 of the predicted rSNPs and to demonstrate that additional 6 polymorphisms with less pronounced changes are also likely to be regulatory. Additionally some position effect of SNP within OTFR was shown. Although all 12 SNPs from different parts of OTFR belonging to APC gene demonstrated profound changes in protein binding pattern, strong tissue-specific effect was observed .All sequences containing experimentally verified SNPs were subjected to the search for putative TF binding sites, which appeared and/or were disrupted due to single nucleotide substitution, using transcription factor binding site profiles from open-access JASPAR database. For this purpose TFBSs were predicted for both sequence variants with the relative profile score threshold of 80% and then motifs represented only in one of them as well as motifs displaying score difference above 5% between variants were extracted. As could be seen from the It is known that the changes in TF binding to the cognate sites can lead to dramatic consequences down to cell death or malignant transformation due to impairments in cell differentiation and proliferation Recently, high-throughput functional assays such as ChIP-seq and DNaseI-hypersensitivite sites identification by sequencing (DNase-seq) have emerged as promising approaches for genome-wide determination of regulatory units, promoting extensive data accumulation and appearance of such large-scale projects as the ENCODE. Several studies showed that in certain cases SNPs falling into ChIP-seq/DNase-seq regions directly affected binding of various TFs clinic Somim and Sgwas) and the sample of random SNPs we have demonstrated that the higher the number of ChIP-seq peaks overlapping with SNPs, the larger is the enrichment of functionally significant samples for the putative rSNPs as compared with the random one  and SNPs . The resulting samples were the same size as the initial sample, but some elements were selected repeatedly while some were excluded by chance. Pearson's chi-squared test (χ2) was used to test the hypothesis of normal distribution of the data. The enrichment E(i) was calculated for each pair of resulting SNP and OTFR(i) samples.(TIF)Click here for additional data file.Figure S2Proportion of the genome falling into OTFRs, depending on the i values. Total length of OTFRs consisting of at least i ChIP-seq peaks was calculated as percent of the genome length.(TIF)Click here for additional data file.Table S1List of oligonucleotide probes tested in EMSA.(DOC)Click here for additional data file.Table S2Total length of OTFRs and proportion of the genome falling into OTFRs depending on the i value.(DOC)Click here for additional data file.Table S3The amount of SNPs falling into OTFRs with at least i TF binding loci, and significance of the corresponding enrichment ), for seven SNP samples analyzed. Sample size is specified in brackets after sample name.(DOC)Click here for additional data file.Table S4The SNPs and corresponding appearing or disrupted TF binding sites, revealed using TFBS profiles from JASPAR database. Relative profile score threshold 0.8.(DOC)Click here for additional data file.Document S1Data infile “TxN.txt” for creation MySQL table TxN see .(GZ)Click here for additional data file.Document S2The algorithm for data management and analysis used in the study.(DOC)Click here for additional data file.Document S3Putative rSNPs selected for EMSA and their position within OTFRs with i≥7.(DOC)Click here for additional data file.


Prunus avium L.), a non-model crop with narrow genetic diversity, is an important member of sub-family Amygdoloideae within Rosaceae. Compared to other important members like peach and apple, sweet cherry lacks in genetic and genomic information, impeding understanding of important biological processes and development of efficient breeding approaches. Availability of single nucleotide polymorphism (SNP)-based molecular markers can greatly benefit breeding efforts in such non-model species. RNA-seq approaches employing second generation sequencing platforms offer a unique avenue to rapidly identify gene-based SNPs. Additionally, haplotype markers can be rapidly generated from transcript-based SNPs since they have been found to be extremely utile in identification of genetic variants related to health, disease and response to environment as highlighted by the human HapMap project.Sweet cherry  sequencing method yielding 43,396 assembled contigs. In order to test our approach of rapid identification of SNPs without any reference genome information, over 25%  of the contigs were screened for the SNPs. A total of 207 contigs from this set were identified to contain high quality SNPs. A set of 223 primer pairs were designed to amplify SNP containing regions from these contigs and high resolution melting (HRM) analysis was performed with eight important parental sweet cherry cultivars. Six of the parent cultivars were distantly related to Bing and Rainier, the cultivars used for initial SNP discovery. Further, HRM analysis was also performed on 13 seedlings derived from a cross between two of the parents. Our analysis resulted in the identification of 84 (38.7%) primer sets that demonstrated variation among the tested germplasm. Reassembly of the raw 3'UTR sequences using upgraded transcriptome assembly software yielded 34,620 contigs containing 2243 putative SNPs in 887 contigs after stringent filtering. Contigs with multiple SNPs were visually parsed to identify 685 putative haplotypes at 335 loci in 301 contigs.This approach, which leverages the advantages of RNA-seq approaches, enabled rapid generation of gene-linked SNP and haplotype markers. The general approach presented in this study can be easily applied to other non-model eukaryotes irrespective of the ploidy level to identify gene-linked polymorphisms that are expected to facilitate efficient Gene Assisted Breeding (GAB), genotyping and population genetics studies. The identified SNP haplotypes reveal some of the allelic differences in the two sweet cherry cultivars analyzed. The identification of these SNP and haplotype markers is expected to significantly improve the genomic resources for sweet cherry and facilitate efficient GAB in this non-model crop. Prunus avium L.), a non-model crop, is an important non-climacteric member of sub family Amygdoloideae where other members like peach and plum demonstrate climacteric fruit ripening. Sweet cherry is a diploid (2n = 16) and is estimated to be slightly larger than peach, 225-300 MB [Sweet cherry  based on variation in the 3'UTR (97% nucleotide identity). Through comparison with sequences in multiple maize databases, 93.8% of the SNPs identified by Eveland et al. were confirmed [Continued improvements in second generation DNA sequencing technologies have increased the ability to obtain significant sequencing depth in a rapid and cost efficient manner, compared to Sanger sequencing approaches . Bundockonfirmed . Use of de novo assembly of 454 generated-3'UTR sequencing reads and strict filtering, we initially identified a putative set of contigs containing SNPs. Primer sets designed to amplify the regions of these contigs with putative SNPs were developed and used for High Resolution Melting (HRM) analysis among eight currently utilized parental cultivars of sweet cherry and 13 hybrid seedlings derived from a cross between two of the parental cultivars, respectively. We determined that 68 out of 223 (30.5%) and 65 out of 217 (30.0%) of the tested primer pairs are able to detect genetic variability. From these polymorphic sites, 685 haplotypes were identified from 301 contigs containing multiple SNPs.In addition to identifying polymorphisms, current research in human genomics has demonstrated the utility of developing haplotype information as a way to more fully understand genotype to phenotype relationships, especially in context of health, disease and response to environmental cues -22. GeneTissue samples from developing floral buds of two commercially important cherry cultivars, Bing and Rainier, were excised from the trees and flash frozen in liquid nitrogen. The frozen tissues were pulverized uniformly in a SPEX SamplePrep 6870 FreezerMill  for five cycles each with cooling for two minutes and grinding at 15 counts per second for four minutes. Total RNA from each sample was extracted using the RNeasy Plant DNA Extraction Kit . First strand cDNA was then synthesized using the Ambion aRNA synthesis kit with a biotinylated poly-T B-adaptor [see Additional File The 3'UTR libraries were sequenced as per the 454 FLX protocol  on a single LR-70 sequencing plate. After sequencing, the 454 produced reads were processed using a custom script [see Additional File For method development, a total of 10,100 contigs were examined for the presence of putative SNPs using Lasergene 7's SeqMan . The high confidence SNPs have at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. Primer pairs flanking potential SNP loci were designed using the PRIMER3 program  to ampli1 mapping population of Selah × Cowiche were used. Leaves of these accessions were collected from the WSU Irrigated Agriculture Research & Extension Center in Prosser, WA and DNA was extracted from dried leaves using a CTAB extraction protocol [® Green, 5 ng of genomic DNA and autoclaved nanopure water to a total volume of 20 μL. The Cultivar panel comprised of 223 primer sets tested on all eight parental cultivars and the Seedling panel included 217 primers sets tested on one reaction of each parent, Cowiche and Selah, and one of each hybrid seedling. Analyses were performed on the LightCycler® 480 System  using the following PCR cycling and HRM conditions. Initial melting for 10 minutes at 95°C was followed by 45 cycles of 95°C for 10 seconds, 57°C for 15 seconds, and 72°C for 15 seconds, then heated to 95°C for 1 minute and cooled to 40°C. High Resolution Melting analysis was then automatically initiated whereby the amplicons were heated from 60°C to 90°C with 25 acquisitions per degree. As the temperature slowly increased, the dye fluorescence was recorded, plotted and later analyzed using the LightCycler® 480 Gene Scanning Software. Since the Tm can vary based on the HRM reaction conditions, curve shapes were visually examined and the number of distinct curve profiles was identified for each primer set.Eight sweet cherry cultivars: Bing, Chelan, Emperor Francis, New York 54, Regina, Selah, Stella and Cowiche used as parental material in the Washington State University (WSU) Sweet Cherry Breeding Program  were used to test the polymorphisms of the identified SNP loci across Bing and Rainier cultivars. For segregant analysis, 13 seedlings from an Fprotocol . The reade novo transcriptome assembly of: 85% match, match size 21, genome length 225 MB. The whole SNP report was initially filtered to retain the HRM confirmed SNPs using a minimum total depth of 10 reads at the polymorphic base and at least 20% variance from the consensus. Further filtering into high confidence SNPs was performed by screening for at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. This minimum depth per allele for each SNP equals or exceeds the published depths using either 454 data [After the HRM analysis, a second assembly using SeqMan NGen v3.0  was performed due to its improved algorithm and the results were used for SNP reporting on the entire data set. This assembly was completed using the default parameters for NGen 3.0's 454 data ,29 or Il454 data ,31. AddiHaplotypes were identified visually by analyzing the combined transcriptome assembly generated using NGen 3.0 in SeqMan (DNASTAR). Similar to the SNP screening, at least three reads of an allele spanning two SNP loci were required to link SNPs into a haplotype. When two or more haplotypes were present at one locus, they were differentiated and recorded as separate haplotypes for their use as haplotype markers.The general method presented in this study is based on four steps as outlined in Figure Pyrosequencing of 3'UTR libraries from Bing and Rainier on a single 454 GS FLX sequencing plate produced a total of 580,455 reads Table . The reaTo test our experimental approach, analysis of a subset of the assembled contigs was performed to identify SNPs within the dataset. The 100 contigs with the highest number of reads and contigs 1-10,000 as produced by SeqMan 7  were analyzed yielding 600 contigs containing at least one high confidence SNP. These high confidence SNPs have at least two alleles represented by a minimum depth of three reads per nucleotide call per allele. Since false polymorphism of indels can be high , indels de novo approach [The automated genotype calling of the LightCycler 480 analysis software v1.5.0 identified only a few SNPs with more than one allele. Modifying the analysis parameters did not provide significant improvement of the automated analysis (data not shown). However, manual analysis identified multiple curve types for many primer sets as well as heritable patterns between Cowiche, Selah, and their seedlings. While the differences in melting curve shape are small, homozygotes and heterozygotes were visibly distinguishable with many of the primer sets Figures  &2B. It approach . The autDNA assembly programs continued to improve since the initial assembly which was used to design primers and analysis of population variability. Subsequently, the trimmed reads were re-assembled using NGen v3.0 . This assembly produced 34,620 contigs [see Additional File JP376615-JP382830 and dbSNP NCBI ss# 469992783-469995036 except 469992784, 469992792, 469992801, 469992809, 469992818, 469992823, 469992825-7, 469992834-5, 469992842, 469992851, 469992853-4, 469992859, 469992866-7). Analysis of the "failed" HRM primer sets on contigs obtained from the NGen assembly showed that none had a significant change in the contig consensus. However, three of the 10 primers did show multiple possibilities for primer binding which could decrease PCR effectiveness.After filtering the new SNP report for a minimum read depth of 10 and 20% variance from the consensus an initial list of SNPs was derived. These SNPs were examined to remove any SNPs resulting from the ends of the reads and filtered requiring a minimum of three confirming reads per base call per allele. A total of 2243 putative SNPs were identified in 887 contigs after this filtering [see Additional File Prunus armenica (genome size = ~200 MB) and apple Malus × domestica (genome size = 740 MB) respectively [The 2243 putative SNPs identified in the assembled gene space (expressed sequences) of 5.19 Mb yields a SNP frequency of 1 in 2,315 bp (0.43 SNPs per kb of gene space). The sweet cherry gene space of 5.19 MB generated in this study represents approximately 2.3 - 1.7% of the estimated genome size of 225 - 300 MB. Previous studies utilizing whole genome sequence have reported a frequency of 1 SNP in 114 bp (8.8 SNPs per kb) and 1 SNP in 208 bp (4.8 SNPs per kb) in almond ectively ,41. The Prunus mume, Japanese apricot [As mentioned earlier, coding regions of genes were also sequenced inadvertently since the poly T primer annealed to regions other than the 3'UTR region, thereby further reducing the number of polymorphic sites in the sequenced regions. Analysis of the putative sweet cherry SNPs for transitions (C/T and A/G) and transversions  yields a transition to transversion ratio  of 1.14/1  as Prunus avium assemblies and SNPs corresponding to these sequences larger than 200 bp were uploaded to dbSNP .Due to the nature of the contigs and SNPs, many of them did not fit the requirements for typical submission to NCBI. All contigs and high quality SNPs are available as additional files to this manuscript. All of the raw sff files were uploaded to NCBI's Sequence Read Archive (SRA046001.1). Contigs greater than or equal to 200 bp in length were added to GenBank's Transcriptome Sequence Assemblies (TSA) database : >1300_8769_5430 length = 258 urnand = JHSK987KJSH2KJHJK8777 AGTCCCCCGGGGTTTAAAGGGGCCCCTTTTAAAAAAGTCGTCAATGCGGT AGTCTGCAAAAAAATTTCCCCCCCCCCGGGGGGGGGGGTAGCCGTATGCA Input (MIDs csv file): Sample1,ATAGTGA Sample2,ATGCATG Output: A fasta file of the remaining sequence after removing the primer/bar code/MID with corresponding header attached as specified in the input "MIDs csv" file.Click here for filePrimers and HRM analysis. The table represents contig number (column B), predicted amplicon length (column C), number of SNPs (column D), forward and reverse primers for each set (column E and F) used for HRM analysis. Included in the table is the Cultivar number of curve profiles (column G), number of Cowiche × Selah curve types (column H) and the Seedling number of curve profiles (column I).Click here for fileContig sequences. A fasta file containing the 34,620 contigs from NGen v3.0.Click here for fileFiltered SNP report. This table is modified output generated from NGen v3.0 and SeqMan. The contig number and all details about the SNP are given including number of calls for each base at the given position from Columns B-L. Column M is the 5' flanking sequence. Column N is the polymorphism. Column O is the 3' flanking sequence. Columns M and O have been provided to enable rapid analysis of other germplasm.Click here for fileHaplotypes identified in sweet cherry. The table presents different haplotypes identified in each contig. Some contigs have multiple positions indicated as A, B or C positions. Nucleotides corresponding to a given position in an allele are presented. Cells are merged when the differences between alleles are no longer traceable. A questions mark (?) symbolizes incomplete depth for a confirmed call at this base.Click here for file


Medicago species. Principal component analysis and model-based clustering showed clear population structure corresponding to subspecies and ploidy levels. Within cultivated tetraploid alfalfa, genotypes from dormant and nondormant cultivars were largely assigned to different clusters; genotypes from semidormant cultivars were split between the groups. The extent of linkage disequilibrium (LD) across all genotypes rapidly decayed to 26 Kbp at r2 = 0.2, but the rate varied across ploidy levels and subspecies. A high level of consistency in LD was found between and within the two subpopulations of cultivated dormant and nondormant alfalfa suggesting that genome-wide association studies (GWAS) and genomic selection (GS) could be conducted using alfalfa genotypes from throughout the fall dormancy spectrum. However, the relatively low LD levels would require a large number of markers to fully saturate the genome.A large set of genome-wide markers and a high-throughput genotyping platform can facilitate the genetic dissection of complex traits and accelerate molecular breeding applications. Previously, we identified about 0.9 million SNP markers by sequencing transcriptomes of 27 diverse alfalfa genotypes. From this SNP set, we developed an Illumina Infinium array containing 9,277 SNPs. Using this array, we genotyped 280 diverse alfalfa genotypes and several genotypes from related species. About 81%  of the SNPs met the criteria for quality control and showed polymorphisms. The alfalfa SNP array also showed a high level of transferability for several closely related  M. sativa L.) is an important forage legume throughout the world. Alfalfa provides high nutrition animal feeds and as a perennial crop, offers numerous environmental benefits. The deep root system stabilizes soil and increases soil fertility through symbiotic biological nitrogen fixation, providing all the nitrogen needed for the alfalfa crop and a subsequent grain crop.Most alfalfa cultivars are synthetic populations developed by recurrent phenotypic selection. Although important agronomic traits such as winter survival and disease resistance have been improved by phenotypic selection, multiple-year phenotypic evaluations per cycle of selection limit rate of genetic gain. Partially due to the long breeding cycle, the improvement of biomass yield in alfalfa has been much slower compared to that in annual crops Alfalfa  in the populations employed. Proper GWAS statistical models need to be used to minimize false positive association caused by the underlying population structure In this study, our objectives were as follows: (a) to develop a high density SNP array that would provide a high-throughput genotyping platform for alfalfa; and (b) to assess patterns of population structure and LD in diverse alfalfa accessions that could guide appropriate experimental designs for subsequent GWAS and GS projects.http://medsa.comparative-legumes.org/). From this set, we developed an Illumina Infinium array consisting of 10,441 SNPs. We identified SNP loci for the array as follows. First, only loci that had at least 10 reads in at least 20 of the 27 sequenced genotypes were retained; this resulted in about 400,000 SNPs. Second, we computed assay designing tool (ADT) scores and GoldenGate scores (http://support.illumina.com/array/array_software/assay_design_tool.ilmn) for these SNPs and about 180,000 having scores for both criteria that were greater than 0.4 were used for further selection. Third, from the literature, we identified candidate genes for aluminum tolerance, cell cycle, cell size, cell wall composition, drought tolerance, flowering time, growth rate, lignin biosynthesis, components of glandular trichomes, and winter hardiness. For sequences of the candidate genes identified in other plant species, we searched the alfalfa transcriptome sequences −5 were selected. Up to three SNPs were selected from a given EST. In total, 6,468 SNPs from 2,640 candidate genes were included on the array  and one F1 progeny from the Altet4 × NECS-141 population We assayed 280 genotypes including 233 cultivated tetraploid subsp.  species  with thiFor each genotype, fresh leaf tissue was used for DNA extraction using the Wizard® Genomic DNA Purification Kit  following the manufacturer's protocol. The purified DNA was quantified with the Quant-iT PicoGreen dsDNA Reagent and Kits  and normalized to 55 ng/µl. Genotyping was performed on an Illumina iScan system at the National Center for Genome research  following the manufacturer's protocol for the Infinium assay. The resulting intensity data were used to score SNPs with the Genotyping Module of the GenomeStudio software  using a GenCall threshold of 0.15. For a given SNP marker, three clusters  could exist in diploid alfalfa but up to five clusters  could be present in tetraploid alfalfa. In this study, 40 diploid alfalfa genotypes were scored at the same time as tetraploid alfalfa. The automated cluster algorithm with a cluster number of three was used to generate the initial calls. The initial SNP clusters were manually edited to refine the cluster position. For the SNPs in which five clusters could be clearly distinguished, manual editing was performed following the polyploidy protocol in GenomeStudio (Illumina Inc.). The diploid genotypes  were useful to determine the tetraploid homozygous classes (AAAA and BBBB) and the duplex heterozygous class (AABB). To simplify analysis of population structure and LD, both diploid and tetraploid genotypes were scored with three classes , where all three heterozygous classes  in tetraploid individuals were scored as heterozygous AB.prcomp. To reduce dimensionality of the data, the first five principal components (PCs) were used for model-based clustering using the R package mclust. Here, the choice to use the first five PCs was based on the scree plot of eigenvalues Each allele of a SNP marker was scored as 1 (presence) or 0 (absence). For a given SNP marker, missing values were first replaced by the mean value for the marker across all individuals. Most SNP loci (92%) had less than 1% missing data (see Results section). Principal component analysis (PCA) was conducted using the R statistical software package 2 and calculated using the software program TASSEL M. truncatula genome was evaluated by fitting a non-linear model. The expected r2 value under drift-recombination equilibrium was E(r2)  = 1/(1+C), and C =  4ad, where d is the physical distance in bp and a is an estimated regression coefficient n, the expectation becomes For each cluster, the LD between pairs of SNPs was estimated as rFor the breeding populations BR1-4, we inferred linkage disequilibrium based on their parental genotypes. To evaluate consistency of LD between populations, we computed the correlation of LD (r_LD) between populations for SNP pairs located at distances less than 0.1 Mbp, 0.2 Mbp, or 1.0 Mbp and then plotted these correlations against the genetic distance between the pair of populations being analyzed.M. prostrata, 91% for M. truncatula and M. secundiflora, 90% for M. lupulina, and 55% for Trifolium pratense.An initial set of 10,441 SNPs were selected to be included in the array, and a total of 9,277 SNPs were ultimately included. A total of 7,476 SNPs (81%) passed the quality control and showed polymorphism. The remaining SNPs either passed the quality control but were monomorphic or showed poor quality genotype clustering . The aveOf the 7,476 SNPs, 7,433 SNPs were polymorphic among the 274 alfalfa genotypes. Of the 7,433 polymorphic SNPs, 6,826 SNPs (92%) had less than 1% missing values, 557 SNPs (7%) had 1–10% missing values, and only 50 SNPs (1%) had more than 10% missing values. Compared to diploids, up to five clusters  could exist in tetraploid alfalfa. In total 1,025 SNPs (13.8%) showed five clearly distinguishable clusters .M. truncatula genotype) from one SNP array chip showed a lower call rate compared to alfalfa genotypes from other chips. The four genotypes from other related species  also showed lower call rates than alfalfa genotypes that were on the same chip. These 28 genotypes with lower call rates were genotyped a second time using this SNP array. The call rates for the 24 samples originating from one chip were higher in the second round of genotyping  was genotyped twice. The proportion of consistent calls for the duplicated sample was 100% . Twenty-notyping . The avenotyping . For thenotyping . Therefo1 progenies were excluded). The first five principal components explained 18.2% of the total variation. The first principal component (PC1) explained 9.4% of the total variation and clearly separated diploid subsp. falcata from diploid subsp. caerulea and tetraploid alfalfa  contained 25 diploid subsp. caerulea genotypes; and Cluster 3 (C3) contained six tetraploid genotypes . Clusters 4 (C4) and 5 (C5) can be differentiated based on the distribution of genotypes from check cultivars with clearly defined fall dormancy scores. While both clusters contained about half of the intermediate dormancy (FD  = 5–7) check cultivar genotypes, C4 included 44 of 45 genotypes from the check cultivars with FD ≤4 and C5 contained 18 of 20 genotypes from check cultivars with FD 8–11  was performed based on 7,433 polymorphic SNP among 263 alfalfa genotypes  but fixed in C4 and C5 (cultivated tetraploid subsp. sativa)  were only polymorphic within C1 or C2 (wild diploid subsp.  sativa) . Of the  sativa) .falcata within the C1 cluster and 16.3% for the 25 diploid subsp. caerulea within C2  between pairs of SNPs) and physical distances on the M. truncatula genome was determined by fitting a non-linear model. The extent of LD rapidly decayed to 26 Kbp at r2 = 0.2 across all the alfalfa genotypes, but varied among clusters and populations   .2 = 0.2 , we inferred LD based on the 30 parents used to develop each of the populations. The average LD level of these four populations was expected to be about 170 Kbp at r2 = 0.2 . However2 = 0.2 .We estimated LD between pairs of SNP in different populations and then correlated these LD estimates (r_LD) between the populations. The r_LD values were negatively correlated to the genetic distance between populations . The LD Medicago species including M. prostrata, M. lupulina, and M. truncatula and reasonable transferability to red clover (T. pratense). In addition, the allelic dosage of 1,025 SNPs (14%) could be determined in tetraploid alfalfa. In tetraploid potato, the Infinium array included 8,303 SNPs and 35% of those SNPs could be clustered into five genotypic groups Illumina Infinium SNP arrays have been developed as a high-throughput genotyping platform for numerous plant species. Genotypes were called for about 72% of array features for apple sativa and subsp. falcata in this study. In a previous experiment, no differentiation of chloroplast haplotypes was found between tetraploid subsp. sativa and diploid subsp. caerulea, which suggested that the tetraploid subsp. sativa simply originated from diploid subsp. caerulea through genome doubling caerulea and tetraploid subsp. sativa could not be distinguished from each other . However, clear differentiation between them was observed using a large set of SNP markers, but only a few genotypes were analyzed caerulea and cultivated tetraploid subsp. sativa that the two subspecies are clearly differentiated genetically. Niche differentiation may have occurred following polyploidization, early domestication, and/or modern selection in tetraploid subsp. sativa, and this may have caused modern alfalfa to diverge from the wild diploid progenitor subsp. caerulea.Population structure has implications for experimental design and data analysis in GWAS and GS. As in previous studies falcata germplasm, which is highly grazing tolerant, and the relationships seen among germplasm sources may reflect the amount of historical falcata introgression into the germplasm.Fall dormancy (FD) in alfalfa refers to the reduction of growth and the development of decumbent shoot orientation that occurs in autumn due to shortening photoperiods and/or lowering temperatures M. truncatula, or the autogamous short-lived perennials M. lupulina and M. secundiflora. The heterozygosity level of M. prostrata was 5.9%, which fell between that of the annual self-pollinated species and perennial outcrossing alfalfa. The perennial Medicago species are known to be outcrossing, although some evidence , Turkistan (12%), Ladak (8%), and Chilean (7%) More than 90% of the SNPs polymorphic in tetraploid alfalfa were polymorphic in both cultivated alfalfa sub-populations. Multiple germplasms have been mixed in most breeding programs since the 1950's 2 = 0.20 within 26 Kbp, but this varied greatly among sub-populations. We estimated LD to be about 170 Kbp at r2 = 0.2 within four newly developed breeding populations , each generated from 30 parents. However, LD rapidly decayed within 54 Kbp when we evaluated all 120 parents together. Populations with fewer founders will require a lower marker density for GWAS or GS, but using populations of this sort may result in inbreeding depression, which can affect results and needs to be carefully considered Linkage disequilibrium is a key factor in determining the number of markers needed for GWAS and GS. Using the Infinium alfalfa SNP array, we evaluated the genome wide LD pattern for the first time using a large set of markers for alfalfa. Overall, LD decayed to an rcaerulea and subsp. falcata together, LD increased, possibly due to the divergent genomes generating extensive admixture LD. Similarly, extensive admixture LD was found in barley when divergent materials were mixed for LD evaluation caerulea genotypes indicates that by selecting subsets of genotypes from modern cultivars, breeders can increase LD to acceptable levels for genome-wide marker-based breeding projects.Many factors such as recombination, mutation, genetic drift, selection, and population admixture affect the extent of LD. If we analyzed diploid subsp. falcata, is divergent from cultivated tetraploid alfalfa and showed a low level of LD consistency at a distance of 0.1 Mbp. Therefore, it is not advisable to combine those germplasms for GWAS and GS.Successful GWAS and GS experiments also rely on the consistency of LD within the employed population. Natural and/or artificial selection that occurred in wild and breeding populations may result in different allelic combinations and in phasing across loci. Marker-trait association in GWAS and estimated marker effects in GS could be biased or obscured when divergent populations are combined www.illumina.com).This experiment reports on the development and testing of an Illumina Infinium array for alfalfa. The array can assay nearly 8,000 SNP markers simultaneously, providing the community with the most advanced genotyping platform currently available. It should prove useful not only for genetic diversity experiments but also for genetic mapping, GWAS, and possibly GS. The array is available to any researcher and can be obtained commercially from Illumina, Inc. Click here for additional data file.Table S2Accessions used for evaluation of the alfalfa Illumina Infinium SNP array.(XLSX)Click here for additional data file.Table S3Call rates of the duplicated and repeated samples.(XLSX)Click here for additional data file.


A pyrographically decorated gourd, dated to the French Revolution period, has been alleged to contain a handkerchief dipped into the blood of the French king Louis XVI (1754–1793) after his beheading but recent analyses of living males from two Bourbon branches cast doubts on its authenticity. We sequenced the complete genome of the DNA contained in the gourd at low coverage (~2.5×) with coding sequences enriched at a higher ~7.3× coverage. We found that the ancestry of the gourd's genome does not seem compatible with Louis XVI's known ancestry. From a functional perspective, we did not find an excess of alleles contributing to height despite being described as the tallest person in Court. In addition, the eye colour prediction supported brown eyes, while Louis XVI had blue eyes. This is the first draft genome generated from a person who lived in a recent historical period; however, our results suggest that this sample may not correspond to the alleged king. The analysis of complete individual genomes is now routinely achieved, providing new data to understand the bases for individuality and also to explore the complexity of modern as well as ancient human diversity12345In 2010, DNA was retrieved from a pyrographically decorated gourd  dated toHowever, a later analysis of three male living Bourbons from two different family branches revealed identical Y-chromosome haplotypes that were different to those found in the remains of Henri IV and the gourd's individualPseudomonas (a group of bacteria comprising plant pathogens), 27% to unknown origin and 1.3% to other organisms, including fungi (0.47%) and of course, Cucurbitaceae (0.1%) (We sequenced DNA extracted from the inside of the gourd using Illumina technology . Due to e (0.1%)  most like (0.1%) .Considering the highly heterogeneous use of a handkerchief and the gourd itself, we anticipated the presence of human contaminants in the sample. To estimate this, we made use of the higher coverage obtained from the mitochondria . Different haplotypes were reconstructed from the mtDNA reads  with a pTo obtain nuclear contamination estimates we followed a previously developed procedureThe significant degree of contamination should emerge as heterogeneities in the resulting genotypes. However, we can safely assume that in regions with significant coverage, the 17–24% of background contamination will consistently emerge as the minor allele. Therefore in subsequent analyses we systematically assessed the major alleles.To remove contaminants by selecting the major allele, we developed a procedure of eliminating SNPs with a substantial skewed allele imbalance . Thus, wThe second set has a higher confidence due to the increased coverage; nevertheless we have calculated a genotype concordance between both datasets of 98%. Obviously, the conservative procedure selected had the side effect of removing real heterozygotes from the gourd's genome. Subsequent Sanger sequencing of 9 sites confirmed the undercalling of heterozygous SNPs  . CombiniWe finally reasoned that our power to make functional interpretations from alleles at individual level could still be compromised; therefore, we applied a final filtering strategy, based on known linkage disequilibrium (LD) patterns in European populations. It is expected that contaminants (we known that there are at least three within the sample) will generate mixed haplotypes, thus breaking the LD blocks on our genome as compared to present-day Europeans. We checked if a random sample of haplotypes found in 60 CEU (Northern and Western European Ancestry) individuals from the 1000 Genomes Project pilot phaseAccording to the historical records, the genealogical track of the king Louis XVI was highly admixed, with a major Central European contribution . His 16 We first determined the gourd's blood paternal ancestry, using diagnostic SNPs in the Y-chromosome, as defined by ISOGG (2014). Several derived SNPs define a G2a2a haplogroup ; again, An identity-by-descent (IBD) tract sharing analysis showed very few IBD tracts with extant Europeans, likely due to gaps in the genomic coverage and undercalling of heterozygous sites. The sequenced individual shared the highest number of fragments with a French individual, but shared the longest tract with a Belgium individual .We also performed a principal component analysis (PCA) on the >9× SNP exome dataset, using 236 individuals from the 1000 Genomes ProjectIn summary, the results of these analyses do not support the royal identity of the sequenced genome. However, given Louis XVI's complex great-great-grandparental ancestry, the low coverage and the aggressive filtering to remove minor alleles from the current dataset, we consider it could still be possible, although implausible, that the gourd's blood could be that of the French king. Alternatively, it could be that one of the so-called contaminants detected at mitochondrial level may in fact correspond to the king's blood; however, we do not have any evidence  that could support this assumption.We performed a functional characterization of the variants found in his genome, including a catalogue of features unique to this individual. A total of 3,850 genetic variants not described in dbSNP were found in the exome . In the We then reviewed different contemporary historical sources, including Marie-Antoinette's correspondence and transcribed conversations from testimonies and relatives  to chara15The remaining phenotypes have complex genetic backgrounds that are currently being investigated from genome-wide association studies (GWAS). Due to the gaps and reduction of heterozygotes associated to the low sequence coverage and the background contamination, we don't have information for all the published SNPs; however, we took advantage of linkage disequilibrium (LD) patterns to recapture SNPs not genotyped and also to confirm those present in the genomic draft.The conservative final set of SNPs after selection and filtering  did not HERC2/OCA2 locus, finding the nucleotide substitution associated to blue eyes in heterozygosity. To further explore the gourd's genome phenotype we determined the allelic state at five additional loci and predicted the eye color with Iris-PlexThe king is depicted with blue eyes in all his portraits, despite the fact that his parents  were brown-eyed. In the previous study, we retrieved by PCR the critical rs12913832*C SNP at the −7 and <5 × 10−8 in GWAS Catalog), but the individual was placed at the 8.37 and 9.78 bottom percentiles, respectively. Similar results were obtained when, rather than assuming HW equilibrium, resamplings were made from the genotypic frequencies in extant CEU samples. In this case, the absolute gain in height for this individual is 5.94 cm, which placed the gourd's individual in the 4.71 bottom percentile . The additional search for possible mutations in genes associated to cases of gigantism produced no results.Complex traits such as height are more difficult to assess from genetic data alone. However, we could evaluate 23 high-quality SNPs influencing this trait . We assuWe have retrieved a draft genome from the gourd's blood, supposed to be that of king Louis XVI, and we have conducted several genomic analyses to characterize this individual. First, we have generated a population analysis with different current European datasets as reference populations. These analyses show that the position of the gourd's individual in the context of modern Europeans is somewhat complex – we see IBD sharing with contemporary French individuals, but also ancestry that is somewhere between contemporary CEU samples and Northern Italian samples. Although it can be argued that Northern Italian populations have clear ancestral ties with Central Europe, this discernible component cannot be easily reconciled with the known Louis XVI family history, given that just one in sixteen great-great-grandparents - Victor Amadeus II, Duque of Savoy (1666–1732)- has some possible Northern Italian ancestry. Additionally, it has to be remembered that the gourd itself has been in Italy for more than a hundred years, but we have no evidence that the contamination found in the sample could be traced back to this period.Secondly, we have provided a functional interpretation of the gourd's genome, both at the level of disease susceptibilities and also physical traits. Due to the limitations associated to the low coverage, we have focused on the study of height and eye colour.Contemporary witnesses stated that the king was the tallest person at Court, which was arguably a representation of the best fed people in the country. Some estimates, including one deduced from the length of the coronation cloak in 1775 (measuring 162 cm), placed Louis XVI as an extremely tall person, around 185–190 cm in height or even moreDespite having in heterozygosis a major-effect SNP for blue eye colour, four other SNPs involved in this phenotype display only the allele associated to brown eyes, to the point that the estimated prediction probability is 0.892 for brown eyes. Although this could be considered an upper limit due to the underrepresentation of heterozygous positions in four SNPs not located in the exome (currently between 1–4× of sequence coverage), the prediction for blue eyes could only be increased up to 0.195 if these SNPs were in fact heterozygous. Thus again, within the limit of the quality of our data, the sequenced genome appears to be inconsistent with that of a blue-eyed person.Finally, we have generated a catalogue of private genetic features, including stop codons and non-synonymous substitutions. If Bourbon genomes contemporaneous to Louis XVI can be retrieved in the future, it would be relevant to the authenticity question to determine if the private mutations found in the gourd's genome are present in members of the French royal family.Although we cannot totally discard that the gourd's sample belongs to Louis XVI on our genomic data alone, several lines of evidence, including the ancestry analysis and the functional interpretation of the genome fail to provide definitive support for the attribution of this specimen to the beheaded French king. Higher quality genomes of this sample as well as that of other possible genomes from the Bourbon family will be needed to make a more definitive claim.Second-generation sequencing techniques will allow the generation of massive genomic data in forensic historical cases like those associated to relicts from the French royal family. These data grant more accurate inferences on ancestry and phenotype than those obtained by traditional forensic methods, although some of the difficulties of identifying personal samples remain. With this approach we have obtained and analysed here the first draft genome from a person of a recent historical period. Irrespective of the historical significance of the individual analysed, this kind of genomic data can provide a new source of information on disease susceptibility, demography, mobility patterns and ancestry in historical periods.About 60 mg of dried blood sample obtained from the inside of the gourd was extracted in a dedicated ancient DNA laboratory in Barcelona, following experimental procedures described elsewhereWe generated in several phases a total of 1,545,311,190 reads with Illumina technology . After r2 = 1 were considered. We then searched those recaptured SNPs associated to our selected traits at the GWAS Catalog. Afterwards, based on sequence data for the CEU population from the 1,000 Genomes ProjectTo recapture non-available SNPs and to confirm genotyped SNPs, we first searched for those SNPs present in the NHGRI GWAS Catalog2 > 0.8, keeping the one with the lowest p-value.Furthermore, the pipeline was used not only to recapture unknown SNPs, but also to verify that the SNPs for which we already had a genotype were correct. We considered as valid those SNPs that were in a congruent haplotype with at least six other SNPs . FinallyD.L. and D.P. collected the sample; A.N., H.H., T.M.-B. and C.L.-F. designed the study; D.L., D.P., contributed to the study design; P.C. performed historical research; I.G., M.M., L.M.-L. and O.R. performed experiments; I.O., F.S.-Q., D.D., U.M.M., C.W.K.C., J.A.R., M.F.-C., S.C., A.N. and H.H. analyzed data; I.O., T.M.-B. and C.L.-F. wrote the paper; all authors discussed the results and commented on the manuscript.Accession codes: The sequencing data have been uploaded to the European NucleotideArchive under accession code SRX504977 and SRX505016.Supplementary Information


Alternative polyadenylation (APA) can for example occur when a protein-coding gene has several polyadenylation (polyA) signals in its last exon, resulting in messenger RNAs (mRNAs) with different 3′ untranslated region (UTR) lengths. Different 3′UTR lengths can give different microRNA (miRNA) regulation such that shortened transcripts have increased expression. The APA process is part of human cells' natural regulatory processes, but APA also seems to play an important role in many human diseases. Although altered APA in disease can have many causes, we reasoned that mutations in DNA elements that are important for the polyA process, such as the polyA signal and the downstream GU-rich region, can be one important mechanism. To test this hypothesis, we identified single nucleotide polymorphisms (SNPs) that can create or disrupt APA signals (APA-SNPs). By using a data-integrative approach, we show that APA-SNPs can affect 3′UTR length, miRNA regulation, and mRNA expression—both between homozygote individuals and within heterozygote individuals. Furthermore, we show that a significant fraction of the alleles that cause APA are strongly and positively linked with alleles found by genome-wide studies to be associated with disease. Our results confirm that APA-SNPs can give altered gene regulation and that APA alleles that give shortened transcripts and increased gene expression can be important hereditary causes for disease. Variants in DNA that affect gene expression—so-called regulatory variants—are thought to play important roles in common complex diseases, such as cancer. In contrast to variants in protein-coding regions, regulatory variants do not affect protein sequence and function. Instead, regulatory variants affect the amount of protein produced. The 3′ untranslated region (UTR) is one gene region that is critically important for gene regulation; cancers for example, often express genes with shortened 3′UTRs that, compared with full-length 3′UTRs, have higher and more stable expression levels. We have investigated one kind of regulatory variant that can affect the 3′UTR length and thereby cause disease. We identified several such variants in different genes and found that these variants affected the genes' expression. Some of these variants were also strongly linked with known markers for disease, suggesting that these regulatory variants are important hereditary causes for disease. In protein-coding genes, the polyadenylation process consists of cleaving the end of the 3′ untranslated region (UTR) of precursor messenger RNA (pre-mRNA) and adding a polyadenylation (polyA) tail. Alternative polyadenylation (APA) can occur when several polyadenylation (polyA) signals lie in the last exon of a protein-coding gene. Many APA signals are evolutionary conserved One functional consequence of APA is transcripts with different 3′UTR lengths and different microRNA (miRNA) regulation FGG gene has for example been shown to affect the usage of this polyA site, and has been associated with increased risk for deep-venous thrombosis CCND1 gene has been shown to create an alternative polyA signal and is associated with increased oncogenic risk in mantle cell lymphoma Data on APA can be used as an efficient biomarker for distinguishing between cancer subtypes and for prognosis Hypothesizing that mutations in DNA elements such as the polyA signal can be an important cause of altered APA, we investigated to what extent SNPs can create or disrupt APA signals (APA-SNPs). Specifically, we tested whether APA-SNPs can give shorter 3′UTRs, increased gene expression through loss of miRNA regulation , and be First, by analysing EST data, we found that SNPs can create polyA motifs and affect 3′UTR length. Second, differential allelic expression from RNA-seq data, as well as mRNA and miRNA microarray expression data revealed an association between alternative polyA site strength , loss of miRNA target sites, and transcript expression. Third, based on these analyses we also identified significant APA-SNPs. Fourth, we mapped the identified SNPs to disease-associated SNPs and found that APA alleles were significantly correlated with disease-risk alleles. Together, these results suggest that APA-SNPs can be a significant causative mechanism in disease .The distribution of SNPs within 3′UTRs is fairly uniform To analyse SNPs in alternative polyadenylation signals, we first identified a set of SNPs that potentially create new APA signals in 3′UTRs. Specifically, we searched for any Hapmap SNP To investigate whether SNPs can create functional alternative polyA sites, we analysed the EST-based polyA sites from the PolyA_Db database ABCC4, AKAP13, FANCD2, KY, MIER1, OSTM1, PNN, RASGRP3, RHOJ, SELS, SHMT1, SLBP, and SLC11A2. Second, for each of these genes, we identified and imputed (see MIER1 (SNP rs17497828) and PNN (SNP rs532) were significant , whereas 3 had the non-APA allele (none of them had a clear polyA tail). Similarly, for PNN, all of the 34 EST sequences ending near the annotated APA site had the APA allele (including 10 with a clear polyA tail). Together, these results suggest that SNPs can create functional APA sites and thereby affect 3′UTR length.For EST data can be used to identify alleles and transcript ending positions , but ESTTo explore whether RNA-seq data could reveal whether APA-SNPs affect transcript expression, we therefore developed and validated an RNA-seq-based genotyping approach , resulting in 362 SNPs. In total, 262 unique SNPs had 3′ end estimates and genotypes available (6852 data points). To analyse the impact of APA-SNPs on 3′ end positions, we only considered SNPs that lie far enough (at least 1500 bp upstream) from the annotated 3′end. This final requirement gave 93 unique SNPs (2340 data points).Fourth, we ran correlation analyses between the genotype  and the negative logarithm of the distance between the estimated and the annotated transcript end see . As expen status , we coulSince RNA-seq data can genotype our candidate SNPs and at the same time determine transcript expression levels, we decided to analyse ratios of allele expression . According to our hypothesis , APA allSome polyadenylation signals occur more frequently upstream of known polyA sites than other signals do , , ) . Strong In addition to having strong polyA signals, functional polyA sites tend to have a GU-rich region downstream of the cleavage site We computed the GU level for each of our candidate SNPs. As the background value outside the GU-rich region is about 0.51 , we used, , ) . In cont)  . This inTo further evaluate the connection between signal strength, GU level, and allelic imbalance, we grouped the SNPs according to their GU level and their signal strength  from two breast cancer cell lines , which we tested individually for significant positive imbalance; that is, an APA allele count significantly greater than the non-APA allele count. We used a et al.Since heterozygous SNPs in strong APA signals can have an increased imbalance towards APA alleles, we investigated whether positive allelic imbalance can be associated with increased gene expression; that is whether a higher proportion of APA alleles than non-APA alleles was associated with an increased total allele count. We focused on the 12 samples from the Burge dataset that we could match to miRNA expression data in similar cell types from Landgraf Based on the 174 allelic ratios, we compared SNP expression  for groups with higher APA allele proportion (positive log AR) with groups with higher non-APA allele proportion  that were significantly different from 0 see . All hadet al.Since SNPs can alter polyadenylation and affect miRNA target sites and gene expression, we wondered whether they can also play an important role in human diseases. We therefore tested if any of our APA-SNPs were linked to trait-associated SNPs from the NHGRI GWAS catalogue We hypothesised that if APA-SNPs play a role in diseases, then APA alleles would be positively  . In contThose results show that a significantly high proportion of our candidate SNPs is in LD with trait-associated SNPs and their APA alleles are positively correlated with risk alleles of trait SNPs. This suggests that those APA-SNPs can potentially be the cause of their corresponding disease-association signals measured and registered in the GWAS catalogue.Our analyses confirmed the hypothesis , loss of miRNA target sites, allelic imbalance, and transcript expression. The Burge dataset was generated by using cDNA fragmentation, which gives a good coverage of 3′UTRs The independent microarray data strongly support the EST and RNA-seq results, however. Specifically, the mRNA and miRNA microarray expression data showed association between alternative polyA site strength , loss of miRNA target sites, and transcript expression. This microarray analysis had the advantage of directly using genotype data from Hapmap, instead of genotyping SNPs through mapped RNA-seq reads. Furthermore, the microarray analysis focused on transcript expression differences between individuals and therefore required data from a unique cell type, whereas the RNA-seq analysis focused on allelic expression differences within a sample and could therefore involve different cell types. As expected, the microarray analysis showed similar results as the RNA-seq analysis, suggesting that the increased allelic ratios from RNA-seq data did not come from a potential bias due to the cDNA fragmentation method, but from the loss of functional miRNA target sites.i.e. predicted heterozygotes that were in fact homozygous).One clear disadvantage of using the RNA-seq data for genotyping and allelic-imbalance-based detection, was false positive homozygotes. We could detect potentially functional candidate SNPs by testing for allelic imbalance, which takes into account the number of reads and their quality, while testing for unusual allele proportion patterns. The difficulty was to find extreme allelic imbalance, as we could miss extreme imbalance by classifying a locus as homozygote because of too few reads  from the human haplotype map project  from PolyA_Db For each of the 13 candidate genes, we downloaded the EST sequences (Expressed sequence tag) from UCSC  Given a 3′UTR region of a gene of interest, we took all the phased SNPs from Hapmap We downloaded RNA-seq data from human primary We mapped RNA-seq reads using the RMAP software We mapped those reads to 3′UTR We counted base calls based on base quality probability score and sequence alignment score: We discarded reads mapped with an alignment score We mapped reads from the Burge dataset using the alignment software Bowtie Log Allelic Ratio for each heterozygous SNP is defined as However, to avoid that a mapping bias towards reference alleles affects allelic ratios, we used a corrected allelic imbalance in our analyses, by combining allelic ratios computed from reads mapped to the reference genome with reference alleles, and allelic ratios computed from reads mapped to the same genome but with non-reference alleles at candidate SNPs. We defined it as the mean of the two log-ratios:where We took all the known coding genes from the UCSC RefSeq gene database (hg18)   window . We theret al.Human miRNA profiles from Landgraf et al.We took human miRNA profile from Wang et al.et al.For each of the candidate SNPs and their corresponding RefSeq genes, we defined a short 3′UTR as the exonic region from the mRNA stop codon to the SNP, and a long 3′UTR, as the reference 3′UTR. We computed miRNA target predictions on those short and long sequences using the prediction tool from Saito We downloaded human gene expression profiling of EBV-transformed lymphoblastoid cell lines from 270 unrelated Hapmap individuals We mapped probe IDs to RefSeq genes using the BioConductor package for R Genotype was encoded as 0, 1, and 2 for non-APA homozygotes, heterozygotes, and APA homozygotes, respectively.We computed bootstraps of median differences: Given two groups with different sizes, we resampled with replacement in each group with their actual original size. We took the median in each resampling and computed the difference. We repeated this procedure 1000 times to create a median difference distribution, which was then used to compute the 95% confidence interval Click here for additional data file.Figure S2Distribution of Hapmap SNPs within 3′UTRs of all RefSeq genes. Panel (A) shows the SNP distribution as a function of relative position within the 3′UTR (coding end site at position 0 and transcript end site at position 1). The SNP distribution, which is based on a kernel density estimate, is relatively uniform across the 3′UTR. Panels (B) and (C) show the SNP distribution from, respectively, 500 bp and 200 bp upstream of the transcription end position to the first 50 bp outside the gene. The SNP density is uniform within the 3′UTR except at the polyA signal position around 30 bp upstream of the transcript end.(PDF)Click here for additional data file.Figure S3Distribution of distance (PDF)Click here for additional data file.Figure S4GU content around transcription end site, based on all RefSeq genes. Mean of curves defined as GU proportion in a 5-nucleotide window sliding from the polyA signal to 70 nucleotides downstream. The GU-rich region is located between the 25th window and the 45th window.(PDF)Click here for additional data file.Table S1N nucleotides upstream of the cleavage site when looking at the reference genome, can be explained by a SNP in the region creating a signal from the SNP's non-reference allele.A portion of the EST-based polyA sites from PolyA_Db that do not have any signal in (PDF)Click here for additional data file.Table S2Checking genotyping of 755 mono-allelic SNPs in 2 datasets (Heap and Burge). Columns correctHOM, incorrectHOM, and incorrectHET show the number and proportion of correctly classified homozygotes and of incorrectly classified homozygotes and heterozygotes among the total number of genotypes, respectively; ‘correct(PDF)Click here for additional data file.Table S3Genotyping results for the 412 candidate APA-SNPs in the Heap and Burge datasets.(PDF)Click here for additional data file.Table S4et al.PolyA signal frequencies. The first three columns show polyA signal ranks, signal hexamers, and their frequencies in human genes from Tian (PDF)Click here for additional data file.Table S5Multiple regression on distance between the estimated and the annotated transcript end ; see  and APA (PDF)Click here for additional data file.Table S6Significant APA-SNPs from the microarray, EST and RNA-seq analyses.(XLS)Click here for additional data file.Text S1Translation of the Abstract into French by LFT.(PDF)Click here for additional data file.Text S2RNA-seq data successfully genotype known SNPs.(PDF)Click here for additional data file.