Francisella tularensis has been documented. Current DNA based genotyping methods for typing F. tularensis offer a limited and varying degree of subspecies, clade and strain level discrimination power. Whole genome sequencing is the most accurate and reliable method to identify, type and determine phylogenetic relationships among strains of a species. However, lower cost typing schemes are necessary in order to enable typing of hundreds or even thousands of isolates.A low genetic diversity in Francisella isolates, including 13 F. tularensis subspecies holarctica (type B) strains, 26 F. tularensis subsp. tularensis (type A) strains and a single F. novicida strain. The tree was generated from global multi-strain single nucleotide polymorphism (SNP) data collected using a set of six Affymetrix GeneChip® resequencing arrays with the non-repetitive portion of LVS (type B) as the reference sequence complemented with unique sequences of SCHU S4 (type A). Global SNP based phylogenetic clustering was able to resolve all non-related strains. The phylogenetic tree was used to guide the selection of informative SNPs specific to major nodes in the tree for development of a genotyping assay for identification of F. tularensis subspecies and clades. We designed and validated an assay that uses these SNPs to accurately genotype 39 additional F. tularensis strains as type A  or type B (B1 or B2).We have generated a high-resolution phylogenetic tree from 40 F. tularensis subspecies and clades, emphasizing the potential power and utility of this methodology for selecting SNPs for typing of F. tularensis to the strain level. Additionally, whole genome sequence based SNP information gained from a representative population of strains may be used to perform evolutionary or phylogenetic comparisons of strains, or selection of unique strains for whole-genome sequencing projects.Whole-genome SNP based clustering was shown to accurately identify SNPs for differentiation of  Francisella tularensis, a Gram-negative bacterium, is the causative agent of tularemia and a Category A select agent. F. tularensis is divided into three subspecies (subsp.): tularensis (type A); holarctica (type B); and mediasiatica [F. tularensis subsp. mediasiatica includes strains predominant in central Asia [F. novicida has been suggested to be a subspecies of F. tularensis based on genetic similarity [F. novicida has been isolated from North America and Australia, and rarely causes human disease even though it can cause a lethal infection in the murine model of disease [asiatica ,2. Tularral Asia . F. novimilarity ,5, but i disease ,6.F. tularensis offer a varying degree of power to discriminate subspecies, clades and strains [F. tularensis subsp. tularensis have been reported based on multiple subtyping methods including multi-locus variable number tandem repeat analysis (MLVA), pulsed-field gel electrophoresis (PFGE) and multi-locus sequence typing [F. tularensis, however it is limited in its ability to perform evolutionary analyses and to estimate relationships among very closely related strains [Current DNA based genotyping methods for typing  strains ,7,8. Twoe typing . Clades e typing . A limit strains .F. tularensis can ideally be met by whole genome sequencing of multiple strains. Whole genome sequencing is the most accurate and reliable method to identify and discriminate strains of a species, especially those species with a high degree of genome homogeneity. Genomic sequence information of several type A and B strains is now available http://www.ncbi.nlm.nih.gov/sites/entrez?db=genomeprj&orig_db=&term=Francisella%20tularensis&cmd=Search. F. tularensis has a single circular chromosome with genome size of ~1.89 Mb. Naturally occurring plasmids have not been reported for F. tularensis strains so far. A low genetic diversity in F. tularensis has been documented. Based on whole genome sequencing, the genetic variation between the type B live vaccine strain (LVS) and two other type B strains, FSC200 and OSU18, is only 0.08% and 0.11% respectively. F. tularensis subsp. holarctica strain FSC200 is a virulent strain of European origin whereas F. tularensis subsp. holarctica strain OSU18 is a virulent strain isolated in the United States. A higher genetic variation of 0.7% has been reported between a type B (LVS) and type A (SCHU S4) strain [Development of high-resolution genotyping methods for ) strain . SNPs al® based approach for whole genome F. tularensis resequencing and global SNP determination [Francisella strains using this approach. Twenty six F. tularensis type A (20 A1 and 6 A2), thirteen F. tularensis type B and one F. novicida strain were used for phylogenetic SNP analysis and identification of high-quality SNPs for use as typing markers. Based on our global analysis of 40 genomes, we were able to identify a series of SNPs at various levels of hierarchy. We used these SNPs to develop and validate a low-cost PCR-based assay for typing and discriminating F. tularensis isolates.Previously, we reported an Affymetrix GeneChipmination . We now Francisella strains used for whole genome sequencing are listed in Table PmeI was performed for CDC strains to characterize type A strains as either A1, A2, A1a or A1b [PvuII restriction enzyme, was used to characterize USAMRIID type A strains as A1 or A2 .a or A1b . RibotypF. tularensis reference strains LVS and SCHU S4 were obtained from Dr. Luther Lindler of Global Emerging Infections Surveillance and Response System of Department of Defense. Genomic DNA was isolated from the strains in Table Genomic DNAs of ® resequencing by hybridization and the details of the design of F. tularensis GeneChip® set has been described earlier [AM 233362) serving as a reference and complemented with unique sequences of SCHU S4 (GenBank Accession: AJ 749949). A total of 1,764,558 queryable bases were identified for resequencing by hybridization after exclusion of ~9.22% of repetitive sequence from the design. This sequence was tiled onto a set of six CustomSeq 300 K GeneChips® by Affymetrix, Inc., . This design provides approximately 91% of the F. tularensis double stranded genome sequence information from holarctica (type B) and tularensis (type A) subspecies. The whole genome resequencing was performed in duplicate for all query strains.The basis of the Affymetrix GeneChip earlier . BrieflyFrancisella genomic DNA amplification, DNA fragmentation, labeling, hybridization and acquisition of raw data was carried out exactly as described earlier [ earlier .® resequencing array platform can lead to incorrect basecalls due to a number of systematic effects that are less prevalent when the sample consists of a purified PCR product. We have developed bioinformatic filters to account for most of these predictable adverse effects. Our bioinformatic filters consist of a set of Perl scripts that operate on the CHP files generated by GSEQ software and produce a list of high-confidence SNP calls from the larger raw set of SNPs calls present in those files. The scripts are available for download from our website http://pfgrc.jcvi.org/index.php/compare_genomics/snp_scripts.html. Each filter serves to reduce the number of candidate SNPs. The output of one filtering step becomes the input for the next. The detailed descriptions of these filters have been reported [Hybridization of a whole-genome sample on an Affymetrixreported .Briefly, the quality filter implemented in GSEQ software initially eliminates SNP calls that have been assigned low quality scores based on the difference in signal intensity between the highest intensity probe pair and the next highest intensity pair at a particular locus. The first filter applied is the "low homology filter" which identified regions that performed poorly as a result of deletions in the sample relative to the reference sequence. The base calls from the CHP files from GSEQ software are scanned to identify regions of adjacent positions that are rich in no-calls and SNP calls. SNP calls that occur within the defined low homology region are removed from the list of high-confidence SNP calls. The next script is referred to as the alternate homology filter. The alternate homology effect is caused by the sequences in the query DNA sample capable of hybridizing with high efficiency to more than one probe pair at a locus on the array. When a locus contains two strongly hybridizing probe pairs, the GSEQ software may make a SNP call, a reference base call or a no-call ("N"), depending on the relative signal strengths of the probe pairs. The alternate homology filter identifies SNP calls that may have arisen as a result of this effect based on the difference in binding energy between the alternate (SNP) sequence and the reference sequence. If the difference between these two binding energies is = 11.5 kcal/mol, the SNP call is assumed to be an artifact of the alternate sequence homology, and it is removed from the list of high confidence SNP calls. The remaining SNP calls are then put through the footprint effect filter. The artifact called the footprint effect is caused by the occurrence of a real SNP in a query sample that results in a destabilizing effect on 25-mers in the immediate vicinity of the SNP. The footprint effect filter algorithm assumes that a genuine SNP is most likely to cause spurious SNP calls at locations within 10 bases on either side of the genuine SNP. Any SNP call that occurs more than 10 base positions from the nearest neighboring SNP call is assumed to be valid, and any SNP call that has one or more neighbors within 10 base positions is subjected to the filter. Since any number of consecutive SNP calls within 10 base positions of each other may occur in the data, this filter is implemented as a recursive algorithm. For each list of consecutive SNP calls that each lies within 10 bases of its neighbors, the algorithm identifies the SNP call having the highest quality score. That SNP call is accepted as valid, and its immediate neighbors are removed from the list of high confidence SNP calls. This action may break the original list of neighboring SNP calls into two separate lists. All resulting lists are processed recursively in the same way, until all of the SNP calls have been accepted or rejected. This algorithm is implemented in the RemoveFootprintEffect.pl Perl program. All the above filters are applied to individual data sets generated for any sample, following which a final filter referred to as the replicate combination filter is applied. The replicate combination filter generates the list of common SNPs present in both the experiments.We generated a phylogenetic tree from the resequencing data by considering only those locations at which a SNP occurred in one or more of the forty strains. For each strain, we constructed a sequence containing the base calls at each of the locations at which a SNP was found in some strain(s). This resulted in forty sequences, each containing 19,897 base calls  which were used for the phylogenetic analysis. The phylogenetic tree was generated using the MrBayes program, version 3.1.2 -17. The Node pairings which discriminated between subspecies or clades were selected for the development of diagnostic typing assays. Criteria used to select SNP locations for the assay were:1. The SNP location must cleanly differentiate the two nodes of interest. Within each of the nodes, all of the member strains must share the same base call at the location, and the two nodes must differ at the location.2. The sequences downstream of the SNP location must be in sufficient agreement among all strains from both nodes so that an appropriate primer can be chosen from the consensus sequence .3. The primer sequences must have melting temperatures within a specific limited range (60°C to 70°C).4. The predicted PCR product size must be within the range 150 to 500 bp.ftp://ftp.jcvi.org/pub/software/pfgrc/SNPTree/SNPTreePackage.tar.gz. These programs along with our bioinformatic filter pipeline can easily be adapted for other bacterial model systems for whole genome resequencing and SNP phylogeny using the Affymetrix resequencing array platform.We developed a set of programs to identify candidate SNP locations for the real-time PCR (RT-PCR) assay. SNPTree uses the phylogenetic tree and the multi-FASTA files from the resequencing experiments as input, assigns arbitrary node numbers to all nodes in the tree, and produces a set of multi-FASTA files, one for each node in the tree, of the consensus base calls for each node. The consensus call is "N" unless all members of a particular node share the same base call at that location. The program also produces a set of files, one for each node, listing the base calls that occur at every SNP location, for all SNP positions detected within the entire set of 40 samples . The program CompareNodes uses the SNP list files for any two nodes and produces a list of SNP locations that cleanly differentiate the two nodes (described above). The program CreatePrimer3 uses a list of discriminating SNP locations and the multi-FASTA files for two nodes, and creates an input file for the Primer3 program [Primer3 software was used to design discriminating PCR primers based on the set of discriminating locations identified. Three primers were designed at each discriminating location: a 5'-forward primer with the node X call in the 3' position; a 5'-forward primer with the node Y call in the 3' position; and a single 3'-reverse primer. A base call at the discriminating location is determined by two PCR reactions where one of the two yields a lower cycle threshold (Ct) value. The RT-PCR primers used are shown in Additional File F. tularensis subspecies and clades were developed using SYBR® Green  which binds all dsDNA molecules, emitting a fluorescent signal of a defined wavelength (522 nm). Reactions were performed in 20 μl volume and contained 80 pg of genomic DNA (0.01 ng/μl), 150 nM of forward and reverse primers and 10 μl of iQ SYBR® Green Supermix . Reaction components were mixed in a V-bottom thin wall PCR 96-well plate . Real-time PCR was performed using the iCycler iQ  with the following thermal cycling parameters: 50°C for 2 min, 95°C for 5 min, 60 cycles of 95°C for 15 seconds and 68°C for 30 seconds, 72°C for 30 seconds, 95°C for 1 min and finally 55°C for 3 min. The fluorescence was measured at 72°C in the cycle program. A cycle threshold (Ct) was automatically generated by the iCycler iQ Version 3.0a analysis software for each amplification reaction . Melt curve analysis was performed to verify that no primer dimers formed.Real-time PCR assays to identify ® array based whole genome resequencing platform for F. tularensis. Our whole-genome sequencing by hybridization approach made use of a set of bioinformatic filters to eliminate a majority of false positives and indicated a base call accuracy of 99.999%  for type B strain LVS [http://pfgrc.jcvi.org/index.php/compare_genomics/snp_scripts.html. Two type A strains, WY96 3418 and SCHU S4 showed base call accuracies of 99.995% and 99.992% with Phred equivalent scores of 43 and 41 respectively [Francisella strains. Table F. novicida strain were resequenced.Previously, we reported an Affymetrix Inc. GeneChiprain LVS . The basectively . We usedF. tularensis A1, A2 and type B strains are shown in Figure F. novicida strain FRAN003  was excluded. The whole genome resequencing call rate was in the range of 94.62% to 97.62% for A1 strains, 92.43% to 97.41% for A2 strains and 94.04% to 97.92% for type B strains. Overall, type B strains displayed the highest average base call rate of 95.97% ± 1.06% and A2 displayed the lowest with 94.40% ± 0.64%. The average base call rate for A1 strains was 95.87% ± 0.64%. The total number of SNPs for all forty strains ranged widely from 15 to 12,407. As expected FRAN003, the F. novicida strain, displayed the highest number of SNPs  compared to the F. tularensis reference (LVS + SCHU S4) sequence. The wide range in SNP differences was reduced almost by half, 15 to 6543, when the F. novicida sequence was excluded.The base call rate and number of SNPs for F. tularensis type B strains displayed the lowest number of SNPs, ranging from 15 to 2915. As expected, LVS strains (LVS and FRAN004) showed the fewest SNP positions (15-16) when compared to the reference sequence. The genomes of all other type B strains, except for FRAN024, contained 497 - 605 SNPs, when compared to the reference sequence. FRAN024 showed a significantly higher number of SNPs (2915) compared to other type B strains. FRAN024 is a Japanese holarctica strain. It has been reported that the F. tularensis subsp. holarctica isolates from Japan are unique, being somewhat intermediate to F. tularensis subsp. tularensis and the other F. tularensis subsp. holarctica isolates [isolates ,21. A1 sFrancisella strains is shown in Figure F. novicida (FRAN003) was distinct from type A and type B and formed its own phylogenetic group. Nodes  of the phylogenetic tree were assigned numbers by the SNPTree program. All type A strains emerged from node 4, whereas all type B strains emerged from node 50. The type A strains were divided into two primary sub-nodes, node 39 and node 5, corresponding to clades A2 and A1 respectively. A1 strains further subdivided into node 8, node 23, and node 5, corresponding to clades A1a and A1b and the MA00-2987 strain, respectively , A2 and B strains (excluding FRAN024) are shown in Figure The cladogram and phylogram generated from the whole-genome resequence SNP data of all 40 da FRAN00 was distF. tularensis strains. The A1a strains, SCHU S4, FRAN023, FRAN031, FRAN032, FRAN026, FRAN030, and FRAN033 all originate from the same temporal location (Ohio) in the 1940's  with no known link to one another were clearly resolved by whole genome SNP based phylogenetic clustering , whereas only strains derived from North America B1 strains) were associated with node 52 Figure , Table 1 strains holarctica strain FRAN024 is excluded from this analysis (B*). However, when all type B isolates, including the Japanese holarctica strain FRAN024, are included in the analysis, our data indicates a similar level of diversity for types A and B .Table The presence of a large number of differentiating SNPs within each phylogenetic node suggests that a deeper level of discrimination can be achieved by identifying SNPs unique to individual strains. The smallest number of differentiating SNPs within a phylogenetic node was 71 (A1b strains). The phylogram Figure  indicateF. tularensis subspecies or within subspecies were selected for the development of SNP diagnostic typing assays  distinguished type A and type B, 3 out of 9 primer sets distinguished A1 and A2 , 2 out of 5 primers sets distinguished A1a and A1b  and 3 out of 9 primer sets distinguished B1 and B2 . The two primer sets from each group displaying the largest difference in Ct values (shown in bold) were pursued further . To determine the robustness of these discriminatory SNP positions, an additional 39 F. tularensis strains   is shown in Figure PmeI PFGE typing results  and B2  strains were included as positive controls. Of the 16 type B strains tested, nine isolates were classified as B2 and 7 isolates were classified as B1. Isolates from Russia (RC 503), Spain (SP03 1782 and SP98 2108) Finland (SP03 1783) and the US were identified as B2 by this assay, whereas isolates from Canada and the US were identified as B1, providing evidence for geographic clustering of type B isolates based on this SNP marker. In summary, this work shows the potential for development of SNP typing markers based on a relatively small number of "complete" genome sequences. For future work, it will be important to define a set of SNPs that could be used for high-resolution discrimination to the strain level.F. tularensis, enabling rapid advancements in our understanding of basic and applied biology of this organism. F. tularensis has been recognized as a causative agent of tularemia for almost a century [Francisella strains using our whole genome high-density resequencing array platform [http://www.biohealthbase.org/GSearch/home.do?decorator=Francisella. BioHealthBase is a Bioinformatics Resource Center (BRC) for biodefense and emerging/re-emerging infectious diseases that is supported by the National Institute of Allergy and Infectious Diseases (NIAID). The data can also be obtained from our web site at http://pfgrc.jcvi.org/index.php/compare_genomics/francisella_genotyping.html or through the JCVI ftp server at ftp://ftp.jcvi.org/pub/data/PFGRC/Ft_DataRelease/. This multi-strain high-quality nearly complete genome sequence and global SNP information provides a unique opportunity to perform comparative genome analysis between F. tularensis strains, thus contributing towards a better understanding of pathogenicity and evolutionary relationships of this species. We have used this information to build a robust whole genome based phylogeny that enabled the identification of SNP discriminatory markers. We further validated high quality global SNP markers for typing of F. tularensis subspecies and clades as a proof of concept that these markers may be used for future development of high-resolution typing methods.Whole genome comparative analysis and collection of high-confidence global SNPs from multiple strains of a given bacterial species has a number of applications in both basic and translational research. Our study was undertaken with an objective of providing the scientific community with whole-genome sequence and SNP information from multiple strains of  century  and is cplatform . All theF. novicida isolate in our study, FRAN003 (U112), had the lowest base call rate (83.041%) and the highest number of SNPs  among our samples. The low base call rate is a likely reflection of the sequence divergence between the F. novicida strain (U112) and the reference sequence on our resequencing chips. Rohmer et. al[F. novicida U112 genomes. The differences in these two approaches may be due to the fact that array-based resequencing is sensitive to sequence divergence, and performs best with samples that are homologous with the reference sequence. In our global SNP phylogenetic analysis, F. novicida (U112) is well separated from the F. tularensis isolates (Figure Previous reports have suggested that greater genetic diversity exists among type A as compared to type B strains . Our whoFRAN003 U2, had ths Figure .Francisella [F. tularensis subspecies have been revealed by these approaches. Differing methods provide differing resolution as most of the methods sample only a subset of the whole genome in order to assess relationships among different isolates [A number of molecular approaches have been used to better understand the diversity of ncisella ,21,25-27isolates . PFGE tyisolates , not preisolates  and a nuisolates ,21-23.Francisella using ~8000 syntenic SNPs from the published whole genome sequences of seven strains. Use of only two type A and two type B genomes was sufficient to reveal that type A strains differ greatly from each other unlike type B strains. More recently, the phylogenetic structure of F. tularensis has been reported based on whole genome SNP analysis of thirteen publicly available genome sequences; 29,774 SNPs were used in this analysis [Francisella strains based on whole genome sequences. This to our knowledge is the first report of a phylogenetic model based on nearly complete genomes of multiple strains of F. tularensis using Affymetrix resequencing arrays.Comparative whole-genome sequence analysis provides the highest level of discrimination among different strains, but has not been widely used due to the high cost of this method. Keim et al  have shoWe have demonstrated that resequencing data may be used to generate high-resolution phylogenetic trees based on global SNPs. The advantage of this sequence-based approach is that SNP based phylogenetic trees can be used for evolutionary analyses. The comparative analysis based on the phylogenetic relatedness of strains can provide significant insights into the varying degree of phenotypes and ecotypes of an organism. The total number of complete genomes required to achieve an optimum phylogenetic profile from the multiple strains of an organism will be determined by the degree of plasticity of the genome. Adequate phylogenetic relationship can be determined with a sufficient number of genomes from diverse isolates of an organism and the whole genome comparative analysis of such related strains can provide real biological insights into the adaptation and evolution of a species. Such phylogenetic-based comparative analysis can capture genomic differences of very closely related strains and provide valuable information for the development of rapid molecular sequence based assays, capable of discrimination to the strain level.Francisella to construct a global phylogeny. Our data shows a good correlation with the previously published reports using limited genomic sequence information and also provides higher strain resolution. We used the whole genome SNP phylogeny to identify informative SNP markers specific to major nodes in the tree and to develop a genotyping assay for subspecies and clades of F. tularensis strains. Less diverse type B strains could even be discriminated into two clades, B1 and B2, based on a single SNP. Our whole genome SNP based phylogenetic clustering shows high potential for identifying SNP markers within F. tularensis capable of discriminating to the strain level. This finding should greatly facilitate the rapid and low-cost typing of F. tularensis strains in the future.The whole genome resequencing array platform provides sequence and SNP information from multiple strains for any infectious agent with an available whole genome sequence. Multi-strain whole genome sequence data allows one to build robust phylogenetic models for an organism based on global SNPs. Whole genome SNP based phylogenetic trees can guide meaningful comparative analysis of strains to better understand the biology of an organism as well as in translational research such as in developing high resolution economical SNP based typing assays. We have collected whole genome sequence and SNP information from forty strains of DNA: deoxyribonucleic acid; LVS: live vaccine strain; MLVA: multi-locus variable number tandem repeat analysis; PFGE: pulsed-field gel electrophoresis; SNP: single nucleotide polymorphism; subsp.: subspecies; RT PCR: real-time polymerase chain reaction.GAP- planned, developed and co-coordinated the project, analyzed the data, wrote the manuscript; MHH - bioinformatic tool development and data analysis, contributed to the progress of the project and manuscript writing; JMP - contributed to the data analysis and manuscript preparation; SP- wet lab analysis, performed resequencing assays of the samples; SAK- bioinformatic data analysis, preparation of tables and figures; MJW- contributed to the data analysis and manuscript preparation; CM- data collection for the SNP typing assay of samples; MJ- contribution towards development and optimization of the SNP typing assay; MES-participated in data analysis and manuscript preparation; RDF-project oversight; SNP-project design, manuscript contribution and project oversight. All authors read and approved the final manuscript.Francisella strains using maximum likelihood methodWhole genome SNP based phylogenetic analysis of Click here for fileList of RT- PCR primers for diagnostic typing assaysClick here for fileF. tularensis strainsWhole genome resequencing call rates and SNPs for Click here for fileQuantitative SNP differences between the major phylogenetic nodes in the cladogramClick here for filein silico identified SNP diagnostic markers.Features of Click here for file


A great amount of data has been accumulated on genetic variations in the human genome, but we still do not know much about how the genetic variations affect gene function. In particular, little is known about the distribution of nonsense polymorphisms in human genes despite their drastic effects on gene products.−3 per site, which is lower than that of nonsynonymous SNPs (2.1×10−3 per site). On average, nonsense SNPs were located 250 codons upstream of the original termination codon, with the substitution occurring most frequently at the first codon position. Of the nonsense SNPs, 581 were predicted to cause nonsense-mediated decay (NMD) of transcripts that would prevent translation. We found that nonsense SNPs causing NMD were more common in genes involving kinase activity and transport. The remaining 602 nonsense SNPs are predicted to produce truncated polypeptides, with an average truncation of 75 amino acids. In addition, 110 read-through SNPs at termination codons were detected.To detect polymorphisms affecting gene function, we analyzed all publicly available polymorphisms in a database for single nucleotide polymorphisms (dbSNP build 125) located in the exons of 36,712 known and predicted protein-coding genes that were defined in an annotation project of all human genes and transcripts (H-InvDB ver3.8). We found a total of 252,555 single nucleotide polymorphisms (SNPs) and 8,479 insertion and deletions in the representative transcripts in these genes. The SNPs located in ORFs include 40,484 synonymous and 53,754 nonsynonymous SNPs, and 1,258 SNPs that were predicted to be nonsense SNPs or read-through SNPs. We estimated the density of nonsense SNPs to be 0.85×10http://h-invitational.jp/varygene/).Our comprehensive exploration of nonsense polymorphisms showed that nonsense SNPs exist at a lower density than nonsynonymous SNPs, suggesting that nonsense mutations have more severe effects than amino acid changes. The correspondence of nonsense SNPs to known pathological variants suggests that phenotypic effects of nonsense SNPs have been reported for only a small fraction of nonsense SNPs, and that nonsense SNPs causing NMD are more likely to be involved in phenotypic variations. These nonsense SNPs may include pathological variants that have not yet been reported. These data are available from Transcript View of H-InvDB and VarySysDB ( Genetic variations in the human genome are maintained by a balance of mutation, selection and random genetic drift. Some of the polymorphisms cause phenotypic variations and diseases. Therefore, many studies have attempted to identify causative variants of genetic diseases and the relationships between genetic variations and phenotypic effects. Genetic variations within linked loci are inherited to the same gamete. Based on the linkage of genetic variations, loci that contain disease-causing genes have been mapped by using polymorphic markers. At present, about 14 million clusters of genetic polymorphisms have been identified in the human genome Another approach in finding pathological variants is to extract polymorphisms that alter amino acids in functional genes or affect gene expression or splicing, using a comprehensive set of functional elements of the human genome. Several studies have analyzed nonsynonymous SNPs to predict pathological variants Although many pathological mutations have been identified In this study, to detect polymorphisms affecting gene function, we analyzed all publicly available polymorphisms in the Single Nucleotide Polymorphism Database (dbSNP) (build 125) in the exons of all 36,712 protein-coding genes that were defined in an annotation project of all human genes and transcripts (H-InvDB ver3.8)We analyzed 9,235,997 polymorphisms (dbSNP build 125) in the human genome with exon positions and predicted ORFs that were revealed in our annotation project of human genes (H-InvDB) . In all −3 per synonymous site, in ORFs , and all 23 were found . NonsensWe examined the positions of 1,183 nonsense polymorphisms in the coding regions. On average, nonsense SNPs were located at 250 codons upstream of the original termination codons. To predict whether a nonsense mutation causes nonsense-mediated decay (NMD) of mRNA, we examined the locations of nonsense SNPs in the exon-intron structure of the genes . As a reAMPD1, rs283413 in ADH1C, rs10250779 in PGAM2, rs17215500 in KCNQ1, rs497116 in CASP12, rs2228325 in ACTN3, rs3092891 in RB1 and rs28989186 in BUB1B) matched the variants in the OMIM database that are known variants with phenotypic variations  database. Only eight of 1,183 nonsense SNPs (rs17602729 in riations . This loriations .DDR2 gene (X74764) is predicted to be a read-through mutation (from TAG to CGA), and the transcript has no other stop codon in the 3′UTR region. The frequency of this SNP is unknown (it is monomorphic in the four populations in HapMap project MFSD3 gene (CR620962), which causes a change from TGA to CGA resulting in a change to arginine.Among the 247 SNPs at termination codon sites, 119 SNP-mRNA pairs were found to be read-through mutations. If the allele having the stop codon is the ancestral type, the SNP is regarded as a change causing elongation of the polypeptide. However, an extended polypeptide would be expected only if there is an additional termination codon downstream. For 108 SNP-mRNA pairs, an additional termination codon was found in the 3′UTR region. The average extension was estimated to be 29 amino acids. Interestingly, we found five SNP-mRNA pairs that have no stop codons in the 3′UTR at all (The remaining six SNP-mRNA pairs do not have 3′UTR regions). For example, the T-to-C substitution (rs15941) in the To see whether there is any functional bias in genes having nonsense SNPs, we examined the frequent biological terms in the genes having nonsense SNPs. We classified the genes having nonsense SNPs into two categories: genes with nonsense SNPs that are predicted to cause NMD and genes with nonsense SNPs that are not predicted to cause NMD. For genes having nonsense SNPs that would cause NMD , the molIn this study, we conducted an extensive analysis of human genome polymorphisms with a comprehensive catalogue of human genes, and detected more than 50,000 polymorphisms that affect proteins. The distribution of polymorphisms showed different densities of polymorphisms among the 5′UTR, ORF and 3′UTR. The density of SNPs was lower in ORFs than in the 5′UTR and 3′UTR. The density of synonymous SNPs in the ORFs was higher than the densities of SNPs in the UTR regions. The reduction in density of SNPs in the UTR regions is consistent that there are functional constraints on nucleotide changes in UTRs related to the transcriptional and translational efficiencyWhile nonsense mutations that cause NMD result in ‘loss of function’, nonsense mutations that do not cause NMD produce truncated proteins which could have the dominant effects. The proportion of predicted nonsense SNPs causing NMD in this study is in agreement with a previous study which showed that dbSNP (build 125) has 1301 nonsense SNPs, about half of which were predicted to result in NMD The molecular functions that were overrepresented in the genes having nonsense SNPs included several molecular functions that were observed in human-specific pseudogenesOur results showed a low proportion of matches of nonsense SNPs with known pathological variants in OMIM, suggesting that the effects of most nonsense polymorphisms are unknown or not reported. Furthermore, the correspondence of the nonsense SNPs to the OMIM allelic variants , Table 7There is a possibility that the nonsense SNPs detected here have pathological effects, in particular, if non-dispensable genes have nonsense mutations. First, a defect in one gene by a nonsense mutation or a frame-shifting indels causing a premature termination codon could be a cause of genetic diseases including complex diseasesThe effect of single nonsense SNPs can be compensated by the products of other genes having similar functionsElongation of polypeptides by read-through mutations can affect protein folding and aggregation of proteins, which could affect phenotypic variations. Furthermore, a read-through mutation can cause more severe effects on translation when no additional stop codon follows. Such mutations are subject to ‘non-stop decay’ Although the present results are based on representative transcripts (one transcript for one gene), the total number of SNPs causing changes between amino acids and stop codons in all the splicing isoforms was much larger . These variations, which cause changes in the length of a polypeptide or which determine whether a protein is translated, may include pathological variants that have yet not been reported. Therefore, it is important to examine their presence in human populations.As data of genetic polymorphisms of human genome, single nucleotide polymorphisms (SNPs) and insertions and deletions (indels) in dbSNP http://www.h-invitational.jp/), created by the annotation project of human genes  The data of human gene structure were obtained from H-InvDB ver3.8  premature termination (nonsense) codon, 2) read-through of original stop codon, and 3) synonymous at stop codon site, by assuming that the cDNA sequence has an ancestral allele. Indels were classified based on whether they are located in ORF. The indels within ORF were further classified by whether the insertion or deletion causes frame shifting in translation.Inference of direction of nonsense and read-through mutations. Ancestral alleles were obtained from dbSNP (build 128) to check direction of mutations for SNPs causing changes between amino acids and stop codons. For nonsense SNPs in protein-coding regions, we checked whether the ancestral allele codes amino acids. In case that the ancestral allele codes stop codon, we do not regard this SNP as nonsense SNP, but is a read-through mutation assuming that there was a variant having a shorter ORF. For read-though SNPs at termination codon site, we checked whether the ancestral allele codes stop codon. In case that the ancestral allele codes amino acids, we regard this SNP not as a read-through mutation, but as a nonsense mutation in a variant having a longer ORF.Number of sites for synonymous, nonsynonymous and nonsense mutations. To estimate densities of synonymous, nonsynonymous and nonsense SNPs, the numbers of potential synonymous, nonsynonymous and nonsense sites by single nucleotide changes were estimated for the ORF sequences. This is an extension of estimation of the numbers of synonymous and nonsynonymous sitesr) was set to be 4.0 . For example of the TTA codon for leucine, the number of nonsense sites was estimated to be 2.0/(r+2.0), because two types of transversional mutations at the second position cause nonsense mutations.NGAS gene).To check whether the polymorphisms that alter proteins are known pathological variants with phenotypic effect, we examined correspondence of SNPs with data of known pathological variants. We used data of ‘allelic variant’ in the Online Mendelian Inheritance in Man (OMIM) database Some of nonsense mutations cause nonsense-mediated decay (NMD), resulting in prevention of translation. It has been reported that mRNA would be destroyed if a stop codon occurs in the 5′ side of the boundary, which is 50–55 nucleotides upstream from the end of the second to last exon www.geneontology.org), a proportion of genes with the biological function in the genes having nonsense SNPs was compared with that in all human genes , and the significance of over representation of a molecular function in the genes having nonsense SNPs was evaluated as the upper probability of the hypergeometric distribution.For each biological term from Gene Ontology Click here for additional data file.Table S1Nonsense SNPs and read-through SNPs on representative transcripts.(4.24 MB DOC)Click here for additional data file.


Vitis), the world's most economically important fruit crop. Reduced representation libraries (RRLs) from 17 grape DNA samples (10 cultivated V. vinifera and 7 wild Vitis species) were sequenced with sequencing-by-synthesis technology. We developed heuristic approaches for SNP calling, identified hundreds of thousands of SNPs and validated a subset of these SNPs on a 9K genotyping array. We demonstrate that the 9K SNP array provides sufficient resolution to distinguish among V. vinifera cultivars, between V. vinifera and wild Vitis species, and even among diverse wild Vitis species. We show that there is substantial sharing of polymorphism between V. vinifera and wild Vitis species and find that genetic relationships among V. vinifera cultivars agree well with their proposed geographic origins using principal components analysis (PCA). Levels of LD in the domesticated grapevine are low even at short ranges, but LD persists above background levels to 3 kb. While genotyping arrays are useful for assessing population structure and the decay of LD across large numbers of samples, we suggest that whole-genome sequencing will become the genotyping method of choice for genome-wide genetic mapping studies in high-diversity plant species. This study demonstrates that we can move quickly towards genome-wide studies of crop species using next-generation sequencing. Our study sets the stage for future work in other high diversity crop species, and provides a significant enhancement to current genetic resources available to the grapevine genetic community.Next-generation sequencing technologies promise to dramatically accelerate the use of genetic information for crop improvement by facilitating the genetic mapping of agriculturally important phenotypes. The first step in optimizing the design of genetic mapping studies involves large-scale polymorphism discovery and a subsequent genome-wide assessment of the population structure and pattern of linkage disequilibrium (LD) in the species of interest. In the present study, we provide such an assessment for the grapevine (genus  The aim of genetic mapping studies is to identify loci that underlie phenotypic variation. Genetic mapping studies are critical for improving crops through marker-assisted breeding and for our understanding of the relationship between genotype and phenotype http://www.hapmap.org/), and similar projects are in progress for Arabidopsis thaliana , rice (http://irfgc.irri.org) and maize (http://www.panzea.org/). While previous SNP discovery initiatives relied on laborious and relatively expensive sequencing and genotyping platforms, SNP discovery has become less time consuming and much more cost-effective since the introduction of next-generation sequencing . SNP discovery using next-generation sequence data is still in its infancy, but several studies have already demonstrated that large numbers of high quality SNPs can be identified in a cost effective manner using next-generation sequence data The first step towards GWA and GS is to discover large numbers of genetic markers, generally single nucleotide polymorphisms (SNPs), across the genome. This initial step of large-scale SNP discovery is already underway in several organisms. For example, in humans the International HapMap Project currently boasts over 3 million SNPs  and population structure in the species of interest. The strategy underlying GWA and GS is to genotype enough markers across the genome so that functional alleles will likely be in LD with at least one of the genotyped markers Vitis). The grapevine is a long-lived woody perennial consisting of dozens of species whose natural habitat spans the northern hemisphere V. vinifera, represents one of the earliest domesticated fruits http://faostat.fao.org/). Previous characterizations of the genetic structure of the grapevine have been restricted to small numbers of microsatellites Here we describe the initial steps we have taken towards genome-wide genetic mapping studies in the world's most economically important fruit crop, the grapevine . We show that the Vitis9KSNP array provides sufficient high-quality genotypes to successfully capture the genetic structure within and between the V. vinifera cultivars and wild Vitis species. Our analyses suggest that the use of SNP arrays for WGA studies will be inadequate for high-diversity plant species in which LD decays rapidly, as in the grapevine. We suggest a stronger focus on experimental design in the anticipation that future mapping populations will be cost-effectively whole-genome sequenced in the near future.Here we describe a simple and rapid procedure for identifying hundreds of thousands of SNPs from 11 V. vinifera varieties, 6 wild Vitis species and the reference genome (inbred Pinot Noir) – see HpaII, which has proved useful in the generation of RRLs by others HpaII sequence tag across the 17 samples.We generated reduced representation libraries (RRLs) from 17 grapevine DNA samples (10 cultivated HpaII sequence tag (CGG). HpaII digested fragments between 40 bp and 250 bp  even at short physical distances  that assays 8898 SNPs selected from the 470K set by relying on several criteria described in V. vinifera accessions, while the second PC differentiates among wild species. The exception is V. sylvestris, the wild ancestor of the domesticated V. vinifera, which clusters with the V. vinifera varieties. We also performed PCA on genotype data generated from the Vitis9KSNP array for the same set of 17 samples  on 14,325 SNPs from the Illumina GA sequence data, which were chosen without regard to the pattern of segregation among the 17 wild and cultivated grapevines  showed a low proportion of matches. The variation across samples in the proportion of matches could be due to numerous factors, including variation in exogenous DNA contamination or quality of the sequence data.We generated ∼2.6 Gb of DNA sequence using the Illumina Genome Analyser, a substantial proportion (32%) of which did not align successfully to the available genome sequence . Some ofHpaII tag . Moreover, this high degree of shared polymorphism is likely an underestimate since polymorphism was often missed due to low read counts. Despite being geographically isolated for more than 20 million years, there is strong evidence of significant degrees of shared polymorphism between North American wild grapevine species and European cultivated grapevines. This observation supports the view that grapevine species have maintained large effective population sizes for millions of years and that, despite having undergone domestication and breeding, V. vinifera cultivars still harbor variation that dates back tens of millions of years.We find relatively few fixed differences (2.7% of SNPs) and a considerable degree of shared polymorphism (24.3% of SNPs) between  species . The wilV. vinifera are low, even at short inter-SNP distances. The median r2 for SNPs within 50 bp of each other is only 0.18, for example. This striking observation suggests that the effective population size of the domesticated grapevine is extremely large and historical recombination has fragmented the V. vinifera genome into very short haplotype blocks. The rapid breakdown of LD in V. vinifera, together with the presence of shared polymorphism between V. vinifera and wild Vitis species, suggests that grapevine domestication did not involve a severe population bottleneck. Future work assessing levels of diversity and LD decay in V. sylvestris, the ancestor of V. vinifera, will allow us to quantify more accurately the severity of the domestication bottleneck in the grapevine.We found that LD decays to background levels at inter-SNP distances of ∼10 kb . ConsistThe consequence of the observed rapid LD decay is that genetic mapping in the cultivated grapevine will not follow other organisms' paths towards genome-wide mapping studies. To date, the path towards GWA and GS has begun with genotyping microarrays that carry tag SNPs, SNPs that effectively capture neighboring variants through LD V. vinifera and ∼250 wild Vitis species from the USDA's grape germplasm collection. We selected SNPs discovered by Illumina GA sequencing to include on the array based on a number of criteria  to generate reduced representation libraries (RRLs). HpaII is a methyl-sensitive enzyme, but the genome amplification step prior to restriction digestion eliminates methylation and HpaII therefore behaves as a non-methyl-sensitive enzyme in this case. The standard library preparation for Illumina's 1G Genome Analyzer was then performed for each RRL with one alteration: size selection by gel excision was not performed as our experience suggests that it makes no difference in sequence quantity or quality . Each RRL was sequenced on a single lane of the Genome Analyzer with 36 cycles to produce 57.3 million reads. The sequences generated in this study have been submitted into the NCBI short read archive (SRA accession: SRA009211.21). Each 36 bp read was first shortened to 32 bp  and aligned to the grape reference genome Genomic DNA was extracted with DNeasy Plant Mini Kits (Qiagen) from young, lyophilized leaves, cambium tissue or leaf bud tissue. Details about the 17 DNA samples are provided in rd and 4th most common alleles was ≥2% of the total read count. We then considered only the two most common alleles as we are only interested in identifying bi-allelic SNPs. The implementation of these preliminary filters resulted in 469,470 SNPs, which we refer to as our 470K SNP set. From the 470K SNP set, we identified a 71,397 high-quality SNPs which we refer to as the 71K SNP set. The 71K SNP set was established by choosing SNPs from the 470K set with average Q scores ≥20 and genotypic contingency test p-values ≤0.01. See Supplementary To obtain a robust set of SNPs from this set of 2,271,594 putative SNPs, we implemented a series of preliminary filters. First, we rejected a putative SNP if the read count for the minor allele(s) was ≤5% of the total read count. This filter aims to distinguish between sequencing error, which should be found at low frequency, and true polymorphism. While this filter likely rejected true low-frequency SNPs in some cases, this is of little concern since we were primarily concerned with identifying intermediate-frequency SNPs. Some putative SNPs were covered by >50,000 reads. Putative SNPs covered by extremely high read counts are more likely to be non-allelic, i.e. the result of paralogy: although a set of reads may align to a single genomic location according to the genome sequence, they in fact are derived from multiple genomic locations that are misrepresented as a single sequence in the currently available genome sequence. To mitigate the paralogy problem, we implemented a second filter whereby putative SNPs were rejected if the total read count was >1000. This second filter also aids computational speed. Third, we implemented an arbitrary read count requirement and rejected SNPs with total read counts <10. Finally, when 3 or 4 alleles were observed, we rejected putative SNPs if the sum of the 3A significant proportion (31.1%) of the grape genome sequence has not been assigned to a chromosome. Another 7.9% of the genome is assigned to chromosomes, but not anchored to a chromosomal location. For our coverage analysis, we considered only the 60.9% of the genome sequence that is assigned and anchored to locations on chromosomes 1 to 19. We refer to this portion of the genome as the “assembled genome”.http://www.genoscope.cns.fr/externe/Download/Projets/Projet_ML/data/annotation/repeats/.A total of 17,326,203 reads  were successfully mapped to the assembled genome. We generated the observed coverage distribution by calculating the coverage for every base in the assembled genome see . The obsTo generate the expectation from sequencing at random without the use of RRLs, we followed the Lander-Waterman model whereby coverage follows a Poisson distribution if sequence is obtained at random from the genome V. vinifera and wild Vitis species using read count data from the 71K SNP set. For this analysis, V. sylvestris, the wild progenitor of V. vinifera, was included in the V. vinifera group. SNPs with ≥1 read carrying the reference allele and ≥1 read carrying the alternative allele within V. vinifera were identified as “segregating” or “polymorphic” within V. vinifera. The same criteria were applied to the wild Vitis species. Fixed differences were identified as SNPs with one allele present exclusively in V. vinifera and the other allele present exclusively in the wild Vitis species.We assessed the pattern of segregation within and between V. vinifera samples were included. D' is an unreliable measure of LD with small sample sizes and we therefore only present r2 values. SNPs with ≥2 missing genotypes were excluded. Singleton SNPs were excluded. Using these criteria enabled us to include 16,486 SNPs and provided sufficient resolution to assess LD decay. The genotype calls are likely sufficiently reliable since comparisons between r2 values generated from this SNP calling method and from the stricter SNP calling method described below under “Vitis9KSNP array” were highly correlated . A table of r2 values and their respective inter-SNP distances was sorted by inter-SNP distance. We calculated the median r2 in sequential bins of 1000 observations along this table and plotted this value against the mean inter-SNP distance for each bin. Background LD was assessed by calculating 20,000 r2 values between pairs of SNPs on different chromosomes. Pairwise LD was calculated using the R package “genetics” which incorporates maximum likelihood phase estimates into the estimation of LD We called genotypes from the raw Illumina GA read data as follows. A genotype was called only if the read count for an individual at that locus was ≥4 reads. Individuals were called homozygous if they carried ≥4 reads for one allele and 0 reads for the other allele. Individuals with ≥4 reads carrying both alleles were called heterozygous. For the analysis of LD decay, only the 10 We called genotypes from the Illumina sequence data and compared them to genotype calls from the Vitis9KSNP array. We attempted to find a set of rules for calling genotypes from the Illumina sequence data that would provide a sufficient number of SNPs for comparison while minimizing the false positive rate. An individual was called a homozygote at a locus if there were> = 5 reads from that individual mapping to that locus and all these reads carried the same allele at that locus. An individual was considered heterozygous at a SNP if it had ≥8 reads mapping to the position and if it passed the heterozygosity test  was performed using the prcomp command in R We called genotypes for SNPs in the 71K SNP set from the Illumina sequence data. To do so, we employed the SNP calling criteria described under the heading “LD decay” of the Methods S1Supplementary Methods(0.10 MB PDF)Click here for additional data file.Figure S1The distribution of assayed accessions for the 470K and 71K SNP set. In many cases, reads covering a SNP are only obtained from a fraction of the total number of samples sequenced. The histograms partition SNPs by the number of accessions from which reads were obtained.(0.01 MB PDF)Click here for additional data file.Figure S2The effect of neighboring polymorphisms on array-based SNP call quality. Each SNP on the Vitis9KSNP array is queried by a probe sequence that is complementary to the 50 bp of sequence adjacent to each SNP. SNPs within this adjacent probe sequence may reduce probe-sequence hybridization and thus result in poor quality SNP calling. The GenTrain score, along the Y-axis, is a metric of SNP quality assigned to every SNP on the Vitis9KSNP array by Illumina's BeadStudio software. The number of SNPs from the 71K set within each SNPs' probe sequence is shown along the X-axis. The boxplot demonstrates that the GenTrain Score decreases as the number of SNPs present in the probe sequence increases. Thus, obtaining reliable genotype calls using SNP arrays in highly diverse species will be challenging.(0.16 MB PDF)Click here for additional data file.Figure S3Plots of the first 10 PCs generated from 14,325 SNPs chosen without regard to the pattern of segregation among wild and cultivated grapevines. The proportion of the variance explained by each PC is in parentheses above each plot.(0.08 MB PDF)Click here for additional data file.Figure S4V. vinifera, hybrid Vitis cultivars and wild Vitis species are easily distinguishable along PC1. PC2 distinguishes among V. vinifera cultivars. V. sylvestris, the ancestor of V. vinifera, is found among the V. vinifera cultivars as expected.A PCA plot of 100 grapevine accessions. The SNP data were generated from the Vitis9KSNP array and only the first 2 PCs are shown. The proportion of the variance explained by each PC is shown in parentheses. The (0.03 MB PDF)Click here for additional data file.Figure S5Vitis accessions. The SNP data were generated from the Vitis9KSNP array and only the first 2 PCs are shown. The proportion of the variance explained by each PC is shown in parentheses.A PCA plot of 50 wild (0.03 MB PDF)Click here for additional data file.Table S1Additional information on grape DNA samples used in the present study(0.20 MB PDF)Click here for additional data file.Table S2Criteria used to choose the 8988 SNPs assayed by the Vitis9KSNP custom genotyping array(0.09 MB PDF)Click here for additional data file.


The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study. In the past several years, genome-wide association studies (GWAS) have achieved great successes in identifying hundreds of genetic variants that affect dozens of complex diseases. Most studies reported to date primarily employed a single-marker-based analysis strategy. As multiple genetic variations and environmental risk factors are expected to jointly affect a complex phenotype, it is natural to ask whether there is any benefit from conducting a joint marker analysis, e.g., a systematic study of all possible pairwise interactions, versus single-marker analysis . Both sij as follows:In the first stage of our proposed method for joint marker analysis, single-nucleotide polymorphisms (SNPs) are screened by using both a marginal search  and 2-D scan. For the marginal search, the simple logistic regression model is employed for each SNP The 2-D scan evaluates all possible SNP pairs by using the following additive models and interaction models:j ≤ k ≤ p index SNPs, genotype values Xj and Xk = 0, 1, or 2 denote the number of the minor allele at each SNP. The overall statistical significance of Models (1), (2), and (3) measures the significance of the marginal effect of SNP j, the additive joint effects of SNPs j and k, and the complete joint effects of SNPs j and k, respectively. In Model (2), the statistical significance of parameters αjk 1(or αjk2) measures the conditional additive effects of SNP j (or k), given SNP k (or j). In Model (3), the significance of interaction effect (epistasis) between SNPs j and k. The corresponding log-likelihood-ratio test statistics (LLR) quantify the statistical significance of models and parameters and thus the corresponding genetic effects. We wrote a C-program to implement logistic regression analysis, allowing for the exhaustive 2-D search (this program is available upon request from the authors). We chose the SNPs from the models that were ranked highest based on LLR.where 1 ≤ After the first stage analysis identifies a set of candidate SNPs and SNP interactions, we apply the LASSO model selection method ,6 to selglmnet [In the LASSO model, the variables to be considered are either genotype values that reveal signals of marginal and conditional additive effects, or the products of genotype values, i.e., interaction terms, which reveal the signals of epistases. We use the R package glmnet  for the p-values < 0.001 or minor allele frequencies < 0.01, and also excluded SNPs or individuals with missing rates >10%. Outliers were removed based on principal component analysis. Consequently, the final data include 500,884 SNPs and 2,002 individuals . In the first-stage analysis the missing observations of the corresponding SNP(s) were eliminated at each model fitting. In the second stage, we imputed the missing SNP genotypes using software Beagle [For GAW16 data quality control, we excluded those SNPs whose Hardy-Weinberg equilibrium p-values < 0.1). Most of these SNPs are located in chromosome region 6p21, with high linkage disequilibrium (LD) existing among some of them. We sorted the test statistics of marginal association in decreasing order. The blue solid curve in Figure There are 395 SNPs showing significant marginal effects with LLR > 27.04 . Hierarchically nested within these full models, 70,795 two-marker additive models in Form (2) contain at least one SNP with a conditional additive effect that has an LLR > 27.04 . These additive models involve 18,388 unique SNPs, 11.46% of which are on chr6.Because a two-marker model can be statistically significant if one of the SNPs has an extremely high marginal association, we did not study the conditional additive effects of the top 200 marginally associated SNPs (LLR > 49) in Model (2). Excluding these 200 SNPs, 71,693 two-marker full models in Form (3)  were found to be significant with LLR > 59.37 . To illustrate the connection between conditional additive effects and marginal effects, Figure genetics [To illustrate the LD pattern of the SNP pairs that have large conditional additive effects, Figure genetics ). In parp-values < 2.54 × 10-10. Of these 208 interactions, 46 are significant . The top 208 epistatic terms have their corresponding LLRs > 40, or unadjusted The goal of the second stage is to jointly select from the candidates: first, the top 395 SNPs with significant marginal associations in Model (1); second, the 506 unique SNPs from the 494 SNP pairs with significant conditional additive effects in model (2); and third, the top 208 epistatic SNP pairs by Model (3). In total, 914 variables were selected as input variables for LASSO selection, in which 706 variables were the genotypes of non-overlapping SNPs; 208 variables were the cross-products of genotype values of the 208 SNP-pairs.PTPN22 (rs2476601), HLA genes, and C5 (rs2900180) [To obtain a model that associates RA disease status with SNPs . Bayesia2900180) -13. FurtLOC389362 and C14orf151  among those 57 genes are not represented in the WTCCC data. Within the rest of genes (or around ± 5 kbp if no SNP is found within the gene), we retrieved WTCCC SNPs. Again, missing genotypes were imputed by Beagle [Using the Wellcome Trust Case Control Consortium (WTCCC) data , we sougPTPN22 and the major histocompatability complex (MHC) region genes HLA and BTNL2 reported in the literature are also contained in our results. Gene C6orf10 is located in the MHC region, but to the best of our knowledge was not previously reported as RA risk genes. Associated genes PGCP and MYO18B are in novel regions on 8q22.2 and 22q11.1, respectively.We got 1,371 WTCCC SNPs from the 61 genes. Their genotypes were fed as candidates into the LASSO model selection. The number of SNPs selected by LASSO depends on the value of tuning parameter λ. In order to guarantee that the LASSO-selected SNPs are statistically significant as a whole set, we chose the value of λ that led to the average number of false positive predictors to be less than 0.05 under the null hypothesis of no association. Specifically, with the selected value of λ, we permuted the responses for 1,000 times and obtained an average model size of 0.05. Table p-value < 0.05). These results show that the important gene-gene interactions for RA interactions are mostly located within the MHC region, but may reflect redundant information about the overlapped regions.We tried to validate the 103 gene-gene interactions (involving 91 unique genes) which were identified by the 208 most significant SNP pair epistases detected with Model (3) in the first stage of GAW16 data analysis. The WTCCC data were used to check whether significant epistases exist between SNPs from the corresponding gene pairs. Applying the same data quality control procedures as for GAW16 data, 1,781 unique SNPs were extracted from the WTCCC data and combined into 35,515 SNP-pairs according to the corresponding gene-pairs. Table Joint SNP analysis can benefit GWAS more than single-SNP analysis in at least two aspects. First, in GWAS many strong marginal associations are likely due to strong LD with a truly associated locus. So single-marker analysis may pick up many SNPs but mostly they are nested within one or two narrow genomic regions. In joint analysis, e.g., LASSO selection, SNPs that have high correlation with those already included in the model are less likely to be added into the model again. This may help us to study more interesting regions while keeping in mind the hotspots. In other words, if we retain the same number of SNPs for follow-up studies, joint analysis likely brings a wider genome region into further consideration. Second, joint analysis can identify truly associated predictors that have small marginal signals but large conditional additive effects or large epistatic effects. Empirical data suggest this scenario does exist. These findings are potentially valuable in further exploring the relationships among genes in pathway studies.Two important issues should be noted with regard to our methodology. First, LASSO tends to over-fit when choosing λ based on the BIC criterion. To illustrate this issue does exist, we permuted the disease status of the WTCCC data set in the validation stage, in which the BIC-controlled LASSO led to false-positive selection. To overcome this problem, permutation was used to determine an appropriate value for λ when quantifying the proportion of false positives. Second, in the second stage of selection, it may cause over-fit when isolated applying the significance control to the SNPs identified by screening the same data set. To prove this problem exists, we permuted the RA status before going through the whole two-stage analysis procedure for the GAW16 data set. In this case, even though no SNP is associated with the disease, LASSO still selected some variables, even if a λ value was chosen in the second stage in the same way as we did for WTCCC validation. To address this problem, we may similarly apply the same procedure using permutations at the outset of screening analysis to select an appropriate λ value for the original data. However, this requires intensive computation and may lead to fewer SNPs to be followed up in other studies. In practice, these issues can be alleviated by using a separate data set to validate the results. In this way we can carry out a screening and then apply LASSO, while properly controlling λ for the final model in the validation stage.p-values at the 0.1 level in the first stage because we would like to avoid missing true associations, and hope that the second stage analysis will be able to select terms according to a more stringent criterion. We have also tried the significance control level of 0.05 that led to slightly fewer gene findings (except obtaining one extra gene C14orf151) from the GAW16 data analysis. However, both control levels led to exactly the same set of detected signals shown in Table We reported our results using a Bonferroni In GWAS, there exist SNPs with small marginal but large joint associations with RA. To extract more information from GWAS data, we have proposed a two-stage association detection method based on an exhaustive two-dimensional screening and the LASSO model selection. Our method studies joint associations including gene-gene interactions. Applying this joint analysis method to GAW16 data and validating the results with a separate data set (WTCCC data), we have found novel genes associated with RA, as well as interactions implying complex RA associations in the MHC region.2-D: Two-dimensional; BIC: Bayesian information criterion; Chr6: Chromosome 6; GAW16: Genetic Analysis Workshop 16; GWAS: Genome-wide association study; LD: Linkage disequilibrium; LLR: Log-likelihood-ratio; MHC: Major histocompatability complex; NARAC: North American Rheumatoid Arthritis Consortium; RA: Rheumatoid arthritis; SNP: Single-nucleotide polymorphism; WTCCC: Wellcome Trust Case Control ConsortiumThe authors declare that they have no competing interests.ZW and HZ conceived and designed the analysis and wrote the manuscript. ZW contributed analysis tools and analyzed the data. CA, DHB, JYL, and JSL participated in the data preparation and analysis and helped to draft the manuscript. All authors read and approved the final manuscript.


Xenopus tropicalis. We show that the NCBI Trace Archive provides a valuable resource for the investigation of the phenomena of DNA and RNA editing, as well as setting the stage for a comprehensive mapping of editing events in large-scale genomic datasets.While it is widely held that an organism's genomic information should remain constant, several protein families are known to modify it. Members of the AID/APOBEC protein family can deaminate DNA. Similarly, members of the ADAR family can deaminate RNA. Characterizing the scope of these events is challenging. Here we use large genomic data sets, such as the two billion sequences in the NCBI Trace Archive, to look for clusters of mismatches of the same type, which are a hallmark of editing events caused by APOBEC3 and ADAR. We align 603,249,815 traces from the NCBI trace archive to their reference genomes. In clusters of mismatches of increasing size, at least one systematic sequencing error dominates the results (G-to-A). It is still present in mismatches with 99% accuracy and only vanishes in mismatches at 99.99% accuracy or higher. The error appears to have entered into about 1% of the HapMap, possibly affecting other users that rely on this resource. Further investigation, using stringent quality thresholds, uncovers thousands of mismatch clusters with no apparent defects in their chromatograms. These traces provide the first reported candidates of endogenous DNA editing in human, further elucidating RNA editing in human and mouse and also revealing, for the first time, extensive RNA editing in  Most biomedical, genomic research begins with the painstaking assembly of a “reference genome” for the organism of interest. Implicit in this process is an assumption that genomic information is constant throughout an organism. There are enzymes, however, that can change, or “edit,” genomic information so that variations from the reference can exist within a single organism. In this work, we analyze the raw data used to assemble the reference genomes of ten organisms to discover evidence for editing. We found candidates for DNA and RNA editing as well as a sequencing error that has become incorporated into commonly used genomic resources. Our analysis demonstrates the utility of raw genomic data for the discovery of some editing events and sets the stage for further analysis as sequencing costs continue to decrease exponentially. With the exception of infrequent random somatic mutations, it is widely believed that the same genomic content should be fixed in an organism throughout its lifetime. This information will also serve as a template for exact RNA copies. Proteins that can modify genomic content, nevertheless, have been identified in humans and in many other organisms.adenosine deaminase that acts on RNA (ADAR) family of deaminases RNA editing involves alteration of particular RNA nucleotides by specifically changing Adenosine (A) into Inosine (I), which in turn is read as Guanosine (G) For many years, the only known human endogenous target of the APOBEC protein family was the apoB RNA transcript. In this case, editing in position 6,666 by APOBEC1 leads to a stop codon and eventually results in two functionally distinct isoforms of apolipoprotein B (ApoB) in vivo.Deamination of cytosines to uracils in DNA (DNA editing) by various APOBEC protein families is characterized, in many cases, by clusters of G-to-A mismatches between the reference genome and the edited sequence. These mismatches are the end product of deamination of “C” into “U” in the other DNA strand. Recently, it was found that APOBEC3G can serve as a potent inhibitor of a wide range of retroviruses, including endogenous retrotransposons. This protein introduces large numbers of C-to-U mutations in the minus-strand of the viral DNA, eventually leading to G-to-A mutations after plus-strand synthesis Although editing of retrotransposons and their integration back into the genome is expected to be rare, very deep DNA sequencing can be used to identify these events. In this paper we report initial results of a novel bioinformatic approach for detection of endogenous RNA and DNA candidate sites in various organisms. We obtained 600 million sequence traces from the NCBI Trace archive. This data repository contains DNA sequence chromatograms (traces) from various large-scale capillary electrophoresis sequencing projects, base calls, and quality estimates. Next, we aligned these traces to their consensus reference genomes and searched for clusters of mismatches. Interestingly, we have found not only evidence of genuine RNA and DNA editing events but have also isolated a very common technical sequencing artifact that leads to such clusters.One hallmark of editing enzymes is a cluster of mismatches of the same type in the edited substrate. While the results of the RNA editing ADARs are clusters of A-to-G mismatches, the hallmark of members of the APOBEC3s protein family is a cluster of G-to-A mismatches in the newly formed DNA strand after reverse transcription. In order to find new endogenous editing events we looked for such mismatch clusters in the largest available repository of “raw” sequencing data, before they have been processed and assembled. We aligned “raw” sequencing reads from the NCBI trace archive to their consensus, reference genome. We repeated this procedure, in parallel, for each of ten organisms  are common in APOBEC targets, such as IAP mouse retroelements edited by APOBEC3 Since editing enzymes have a preferred sequence context, the large data set allows us to restrict our search to traces with the same three base-pair motif centered at each mismatch site in the trace Out of the 53,639 total examples conforming to the above criteria, we found 46,483 (82%) examples of G-to-A traces in human. Thus, the restrictions above reduced the total number of traces more than 12-fold while only reducing the number of G-to-A examples by less than 5-fold. Moreover, we found a striking preference for either an “AGA-to-AAA” mismatch motif  or an “AGG-to-AAG” motif . This tendency was observed in traces from all sequencing centers tested but one (Celera) see . Since mSequence traces are derived from both DNA strands, thus one would expect to observe a symmetric over representation of C-to-T mismatch clusters. Lack of similar numbers of complementary mismatches led us to the conclusion that most of these mismatches are not caused by a biological source but rather are sequencing artifacts.In order to understand the origin of the artifact, we analyzed sample traces, and noticed that traces with “runs” of mismatches, with identical three base-pair motifs, centered on the mismatch, often had a peculiar defect in their chromatograms. Such defects can arise when the florescent dyes used in DNA sequencing have sequence specific incorporation differences which lead to unevenly spaced or shaped peaks in the electronic trace chromatogram after capillary electrophoresis. We used strict criteria to construct the artifact set, thus the actual number of those errors is probably much larger than the 260K we found and may disrupt the accuracy of genomic assemblies. Indeed, we found evidence that these common errors influence the consensus sequence of a few genomes. The number of runs of G-to-A mismatches with the AGA motif was much higher in genomes with high coverage, where each position in the reference genome has many traces to support each call. In these cases, the reference is determined according to the “majority voting” of all the supporting traces. Since the reported type of mismatch is much less abundant than the correct call, the reference will have the correct “G” in virtually all cases. In genomic projects with lower coverage, however, such events can become part of the reference genome and therefore could not have been detected by our method. Indeed, we found that genomes with lower coverage tended to be free of G-to-A mismatches. This effect is most striking in drosophila where mean coverage of the reference by aligned traces is only 2.5 See . This fip-value≪e-200, Fisher's Exact Test). Over-representation of G as the observed allele in A/G SNPs (or C in C/T SNPs) in the group of SNPs that have only one observed allele, when comparing it to the SNPs with two observed alleles, suggests that up to 1.8% of the HapMap SNPs are a result of the artifact and are not real SNPs (data not shown) the ratio is probably larger in dbSNP which is less curated.Another effect of this error was found in the assignment of single nucleotide polymorphisms (SNPs). A sequencing error in one genomic trace will not usually lead to the determination of a SNP at this position. However, since many of the “AGA” mismatches have a quality score of phred 20 or higher, which is considered an acceptable quality with an estimated error probability of only 1% Once we realized that the majority of “AGA” and “AGG” mismatch motifs were caused by a sequencing error, we endeavored to eliminate such errors from our dataset. To do so, we incorporated phred quality scores, also available from the trace archive. We obtained quality scores for all traces with a run of three or more substitutions of the same type. This set contains 20.7 million traces out of the 300 million that aligned uniquely. We then applied various quality score thresholds on to the data see . At qualin vivo is not yet elucidated. We wanted to investigate whether our curated dataset of G-to-A mismatch clusters may actually include some examples of DNA editing. To test this assumption we looked at mismatch clusters in the mouse genome. We found that the total number of A-to-G and T-to-C mismatches was similar to the number of C-to-T and G-to-A mismatches . However, in genomic regions of IAP  elements, for which a few members are still active, there was a significant dominance of the G-to-A / T-to-C mismatches (114 compared to 49 A-to-G / T-to-C) . This supports the idea that the origin of the mismatches is a result of editing by APOBEC after reverse transcription of the retrotransposons. An example of a DNA editing candidate, in a mouse retrotransposon, is given in Recently, DNA editing has been reported to be a powerful defense mechanism against the threat of genomic instability imposed by viruses and retrotransposons. However, the full magnitude of the phenomenon p-value of 0.0000017, Fisher's Exact Test). One such candidate of editing by APOBEC in human retrotransposon HERVL-A1 is shown in AluY. All of these mismatches have high sequencing quality (Phred 40 or greater). Moreover, previously it was demonstrated that APOBEC3 can inhibit retrotransposition of Alu Active retrotransposons exist in human. For example, two edited HERVK elements have been recently discovered The actual number of edited traces in the trace archive is most probably much higher than we have found, for several reasons: More than half of all traces were rejected with our alignment parameters, at least partially due to the fact that DNA editing tends to lead to hyper-mutation in its target sequences Alu repeats RNA editing is a general term for the modification of RNA after it is transcribed from DNA. The most common modification in mammals is A-to-I editing by the ADAR protein family. As I (Inosine) is read as a G (Guanosine) after sequencing, this editing type manifests itself as an A-to-G substitutions after cDNA sequencing and alignment to the original genomic locus. Recently it was found that the human genome harbors large numbers of editing events that are located in clusters, mainly in Xenopus tropicalis sequences obtained from the trace archive are labeled as derived from RNA, rather than DNA. In total, after passing the stringent alignment criteria, 250K, 513K and 454K traces, respectively, of those genomes have RNA origin, thus A-to-G or T-to-C mismatches in these traces could be the result of RNA editing. No over-representation  of A-to-G or T-to-C clusters appear in the RNA trace set  These observations suggest that RNA editing is the cause of the mismatches in the higher quality RNA sets.A fraction of the human, mouse and race set , but as race set , we findp-value 1.9e-143) Alu repeats and indeed 72% of the mismatches in the higher quality set are located in Alu repeats while Alu represents only about 10% of human DNA .Further evidence that the higher quality set is indeed a result of RNA editing comes from two additional observations. First, a significant under-representation of “G” immediately upstream to the editing sites which is in agreement with the known sequence motif of the ADAR proteins Detection of RNA editing from short EST sequences has proven to be challenging, due to their relatively low sequence quality Xenopus tropicalis had significant numbers of RNA traces. If we use our enriched, higher quality set as a proxy for the total number of editing events, our data shows that in mouse, editing occurs at an estimated rate of 1 mismatch per 100,000 unique, expressed base-pairs. In human, in agreement with previous publications Xenopus tropicalis. A closely related species, Xenopus laevis, is a principal model organism for the study of RNA editing as ADAR activity was first described in Xenopus laevis oocytes Xenopus laevis lead to the suggestion that editing can down-regulate gene expression in transXenopus tropicalis. In the enriched set 83% of the mismatch clusters are of the A-to-G and T-to-C type, while these types contribute only 39% of the mismatch clusters in the non-enriched set   . This stXenopus tropicalis genome has not been completed yet and the annotation is still partial. Thus, we cannot determine if the editing sites are located in one type or a small number of genomic repetitive regions. Interestingly, we found that 10001 out of the total 18161 mismatches in our editing-enriched, higher quality set occur in clusters of ten sites or more, larger than the common clusters detected in human RNAs sequences which have a typical size of less than 6 mismatches. By further examining a few mismatch clusters, we found that they tend to occur in palindromic regions that can form tight double stranded RNA. These structures are known to be required for ADAR editing  and aligned them with their reference genomes obtained from the UCSC Genome Browser We obtained all traces for 10 organisms , in FASTA format, at the NCBI Trace Archive Xenopus tropicalis these tables also provide (in brackets) the number of traces that likely originated from RNA.We augmented the above data by downloading auxiliary information and quality scores for a subset of about 20.7 million traces which were, potentially, enriched for editing events. We used runs of three consecutive mismatches of the same type as the enrichment criteria. The number of high quality traces for each editing type  - is listed in The complete set of mismatches found in these two sets of traces is available to the community as two files, “all.c2.t100.q40+.bed.gz” (5.95MB) and “all.c2.t100.q0-9.bed.gz” (122MB), respectively. The first set is included on the journal's web-site while the second file is available, on request, from the authors. The files contain: the genomic coordinate of the mismatch, the mismatch type, the position on the trace, the quality of the mismatch, the length of the run in which the mismatch was found, the sequencing center, the trace id, the organism, and the likely origin of the trace, DNA or RNA. In order to be counted, each trace must have at least two mismatches with phred 40 or greater that are separated by 100bp or more. Only mismatches with phred scores of 40 or greater are included in the high quality set Click here for additional data file.Dataset S2Xenopus RNA editing sites.(0.36 MB TXT)Click here for additional data file.Figure S1DNA editing of mouse MMTV-int retrotransposons (both clone mates). DNA editing in a mouse retrotransposon. Two traces (ti#71971190 and ti#71976546 which are mate pairs from one sequencing clone) are aligned to the mouse genomic full length MMTV-int retrotransposon (ERVK family) locus (chr6:68193707-68200951). Both aligned with a large number of G-to-A mismatches, an indication of DNA editing in this active retrotransposon. Additional mismatches are present as well, probably due to the activity of DNA damage proteins.(0.03 MB DOC)Click here for additional data file.Figure S2Substitution spectrum, by quality score, sampled from runs of three substitutions of the same type in ten organisms. In all organisms examined the abundance of G-to-A mismatches dominates all other substitution types for mismatches with Phred quality scores between 10 and 40. From Phred40 and onward the spectrum becomes more even with G-to-A, C-to-T, A-to-G and T-to-C all roughly the same with each of those mismatch types representing 20% of all substitutions.(0.06 MB TIF)Click here for additional data file.Figure S3Absolute abundance of mismatches in human w/100 bp runs. Shows absolute abundance of runs from (0.03 MB TIF)Click here for additional data file.Figure S4Absolute abundance of mismatches in human. Shows absolute abundance of runs from (0.08 MB TIF)Click here for additional data file.Table S1Summary of traces without enrichment (RNA origin) by mismatch type. “Other” indicates the most abundant type other than those listed. No enrichment for the ADAR derived mismatches are observed in the full set.(0.03 MB DOC)Click here for additional data file.Table S2Sequence context preceding mismatch . There is a clear under representation of the “G” nucleotide upstream to the mismatch, in agreement with known ADAR signatures in both human and Xenopus. RNA editing is known to be less common in mouse, thus, this is consistent with a lack of depletion.(0.03 MB DOC)Click here for additional data file.Table S3Sequence context preceding mismatch . The position preceding an edited site is known to be depleted in “g”. We looked at the position preceding an A-to-G or T-to-C mismatch in RNA derived traces. The depletion is clearly visible in the enriched set see  but no s(0.03 MB DOC)Click here for additional data file.Table S4Summary of Traces without enrichment (RNA origin). “unique bp” indicates the total number of genomic positions covered by the placed traces of the RNA traces.(0.03 MB DOC)Click here for additional data file.Table S5Xenopus tropicalis these tables also provide (in brackets) the number of traces that likely originated from RNA. See Editing enriched traces-lower quality. Number of traces, by mismatch type, with two or more mismatch below a quality threshold of Phred 10, spanning 100 bp or more. For Mouse, Human, and (0.03 MB DOC)Click here for additional data file.


Although the Illumina 1 G Genome Analyzer generates billions of base pairs of sequence data, challenges arise in sequence selection due to the varying sequence quality. Therefore, in the framework of the International Porcine SNP Chip Consortium, this pilot study aimed to evaluate the impact of the quality level of the sequenced bases on mapping quality and identification of true SNPs on a large scale.DraI; 150–250-bp fragments were isolated and end-sequenced using the Illumina 1 G Genome Analyzer, yielding 70,348,064 sequences 36-bp long. Rules were developed to select sequences, which were then aligned to unique positions in a reference genome. Sequences were selected based on quality, and three thresholds of sequence quality (SQ) were compared. The highest threshold of SQ allowed identification of a larger number of SNPs , distributed widely across the pig genome. In total, 3,142 SNPs were validated with a success rate of 96%. The correlation between estimated minor allele frequency (MAF) and genotyped MAF was moderate, and SNPs were highly polymorphic in other pig breeds. Lowering the SQ threshold and maintaining the same criteria for SNP identification resulted in the discovery of fewer SNPs , of which 259 were not identified using higher SQ levels. Validation of SNPs found exclusively in the lower SQ threshold had a success rate of 94% and a low correlation between estimated MAF and genotyped MAF. Base change analysis suggested that the rate of transitions in the pig genome is likely to be similar to that observed in humans. Chromosome X showed reduced nucleotide diversity relative to autosomes, as observed for other species.DNA pooled from five animals from a commercial boar line was digested with Large numbers of SNPs can be identified reliably by creating strict rules for sequence selection, which simultaneously decreases sequence ambiguity. Selection of sequences using a higher SQ threshold leads to more reliable identification of SNPs. Lower SQ thresholds can be used to guarantee sufficient sequence coverage, resulting in high success rate but less reliable MAF estimation. Nucleotide diversity varies between porcine chromosomes, with the X chromosome showing less variation as observed in other species. HowevSeveral methods for ultra high-throughput DNA sequencing that reduce the cost and labor demands of Sanger sequencing are currently available ,4. The ISequencing of reduced representation libraries (RRLs), which are reproducible subsets of the genome, allows cost-effective genome-wide SNP discovery with accurate estimations of minor allele frequencies (MAF) . BecauseDespite the efficiency of massive parallel sequencing for providing large amounts of sequencing data, a sequence selection stage is still required. Previous studies have applied various rules for sequence selection: sequences must start with a restriction motif , sequencWith increasing attention being paid to genomic selection by animal breeders, there is a need for high-density SNP maps of the genomes of farm animals. Experimental evidence has shown that linkage disequilibrium extends from 0.1 to 2 cM in European commercial pig breeds ; thus, aIn this study, an RRL generated from a DNA pool of a boar line was sequenced using the Illumina 1 G Genome Analyzer. The two main goals of our study were (a) to develop rules for decreasing sequence ambiguity , which would decrease noise and increase efficiency in sequence alignment and SNP identification, and (b) to evaluate the effects of different SQ filtering strategies for cost-effective, large-scale identification of SNPs.vs. Pietrain) commercial boar line (PW), using the restriction enzyme DraI, which recognizes pattern "TTTAAA" and generates blunt-ended fragments starting with AAA. Fragments 150–250-bp long were selected and end-sequenced using the Illumina 1 G Genome Analyzer. Short sequences will align to a unique genomic location (target), creating groups (clusters) with a number of sequences (target depth) sufficient for SNP identification. The in silico digest of pre-EnsEMBL Sus scrofa build 7 [An RRL was produced from a DNA pool of five boars from a crossbred . The quality score of a base is calculated by comparing the fluorescence signals of the previous and following bases. The algorithm does not expect a repeated motif in the beginning of a sequence (AAA) and therefore estimates poor quality scores. The severe decrease in base quality at the 3' end of the sequence indicates the existence of a higher level of sequencing errors at the 3' end. The proportion of unique sequences (sequences occurring only once) ranged from 35% when considering a sequence length of 29 bp to 55% when considering a sequence length of 35 bp, which again indicates an increase in sequencing errors with an increase in sequence length. Therefore, sequences were trimmed at 33 bp and filtering rules were applied.In total, 70,348,064 sequences were generated during three different runs. In addition to sequence information, this second generation platform generates quality scores that are analogous to Phred scores (which assign a probability to the four possible nucleotides for each base in the sequence) . Levels Sus scrofa build 7 [in silico digest. This ratio, the estimated average level of sequence re-sampling, was estimated at 35×, and sequences with a frequency approximately 3-fold greater (>100×) were removed.We applied a number of rules to select sequences for further analysis: (a) screening for properties of the RRL ; (b) filtering for sequence ambiguity, and (c) filtering for SQ. Filtering for sequence ambiguity was performed by removing sequences with homopolymers and removing sequences with a high rate of re-sampling. Sequences with a re-sampling rate above an expected level were discarded as potentially paralogous sequences. Because pre-EnsEMBL  build 7  comprise® produces quality scores analogous to PHRED scores. SQ has been defined in previous studies as the average of the quality scores of all bases in a sequence, and the threshold has been set to a minimum of SQ = 20 [The Illumina 1 G Genome Analyzer SQ = 20 ,9, whichSus scrofa build 7 [in silico analysis have a lower mapping quality and are less reliable for SNP identification. Additional filters were used to further decrease the rate of false-positive SNPs: 1) occurrence of the minor allele in a minimum of three sequences (to increase the accuracy of detecting SNPs with high MAF), and 2) a maximum target coverage of 100 reads. Again, the restriction of maximum target coverage aims to decrease the rate of false-positive SNPs identified in potential paralogous regions that align to each other because the available assembly only comprises around 70% of the total pig genome. The results allowed us to identify a larger number of SNPs in Data 20  and the genotyped MAF in the animals used in the discovery panel was calculated for the 2,879 SNPs shared between Data 12, Data 15, and Data 20. The observed correlation of 0.32 was somewhat lower than that reported by Wiedmann [Table We presented a strategy for using short sequences derived from second generation sequencing technology to efficiently identify large numbers of SNPs with MAF estimates at a low false discovery rate. These results show that by lowering the SQ it is possible to identify SNPs while still keeping the false discovery rate low, although the cost is a lower correlation between the estimated and true MAFs. Finally, our data show that nucleotide diversity is quite variable among porcine chromosomes and is particularly low on SSCX.DraI  at 37°C for 16 hours. The fragments were separated by 1.2% agarose gel electrophoresis , and 150–250 bp-fragments were eluted (yielding 1069 ng), end-repaired, and ligated to Illumina's oligonucleotide adapters. Fragments were end-sequenced using the Illumina 1 G Genome Analyzer.DNA was extracted from five individual boars produced from a cross between Landrace and Pietrain. Extracted DNA was pooled (60 ng) and digested with ® into sequences and base quality scores similar to Phred [The Illumina 1 G Genome Analyzer generates image information that is translated by BUSTARDto Phred . Using PFiltering rules were applied to select sequences that were mapped to the pig genome  using MAcns.view of MAQ [The total aligned consensus sequence was obtained using option w of MAQ . Full ouw of MAQ , and GC Sus scrofa build 7 [® Genotyping assay on an Illumina® BeadStation.A total of 3,142 SNPs located in Oligonucleotides were designed, synthesized, and assembled into oligo-pooled assays by Illumina Inc.Individual DNA samples from the animals used for the SNP identification panel (PW) were genotyped, plus one more animal from PW, 20 samples of Wild Boar, 136 samples of Large White, 80 samples of Landrace, 82 samples of Duroc, 90 samples of Pietrain, 67 samples of Berkshire, and 39 samples of Hampshire.in silico digests. HHDK developed a PERL pipeline to parallelize sequence mapping analysis and to extract information for SNP statistical analysis. HCMH was involved in discussions about all of the analyses performed. BD and RPMA collected and prepared the samples for sequencing. MAMG coordinated and supervised the project. JD provided facilities for DNA sequencing. MAMG, H-JM, and HCMH assisted in manuscript preparation. All authors read and approved the final manuscript.AJA designed and developed the PERL pipeline for sequence filtering, performed all bioinformatics and statistical analyses, and wrote the manuscript. H-JM was involved in discussions about all of the analyses performed, developed the PERL script to perform nucleotide diversity analysis, GC content, mapping density, and 120015817 to 120032847.The sequences and nucleotide variations have been submitted to GenBank dbSNP database with accession numbers ranging from Supplemental Figures 1–3. Supplemental Figures 1, 2, and 3 cited in manuscript.Click here for file