The dissection of complex traits of economic importance to the pig industry requires the availability of a significant number of genetic markers, such as single nucleotide polymorphisms (SNPs). This study was conducted to discover several hundreds of thousands of porcine SNPs using next generation sequencing technologies and use these SNPs, as well as others from different public sources, to design a high-density SNP genotyping assay.AluI, HaeIII and MspI) were sequenced using Illumina's Genome Analyzer (GA). The SNP discovery effort resulted in the de novo identification of over 372K SNPs. More than 549K SNPs were used to design the Illumina Porcine 60K+SNP iSelect Beadchip, now commercially available as the PorcineSNP60. A total of 64,232 SNPs were included on the Beadchip. Results from genotyping the 158 individuals used for sequencing showed a high overall SNP call rate (97.5%). Of the 62,621 loci that could be reliably scored, 58,994 were polymorphic yielding a SNP conversion success rate of 94%. The average minor allele frequency (MAF) for all scorable SNPs was 0.274.A total of 19 reduced representation libraries derived from four swine breeds  and a Wild Boar population and three restriction enzymes (Overall, the results of this study indicate the utility of using next generation sequencing technologies to identify large numbers of reliable SNPs. In addition, the validation of the PorcineSNP60 Beadchip demonstrated that the assay is an excellent tool that will likely be used in a variety of future studies in pigs. The genetic regulation of quantitative traits is complex and the identification of the genes that underlie genetic variation requires large numbers of genetic markers, such as microsatellites or SNPs. To date, many QTL (quantitative trait loci) have been localized to large chromosomal regions in several species of domestic animals, including the pig In recent years, new sequencing technologies have emerged which offer great promise for marker discovery due to their ability to efficiently generate large amounts of sequence data, both in terms of time and cost. They are usually referred to as “second generation” or “next generation” sequencing technologies and include the Illumina Genome Analyzer (previously Solexa), Roche's 454 FLX system and Applied Biosystems' SOLiD. The chemistry used by each technology, as well as the read length and the sequence output vary Genome assemblies are being produced for a growing number of animal and plant species, including several species of domestic animals like the cow http://www.illumina.com; http://www.affymetrix.com), including the first application of a dog SNP assay de novo discovered and other SNPs from public sources.The most efficient way to genotype large numbers of SNPs is to design a high-density assay that includes tens of thousands of SNPs distributed throughout the genome. These SNP “chips” are a valuable resource for genetic studies in livestock species, such as genomic selection DNA samples were obtained from five pig breeds, including Duroc (DU), Pietrain (PI), Landrace (LR), Large White (LW) and Wild Boar (WB). For each breed, a single DNA pool containing equal DNA amounts from all of the samples was prepared. The number of animals per pool was 34, 23, 29, 36 and 36 for DU, PI, LR, LW and WB, respectively. The DNA samples for the commercial breeds were representative of the worldwide distribution of the breeds , whereas the WB samples were collected mainly in Europe, with five samples originating from Japan.AluI, HaeIII and MspI , following the manufacturer's recommendations. For each restriction digest, 10 units of enzyme were used in a final volume of 10 µl and incubated at 37°C for 16 hr. Upon completion of each digestion, fragments were fractionated with a 10% non-denaturing polyacrylamide Criterion TBE gel  at 100 V for 190 min. DNA fractions were stained by immersing a gel for 15 min in a TBE 1× solution containing ethidium bromide. Fragments in particular size ranges were excised from the gel. The size ranges for fragments excised differed between libraries and included fragments of 160–200 bp (AluI short), 200–240 bp (AluI long), 160–200 bp (HaeIII) and 100–200 bp (MspI). After excision, the gel pieces were placed in a 0.5 ml microcentrifuge tube that had been pierced in the bottom, and then placed in a collection tube and centrifuged for seven min at 16000×g. A total of 400 µl of recovery buffer was added to the disrupted gel pieces, and the samples were kept overnight at 4°C. DNA was purified from the gel slurry by passing the samples through Sample Filter Cups from the DNA Gel Extraction Kit  by centrifugation at 4965×g for 10 min at room temperature. Next, 200 µl of recovery buffer was added and the samples were centrifuged at 16000×g for 15 min, followed by an ethanol precipitation. DNA was dissolved in 40 µl of Puregene DNA Hydration Solution . Samples were quantified by spectrophotometry, and at least 300 ng of purified DNA was used to construct a RRL (reduced representation library) for sequencing. A total of 19 breed specific RRL were prepared, including 4 libraries each for DU, PI, LR and LW, but only three libraries were available for WB, because the AluI short library was not prepared.For each breed, a total of 25 µg from each DNA pool, divided in five aliquots of 5 µg each, was digested with each of three restriction enzymes AluI RRLs were prepared and sequenced using a 454 FLX system  on the GS FLX platform. For the Genome Analyzer, library constructions followed the protocol supplied with the Genomic DNA Sample Prep Kit  with minor modifications. Library construction started with the addition of an adenosine base to the 3′ end of the DNA fragments, except for the MspI digested samples which were initially end repaired. The volumes of adapter reagent used in the adapter ligation reactions were titrated accordingly to the amount of input DNA to maintain a 10∶1 molar ratio of adapter to DNA. The concentrations of the libraries were determined using a NanoDrop ND-1000 Spectrophotometer  and the size and purity were determined using an Agilent 2100 Bioanalyzer in combination with the Agilent DNA 1000 Kit . The libraries were diluted in buffer EB (QIAGEN) to 10 nM, denaturated with 2 N NaOH to a final DNA concentration of 0.5 nM, diluted to 2–4 pM with pre-chilled Hybridization buffer (Illumina) and loaded in individual lanes into a 1.0 mm flowcell together with a single lane of a 2 pM PhiX control library (Illumina). Following cluster amplification, linearization, blocking, and primer hybridization, 36 cycle sequencing were conducted on an Illumina Genome Analyzer (version I) using the Genomic DNA Sequencing Primer in combination with clustering and sequencing kits supplied by Illumina. The resulting images were transferred to the pipeline computer and analyzed using the Genome Analyzer Pipeline Software  generating the raw fastq files. For FLX sequencing, the blunt fragments produced by AluI digestion were ligated directly to the adapters provided in the library preparation kit without the shearing, sizing or end-polishing steps. Single stranded libraries were then prepared and sequenced according to the manufacturer's instructions. The AluI short and AluI long libraries were sequenced using 5.5 and 4.5 full machine runs, respectively, using the two-region gasket.All 19 libraries were sequenced on a 1G Genome Analyzer . The length of all sequences generated in this study was 36 nucleotides. In addition, two pooled samples derived from each of the AluI, MspI, and HaeIII restriction digests, respectively. Sequences that contained the same nucleotide at more than 18 continuous positions were also eliminated. The average quality score was next calculated for each read by averaging the individual score for each of the 36 base positions. Previous results obtained in a smaller pilot study The raw sequence data were filtered according to different criteria. Each sequence was first evaluated for the presence of the expected sequence motif for each restriction enzyme; sequences not containing the expected sequence were discarded. The acceptable sequences began with CT, CC or CGG for the Sus scrofa assembly 7 of the pig genome available on July 28th 2008 was used for this study, and that resource was obtained from: ftp://ftp.sanger.ac.uk/pub/S_scrofa/assemblies/PreEnsembl_Sscrofa7/.The reference genome against which the 36 bp sequences were aligned included all the porcine autosomes and the X chromosome. The state of completion of the assembly of each chromosome varied because the sequencing of the pig genome has not yet been completed. This genome sequencing and assembly effort is being performed under the direction of the international Swine Genome Sequencing Consortium AluI libraries. For each library, all sequences were first aligned to the Sus scrofa assembly with BLAT To represent regions of the genome that had not yet been assembled, two pseudo chromosomes were built from 454 sequence data generated from the two de novo identified SNPs that had passed all filters. Finally, SNP MAFs were estimated by directly counting the number of reads for each allele. All de novo identified SNPs have been submitted to dbSNP (accession numbers from ss131027063 to ss131629651). The flanking sequences for the SNPs used to build the genotyping assays were derived from the ongoing sequencing of the porcine genome by the Sanger Institute and these sequences are regularly uploaded to Genbank.Sequences were aligned against the assembled reference genome and initial SNP detection was performed using MAQ de novo identified SNPs, the final porcine SNP dataset also comprised previously identified SNPs that were available from different public sources, including SNPs detected in a pilot study conducted at Wageningen University using a DraI RRL In addition to these HaeIII library with flanking sequence information was available for validation of the SNP calling procedure. These markers were sorted by predicted location within the genome, and every fortieth sequence was processed using the Sequenom Assay Design software. From the output, assay groups were designed to have 32 multiplexed SNPs per well. Critical features to be tested were the position of the SNP within the GA sequence read, average quality score of the most frequently called base, number of alleles identified, as well as position within the genome. Nine assay groups  were selected to represent SNPs in all tested categories. Reactions were set up per manufacturer's instructions for the Sequenom iPLEX GOLD chemistry and the 128 boars that contributed to the commercial breeds RRLs were individually genotyped to allow the determination of the true SNP allele frequencies in the discovery population.Information on 44,236 SNPs derived from the de novo identified and publicly available SNPs, was submitted to Illumina for design score calculation, which was performed with Illumina's Assay Design Tool for Infinium. Type II Infinium SNPs were prioritized for selection in the design of the assay because they require only one bead type on the chip. In addition to the Illumina design score and the type of Infinium assay, we also considered other parameters, such as the estimated SNP MAF, spacing of the SNPs along each chromosome, genome-wide coverage (number of SNPs selected for each chromosome), presence of other SNPs within 40 bp of each target SNP and available information concerning the conversion rate of the SNP . Finally, any SNP known to be linked to a patent was excluded from the list. Information for these parameters was collected for all available SNPs and was used to assign SNPs to waves A list which included the final porcine SNP dataset comprising 549,282 Because the available porcine genome sequence (build 7) only covered 70% of the genome, 70% of the SNPs selected to be included on the assay came from those for which position on genome build 7 was known. To ensure a complete coverage of the porcine genome, the remaining 30% of SNPs were selected from the list of SNPs that could not be mapped to porcine genome build 7.To evaluate the performance of the Illumina iSelect BeadChip, which is now commercially available as the PorcineSNP60, the panel of 64,232 markers that passed the assay design tool informatics screen was used to genotype all 158 individuals from the five breeds that had been included in the original discovery panel. Samples were genotyped in the Illumina services lab and data was evaluated for robust genotyping AluI short, AluI long, HaeIII and MspI) and that had not been mapped to the sex chromosomes. Since the X chromosome contains pseudoautosomal regions that are largely unknown it would not be possible to correct for the effect of gender. For each SNP the reference allele was set as the major allele over all breeds as derived from sequencing. For each of the breeds the sequence derived frequency was matched with the genotype derived frequency. The genotyping was performed on the same individuals that were used to make the sequencing pools. Comparisons between sequence-derived and genotype-derived frequencies were done on a breed by breed basis since the number of alleles counted per breed could vary widely between breeds. Correlations were calculated using Pearson's product-moment correlation as implemented in R (www.r-project.org). The correlation between genotype-derived and sequence-derived frequencies was plotted against sequence depth and fitted using locally weighted scatter plot smoothing (LOESS function in R).Since allele frequency data were available from both the sequencing and genotyping efforts we investigated the relationship between the allele frequency estimates derived from both approaches. To obtain the best possible estimate for the correlation we applied some filters to the dataset. We used only SNPs that had been identified in the four RRLs prepared for this study  were also sequenced on Roche's 454 platform. The total number of sequences generated on this platform was 2,121,435 and 1,922,150 for the AluI short and AluI long RRLs, respectively.To identify SNPs in the 30% of the porcine genome that was not represented in genome build 7, the two largest RRLs . The final extended reference genome used with MAQ and to which all GA sequences were aligned consisted of porcine genome build 7 and the two pseudo 454-AluI chromosomes. Build 8 of the pig genome, released in October 2008, was also used to compare the distances between SNPs calculated using builds 7 and 8 of the pig genome.The genome of the pig is comprised of 18 autosomes and the X and Y sex chromosomes with an estimated total size of 2.7 Gb similar to that of human. The reference genome (build 7) against which the GA sequences were aligned for SNP discovery, represented approximately 70% of the porcine genome with the state of sequence completion of each chromosome differing from less then 50% to up to 99% for individual chromosomes. To discover SNPs in the 30% of the porcine genome not represented in the assembly, the 454 sequences from the two HaeIII RRL showed that a good indicator of the likelihood of SNP failure was the quality of the nucleotide at the SNP . Hence, we examined the quality score at each of the SNP bases in the two reads and established a threshold of 20 to discover reliable SNPs in those cases where the minor allele was seen only twice. The final total number of high confidence SNPs identified by using these strategies was 372,886.All 247 million sequences that passed our selection criteria  were aliTo further evaluate the conversion rate of the SNPs discovered by our approach we looked at the ratio between the number of transitions and transversions. For those SNPs where the minor allele was seen at least three times, 69.4% were transitions and 30.6% were transversions. Almost the same values were found for the 57,756 additional low MAF SNPs identified using the alternative thresholds (69.6% transitions and 30.4% transversions). Finally, because the error rate in GA sequences increases towards the end of the sequences, we also plotted the distribution of the identified SNPs against the nucleotide position within the GA sequences , 2.Before selecting the final 64,232 SNPs to be included on the Beadchip, we decided to validate the selection criteria used during the SNP discovery phase by genotyping a representative sample of 288 SNPs in the individuals used to create the discovery panel. Of the 288 assays attempted on the Sequenom MassArray platform, 241 (83.7%) assays yielded a sufficient number of genotypes to draw useful conclusions from the data  across all 128 DNA samples. Of the 241 assays, 20 were monomorphic in the DNAs of the boars used to generate the RRL libraries (8.3%). An assessment of the quality score of the predominant base indicated that when the most predominant base scored was of low quality the frequency of monomorphic assays was tenfold higher than when the predominant base called was of high quality (44.0% vs 4.2%). We, therefore, added this as an additional selection criterion for SNP discovery. Twenty-four successful Sequenom assays were designed for SNPs with 3 alleles detected in sequencing the RRL. Only one of the 24 assays was actually tri-allelic, while two of the assays were monomorphic, representing a total drop-out rate of 12.5%. The average read depth in the RRL for the 288 SNPs tested was 37.4. Assays designed for SNPs with over 2× of the mean read depth resulted in 23.5% monomorphic assays (4 of 17) and 11.8% of assays with excess heterozygotes (2 of 17), likely due to duplicated regions in the genome. While an initial assessment of position within the read indicated that as the SNPs were located towards the end of a read the percentage of monomorphic markers increased, removal of SNPs detected with low quality bases eliminated this trend.de novo generated SNPs, an effort was made to assemble a collection of porcine SNPs from other public sources. The final combined SNP collection surpassed 549K SNPs and this set was used to select the SNPs that were included on the PorcineSNP60 Beadchip. The information regarding the results for the Beadchip design is summarized in Besides the The selection of SNPs to be included on the Beadchip was based on specific wave numbers assigned to each individual SNP based on quality parameters, MAF, type of assay (Infinium type I or II) and location in the genome. Final SNP selection was performed in waves After the initial SNP discovery and our design of the Beadchip a new build of the pig genome became available (build 8). To increase the number of SNPs mapped to a specific position within the genome, the flanking sequences for all 64,232 SNPs were aligned to the new build using BLAT The panel of 64,232 porcine SNPs was used to genotype the 158 individuals from the five pools that were used for SNP discovery. SNPs were validated for robust genotyping quality by evaluating signal intensity and cluster separation. From the initial panel of SNPs, 62,121 (97.5%) genotyped successfully, while 1,611 SNPs (2.5%) could not be reliably genotyped. Of the 62,621 validated loci, 58,994 were polymorphic which indicates that the SNP conversion success rate was 94%. Furthermore, for 57,109 SNPs, the MAF was>5% and for 58,737 SNPs the MAF was>1%. The average MAF for all scorable SNPs was 0.274 . The DNAMspI RRL presented higher correlation values when compared with the other RRLs), although these differences were small  as well as the wild boar (WB), the ancestor of all modern pig breeds, it is anticipated that the PorcineSNP60 Beadchip will be highly efficient for genomic selection in silico will translate to working SNP assays. Additionally, MAF estimates can also be precisely estimated from the GA sequence data. The frequency should be calculated on a breed by breed basis since there is large variation in the number of reads each breed contributed to the overall allele count, and the allele frequencies can vary widely between breeds. Simulations showed that given the distribution of allele frequencies, the correlations between genotype-derived and sequence-derived allele frequencies could reach values of 0.95 and higher. The maximum achieved for this study was 0.8, indicating there are additional sources of variation. One source may be the variation in constructing the pools, although previous use of pools for genotyping efforts have shown that variance introduced this way in large pools is minor . A second source is due to the SNPs that were identified in the sequencing, but turned out to be monomorphic in the genotyping assay. Many of these were identified with allele frequencies derived from the sequencing to be approximately 0.5  were identified using a very stringent threshold for the quality of the nucleotide used to call the SNP but where the minor allele was only seen twice. To detect rare SNPs with a MAF between 1–5% it will be essential to increase the depth at which each base is sequenced. However, increasing the sequence depth also directly affects the false discovery rate due to sequencing errors, further emphasizing the need to use stringent sequence quality parameters.The overall confidence of the SNPs identified by our approach is high. (1) Application of the PorcineSNP60 Beadchip demonstrated that>95% of the predicted SNPs were validated. 2) The ratio of transitions to transversions was 2∶1 which is similar to the transition/transversion ratio observed in humans  The ratiIt is generally accepted that SNPs are the most common source of variation in vertebrate genomes and that significant numbers of SNPs will be needed for future studies in all species. It is now possible to conduct studies aimed at the molecular dissection of complex traits of economic importance. For this purpose, a high density porcine SNP genotyping Beadchip is now available to the community. This resource was developed using the four breeds primarily used in modern pig production and wild boar samples, which should make the Beadchip a very valuable resource for most types of pig genetic studies. The PorcineSNP60 Beadchip was tested using 158 samples and results confirm the high utility and importance that this resource will have, both in terms of working SNPs  and polymorphic SNPs (over 57K SNPs with MAF>0.05). This study also confirmed the utility of next generation sequencing technologies for the mass identification of genetic variation in any genome, including the identification of SNPs in regions of the genome that have not been previously sequenced. In addition, a very large number of additional SNPs were also identified, and even though they were not included on the Beadchip they are now publicly available for pig researchers worldwide.Figure S1Scatter plots for the correlation between sequence-based and genotype-based allele frequencies. The correlation plots are illustrated for the porcine breeds and RRLs analyzed in this study. The PorcineSNP60 SNPs derived from the RRLs generated were used to determine the correlations.(9.73 MB TIF)Click here for additional data file.Table S1The correlation plots are illustrated for the porcine breeds and RRLs analyzed in this study. The PorcineSNP60 SNPs derived from the RRLs generated were used to determine the correlations.(0.05 MB DOC)Click here for additional data file.


We have developed a web-based query tool (GLIDERS: Genome-wide LInkage DisEquilibrium Repository and Search engine) that enables the retrieval of pairwise associations with r2 (0.3 to 1), HapMap population sample  and HapMap build/release. All resulting genome-wide inter-SNP associations are displayed on a single output page, which has a link to a downloadable tab delimited text file.GLIDERS is an easy to use web tool that only requires the user to enter rs numbers of SNPs they want to retrieve genome-wide LD for (both nearby and long-range). The intuitive web interface handles both manual entry of SNP IDs as well as allowing users to upload files of SNP IDs. The user can limit the resulting inter SNP associations with easy to use menu options. These include MAF limit (5-45%), distance limits between SNPs (minimum and maximum), rGLIDERS is a quick and easy way to retrieve genome-wide inter-SNP associations and to explore LD patterns for any number of SNPs of interest. GLIDERS can be useful in identifying SNPs with long-range LD. This can highlight mis-mapping or other potential association signal localisation problems. The discovery of the block structure of haplotypes has led to much research into patterns of local linkage disequilibrium (LD) in the genome -3. The I2 and D' among all pairs of SNPs based on genotype data from HapMap2 (release 21 and 23) and HapMap3 (release 2) in three analysis panels  [-7. Additionally, HapMap3 central QC removed many SNPs investigated in HapMap2. The final number of SNPs analyzed after the above QC procedures are seen in Table 2 ≥ 0.3. GLIDERS does not handle SNP aliasing created by dbSNP updating. The data are stored as text files in a tree-based directory structure for fast query performance. Our analysis shows a nearly linear-time query performance with the number of query SNPs. The parameters that affect query performance are the distance and r^2parameters. The data are accessed and queried by a Perl CGI script.We computed pairwise rand YRI) ,5. We stand YRI)  and exclrelease 2 and 23 a. Detailed documentation can be accessed by a link at the top of the page and includes examples of how to use the application. Users can select which HapMap version and release, and which study population they want to investigate from drop-down lists as seen in Figure 2 value between SNPs. The user is then presented with a table of results, as seen in Figure 2 with query SNP, D' with the query SNP, and all the commercially available chips that the SNP is included in. Further information is available by clicking the rsID, which takes the user to the dbSNP record for that SNP [The GLIDERS search engine is publicly available at that SNP . In addi2 lower bound of 0.3, whereas SNAP has an unlimited r2 search space for SNP associations. Therefore, GLIDERS should be viewed as a complementary tool to SNAP. GLIDERS provides a quick list of potential proxy SNPs and an indication of the extent of LD between the potential proxies and the query SNP(s). GLIDERS also provides insight into potential SNP mapping errors or more interesting biological processes by revealing strong associations between SNPs on different chromosomes. The number of HapMap phase 3 SNPs demonstrating at least one trans-chromosomal association with r2 ≥ 0.3 in the three panel populations are: 17,562 in CEU, 27,064 in YRI, and 1,497 in JPT+CHB. These associations either reveal mapping errors, interesting biology, or that these populations are still young populations that have not had enough time to degrade the LD amongst unlinked loci. Our analysis has also revealed potential data quality issues between HapMap2 and HapMap3, since many of the long-range associations we discovered in the HapMap2 data disappear in HapMap3. This, we believe, is likely to be due to the increased sample size in phase 3 and the removal of poorly performing phase 2 SNPs from phase 3. Therefore we recommend querying your SNPs against HapMap3, with the trade off that there are fewer SNPs analyzed in HapMap3 because of the more stringent QC analysis.GLIDERS is an efficient and easy tool allowing researchers to find genome-wide LD between all SNPs investigated in HapMap phase 2 and 3. GLIDERS is not the only tool for interrogating LD in the HapMap data, but it has certain features that set it apart. Firstly, GLIDERS does not require users to download and install software on their local machine, like the popular Haploview program requires. AdditioWith the large number of GWAS being carried out and the large number of SNPs showing disease association, it is important to be able to check quickly and easily for distant SNP proxies which might affect disease gene localization. GLIDERS is an ideal utility for this as it contains all genome-wide inter-SNP associations between HapMap markers from the three main population samples calculated using both phase 2 and phase 3 genotypes. GLIDERS is an easy to use inter-SNP association web database tool that can be used by any researcher with internet access.. There are no restrictions to the use of GLIDERS by academic or commercial entities.The GLIDERS search engine is publicly available at GWAS: (genome-wide association study); LD: (linkage disequilibrium); MAF: ; SNP: (single nucleotide polymorphism); GLIDERS: (Genome-wide LInkage DisEquilibrium Repository and Search engine); CEU: (CEPH (Utah residents with ancestry from northern and western Europe)); YRI: ; JPT: ; CHB: ; CGI: (Common Gateway Interface); SNAP: (SNP Annotation and Proxy Search); rsID: (reference SNP identification); QC: ; Kb: (kilobase).RL carried out LD calculations, developed the database and drafted the manuscript. ADW drafted the manuscript and contributed to the web server set-up. RM provided software for LD calculation. JB contributed to website design. LC contributed to database design and supervised the project. EZ supervised the project and drafted the manuscript. All authors have read and approved the manuscript.


Cucumis melo L.), a member of the Cucurbitaceae, despite its importance as a crop. Among these tools, genetic maps have been constructed mainly using marker types such as simple sequence repeats (SSR), restriction fragment length polymorphisms (RFLP) and amplified fragment length polymorphisms (AFLP) in different mapping populations. There is a growing need for saturating the genetic map with single nucleotide polymorphisms (SNP), more amenable for high throughput analysis, especially if these markers are located in gene coding regions, to provide functional markers. Expressed sequence tags (ESTs) from melon are available in public databases, and resequencing ESTs or validating SNPs detected in silico are excellent ways to discover SNPs.There are few genomic tools available in melon  × 'Piel de sapo' (PS) genetic map or using This is the first example of a genetic map in a cucurbit species that includes a major set of SNP markers discovered using ESTs. The PI 161375 × 'Piel de sapo' melon genetic map has around 700 markers, of which more than 500 are gene-based markers . This genetic map will be a central tool for the construction of the melon physical map, the step prior to sequencing the complete genome. Using the set of SNP markers, it was possible to define the genetic relationships within a collection of forty-eight melon accessions as efficiently as with SSR markers, and these markers may also be useful for cultivar identification in Occidental melon varieties. Single-nucleotide polymorphisms (SNPs) are the most frequent type of variation found in DNA  and are Cucumis melo L.) is an important crop worldwide. It belongs to the Cucurbitaceae family, which also includes cucumber, watermelon, pumpkin and squash. The melon genome has an estimated size of 450 Mb [Melon  was the basis for the construction of a genetic map with 221 co-dominant, transferable RFLP and SSR markers [A double haploid line (DHL) population from the cross between the Korean accession PI 161375 (SC) and the  markers . The avainodorus and the cantalupensis melon types.Genetic markers can also be used for variability analysis studies. In melon, there have been several attempts to elucidate intraspecific relationships among melon germplasm, using isozyme , RFLP 2, RAPD 2, AFLP 2 and SSR The objectives of this work were to increase the marker resolution in the melon genetic map, discovering EST-SNPs in a melon EST database, and to study the performance of a subset of EST-SNPs for variability analysis in a collection of melon accessions.Two strategies were used to discover SNPs in melon. The first was based on producing amplicons from randomly selected melon ESTs and resequencing the parental lines of the melon genetic map PI 161375 (SC) × 'Piel de sapo' T111 (PS). Primers were designed from 223 melon ESTs . From the ESTs reported by Gonzalez-Ibeas et al. [agrestis accession (23.4%). The pSNPs and pSCHs were deduced from this set of EST sequences, with a high proportion found between pat81 and 'Piel de sapo', and SNPs experimentally validated after resequencing amplicons from PS and SC. SC belongs to the agrestis melon type as the accession pat81 but has a different origin, so, as expected not all the SNPs were conserved between SC and PS, giving a pSNP validation of 51.8%. On the other hand, only 21.3% of the pSCHs were validated, indicating that many may represent sequencing errors or mutations introduced during the cDNA synthesis procedure. The SNPs in a subset of amplicons containing in silico SNPs between 'Piel de Sapo' and pat81 were validated using different genotyping methods (see below) rather than resequencing in PS and SC.The second strategy was the validation of database . Three hs et al. , 47.3% win silico SNPs) were resequenced in PS and SC and produced 177.5 kb of melon DNA, with 431 SNPs and 59 short indels, at an average of one SNP every 412 bp and one indel every 3.0 kb, (Table agrestis (C. melo ssp. agrestis) and inodorus (C. melo ssp. melo) melon groups, respectively, which are two of the more distant groups in the species [A total of 368 amplicons  were also variable among more closely related genotypes.Forty-five SNPs  and Asiatic ananas and chandalak types; group 3, modern cantalupensis cultivars; group 4, mainly traditional varieties and wild melons from India and Africa and group 5 included conomon accessions from the Far East. The population structure should be taken into account when establishing a collection of genotypes for association mapping studies in melon and models including population structure should be used [inodorus melon accessions included in our studies, could be used.The population structure was estimated using the STRUCTURE software . The a p be used . AlternaThese results demonstrate that SNPs discovered using a small germplasm sample can be transferred to different cultivar groups, being useful for depicting genetic relationships as well as for cultivar identification.Two hundred and seventy-eight SNP-containing ESTs Table  plus tweEssentially the new version of the melon bin-map is a gene-based map, with 412 markers (78%) obtained from gene sequences. Additionally, 114 RFLPs derived from ESTs were previously mapped in an F2 population from the cross SC × PS , and theEucalyptus grandis more than 23,000 SNPs were discovered using 454 sequencing technology, with a validation rate of 83% [With the advent of next generation sequencing technologies, SNP discovery has become more feasible in non-model crop species, allowing the discovery of thousands of SNPs in a single experiment . In Eucae of 83% . In meloCucurbitaceae, for the construction of the melon physical map and for sequencing the melon genome. Additionally, the set of SNPs has proven to be as useful as microsatellites for studying genetic relationships in melon and for varietal identification.The set of 200 SNP markers discovered and mapped have increased the marker resolution of the melon genetic map by defining new bins. The genetic map contains more than 500 gene-based codominant markers , which can be used as anchor points with other genetic maps in this species. This genetic map is also a useful resource for comparative mapping in the The parent lines of the melon double haploid line (DHL) mapping population, PI 161375 'Songwan Charmi' (SC) and 'Piel de sapo' line T111 (PS), were used for SNP discovery . Fourteein silico SNPs previously identified [SNPs were discovered using two different strategies. Firstly, random ESTs were selected from the International Cucurbit Genomics Initiative (ICuGI) webpage . Primer entified  using ESentified  were claBioedit software was used to generate restriction maps from sequences obtained from SC and PS. SNPs (or indels) showing differential restriction maps were used to develop cleaved amplified polymorphic sequence (CAPS) markers. When no differential restriction maps were available, the ABI Prism SNaPshot ddNTP Primer Extension Kit (Applied Biosystems) was used for SNP genotyping . MarkersSNPs and indels were mapped by selective genotyping using the bin-mapping strategy , adaptedForty-five SNPs from 44 amplicons (two SNPs were selected from F241) were chosen for genetic variability analysis. SNPs were genotyped as CAPS or by pyrosequencing as shown in Additional file Eight SNPs were genotyped by minisequencing the region surrounding the polymorphism (two SNPs were detected for F241 in the same reaction). Pyrosequencing was performed using a PSQ™ HS 96 system  following the manufacturers' instructions. Primers were designed with the Pyrosequencing™ Assay Design Software . One of the amplifying primers was 5' end labeled with biotin, allowing the immobilization of the fragment onto M-280 streptavidin coated Sepharose™ dynabeads . The genotyping primer was hence designed to anneal several nucleotides upstream of the SNP. After denaturation of the streptavidin-captured PCR fragments, the single stranded DNA fragments were released into the wells of the PSQ HS 96 plate. Pyrosequencing was performed using the PSQ HS SNP Reagent kit , and bioluminometric quantification of pyrophosphate (Ppi) released as a result of nucleotide incorporation during DNA synthesis was measured with the PSQ™ HS 96 system.Allele frequencies, major allele frequency, gene diversity , genetiA posteriori probability for each K (Pr(K)) can be calculated, which is very small for K values lower than the appropriate value. Usually, the researcher fixes a minimum K (for example K = 1), recording Pr(K) after the analysis, and tests increasing Ks, plotting K against Pr(K). The final K is defined when Pr(K) reaches a plateau for higher K values. Consequently, in the current report, several number of populations (from K = 1 to 8) were tested with the software and the total number of populations was set when the probability reached a plateau for higher K.The number of populations in our collection was deduced with the STRUCTURE software . This pain silico SNPs. JB carried out the bioinformatics analyses for in silico SNPs. AJM performed the variability analysis, coordinated the SNP mapping and participated in the drafting of the manuscript. MBP prepared DNAs for the melon accessions and participated in the genotyping for the variability analysis and in the drafting of the manuscript. JGM, PA, FN, MBP and MAA were involved in the conception of the study. JGM is the principal researcher of this work, supervised it and wrote the manuscript. All authors read and approved the final manuscript.WD discovered and mapped the SNPs and performed the genotyping for the variability analysis. CE and MGT discovered and mapped SNPs. CR discovered SNPs. IFS mapped SNPs. DGI identified and selected SNPs markers mapped in the SC × PS genetic map. Shown here, for each SNP marker: the EST and accession number from where it was obtained; best BlastX hit and E-value for each EST; amplicon primer sequences; SNP/indel position; SNP detection method; linkage group and BIN where the marker maps to. asequence available in [ without accession number. bSNPs published by Morales et al (2004). cSNP position is provided when located in exons and referred to EST in first column. dthird primer was used for SNaPshot genotyping.lable in  or  withClick here for fileSNP markers used for genotyping the melon accessions. The EST from where the SNPs were discovered, the genotyping method (CAPS or pyrosequencing), linkage group where the marker maps to, and source of the marker are given. For unmapped SNP markers, the amplicon primer sequences are given. For SNP markers genotyped using pyrosequencing, forward, reverse and internal primers were used for genotyping. 5'bio: Forward or reverse primer was 5' labeled with biotine. ST1: Additional file Click here for fileForty-eight melon accessions that were examined in this study. Plant assignation (or common name), code used in the current study, accession number from the respective gene banks, cultivar group, origin and seed bank donor  are specified for each genotype. Accessions marked with (*) were previously used by Monforte et al. (2003) for an SSR study.Click here for file


International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes will help to describe epidemiology, monitor trends, and propose interventions for this important complication in patients with diabetes. Prior hypoglycemia studies utilized incomplete search strategies and may be methodologically flawed. We sought to validate a new ICD-9-CM coding algorithm for accurate identification of hypoglycemia visits.Accurate identification of hypoglycemia cases by This was a multicenter, retrospective cohort study using a structured medical record review at three academic emergency departments from July 1, 2005 to June 30, 2006. We prospectively derived a coding algorithm to identify hypoglycemia visits using ICD-9-CM codes . We confirmed hypoglycemia cases by chart review identified by candidate ICD-9-CM codes during the study period. The case definition for hypoglycemia was documented blood glucose 3.9 mmol/l or emergency physician charted diagnosis of hypoglycemia. We evaluated individual components and calculated the positive predictive value.We reviewed 636 charts identified by the candidate ICD-9-CM codes and confirmed 436 (64%) cases of hypoglycemia by chart review. Diabetes with other specified manifestations (250.8), often excluded in prior hypoglycemia analyses, identified 83% of hypoglycemia visits, and unspecified hypoglycemia (251.2) identified 13% of hypoglycemia visits. The absence of any predetermined co-diagnosis codes improved the positive predictive value of code 250.8 from 62% to 92%, while excluding only 10 (2%) true hypoglycemia visits. Although prior analyses included only the first-listed ICD-9 code, more than one-quarter of identified hypoglycemia visits were outside this primary diagnosis field. Overall, the proposed algorithm had 89% positive predictive value  for detecting hypoglycemia visits.The proposed algorithm improves on prior strategies to identify hypoglycemia visits in administrative data sets and will enhance the ability to study the epidemiology and design interventions for this important complication of diabetes care. Hypoglycemia is an important complication in patients with diabetes that has a profound impact on quality of life and self-management. While tight glycemic control remains a hallmark to lower rates of complications -3, the bAlthough some episodes of severe hypoglycemia are treated at home by administration of oral glucose or intramuscular glucagon by family members, the most severe episodes require ambulance or emergency department (ED) visits. Although ED visits for severe hypoglycemia represent a small percentage of the total episodes of hypoglycemia in diabetes, they do serve as a good epidemiological marker of the complication and result in significant economical and psychological costs .International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes may help to describe the epidemiology, monitor trends, and propose interventions to improve diabetes care through national administrative databases and local chart reviews [Most data on the epidemiology of hypoglycemia are based on highly selected patients in the setting of large clinical trials -3,7, andtion ICD--CM codes reviews -14, only reviews .Building on this prior work, we evaluated the accuracy a new ICD-9-CM coding algorithm to identify cases of hypoglycemia presenting to the ED. We hypothesized that our proposed algorithm would identify hypoglycemia with a positive predictive value (PPV) of at least 80%.This was a multicenter, retrospective cohort study using a structured medical record review for validation of a prospectively derived ICD-9 coding algorithm. We obtained ethics approval with a waiver of informed consent from the Partners Human Research Committee and the Beth Israel Deaconess Medical Center Committee on Clinical Investigation.This study was conducted at three urban, academic EDs, which are active participants in the Emergency Medicine Network . The EDsUsing the electronic medical records system at each site, we searched the following ICD-9-CM codes to identify possible visits for hypoglycemia: 250.3 (diabetes with other coma), 250.8 (diabetes with other specified manifestations) 251.0 (hypoglycemic coma), 251.1 (other specified hypoglycemia), 251.2 , 270.3 (leucine-induced hypoglycemia), 775.0 (hypoglycemia in an infant born to a diabetic mother), 775.6 , and 962.3 (poisoning by insulins and antidiabetic agents).Given the diversity of potential ICD-9-CM codes, we searched this broad range of codes and in all diagnosis fields (up to ten listed) in an attempt to capture all possible ED hypoglycemia visits. For admitted patients, we examined only ED-based codes, to avoid inclusion of incident hypoglycemia that occurred during inpatient hospitalization. In cases where multiple candidate codes were present, we recorded only the first-listed code. The exception to this was for the more ambiguous codes 250.3 and 250.8, for which we preferentially recorded any of the other candidate codes if present. We based this strategy on detailed examination of the ICD-9-CM coding manual -14, and The code 250.8 may be used for other specific diabetes-associated complications in addition to hypoglycemia, including: 259.8 (secondary diabetic glycogenosis), 272.7 (diabetic lipidosis), 707.xx (ulcers of the lower extremity), 709.3 (Oppenheim-Urbach syndrome), and 730.0–730.2, 731.8 (osteomyelitis). Based on discussion with coding experts, we determined that 681.xx (cellulitis of fingers/toes), 682.xx (other cellulitis), and 686.9x  may also be utilized as a co-diagnoses for 250.8, although not specifically mentioned in the manual. We prospectively proposed the coding algorithm displayed in Figure We identified all ED visits with candidate ICD-9-CM codes between July 1, 2005 and June 30, 2006 at each site, and obtained written ED charts. For patients with multiple ED visits during the data collection period, we requested only the first visit to avoid overrepresentation by certain patients. Trained research staff abstracted all charts using a standardized form, and the research group met weekly to discuss data collection and resolve abstraction issues. Additionally, two reviewers independently abstracted 10% of charts to evaluate inter-rater agreement in data collection. To enhance the reliability of our chart review, we abstracted only charts with complete ED triage assessment, nursing notes, and emergency physician notes and considered all other charts incomplete.We considered this chart validation as the gold standard for confirmation of true hypoglycemia visits. In reviewing the ED chart, we confirmed cases of hypoglycemia based on the following criteria: 1) Any documented pre-hospital or ED glucose value (serum or capillary) 3.9 mmol/l, or 2) Charted physician discharge diagnosis of hypoglycemia. We based the glucose threshold on the consensus recommendation defined by the American Diabetes Association Workgroup on Hypoglycemia . We inclWe performed statistical analyses using Stata 9.0 . We summarized data using basic descriptive statistics. We determined accuracy of specific codes and coding algorithm by calculating PPV with 95% confidence intervals (CI). Although we included a broad range of possible ICD-9-CM codes, the numbers of missed hypoglycemia visits, not captured by the candidate codes, were unknown. Under the ideal assumption that no cases were missed, we calculated estimated point estimates for sensitivity, specificity, and negative predictive values (NPV). Additionally, we performed a sensitivity analysis by increasing the presumed number of missed cases by 10%.We determined inter-rater agreement for chart abstraction by calculating the kappa statistic for the subgroup of charts abstracted by two investigators. Additionally, we compared accuracy of the algorithm stratified by candidate ICD-9-CM code position, study site and ED disposition using chi-squared test and with two-tailed p < 0.05 considered statistically significant. Finally, we evaluated the accuracy of all ICD-9-CM codes to identify potential improvements and calculated the accuracy of the revised algorithm including the proposed modifications.Of the 174,134 ED visits at the three institutions during the data collection period, we identified 901 patients with candidate ICD-9-CM codes. These patients accounted for 1,139 visits with possible hypoglycemia . We abstracted 679 charts (75% of visits by unique patients) and excluded the remaining 222 patients with missing or incomplete documentation. There were no significant differences in age, sex, race/ethnicity, and distribution of candidate ICD-9-CM codes comparing abstracted and excluded patients .Based on the chart review, we confirmed 436 (64%) cases of hypoglycemia. Confirmation of hypoglycemia had a very high inter-rater agreement (kappa = 0.97). Predictive values for individual ICD-9-CM codes are presented in Table Table Overall, the proposed algorithm had PPV of 89%  for detecting hypoglycemia visits. There was no significant difference in PPV when data were stratified by site or by ED disposition . Based on the assumption that the candidate ICD-9-CM codes captured all ED presentations with hypoglycemia, we estimate that the algorithm had 97% sensitivity, >99% specificity, and >99% NPV. Assuming the algorithm missed at most 10% of hypoglycemia visits, we estimate a minimum sensitivity of 88% with NPV and specificity remaining >99%.Table Post-hoc review of the algorithm performance suggested that cases identified by 962.3 demonstrated low PPV (54%), primarily representing patients with excessive insulin or oral hypoglycemic use without development of clinical or laboratory hypoglycemia. We evaluated the full list of ICD-9-CM codes for all visits coded as 962.3 and found that 10 of 13 confirmed cases of hypoglycemia had 250.8 listed as a co-diagnosis and only 1 of 11 cases without hypoglycemia had 250.8 as a co-diagnosis. When we removed 962.3 as a candidate code and relied only on coding for 250.8 in these cases, the PPV for the overall algorithm increased from 89% to 91%  while the estimated sensitivity remained at 97%.In this study, we demonstrated high predictive value for detection of ED hypoglycemia visits using the new ICD-9-CM coding algorithm. To our knowledge, this is the first chart validation of the accuracy of ICD-9-CM codes for hypoglycemia, an important complication in diabetes and common ED presentation.Our algorithm differs in several important ways from prior reports on hypoglycemia. Two separate analyses of the relationship between anti-hypertensive agents and hypoglycemia in patients with diabetes, Herings et al. and Morris et al. used ICD-9-CM codes 251.0, 251.1, and 251.2 to identify possible episodes of hypoglycemia ,13. SchoJohnson, et al. first reported the utility of 250.8 in their strategy to identify ED visits for hypoglycemia . In theiOverall, 250.8 with exclusion of alternate co-diagnoses and 251.2 comprised >95% of hypoglycemia identified by the algorithm. Although there were insufficient data to evaluate 250.0 and 250.1 in this analysis, we recommend inclusion of these codes in any search for hypoglycemia. These codes are likely to have high specificity/PPV, and their utilization may be higher in different institutions and practice environments. As anticipated, codes 270.3, 775.0 and 775.6 were rare in the ED setting but may have utility for other settings, such as maternity and neonatal units.We hypothesized that we would not be able to determine if diabetes with other coma (250.3) were caused by hypoglycemia or diabetic ketoacidosis (DKA). There were insufficient data to evaluate this hypothesis (only one case), and charts identified with this code should be interpreted with caution. Compared to prior 100% PPV in prior analysis [The ICD-9-CM classification system is imperfect for case identification, as it was created for reimbursement rather than research purposes . Given tThe current study has some potential limitations. We did not abstract the 25% of charts that were missing or incomplete. While this lowered the number of total cases, the distribution of ICD-9-CM codes was similar in abstracted and non-abstracted charts, and the likelihood of biased accuracy estimates was small. The coding of hypoglycemia visits was based on three academic EDs and may not generalize to other areas of the hospital, outpatient providers, and other geographic areas. However, there are a finite number of options for coding hypoglycemia, and although the frequency of individual code usage will likely vary based on local practice and familiarity of the coding personnel, we anticipate that accuracy will not vary substantially. We were unable to formally evaluate false-negatives in our data set. Thus, we were only able to calculate PPV with accuracy, and our estimates for sensitivity, specificity, and NPV are significantly limited. To help address this limitation, we performed a sensitivity analysis, which demonstrated a high level of accuracy even when we increased the assumption of missed cases, but inferences based on these values should be guarded. Finally, the accuracy of case confirmation by chart review depended on a retrospective evaluation for hypoglycemia, which may overestimate or underestimate the true number of cases. Standardized definitions and training of reviewer limited the potential for such bias, and high inter-rater agreement demonstrated internal reliability of the chart review.We derived and validated a new ICD-9-CM coding algorithm to identify hypoglycemia visits with high predictive validity. This algorithm improves on prior strategies to identify hypoglycemia cases in administrative data sets and will enhance the ability to study the epidemiology and design interventions for this important complication of diabetes care.AAG has received investigator-initiated research funding from Novo Nordisk and Bayer in the past 12 months. He does not own stocks or other ownership interest in any company with an interest in diabetes. CAC has received financial support from a variety of groups for participation in conferences, consulting, and medical research. Over the past 12 months, industry sponsors with an interest in diabetes were AstraZeneca, Bayer, GlaxoSmithKline, Merck, Novartis, and Novo Nordisk. He does not own stocks or other ownership interest in any of these companies. The other authors do not have any potential competing interests to report.All authors participated in study conception and design. AAG participated in data collection, performed the statistical analysis, drafted the manuscript, and takes responsibility for the manuscript as a whole. PGB and RML participated in data collection. CAC provided study supervision and statistical expertise. All authors read the manuscript, participated in critical revision, and approved the final version.The pre-publication history for this paper can be accessed here:


The use of pooled DNA on SNP microarrays (SNP-MaP) has been shown to be a cost effective and rapid manner to perform whole-genome association evaluations. While the accuracy of SNP-MaP was extensively evaluated on the early Affymetrix 10 k and 100 k platforms, there have not been as many similarly comprehensive studies on more recent platforms. In the present study, we used the data generated from the full Affymetrix 500 k SNP set together with the polynomial-based probe-specific correction (PPC) to derive allele frequency estimates. These estimates were compared to genotyping results of the same individuals on the same platform, as the basis to evaluate the reliability and accuracy of pooled genotyping on these high-throughput platforms. We subsequently extended this comparison to the new SNP6.0 platform capable of genotyping 1.8 million genetic variants.We showed that pooled genotyping on the 500 k platform performed as well as those previously shown on the relatively lower throughput 10 k and 100 k array sets, with high levels of accuracy (correlation coefficient: 0.988) and low median error (0.036) in allele frequency estimates. Similar results were also obtained from the SNP6.0 array set. A novel pooling strategy of overlapping sub-pools was attempted and comparison of estimated allele frequencies showed this strategy to be as reliable as replicate pools. The importance of an appropriate reference genotyping data set for the application of the PPC algorithm was also evaluated; reference samples with similar ethnic background to the pooled samples were found to improve estimation of allele frequencies.We conclude that use of the PPC algorithm to estimate allele frequencies obtained from pooled genotyping on the high throughput 500 k and SNP6.0 platforms is highly accurate and reproducible especially when a suitable reference sample set is used to estimate the beta values for PPC. Genome-wide SNP association screening has become a launching pad to the identification of genes or loci contributing to the susceptibility of complex diseases. With the advent of high-throughput genotyping microarrays such as those from Affymetrix and Illumina, a genome-wide scan for up to half a million genetic variants has become possible for even smaller laboratories which normally wouldn't be able to afford the manpower for large scale genotyping. In either platforms, single nucleotide polymorphisms (SNPs) were chosen as the markers of choice due to their abundance in the genome, their bi-allelic nature, and because they are stably inherited from generation to generation . When stFor a genome-wide case-control study to be able to associate a particular genetic variant present at a frequency of at least 5% in the population with about 80% power, the samples that would have to be genotyped would number in the thousands . DespiteThe accuracy and validity of pooled genotyping on microarrays has been extensively studied on the Affymetrix 10 k ,11-15 anThe purpose of this study is to comprehensively evaluate the ability of these higher throughput SNP genotyping platforms to estimate allele frequencies from pooled DNA samples. The full repertoire of 500,568 SNPs from the Affymetrix 500 k array set is used as the basis of an un-biased evaluation of the accuracy of pooled genotyping in comparison to allele frequencies obtained from individual genotyping with estimated allele frequencies calculated using the PPC algorithm . This coOn the Affymetrix 500 k platform, the detection rate for the individually genotyped samples varied between 90% and 99% . Samples which had lower detection rates than 90% were repeated. Pooled genotyping, yielded lower detection rates than individually genotyped samples of between 86% and 88%, likely due to the highly heterogeneous nature of the sample. These detection rates were nevertheless comparable with that published from other studies working on the Affymetrix 100 k and 500 k array sets. On the SNP6.0 platform, detection rates for pooled genotyping were between 96% and 98% . As no prior-art exists for comparison, these high detection rates could be considered as par for pooled genotyping on the new platform.To quantify the reliability of each of the three pools in the study groups  improved from 90.8% to 98.6%, while the average error in the estimates improved by 0.06. To further confirm that such a difference exists when different reference samples are used to calculate beta values, allele frequencies were estimated for a second set of 60 cases and 60 controls pooled via the same strategy, using beta values calculated from the 500 k Sample Data Set and the individually typed samples Table .Even in the second set of pooled samples, the average accuracy of allele frequency estimation seems to have improved by approximately 3% while error in allele frequency estimates improved by 0.05 when using the beta values obtained from in-house genotyped samples over those calculated from the reference sample set provided by Affymetrix.For the pooled samples genotyped on the SNP6.0 platform, beta values calculated from the four Hapmap populations in the Sample Data Set were used to separately estimate different sets of allele frequencies. These estimates were compared with the known allele frequencies from the respective Hapmap populations Table .Estimated allele frequencies were most highly correlated with actual allele frequencies when the CHB Hapmap data set was used as a reference to calculate beta values with median absolute error of 0.036 (comparable to that obtained on the 500 k platform). When the CEU and YRI sample data sets were used to calculate allele frequencies from our pooled data, the accuracy of the allele frequency estimates were much lower (0.780 to 0.89). While the level of accuracy of estimation was higher when all the Hapmap samples were used . Figure We calculated absolute errors in allele frequencies between the individual and pooled genotyping results, and looked at the distribution of these errors Figure .While the bi-plot comparison revealed that there were still allele frequency estimation errors of up to 0.2, studying the distribution of errors Figure  showed tIn individual genotyping, if a genotype cannot be assigned for a particular SNP for any particular sample, it is assigned as "NoCall". If a SNP is not called in the majority of samples genotyped, the allele frequency averaged across the remainder of the samples in which the SNP was properly called would not be representative of the whole sample population. As such, SNPs which had "NoCall" in too many samples could potentially be considered to have lower quality genotype calls. The genotype calling rates for all 500,568 SNPs were analysed across the 120 genotyped samples, and the SNPs were filtered based on number of samples with NoCall for each SNP  reduced from 96.9% to 95.1%. This somewhat confirms the results observed in Table We hereby present a comprehensive genome-wide validation of pooled genotyping on the higher throughput SNP genotyping platforms. Using the complete Affymetrix 500 k array set as the basis of comparison, we have shown that the reliability and accuracy of pooled genotyping is as good as or improved over the previously tested 10 k and 100 k array sets. This comparison has been extended to the new SNP6.0 platform, which has yet been shown to be useful for pooled genotyping. We believe that this work would reaffirm that SNP-MaP is still a viable alternative to individually genotyping a large sample population.Strategies for pooled genotyping have classically followed the path of having 3 identical replicate pools at the very least with the intent of "averaging" out the error normally associated with pooling ,17,22. TIndividual genotyping classically produces genotype calls for each sample from which an average allele frequency can be calculated. However, in pooled genotyping, the microarray software is unable to assign a genotype due to the heterogeneous nature of the pooled sample, and the unequal hybridization to the various probes. As such, an algorithm to estimate allele frequencies from probe intensities was necessitated. To account for the unequal allelic amplification in pooled genotyping, relative allele signals (RAS) used together with a k-correction to improve accuracy of estimates was used initially . This alRegardless of the method used to estimate allele frequencies from the probe intensity data of pooled genotyping, the necessity of a set of reference samples is paramount. In most situations, allele frequency data from reference samples  are used as a benchmark to compare the allele frequency estimates against. While the issue of reference samples was brought up  in the cOur initial comparison of the accuracy of allele frequency estimates from pooled genotyping on the 500 k platform revealed that using a genetically homogeneous reference sample set, such as one from a particular ethnic group, produced estimated allele frequencies which were more accurate than using a more heterogeneous one. While our use of the same set of samples for individual and pooled genotyping provided a better indication of the capabilities of the 500 k platform in allelotyping, it might be thought that such a result would be expected given that the same samples were used for both. Our results from the first individual vs pool comparison Table  were conThis difference can be possibly attributed to availability of samples with all 3 genotypes for SNPs in the reference sample set. For the RAS method of calculating allele frequencies, the presence of heterozygous samples together with both homozygotes, allow the calculation of the k-correction which helps improve the accuracy of allele frequency estimates. Similarly, for PPC, the heterozygous samples allow the derivation of second-degree polynomials which increased accuracy of estimated allele frequencies by accounting for unequal hybridization efficiencies of different SNPs . So a reWe believed that SNP variability was related to the ethnicity of the samples in the reference data set. While complete reference sample data sets from different ethnicities were not easily available for the 500 k platform, complete data for all 270 Hapmap samples was made available by Affymetrix when the SNP6.0 was released. This allowed us to compare the accuracy of allele frequencies from our pooled genotyping calculated using beta values from the four major Hapmap populations against the allele frequencies of those very populations. While we have yet to do individual genotyping on the SNP6.0 platform, such a comparison would still be valid as we have already shown that our Singapore Chinese samples are similar to the Hapmap Han Chinese (CHB) population (unpublished data). Our results , they would need to perform a round individual genotyping so as to generate a set of reference data which they can use for subsequent pooling experiments. This greatly detracts from the benefits offered by pooled genotyping as a more economical and more efficient way of performing an initial whole genome scan as part of an association study. However, this is where genotyping repositories, as suggested by various authors ,13, woulIn this paper, we reinforce the capabilities of SNP-MaP as an alternative to individual genotyping of hundreds or thousands of samples in a genome-wide case-control association study. While pooled genotyping had been previously validated on the smaller scale Affymetrix 10 k and 100 k array sets, similarly detailed analysis had not been done on the 500 k or newer SNP genotyping platforms. Previous validation studies have shown accuracies of pooled genotyping on the 10 k platform ranging from 0.923  to 0.987Nonetheless, validation of pooled genotyping was indeed carried out on the 500 k arrays with estimation accuracies ranging from 0.926 . While WWe took this analysis one step further by focusing on the currently available ultra high-throughput SNP genotyping SNP6.0 platform and the 906,600 SNPs it covered . Estimated allele frequencies from our pooling experiment highly represented those from our selected reference data set . Despite the reduction in intensity data available per-SNP, the SNP6.0 platform seems equally well suited as its predecessors for SNP-MaP. Although our allele frequency estimates from pooled genotyping on the SNP6.0 platform were based on individual genotyping data of Hapmap CHB samples instead of the samples in the pools , we are still highly confident of its relevance due to the ethnic similarity of Hapmap CHB and our Singapore Chinese samples.In the 10 k and 100 k arrays, relative allele signal data was readily available thus allowing the use of the RAS method to estimate allele frequencies together with the k-correction to account for unequal hybridization. While such data was directly unavailable for the 500 k data, various authors ,15 proviPrevious studies ,12,18 haRegardless of how we compared our pooled estimates of allele frequencies with the actual allele frequencies obtained from our individually typed samples and known allele frequencies from Hapmap CHB samples, the allele frequency estimates that we obtained proved to be extremely reliable. With reliability and validity improvements over that previously demonstrated on 10 k, 100 k and 500 k arrays, we have shown that both the 500 k and SNP6.0 platforms perform well in pooled genotyping.While we have showed that pooled genotyping allows the estimation of allele frequencies which are highly accurate compared to the actual allele frequencies, it cannot be used to completely replace individual genotyping; the availability of actual genotype data as obtained from individual genotyping allows a more detailed analysis and understanding of the genomic variability in the sample population, and also permits linkage and haplotype analysis within the population. Furthermore, while the genotyping of pooled samples introduces errors, and the errors due to pooling are usually minimal, and random errors due to the array itself can be corrected for by having multiple pooled replicates , systemaIn this study, we have successfully shown that pooled genotyping is a reliable and accurate method of performing a truly genome-wide scan by analysing the performance of the complete repertoire of SNPs on the Affymetrix 500 k high throughput genotyping platform. Using this comparison as the basis, we showed that SNP-MaP is highly viable on the latest even-higher throughput SNP6.0 platform. We believe that newer, even higher density SNP microarrays will be amenable to pooled genotyping following the strategy outlined. The accuracy of allele frequency estimates was shown to be improved by using the PPC algorithm and by using a reference population data set of similar ethnicity to the study population for the calculation of beta values for PPC. Lastly, a novel pooling strategy was explored, and was found to provide similar benefits as that observed in others' replicate pools.The DNA samples used in this study were collected from ethnic Chinese participants as part of an on-going retrospective cross-sectional study on allergic diseases in Singapore (unpublished data) following standard protocols for informed consent. Approval to conduct the study was obtained from the National University of Singapore Institutional Review Board (NUS-IRB Reference Code: 07-023). Genomic DNA was extracted from buccal cells obtained from a mouthwash in 0.9% saline solution. In short, the buccal cells were pelleted and lysed; DNA was extracted using the phenol-chloroform phase-separation technique , purifieSamples were subsequently stratified into case and control groups according to their disease status as determined by ISAAC-derived questionnaires  and skinA novel pooling strategy, involving over-lapping sub-pools to form a larger pool, was used for pooled genotyping on the 500 k platform. Twenty samples from either the case or control group of samples were pooled in equal quantities of DNA to form a single sub-pool. This was repeated twice further to produce a total of 3 sub-pools  for separate analysis of individual and pooled genotyping. In total, 120 chip pairs (NspI and StyI) were used for individual genotyping of 120 samples. Six chip pairs were used for pooled genotyping of the same 120 samples which were individually genotyped. Subsequently, 6 more chip pairs were used for pooled genotyping of the second set of 120 samples on the 500 k array platfrom. For the SNP6.0 arrays, while the protocol was similar, genotyping was outsourced to Origen Laboratories Pte Ltd in Singapore. Both the case and control pooled samples were genotyped in triplicate. In total, 6 chips were used for pooled genotyping of 320 samples on the SNP6.0 platform.The CEL files generated by GCOS were processed using the BRLMM algorithm as implemented in the Genotyping Console v2.1, and genotypes were called using the default settings. The exported genotypes, in the two-letter format  were converted to allele frequencies of the A allele  via a Perl script to simplify subsequent analysis in R .The Mapping 500 k Sample Data Set is made up of thirteen trios  and 9 unrelated HapMap Asian samples . The Genet al., 2005 [The genotype call files together with the probe intensities of the individually typed samples were processed via Perl and R scripts to calculate the three polynomials  required by the PPC algorithm. The 500 k and SNP6.0 Sample Data Sets obtained from Affymetrix were also similarly processed to calculate a separate set of beta values. Allele frequencies were subsequently estimated from the probe intensities of the pooled genotyping data following the PPC algorithm as outlined by Brohede l., 2005 . Separatet al., 2005, and are available in Additional file Pearson's correlation coefficient was calculated to determine "correlation" as a measure of accuracy between actual and estimated allele frequencies. The error in the estimation of allele frequencies was the absolute difference between actual and estimated allele frequencies. Median absolute deviation (MAD) was used as a more robust estimator of dispersion of errors than standard deviation or variance. A 95% confidence interval of the distribution of errors was used as an interval estimate of spread of estimation errors. All other statistical analyses and data manipulations were carried out in Microsoft Excel, Affymetrix Power Tools , R and vOperating system(s): Platform independentProgramming Language(s): Perl, R scriptingLicence(s): GNU General Public LicenceRestriction(s): No restrictions to use by non-academics.RA performed the individual and the majority of the pooled genotyping, carried out all the microarray data analysis, and drafted the manuscript. FTC conceived, designed and planned the study and edited the manuscript. Both authors have read and approved the final manuscript.Script.zip containing the following Perl and R scripts  Scripts.txt – All R scripts, including usage of Perl scripts call.p – Perl script to process genotype calls (500k data set)  extractfields.p – Perl script to extract range of columns from data file  extractprobes.pl – Perl script to process probe intensities (SNP6.0 data set)  field.p – Perl script to extract single column from data file  int.p – Perl script to process probe intensities (500k data set)  relint.pl – Perl script to process relative intensities (SNP6.0 data set)  sepprobe.pl – Perl script to separate probe intensities (SNP6.0 data set)  Click here for file


Identification of disease-related genes in association studies is challenged by the large number of SNPs typed. To address the dilution of power caused by high dimensionality, and to generate results that are biologically interpretable, it is critical to take into consideration spatial correlation of SNPs along the genome. With the goal of identifying true genetic associations, partitioning the genome according to spatial correlation can be a powerful and meaningful way to address this dimensionality problem.We developed and validated an MCMC Algorithm To Identify blocks of Linkage DisEquilibrium (MATILDE) for clustering contiguous SNPs, and a statistical testing framework to detect association using partitions as units of analysis. We compared its ability to detect true SNP associations to that of the most commonly used algorithm for block partitioning, as implemented in the Haploview and HapBlock software. Simulations were based on artificially assigning phenotypes to individuals with SNPs corresponding to region 14q11 of the HapMap database. When block partitioning is performed using MATILDE, the ability to correctly identify a disease SNP is higher, especially for small effects, than it is with the alternatives considered.Advantages can be both in terms of true positive findings and limiting the number of false discoveries. Finer partitions provided by LD-based methods or by marker-by-marker analysis are efficient only for detecting big effects, or in presence of large sample sizes. The probabilistic approach we propose offers several additional advantages, including: a) adapting the estimation of blocks to the population, technology, and sample size of the study; b) probabilistic assessment of uncertainty about block boundaries and about whether any two SNPs are in the same block; c) user selection of the probability threshold for assigning SNPs to the same block.We demonstrate that, in realistic scenarios, our adaptive, study-specific block partitioning approach is as or more efficient than currently available LD-based approaches in guiding the search for disease loci. Technological progress is now enabling the genotyping of up to one million SNPs at a time, a number also expected to increase rapidly. This provides scientists with a considerable amount of information for the study of gene-disease associations   .1 the subset of θs estimated from SNPs in true LD, and by Θ0 the subset of θs estimated from SNPs which are not in LD. Since no other intermediate option is allowed between the LD and the absence of LD status, then Θ1 ∪ Θ0 ≡ Θ. Under the assumption that two SNPs are in LD only if they belong to the same haplotype block, the partition of Θ can be uniquely identified by a binary vector γ = ' where γi = 1 means that the a border of a haplotype block falls between SNPs (i - 1) and i; γi = 0 means that SNP (i - 1) and SNP i belong to the same block. By definition, γ1 = 1 and γS+1 = 1. SNPs not belonging to any block are classified as blocks by themselves, with borders γi-1 = γi = 1. In the following, γ will be referred to as block border vector.Denote by Θθs, f(θ), is generally skewed to the right, often with a mass close to 1. The magnitude of this mass depends on the LD statistic used and on the study sample size. This property of θ, made us to assume that f(θ) is composed of two underlying distributions, so thatEmpirical evidence and theoretical studies -59 showeθs being drawn from f1 when the SNPs are in LD, from f0 otherwise.the f0, f1, and γ, and assuming conditional independence of the θs, the likelihood isFor given The assumption of conditional independence does not correspond closely to how the data are generated, and is made pragmatically, to simplify an otherwise nearly intractable problem. We consider it unlikely that this assumption will significantly affect the accuracy of the classification, although it may affect the uncertainty assessment. Alternatively, one can model the joint distribution of haplotypes directly and address blocking, for example, as a model selection problem . This apγ and {Θ0, Θ1}, the (1) can be written asBecause of the one-to-one correspondence between f0 can be estimated non-parametrically by randomly permuting the genotypes between subjects. LD is estimated from the genotype distribution via an EM algorithm . This statistic is a way to reward the method which is faster in finding the area where the right SNP is, irrespective of the dimension of blocks.From the standpoint of evaluating the quality of the dimension reduction methodology, it is useful to reward approaches that give a high ratio Bk is the number of SNPs in the kth block. B is the number of SNPs that should be screened before discovering the true SNP, thus the smaller the B the better the method.When the dimension of blocks matters, it could be more interesting to count the number of SNPs classified as good as, or better than, the right SNP, that is The scientific motivation and statistical models were defined by CP and GP with the support of IR for the computational expertise and MDF for epidemiological expertise. Simulations were performed by CP. The article was written mainly by CP with substantial contribution by all the other authors. The project was supervised by GP.MATILDE's core algorithm.Description of the tth iteration of the Metropolis-Hastings algorithm used to explore the (4) is described. The parameters  were updated in three steps as follows:Here the st step 1γt as described below;i) sample γt, split Θ into given lt; r = exp {lt - lt-1};compute u ~ U;ii) sample u <min then {Θ0, Θ1} ← {if γt ← γt-1 and lt← lt-1;else nd step2βt ~ U;i) sample l*; r = exp {l* - lt};compute u ~ U;ii) sample u <min then lt← l*;if βt ← βt-1;else rd step3a* ~ U;i) sample αt ← max;l*; r = exp {l* - lt};compute u ~ U;ii) sample u <min then lt ← l*;if αt ← αt-1.else t, the key point is the proposal of the new block border vector, which is sampled as follows: first, let's decide either to move a boundary (i) or to change the number of blocks. Option (i) corresponds to changing the size of two neighboring blocks, option (ii) corresponds to joining or splitting two neighboring blocks. The choice is done by sampling from a Bernoulli(p), with p defined by the user on the basis of sample size and number of SNPs.At each iteration, γt,1··γt, S+1, is sampled with equal probability; then the border is moved one step to the left or to the right at random: of the two blocks sharing the boundary, one will increase its size of one SNP, while the other will be shortened by one. When this move is chosen, the total number of blocks does not change.Under the option (i), one of the existing boundaries, γt: one point, γt, i, between γt,2 and γt, S, is randomly chosen; if γt, i = 0 then γt, i ← 1 (this means to join two contiguous blocks into a bigger one), else γt, i ← 0 .Under the option (ii), one of two actions is sampled with equal probability: I) split one block: one block is sampled at random and one point inside the block is also chosen at random and turned into a border, generating two contiguous and smaller blocks; II) modify a random value of MATILDE block structures are reported at different probability cutoffs.Comparison of the block partitions on a simulated sample of 200 subjects. The method used is indicated on the left. On the fifth, unlabeled line, ticks are at the positions where at least three of the four methods above it agreed. Click here for fileMATILDE block structures are reported at different probability cutoffs.Comparison of the block partitions on a simulated sample of 400 subjects. The method used is indicated on the left. On the fifth, unlabeled line, ticks are at the positions where at least three of the four methods above it agreed. Click here for fileMATILDE block structures are reported at different probability cutoffs.Comparison of the block partitions on a simulated sample of 600 subjects. The method used is indicated on the left. On the fifth, unlabeled line, ticks are at the positions where at least three of the four methods above it agreed. Click here for fileMATILDE block structures are reported at different probability cutoffs.Comparison of the block partitions on a simulated sample of 800 subjects. The method used is indicated on the left. On the fifth, unlabeled line, ticks are at the positions where at least three of the four methods above it agreed. Click here for fileκ s between the four most common methods, by sample size. Lower panel: κ s between MATILDE and the four most common methods, by probability cutoff (x-axis) and sample size. Symbols: triangle = DprimeCI, diamond = SSD, reverse triangle = 4Gamete, and square = HapBlock.Agreement between all block partitioning methods. Upper panels: pairwise Click here for fileMATILDE . In addition an allele-based single-SNP association analysis is represented by an "x" while a genotype-based single-SNP association analysis is represented by a "+". Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively.Comparison of methods' sensitivity and specificity. Data refer to the simulation of 100 cases and 100 controls assuming a dominant model. Each panel reports the sensitivity/specificity tradeoff for DIprimeCI (triangle), 4Gamete (reversed triangle), the SSD (diamond), HapBlock (square) and Click here for fileMATILDE . In addition an allele-based single-SNP association analysis is represented by an "x" while a genotype-based single-SNP association analysis is represented by a "+". Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively.Comparison of methods' sensitivity and specificity. Data refer to the simulation of 200 cases and 200 controls assuming a dominant model. Each panel reports the sensitivity/specificity tradeoff for DIprimeCI (triangle), 4Gamete (reversed triangle), the SSD (diamond), HapBlock (square) and Click here for fileMATILDE . In addition an allele-based single-SNP association analysis is represented by an "x" while a genotype-based single-SNP association analysis is represented by a "+". Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively.Comparison of methods' sensitivity and specificity. Data refer to the simulation of 300 cases and 300 controls assuming a dominant model. Each panel reports the sensitivity/specificity tradeoff for DIprimeCI (triangle), 4Gamete (reversed triangle), the SSD (diamond), HapBlock (square) and Click here for fileMATILDE . In addition an allele-based single-SNP association analysis is represented by an "x" while a genotype-based single-SNP association analysis is represented by a "+". Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively.Comparison of methods' sensitivity and specificity. Data refer to the simulation of 400 cases and 400 controls assuming a dominant model. Each panel reports the sensitivity/specificity tradeoff for DIprimeCI (triangle), 4Gamete (reversed triangle), the SSD (diamond), HapBlock (square) and Click here for fileMATILDE at various cutoff thresholds are listed.Parallel distribution of the statistics R (relative position of the block containing the right SNP) and B (number of SNPs belonging to blocks classified not worse than the true SNP) for a sample size of 100 cases and 100 controls. Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively. For each panel, the results of simulation with the allele-based single-SNP method, the genotype-based single-SNP analysis, the four common methods  and the Click here for fileMATILDE at various cutoff thresholds are listed.Parallel distribution of the statistics R (relative position of the block containing the right SNP) and B (number of SNPs belonging to blocks classified not worse than the true SNP) for a sample size of 200 cases and 200 controls. Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively. For each panel, the results of simulation with the allele-based single-SNP method, the genotype-based single-SNP analysis, the four common methods  and the Click here for fileMATILDE at various cutoff thresholds are listed.Parallel distribution of the statistics R (relative position of the block containing the right SNP) and B (number of SNPs belonging to blocks classified not worse than the true SNP) for a sample size of 300 cases and 300 controls. Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively. For each panel, the results of simulation with the allele-based single-SNP method, the genotype-based single-SNP analysis, the four common methods  and the Click here for fileMATILDE at various cutoff thresholds are listed.Parallel distribution of the statistics R (relative position of the block containing the right SNP) and B (number of SNPs belonging to blocks classified not worse than the true SNP) for a sample size of 400 cases and 400 controls. Four effect sizes were considered: the OR is 1.2, 1.4, 1.6 and 1.8, respectively. For each panel, the results of simulation with the allele-based single-SNP method, the genotype-based single-SNP analysis, the four common methods  and the Click here for file


The next generation sequencing of a single cow genome with low-to-medium coverage has revealed 2.44 million new SNPs. The majority of the 2 million bovine single nucleotide polymorphisms (SNPs) currently available in dbSNP have been identified in a single breed, Hereford cattle, during the bovine genome project. In an attempt to evaluate the variance of a second breed, we have produced a whole genome sequence at low coverage of a single Fleckvieh bull.We generated 24 gigabases of sequence, mainly using 36-bp paired-end reads, resulting in an average 7.4-fold sequence depth. This coverage was sufficient to identify 2.44 million SNPs, 82% of which were previously unknown, and 115,000 small indels. A comparison with the genotypes of the same animal, generated on a 50 k oligonucleotide chip, revealed a detection rate of 74% and 30% for homozygous and heterozygous SNPs, respectively. The false positive rate, as determined by comparison with genotypes determined for 196 randomly selected SNPs, was approximately 1.1%. We further determined the allele frequencies of the 196 SNPs in 48 Fleckvieh and 48 Braunvieh bulls. 95% of the SNPs were polymorphic with an average minor allele frequency of 24.5% and with 83% of the SNPs having a minor allele frequency larger than 5%.This work provides the first single cattle genome by next-generation sequencing. The chosen approach - low to medium coverage re-sequencing - added more than 2 million novel SNPs to the currently publicly available SNP resource, providing a valuable resource for the construction of high density oligonucleotide arrays in the context of genome-wide association studies. This value is higher than the nucleotide diversity observed in humans . SNPs have been submitted to dbSNP ([dbSNP:ss140006985] to [dbSNP:ss142339932]).Indel: small insertion/deletion event; MAF: minor allele frequency; MALDI-TOF: matrix-assisted laser desorption/ionization time-of-flight; SNP: single nucleotide polymorphism.RF, TM and TMS conceived of the study and participated in its design and coordination. ABP with KF performed the sequencing experiments. SHE and TMS performed the data analysis. SHE, TM, RF and TMS drafted the manuscript. All authors contributed to and approved the final manuscript.The following additional data are available with the online version of this paper: a table showing the number of homo- and heterozygous SNPs depending on different read depth (Additional data file Number of homo- and heterozygous SNPs depending on different read depth.Click here for fileEmpirical cumulative distribution of read depth of the SNPs selected for MALDI-TOF genotyping in comparison to the entire SNP set.Click here for fileTesting of Hardy-Weinberg equilibrium was performed with Pearson's goodness-of-fit chi-square test with one degree of freedom.Click here for fileCalls by MAQ were checked by MALDI-TOF spectroscopy and capillary sequencing.Click here for filePart of the false-positive calls are obviously located in regions with higher than average read depth, suggesting duplications. Read depth in approximately unique regions was calculated for non-overlapping windows of 500 bp by the MAQ software. SNP calls are indicated by grey vertical lines. The identifier of the SNP is indicated in the figure legend.Click here for file