High density genetic maps of plants have, nearly without exception, made use of marker datasets containing missing or questionable genotype calls derived from a variety of genic and non-genic or anonymous markers, and been presented as a single linear order of genetic loci for each linkage group. The consequences of missing or erroneous data include falsely separated markers, expansion of cM distances and incorrect marker order. These imperfections are amplified in consensus maps and problematic when fine resolution is critical including comparative genome analyses and map-based cloning. Here we provide a new paradigm, a high-density consensus genetic map of barley based only on complete and error-free datasets and genic markers, represented accurately by graphs and approximately by a best-fit linear order, and supported by a readily available SNP genotyping resource.Approximately 22,000 SNPs were identified from barley ESTs and sequenced amplicons; 4,596 of them were tested for performance in three pilot phase Illumina GoldenGate assays. Data from three barley doubled haploid mapping populations supported the production of an initial consensus map. Over 200 germplasm selections, principally European and US breeding material, were used to estimate minor allele frequency (MAF) for each SNP. We selected 3,072 of these tested SNPs based on technical performance, map location, MAF and biological interest to fill two 1536-SNP "production" assays (BOPA1 and BOPA2), which were made available to the barley genetics community. Data were added using BOPA1 from a fourth mapping population to yield a consensus map containing 2,943 SNP loci in 975 marker bins covering a genetic distance of 1099 cM.The unprecedented density of genic markers and marker bins enabled a high resolution comparison of the genomes of barley and rice. Low recombination in pericentric regions is evident from bins containing many more than the average number of markers, meaning that a large number of genes are recombinationally locked into the genetic centromeric regions of several barley chromosomes. Examination of US breeding germplasm illustrated the usefulness of BOPA1 and BOPA2 in that they provide excellent marker density and sensitivity for detection of minor alleles in this genetically narrow material. Complete genome sequences of many plants, including economically important small grain cereals such as barley, are unlikely to be available in the near future if they have large genomes and contain much repetitive DNA. The barley genome is 5200 Mbp, which is more than twelve times rice, and composed of at least 80% highly repetitive DNA, which is likely to preclude a whole-genome assembly from shotgun sequences obtained with currently available technologies. However, access to most of the genes of barley and numerous other organisms can be gained through cDNAs  and sequenced PCR amplicons, which provide a facile route to single nucleotide polymorphisms (SNPs) in protein-encoding transcribed genes. As of the January 2, 2009 release of dbEST, there were 525,527 Sanger-sequenced ESTs from barley. These were derived principally from eight malting barley cultivars and one wild barley accession, with a minor fraction from several other barley genotypes. Here we describe the use of the majority of this transcriptome sequence resource to develop high-throughput SNP genotyping in barley, application of the new SNP methods to the production of a high-density and high quality SNP map that can be related readily to prior maps through shared markers and other grass genomes through synteny, and deployment of these new resources in support of marker-assisted breeding and association genetic analyses.In recent years there has been a surge in marker density and convergence toward consensus maps for barley. Rostoks et al.  developeDetails of the identification of approximately 22,000 SNPs from EST and PCR amplicon sequence alignments, and development of three test phase and two production scale Illumina GoldenGate oligonucleotide pool assays (OPAs), are briefly summarized in Methods and provided more fully in Supplemental Text  between the number of SNPs and the product of the number of ESTs. For example, the initial set of 36 pairwise genotype comparisons between eight malting barley cultivars and one wild barley accession (used for POPA1 and POPA2) is provided in Figure S1, Additional File The yield of SNPs from each of 253 pairwise genotype alignments of ESTs  at LOD 4 or 5 and generally remained intact at higher LOD values. The four maps from individual crosses were fused using MergeMap (see Methods) to form a consensus map containing 2943 SNP loci with a total map length of 1099 cM  are provided in Table S2  in the pericentric region but toward the paternal allele (Barke) on the long arm.http://wheat.pw.usda.gov/GG2/index.shtml at a cut-off of 1e-10. Probes that matched more than three U35 unigenes were ignored. The result was 636 previously mapped probes matching 1114 unigenes. The list of probes was then reduced to those mapped in Marcel et al. [Table S3  version 5 and version 6 gene models [Each barley SNP source sequence was compared to the rice . Similarly, the level of difficulty in map-based cloning efforts will also depend on the gene density in regions of low recombination. Due to high gene density in ancestral telomeric regions which are now nested within barley chromosomes, the pericentric regions of five barley chromosomes  have high gene density.An interesting consequence of the evolutionary history of barley chromosomes is that the number of expressed genes in the pericentric regions is highly variable, ranging from relatively few in the cases of barley 3H and 6H to very many in the most extreme case of barley 7H. The relative genetic map density of expressed genes has major implications for plant breeding efforts. If, for example, a trait maps to an ancestral telomeric region within the pericentric region of barley 7H then it will be much less likely that the gene controlling that trait can be separated from neighbouring genes by recombination than, for example, a gene located in the ancestral centromeric region of rice chromosome 9, which is located in a more recombinationally active region on barley chromosome 5H. For example there have been several as yet unsuccessful attempts to map-base clone -5. A total of 36 of these had a best BLASTX against a gene positioned to different rice chromosomes when comparing ortholog locations in rice version 5 to version 6. It is interesting that 14 of the 36 (39%) changes in the rice genome annotations brought rice-barley synteny into line with the barley genetic map, 9 of the 36 (25%) changes degraded rice-barley synteny and 12 (33%) had a neutral effect because neither the version 5 nor 6 rice annotations were syntenic with barley. One had a neutral effect because both BLAST hits were consistent with syntenic duplications in the barley and rice genomes. It is not unusual to find imperfect synteny such as the 12 of 36 (33%) revised but non-syntenic positions; in fact 745 of the 2703 mapped barley SNP source sequences (27.6%) do not point to a best rice BLASTX within the major synteny block. However, from this comparison of the edited positions in rice versions 5 versus 6 to the 2943 SNP barley genetic linkage map, it appears that the barley SNP map is the more stable point of reference. Consequently, it may be of some benefit to use the barley genetic map for further revisions of the rice genome sequence.The version 6 rice genome sequence coordinates, including chromosome, arm and base pair position, are included in Table S4 , IPK (985 SNPs) and UCR . SCRI and IPK SNPs were derived from PCR amplicon sequences, whereas UCR SNPs were derived nearly entirely from EST sequences. In the selection of SNPs for the OPAs, preference was given to SNPs derived from amplicon sequences. Nearly all SNPs on POPA1 and about 60% of the SNPs on POPA2 targeted stress-regulated genes. The composition of POPA1 included 1524 barley SNPs, one per gene, of which 1033 were derived from ESTs and 491 from amplicon sequences. The composition of POPA2 included 1536 barley SNPs, one per gene including 258 genes represented on POPA1, of which 1456 were from ESTs and 80 from amplicon sequences.BOPA1 represented 705 SNPs from POPA1 and 832 from POPA2, including one SNP in common. All BOPA1 SNPs had a satisfactory technical performance on POPA1 or POPA2 and a minor allele frequency of at least 0.08. To the extent of results presented in this manuscript, BOPA1 included 1414 mapped and 122 unmapped SNPs.Residual SNPs from the sources of POPA1 and POPA2 were insufficient to complete the design of POPA3 without compromising on the SNP selection criteria. Additional SNPs for POPA3 came from three sources: 1) an extended list of 5,732 SNPs identified in SCRI amplicon sequences, 2) colleagues who contributed SNPs from amplicon sequences of specific genes of biological interest and 3) an expanded barley EST resource. The first two of these additional sources were exhausted for POPA3 design. In the selection of EST-derived SNPs, priority was given to genes previously classified as having interesting expression patterns during malting or upon exposure to pathogens, or relevant to malting, brewing quality, abiotic stress or phenology. The composition of POPA3 included 1536 barley SNPs, in many cases more than one per gene and in some cases including genes represented on POPA1 or POPA2. In total, 967 POPA3 SNPs were derived from ESTs and 569 from amplicon sequences.BOPA2 represented 406 SNPs from POPA1, 178 from POPA2 and 952 from POPA3. The primary emphases of BOPA2 were representation of mapped SNPs that were not included on BOPA1 and inclusion of multiple SNPs for certain genes to reveal haplotypes at these loci, with some weight given to MAF. BOPA2 contained 921 SNPs with MAF at least 0.08, 256 SNPs with MAF at least 0.04 but less than 0.08, 345 SNPs with MAF least 0.005 but less than 0.04, and 14 SNPs with only one allele (MAF = 0) in the germplasm examined using POPA3. To the extent of results presented in this manuscript, BOPA2 included 1263 mapped and 273 unmapped SNPs. A total of 967 SNPs were from ESTs and 569 from amplicon sequences.Table S4  of the Oregon Wolfe Barley (OWB) population ,27 148 dDNA concentrations were re-checked using Quant-iT PicoGreen  and standardized to 80 ng/μl in TE buffer in preparation for the GoldenGate assay and 5 μl (400 ng) were used for each assay. Data were generated from each progeny line in the OWB, SxM and MxB doubled haploid populations using POPA1 and POPA2. Data were also produced using POPA3 from the complete OWB and MxB sets of DNA samples, but from only 92 SxM doubled haploids. Data from 95 HxO doubled haploids using BOPA1 were also generated. For each of these four mapping populations, extensive integration of SNP data with other types of marker data will be described elsewhere . Data uRaw data were transformed to genotype calls, initially using Illumina GenCall and subsequently using Illumina BeadStudio version 3 with the genotyping module. For each OPA, the data from all samples were visually inspected to manually set 1536 archetypal clustering patterns. The cluster positioning was guided by knowledge that heterozygotes are nearly non-existent in doubled haploids and rare in highly inbred parental genotypes and germplasm samples. Several "synthetic heterozygote" DNA samples were made by mixing parental DNAs in a 1:1 mass ratio Figure . FollowiIndividual maps were made principally using MSTMap ,30 for eAs part of Barley CAP , the twoAFLP: amplified fragment length polymorphism; DAG: directed acyclic graph; DArT: diversity array technology; EST: expressed sequence tag; QTL: quantitative trait locus; RFLP: restriction fragment length polymorphism; SNP: single nucleotide polymorphism; SSR: simple sequence repeat; STS: sequence tagged site.The contributions of authors and other colleagues, locations of work conducted and cost sharing are detailed in Supplemental Text (Additional File Supplemental TextClick here for fileFigure S1. SNP yield. The near-linear relationship between the number of SNPs and the product of the number of EST sequences for pairwise genotype comparisons is shown by plotting all values versus a linear regression line. Each axis is on a logarithmic scale. Oval shapes indicate a comparison involving the wild barley accession OHU602. See text for additional details.Click here for fileTable S1. All individual and consensus maps, including SxM bin markers.Click here for fileFigure S2. Consensus 2943 SNP genetic linkage map.Click here for fileTable S2. All data from MxB, OWB and SxM mapping populations.Click here for fileFigure S3. Complete consensus directed acyclic graphs for barley chromosomes 1H.Click here for fileFigure S4. Complete consensus directed acyclic graphs for barley chromosomes 2H.Click here for fileFigure S5. Complete consensus directed acyclic graphs for barley chromosomes 3H.Click here for fileFigure S6. Complete consensus directed acyclic graphs for barley chromosomes 4H.Click here for fileFigure S7. Complete consensus directed acyclic graphs for barley chromosomes 5H.Click here for fileFigure S8. Complete consensus directed acyclic graphs for barley chromosomes 6H.Click here for fileFigure S9. Complete consensus directed acyclic graphs for barley chromosomes 7H.Click here for fileTable S3. Synonymous marker names.Click here for fileTable S4. All marker consensus map coordinates, names, source types, BLASTs, probe sets, sequences.Click here for fileTable S5. Minor allele frequencies for each SNP on BOPA1 and BOPA2.Click here for fileTable S6. POPA1 SNPs.Click here for fileTable S7. POPA2 SNPs.Click here for fileTable S8. POPA3 SNPs.Click here for fileTable S9. BOPA1 SNPs.Click here for fileTable S10. BOPA2 SNPs.Click here for file


HLA-DRB region.Multivariate techniques are an important area of investigation for studying contributions of multiple genetic variants to disease onset and pathology. We analyzed the Genetic Analysis Workshop 16 North American Rheumatoid Arthritis Consortium (NARAC) data using a principal-components analysis (PCA) with an orthoblique rotation to identify specific subsets of single-nucleotide polymorphisms (SNP) in the major histocompatibility complex (MHC) region associated with rheumatoid arthritis (RA) and rheumatoid factor IgM (RFUW), and compared this method with a traditional PC approach. Using the orthoblique PC-based clustering method, we identified new clusters of SNPs across the MHC region associated with RA and RFUW, and replicated known SNP cluster associations with RA, such as those in the  Testing a candidate gene or region for association with phenotypes typically involves testing multiple single-nucleotide polymorphisms (SNPs). This necessarily introduces the issue of multiple test corrections, which reduce power in order to control the type 1 error rate. Therefore, development of multivariate methods to identify causal loci and to reduce the burden of multiple testing is an area of ongoing investigation for complex diseases. Several multivariate techniques have been used to examine whether multiple SNPs are associated with disease or quantitative traits . HoweverGauderman et al. introduced a principal-components method to assess whether multiple variants within a candidate gene are associated with disease . PrincipThe PC approach has been shown to have greater power than standard joint SNP or haplotype-based tests to detect association between multiple SNPs and disease, especially when the number of haplotypes is large . HoweverWe propose a PC-based clustering method as an alternative approach that reduces dimensionality of the data and maintains the power of a PC approach, but allows for unique identification of multiple SNPs in the region being tested. The algorithm uses an orthoblique rotation of PCs on genotype data to form distinct clusters, where each cluster is defined by a specific array of SNPs. A subset of clusters that explains a large proportion of the total locus variation is selected, such that those clusters can be tested for association with disease outcomes.The PC approach and our proposed oblique PC-based clustering method were applied to the analysis of rheumatoid arthritis (RA) data from Genetic Analysis Workshop 16 (GAW16) (Problem 1). We compare and contrast results from these two approaches and compare findings with previously published results for these data -8.n = 1,187 on 838 SNPs).Sample data included genome-wide association data from Affymetrix GeneChip 100 k Mapping Array containing 116,204 SNPs for RA and RA-related traits, such as rheumatoid factor IgM (RFUW) and anti-cyclic citrullinated peptide (anti-CCP), on 2,062 North American Rheumatoid Arthritis Consortium (NARAC) subjects. Of the 1,250 SNPs in the major histocompatibility complex (MHC) region on 6p21 spanning 3.2 Mb, we restricted analysis to individuals with RA status and complete genotype data . For these analyses, we excluded markers with minor allele frequencies (MAFs) < 0.01, and coded genotypes as 0, 1, or 2 according to the number of minor alleles. Log transforms were applied to quantitative trait data to approximate univariate normality. We performed PCA as described by Gauderman et al. . The subk SNPs initially grouped into a single cluster. Standard PCA is performed on the initial cluster, with an orthoblique rotation . While all SNPs are subdivided into a total of n clusters, the number of SNPs within each cluster varies, yielding cluster coefficients equal to zero for SNPs not included in the nth cluster. Given the cluster coefficients and individual genotype scores, cluster scores are computed for each individual. For comparison with traditional PC analysis, n was determined by the number of clusters that accounted for 80% of the total locus variation.where Cs) or cluster score (Cn), using the following logistic framework:For disease status, we analyzed PC scores  or n clusters  to assess significance, with s or n degrees of freedom (df). Given significant association under omnibus test of all PCs or PC-based clusters, 1-df Wald tests were used to test association between RA or RA-related trait with each PC or PC-based cluster conditional upon all PCs or clusters. Because PC and cluster scores are estimated from the correlation structure of the genotype data, it should be noted that p-values resulting from any association testing framework may not be completely accurate. A bootstrap or randomization procedure that includes computation of PC scores or cluster components would likely yield more accurate p-values. For the purposes of this paper, nominal p-values are reported. All analyses were performed using SAS (v.9.1).For continuous outcomes, we fit a multiple linear regression model with PC scores or cluster scores as covariates. Likelihood-ratio tests were used to contrast the null model (intercept only) to that with either Demographic data for 1,187 subjects  included in this analysis are shown in Table p = 4.8 × 10-85) and RFUW (p = 0.0159) (Table p = 0.62). Of the 53 PCs tested for association, 29 individual PCs were significantly associated with RA (p ≤ 0.05) and 8 were significantly associated with RFUW (p ≤ 0.05); 5 PCs associated with both RA and RFUW. Among all PCs significantly associated with either RA or RFUW, the eigenvector coefficients ranged from -0.1480 to 0.1453. Therefore, this analysis allows us to conclude that the MHC region is associated with both RA and RFUW, but does not permit us to distinguish the relative contribution of each of the 822 SNPs to this association.Of the 838 SNPs in the MHC region, nine were monomorphic and seven had MAFs < 0.01. PC analysis yielded 53 PCs that accounted for 80% of the variance and were used for association testing. The MHC region was significantly associated with RA status (9) Table . We founp = 1.4 × 10-74) and RFUW (p = 5.7 × 10-7) (Table p = 0.21). Twenty-four SNP clusters were associated with RA (p ≤ 0.05) and 36 SNP clusters showed association with RFUW (p ≤ 0.05); 2 clusters were common to both outcomes.Our PC-based clustering algorithm identified 188 clusters that accounted for 80% of the variance and were used to test for association. Cluster size ranged from 1 to 14 SNPs. Using likelihood-ratio tests, the PC-based cluster method also found significant association between the MHC region and RA (7) Table . SimilarHLA-C and HLA-B. The squared correlation coefficients between each SNP and its assigned cluster (RO2), shown in Table N2). Additionally, low values for the ratio of one minus each of these correlations indicates relatively stable SNP-cluster assignment. The SNPs in Cluster 23 primarily span ~295-kb region of MHC class II bounded by HLA-B and HLA-DQA1. The majority of these SNPs cluster around the HLA-DRA, HLA-DRB5, and HLA-DRB1 loci, are highly correlated with each other, and show stable SNP-cluster assignment.The two SNP clusters most significantly associated with RA are shown in Table HLA-A and HLA-C, while SNPs in Cluster 5 span a 74-kb region between HLA-C and HLA-B. Because the SNPs in Cluster 2 reside proximally upstream of HLA-C and those in Cluster 5 immediately downstream of the gene, it is likely that both clusters jointly capture the variation in the HLA-C locus. The SNPs in Cluster 20 are located in the MHC class II region. Interestingly, three of the nine SNPs in Cluster 20 are located within HLA-DQB2, and the remaining six are situated in regions directly flanking HLA-DQB2, implying that Cluster 20 may represent the DQB2 locus. Also shown in Table The five SNP clusters most significantly associated with RFUW are shown in Table Traditionally, investigators examining gene regions or specific candidate genes might genotype hundreds of SNPs, possibly perform tag SNP selection, and test each SNP for association with disease or disease-related traits. Unfortunately, this approach necessitates multiple test correction, resulting in a significant reduction in power. PC analysis has been suggested as an exploratory approach that parses the information contained in a large number of correlated SNPs into a smaller number of orthogonal PCs that can be analyzed for association instead of individual SNPs ,4. A sigHLA-C and HLA-B, Cluster 23 comprises SNPs surrounding the HLA-DRA, HLA-DRB5, and HLA-DRB1 loci. In fact, rs3099844 and rs2857595 found in Cluster 1 were previously identified by Lee et al. [While traditional PC analysis makes it possible to analyze only the subset of PCs that represent most of the variation in a candidate region, PCs still represent linear combinations of all SNPs in the data set, which makes interpretation of significant PCs difficult. Upon inspection of the 29 PCs from the full model found to be significantly associated with RA status, we found the 822 eigenvector loadings on these PCs to range from -0.148 to 0.145, with most hovering close to 0. Thus, we were only able to infer from PC analysis that variation in the MHC region, as represented by these 822 SNPs, is strongly associated with RA risk. Additional interpretation of the specific SNP(s) driving significant associations between PCs and phenotypes can only be achieved by testing all 822 SNPs individually for association. In contrast, the PC-based clustering algorithm we employed reduced 822 SNPs to 188 discernable SNP clusters that also accounted for 80% of the regional variation. The clusters, which are subsets of the 822 SNPs analyzed, allow unique identification of those SNPs that may contribute to the evidence for association. For example, of the 24 SNP clusters associated with RA status, Cluster 1 and Cluster 23 were found to be the most significant. Cluster 1 represents a distinct set of SNPs covering ~883 kb of the 3.2-Mb region examined, while Cluster 24 covers a non-overlapping region of ~295 kb. While Cluster 1 represents SNPs flanking HLA-A and HLA-C, with Clusters 2 and 5 capturing the specific variation in and around HLA-C. Interestingly, Yen et al. demonstrated that HLA-C alleles may modulate the pattern of RA progression [HLA-DQB2. Previous examination of genes in the MHC class II region, conditional on the HLA-DRB loci, has shown the HLA-DQB2 locus to have a vital role in RA [The clustering algorithm also identified 36 SNP clusters found to be associated with variation in RFUW among RA cases. The most significant associations included Clusters 2, 5, 20, 24, and 183. Clusters 2, 5, 24, and 183 are composed of SNPs located in the chromosomal region between gression . Moreovele in RA ,12. As Rle in RA , further1 and PC2 are computed from a distinct set of SNPs that have been assigned to a given cluster, such that the first PC of one cluster may be correlated with the first PC of another cluster. Thus, although each SNP is assigned to the cluster with which it has the highest squared correlation, all SNPs share some degree of correlation with the other clusters they were not assigned to. This underlying correlation among clusters may be indicative of the correlation pattern among SNPs, although not necessarily haplotype blocks, and thus better reflect the true relationship of the variants within the MHC candidate region, but may also result in slightly reduced power to detect association.While our PC-based clustering method offers the interpretability a traditional PC approach lacks, there are other issues to be considered. First, we required more clusters than PCs to satisfy the 80% explained-variance threshold, which increased the degrees of freedom utilized for the omnibus test of association. The additional degrees of freedom usually results in reduced power to detect global association compared to the traditional PC approach. This may be due to the fact that while PCs are orthogonal, or independent, cluster components formed by the clustering algorithm are oblique. At each iteration, PCBoth traditional PC and PC-based clustering methods indicate the MHC gene region is significantly associated with RA and RFUW. However, traditional PCA is unable to highlight which SNPs contributed to this association. In contrast, the PC-based clustering method maintains many of the virtues of the traditional PC approach, but has the advantage of isolating the SNP(s) contributing to evidence for association. Therefore, the PC-based clustering method may be a better approach to testing multiple variant associations with phenotypes of interest.Anti-CCP: Anti-cyclic citrullinated peptide; GAW16: Genetic Analysis Workshop 16; MAF: Minor allele frequency; MHC: Major histocompatability complex; NARAC: North American Rheumatoid Arthritis Consortium; PCA: Principal-components analysis; RA: Rheumatoid arthritis; RFUW: Rheumatoid factor IgM; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.MHB conceived of the study, analyzed the data, and drafted the manuscript. RMW advised on the manuscript. Both authors read and approved the final manuscript.


Gossypium hirsutum L. or upland cotton, and G. barbadense L., extra long-staple fiber Pima or Egyptian cotton. These two cultivated species are among five allotetraploid species presumably derived monophyletically between G. arboreum and G. raimondii. Genomic-based approaches have been hindered by the limited variation within species. Yet, population-based methods are being used for genome-wide introgression of novel alleles from G. mustelinum and G. tomentosum into G. hirsutum using combinations of backcrossing, selfing, and inter-mating. Recombinant inbred line populations between genetics standards TM-1, (G. hirsutum) × 3-79 (G. barbadense) have been developed to allow high-density genetic mapping of traits.Cultivated cotton is an annual fiber crop derived mainly from two perennial species, G. hirsutum and G. barbadense were genotyped across a standard panel of 24 lines, 16 of which are elite cotton breeding lines and 8 mapping parents of populations from six cotton species. Over 200 loci were genetically mapped in a core mapping population derived from TM-1 and 3-79 and in G. hirsutum breeding germplasm.This paper describes a strategy to efficiently characterize genomic variation (SNPs and indels) within and among cotton species. Over 1000 SNPs from 270 loci and 279 indels from 92 loci segregating in In this research, SNP and indel diversity is characterized for 270 single-copy polymorphic loci in cotton. A strategy for SNP discovery is defined to pre-screen loci for copy number and polymorphism. Our data indicate that the A and D genomes in both diploid and tetraploid cotton remain distinct from each such that paralogs can be distinguished. This research provides mapped DNA markers for intra-specific crosses and introgression of exotic germplasm in cotton. Gossypium hirsutum L. or upland cotton, and G. barbadense L., extra long-staple fiber Pima or Egyptian cotton. These two cultivated species are among five allotetraploid species presumably derived monophyletically from a single polyploidization event that occurred 1-2 MYA between ancestors most closely represented today by G. arboreum (A2 genome) and G. raimondii (D5 genome). Breeding of cotton is primarily focused on intra-specific crosses to introduce transgenic traits and to improve baseline lint yield and quality 1) and G. barbadense ([AD]2), c) the A and D subgenomes of contemporary AD genomes are distinct from each other; and d) the A subgenomes of G. hirsutum and G. barbadense are moderately divergent. The above conclusions are emphasized in the present study first by amplifying and sequencing single-copy loci and being able to assay the same sequences using an independent assay, Illumina Golden Gate. Because our template sequences were primarily drawn from G. arboreum and selected for both PCR function and single-locus attributes using SSCP, it is quite possible that the populations of selected loci in the two subgenomes would have been differentially affected. The potential for bias across the two subgenomes precludes using these amplicon sequence or genotyping data to infer relative diversity of the A versus D subgenomes of G. hirsutum and G. barbadense. Our findings are not discordant with the theory that these two extant tetraploids originated from a common allopolyploid ancestor and have evolved independently long enough to create the detected variation and a significant degree of diploidization.Although markers were evenly distributed within linkage groups, there was a disproportionate number of markers on linkage groups in the A subgenome  vs. the D subgenome  at the nucleotide level. G. barbadense germplasm sampled was 1.8 times as diverse  as G. hirsutum even though less than one fifth as many G. barbadense lines were sampled. The A and D genomes of tetraploid cotton are significantly divergent such that individual SNP loci can be assayed with high specificity. Although the above strategy for SNP identification and validation was fruitful, new high-throughput sequencing technologies such as 454  Illumina Genome Analyzer  and SOLiD  offer an opportunity to complement the current strategy to rapidly uncover nucleotide diversity at the whole genome level in multiple breeding lines. The present study demonstrates that sufficient SNP diversity exists in tetraploid cotton populations for genetic and breeding studies and it can be efficiently assayed.SNP diversity depends on population size sampled and the natural evolutionary and directed selection within those populations. The present study indicates that a moderate amount of variation associated with genes exists in breeding germplasm  lines were selected. An additional landrace, TX2094, was added to represent G. hirsutum race yucatanense [The 24 cotton lines and species screened for this study were chosen based on an expansion of the Cotton Marker Database Table . The panatanense . All accG. arboreum ESTs [G. arboreum and G. raimondii ESTs were trimmed and assembled into 7,666 contigs with SNPs and deletions between these genomes. To evaluate the usefulness of cotton genome-specific primers, 48 primer pairs (GSP) were tested with a deletion in the forward primer and 48 with a SNP at the 3' end of the forward primer. A Conserved Orthologous Set for cotton of 2,390 contigs based on 27,878 G. arboreum, 35,509 G. raimondii and 14,354 G. hirsutum ESTs using the procedures described in Van Deynze et al  was alsG. arboreum and G. hirsutum breeding lines) were amplified and tested on agarose gels for amplification  in forward and reverse directions using a ABI 3730 . Trace files were trimmed with Phred options -trim_cutoff 0.02" which translates to Phred 17 score . AssemblG. hirsutum), was used as reference to confirm that amplicons of single-copy loci were represented. Amplicons with putative SNPs were then amplified in the individual 24 lines  in G. hirsutum germplasm sequenced (Table 1 as per manufacturer protocols at the University of California, Genome and Biosciences Facility, Davis, CA. Data were extracted and exported and mapped using JoinMap 4.0 [A set of 384 SNPs was selected to develop an Illumina Golden Gateed Table . SNPs wenMap 4.0  with thenMap 4.0 . A LOD sBAC: bacterial artificial chromosome; COS: conserved orthologous set; EST: expressed sequence tag; Indel: insertion/deletion; SNP: single nucleotide polymorphismAV conceived, supervised and wrote the manuscript; KS carried out the research, contributed to methods and research and edited the paper; MK carried out the research, contributed to methods and research; AK helped conceive, analyse and edit the paper; TW aided in initiating the research, the EST database and edited the paper; RC inititated the research and developed the germplasm panel; JY contributed the mapping population, analysed the mapping data and edited the paper; RK contributed to the mapping population and analaysis; DS conceived the research, developed the germplasm panel, analyzed the data and edited the paper.All authors have read and contributed to the writing of the manuscript.Supplementary tables. Table S1. SNPs and associated sequence information. This worksheet describes the SNPs and the sequences they are derived from. Table S2. Indels and associated sequence information. This worksheet describes the SNPs and the sequences they are derived from. Table S3. Mapping data for TM-1 × 3-79 RIL population. This worksheet contains the data used for the mapping the SNPs. Table S4. Amplification and sequencing conditions used in this research. This worksheet describes the PCR protocol used to amplify and sequence genomic DNA.Click here for file


The most common application for the next-generation sequencing technologies is resequencing, where short reads from the genome of an individual are aligned to a reference genome sequence for the same species. These mappings can then be used to identify genetic differences among individuals in a population, and perhaps ultimately to explain phenotypic variation. Many algorithms capable of aligning short reads to the reference, and determining differences between them have been reported. Much less has been reported on how to use these technologies to determine genetic differences among individuals of a species for which a reference sequence is not available, which drastically limits the number of species that can easily benefit from these new technologies.We describe a computational pipeline, called DIAL (De novo Identification of Alleles), for identifying single-base substitutions between two closely related genomes without the help of a reference genome. The method works even when the depth of coverage is insufficient for de novo assembly, and it can be extended to determine small insertions/deletions. We evaluate the software's effectiveness using published Roche/454 sequence data from the genome of Dr. James Watson (to detect heterozygous positions) and recent Illumina data from orangutan, in each case comparing our results to those from computational analysis that uses a reference genome assembly. We also illustrate the use of DIAL to identify nucleotide differences among transcriptome sequences.http://www.bx.psu.edu/miller_lab/.DIAL can be used for identification of nucleotide differences in species for which no reference sequence is available. Our main motivation is to use this tool to survey the genetic diversity of endangered species as the identified sequence differences can be used to design genotyping arrays to assist in the species' management. The DIAL source code is freely available at  Next-generation sequencing technologies have revolutionized genomics, leading to a tremendous increase in the amount of available sequence data, while bringing down the cost per base. However, de novo assembling of mammalian genomes using these short reads has met with limited success , despiteMuch less has been published about identifying genomic differences in species where an assembled reference sequence is lacking. We describe our computational pipeline, called DIAL, for deducing genetic differences within a target species without the help of a reference genome. The method works even when the depth of coverage is insufficient for de novo assembly. It is also designed to work with sequences from multiple individuals, a situation that can pose difficulties for de novo assembly. We evaluate the performance of DIAL on several extensive datasets.The directions taken in this study were shaped by our immediate needs, which were to identify a pre-determined number of single-base differences to design a custom genotyping array using only as much sequence data as necessary. With current array technology, at most a few thousand differences can be assayed at an affordable cost (a few tens of thousands of dollars), so our main interest here lies in knowing how to identify that many differences with a low false-positive rate, and in understanding the main sources of error at low levels of sequence coverage.DIAL is implemented as a pipeline where the output from one stage serves as the input to the next stage. SNPs (Single Nucleotide Polymorphisms) are called every time a new set of sequences is added. A set can be the yield from a lane of Illumina sequence, a quadrant of 454 sequence, or any subset as decided by the user. Once the number of called SNPs is adequate for the desired application, we can stop sequencing. The general flow of the pipeline can be divided into several steps, as summarized in Figure Duplicate reads can arise from PCR duplicates, sequencing artifacts such as poly-A and poly-N reads, noise in cluster detection, and from genomic DNA shearing at the same location in different molecules. These clones can lead to incorrect coverage assessments and erroneous SNPs. We collapse all such clones of a read into a single sequence by using a simple hash function. We identify reads with the same MD5 checksums  and if tK-mers) by using lower-case letters for nucleotides, so that alignment anchors are not chosen in those regions. We achieve this in three sequential passes, as described below.We then feed the non-duplicate reads to another module, which is based on WindowMasker .  WindowK, the size of the K-mers to be used, as the largest integer such that (N/4K) > 5, where N is the total sequence yield from the current input set. We calculate and store the frequency of each K-mer, freq(S), in a splayhash for efficient access, then sort the K-mer frequencies and store the results in a temporary array F. If C is the total number of unique K-mers from the sequences in the set, then cutoffs Thigh, Tthreshold, Textend and Tlow are calculated as F [int(0.998 * C)] + 1, F[int(0.995 * C)] + 1, F[int(0.99 * C)] + 1 and F[int(0.9 * C)] + 1, respectively. We compute a score for each K-mer to mitigate the effects of a few K-mers with very high frequencies and to avoid keeping track of counts for many infrequently occurring K-mers. The score is calculated as:1. We use the first pass to calculate appropriate parameters and cutoff values to be used by the module. We define K-mers, and we calculate the score of each window by averaging the scores of the K-mers in it. Adjacent windows share 4 K-mers, and we mask the contents of a window if it has a score greater than Tthreshold. We also mask the interval between two masked windows, if the score of each of the windows in it exceeds Textend.2. We use the second linear scan to divide each sequence into a set of overlapping windows. Each window contains 5 K-mers within a read, and if any of the frequencies exceed Textend, then we mask the whole read. This is particularly effective in reducing the time required to calculate overlaps between read sequences. This pass also unmasks any masked segment less than 40 bp long; the motive here is to unmask the high-frequency K-mers in non-repeat sequences.3. The last pass of the masker looks for local repeats. We calculate the frequency distribution of the We align the masked sequences from the current input set against all of the masked reads from the sets processed earlier, using a "seed and extend" strategy so that most of the spurious comparisons are discarded at the outset. We use the LASTZ aligner , which iWe modified this score set for use with the shorter 75 bp Illumina reads; the gap open and gap extend penalties are both set to 10, while the match score is 1 and the mismatch score is 10. We find the endpoints of the alignment when the score falls 20 points below the maximum score observed on that path. Two reads are called as "aligned" if they have an overlapping segment of 50 bp with an identity of 96% or greater.For every read in the input, we store its header and sequence along with the names and observed differences of all the other reads that align to it, in a data structure we term as a "cluster". These clusters are cumulative over the sequencing runs. Each new sequence set is initialized with its own set of clusters, in one-to-one correspondence with the reads. The set of masked sequences is then aligned to all of the masked reads from the previous sets, growing the clusters corresponding to each of two reads if the two reads align. We mark the clusters as "candidate repeats" if they exceed thresholds for the number of members  or the number of observed differences. This is done to prevent a combinatorial explosion that would occur if we stored all the details of all the clusters. We stop adding reads to the cluster once it is marked as a candidate repeat, even though its original corresponding read can still be added to other clusters if it aligns with their sequences. The only exception to this filter is in the case of transcriptome datasets, as transcripts have variable abundance depending on their expression in the sample.C, the coverage k at each base of the genome approximately follows the Poisson distributionOne of the inputs to DIAL is the expected length of the target genome. This information is used to calculate the threshold for identifying a pileup of reads. We normally set it to the expected size of the closest available genome sequence if the expected size of the target genome is not known. If the average depth-coverage is Cthreshold that equals the 99.5th percentile of the cumulative distribution function for the coverage depth. When a sequence set is added and the number of members in a cluster exceeds Cthreshold, then the cluster is marked as a candidate repeat. A cluster is also marked as a candidate repeat if the number of differences in it exceeds a limit Dthreshold. For the reported datasets, this threshold was set to 10.DIAL calculates a value We can call SNPs after the overlaps from two input sets are calculated. The determination takes place in several steps:1. First, we filter the clusters to consider only those which have substitution differences supported by at least two reads for each allele (variant nucleotide at the location of a SNP). Such clusters with more than 3 substitution differences, or with two or more such differences that are within 50 bp of each other, are ignored. If the input to the pipeline consisted of sequences from more than one individual, then we also filter to keep only clusters that have at least two reads from each of at least two individuals. These filters are restrictive and increase the false negative rate of SNP discovery. However we find that they also help in reducing the false positive rate, especially at low coverage. This step also ignores clusters which were marked as "candidate repeats"; they are not considered in the SNP calling stage.2. We then iterate through the filtered clusters and select those containing only member reads that have not been observed in any of the clusters selected before it. This ensures that a read appears in at most one SNP call.3. We also ignore clusters whose sequences align to any of the completely masked sequences in the earlier steps. This includes reads that have local repeats as determined earlier and reads that had less than 40 unmasked bp. For this we use the same LASTZ parameters and score sets as we did for calculating the overlaps between the reads. This step ameliorates any local frequency bias in a particular input set. We also remove reads whose clusters have been marked as candidate repeats from the member lists of all other clusters.4. Next we create "micro-assemblies", i.e., de novo assemblies for each selected cluster. For the 454 technology we extract the reads corresponding to the members of a cluster from the original SFF file to create an input file for Newbler, which is part of the software package distributed by Roche/454 with their sequencing machines. We use Velvet  for asse5. We then read the ACE file output and deduce positions in the assembly where two different alleles are observed with support from at least two reads for each allele.6. A final, optional step filters the candidate locations to find SNPs which are suitable for genotyping. The filters involve a combination of factors, including the number of variants and reads found in the assembled contig, the distance of the candidate SNP from the ends of the contig and the quality scores for the reads. Another optional filter disallows SNPs in homopolymer regions. In the reported datasets (generated by 454 and Illumina) the following requirements were imposed:(a) Only one SNP is found in a micro-assembled sequence.(b) The SNP has at least 50 bp on both sides in the assembled sequence for the 454 datasets, and at least 40 bp on both sides for the Illumina reads.(c) The minimum quality score for the reads at the site is 20. Reads supporting an allele with a lower quality score are not counted.(d) The SNP is not part of a homopolymer of length greater than 4 bp in a window of length 13 bp centered on the SNP.Our strategy is to observe the fragments of DNA sequence data produced by a sequencing machine and identify pairs of fragments that overlap, but differ in one or several of the overlapped positions. When the total number of identified variant positions reaches a desired level (depending on the application at hand), sequencing can stop. If the fragments are all from the genome of one individual, then we are looking for heterozygous positions , whereas with sequence fragments from more than one individual, we can also find positions where every individual is homozygous , but at least two individuals differ. The differences we seek are typically single-nucleotide substitutions that occur within a species or population of interest. We call each position at which different nucleotides appear among the individuals being sequenced a "SNP", short for Single Nucleotide Polymorphism. Each variant nucleotide that appears at a SNP we call an "allele"; for this project we make no requirements concerning the frequency of an allele in the population, other than that it occurs in our samples, and we assume there are precisely two alleles at each SNP. Our approach can also detect insertions/deletions of one or several nucleotides, but these are of less interest to us because high-throughput genotyping methods  focus on nucleotide substitutions. Here we describe two difficulties with our strategy, explain how we address them, and analyze their effects on our results.λ-fold genome coverage in random fragments of an individual genome , what fraction of the genome positions will be contained in two or more fragments from each parent? Or similarly when looking for homozygous differences between two individuals that are each sequenced to depth λ/2? In theory, the answer is z2, where z = 1 - eλ/2-(1 + λ/2). (See the next section for a derivation.) For instance, given 1-fold coverage of an individual, the expected genome fraction where heterozygous positions can be identified is 0.0081. Thus, if a genome has 1 million heterozygous positions, we expect around 8100 of them to be detectable with 1× sequence coverage if no other issues are considered. Under the same conditions, 2× coverage should put 70,000 SNPs within reach of our method. For a genome with fewer heterozygous positions, our potential yield is proportionately less, e.g., 810 SNPs from 1× coverage of a genome with 100,000 heterozygous positions. Typically, we will not know the SNP density in the sequenced individuals when we start a project; we are more likely to know the number of SNPs that can be accommodated on a genotyping array that is within our budget.The first difficulty is dealing with sequencing errors, which can masquerade as SNPs. The actual frequency of SNPs depends on the genetic diversity of the species being sequenced. For instance, the frequency of SNPs between two humans averages around one per 1,000 positions, but in some species SNPs are much less frequent. In any case, the rate of errors in individual fragments produced by the sequencing instrument (regardless of the brand) is probably much higher than the rate of SNPs. This means that when we see a nucleotide difference between two overlapping reads, the odds are that it is a sequencing error rather than a true SNP. A cornerstone of our approach is to require that each allele of a putative SNP be observed at least twice, which we believe is significantly more likely to indicate an actual difference than a repeated sequencing error.  This requires the genomic position to appear in at least two fragments with one allele and two fragments with the other, and raises the following question: Given The second difficulty is that overlapping reads differing at one position may not be from the same part of the genome; instead they may be from the two copies of a recent duplication that have accumulated a mutational difference. In that case, a detected difference is of no use for a population-genetic analysis because every individual genome of that species may contain both variants and thus falsely appear to be heterozygous at a single location in a genotyping assay. Hence we consider a SNP prediction to be in error if it turns out to be caused by our failure to distinguish copies of a repetitive region. Our main defense against this possibility is to limit the number of sequenced fragments that contain a given SNP. If a genomic interval has hundreds of nearly identical copies throughout the genome , then even with only 0.5× coverage we will see a pile-up of overlapping reads, which can be easily rejected. The problem comes from regions that have only a few nearly identical copies in the genome. To give a sense of the frequency of these low-copy repeats in one particular species, consider NCBI Build 36 of the human reference genome. There, 2.16% of the bases are in a repeated region (defined here as at least 97% identity over 300 bases) with precisely 2 copies; for 3 or 4 copies the fractions are 0.72% and 0.43%, respectively. It is not obvious how to extrapolate that estimation to the parts of the human genome not represented in the reference assembly (but which may be represented in next-generation sequenced data), or to another species. However, success of our method clearly requires that the fraction of the genome contained in such high-identity, low-copy-number repeated regions be very small, particularly since the fraction of nucleotides in a duplicated region that differ among the copies is likely to exceed the fraction of positions in single-copy regions that are SNPs.x, on the number of reads that appear to contain a SNP. For identifying heterozygous SNPs in a single genome, the fraction that can in theory be identified when requiring two observations of each allele and at most x overlapping reads, given λ-fold coverage of the genome, is:These two difficulties lead to competing constraints on our strategy; we need to thread our way between not having enough overlapping reads and having too many. Moreover, this threading must be done in the dark, because we typically will not know all of the critical parameters, including the number of actual SNPs among the individuals being sequenced and the frequency of recent genomic repeats in the target species. However, we can obtain guidance by using theoretical models that we fit to observed data. For instance, one decision that needs to be made is the upper limit, call it P (2λ).  If we assume there are 800,000 true SNPs in single-copy regions, 30,000 differences in duplicated regions, and 2.3 copies of a duplicated region on average, then Table For intervals that appear precisely twice in the genome , the fraction of differences that will be mis-identified as SNPs is approximately x = 5 overlapping reads makes little difference, while at 2.5× coverage, setting x = 6 produces substantially more correct SNP calls, but with a substantially higher false-positive rate.  The specific values in the table depend heavily on the assumed numbers of SNPs and of differences within low-copy repeats, but similar trends are observed over a wide range of conditions and provide insight into the problem at hand.This model shows the following general trends. The main trend in the percentages is a difference among the rows. Namely, at 0.5× coverage, the number of incorrect calls caused by low-copy repeats is roughly 60% of the number of correct calls, but as the coverage increases, that percentage drops significantly, reaching around 10% at 2.5× coverage. A less dramatic but still noteworthy trend is seen within rows: at 0.5× coverage, allowing more than Our goal is to perform only as much sequencing as is needed to produce a desired number of SNP predictions with an acceptable false-positive rate. In practice, we impose additional restrictions on our SNP predictions, as described above in the Implementation section, but the two main conditions are that we see each putative allele of a SNP at least twice, and that there not be too many reads that appear to overlap the SNP. The additional conditions, such as high quality scores and lack of other SNP predictions in the assembled region around the SNP, are intended to decrease the fraction of erroneous SNP calls and to provide predictions that are appropriate as input for certain experimental protocols (such as PCR amplification of the position). However, the extra restrictions unavoidably reduce the number of correct SNP calls.L, the read length by l (≪ L), and the total number of reads by n; the read coverage per base pair can then be calculated as λ = x times is therefore f(λ) = 1 - eλ-(1 + λ).Assume that each position in the genome has equal probability to be covered by a read, and that the reads are independently generated. With small read lengths and sparse SNP distributions over the genome, we ignore the correlation of read coverage for adjacent positions. As a result, the distribution of the number of reads covering each position in the genome can be well approximated by a Poisson distribution, whose mean is the average read coverage per base pair. Denote the genome size by λ. For simplicity, we further assume that each putative SNP is biallelic, and we denote the proportion of each allele over all individuals by p and q, where p + q = 1. For example, if only one individual is sequenced, a SNP must be heterozygous with p = q = 0.5. We can calculate SNP detection probabilities according to the first rule as f (λp)f (λq). As a special case, if we sequence two individuals, we have the following results:Our first rule for calling a SNP is that each allele must be observed at least twice. Assume that we sequenced one or more individuals from the same species or population, with equal read coverage for each individual and the total coverage 1. If both individuals are heterozygotes at the SNP, or they are homozygotes but with different alleles, the probability of both alleles being sequenced at least twice is 2. If one individual is a heterozygote and the other is a homozygote at the SNP, the probability of both alleles being sequenced at least twice is lt denote the total length of all distinct segments that have precisely t copies in the genome, e.g., l1 denotes the total length of segments unique in the genome, and l2 denotes the total length of (one copy of) all segments that appear twice. The genome size is then given by λt. The larger t is, the higher the chance that there has been a nucleotide substitution in one copy, which could be mistaken by DIAL for a putative "SNP". The term "SNP" is quoted because it may correspond to mutations occurring in different copies of a duplicated region. Our goal is to call SNPs within unique regions in the genome and discard all "SNPs" in duplicated regions . Without a reference genome, however, we do not know whether a segment is duplicated or not. We therefore select an upper bound for the observed read coverage per base pair, beyond which the corresponding "SNP" will be treated as a duplication difference and discarded. The choice of the upper bound for read coverage can be used to strike a balance between sensitivity and specificity. Suppose that we only call a SNP if (1) both alleles are observed at least twice; and (2) the number of reads covering the "SNP" is at most x (≥4). Within unique regions , the calling probability of each SNP can be calculated as:Let λ replaced by λt, i.e., Pr = P (λt).The calling probability of a "SNP" in duplicated regions can be similarly calculated with p and q in unique regions, we may let p = q = 0.5, which is exact if only one individual is sequenced. For regions with t copies, the frequency of duplication differences is typically much higher than that of SNPs, and thus when t = 2, we will have p = q = 0.5 most of the time. When t > 2, without additional information, we may assume a star tree topology for the evolution of duplicated regions, and thus we can let p = 1 - q = To assign the values of allele proportions Given the above equation, we can define and calculate the sensitivity of our SNP calling  as:t-copy region is pt, and thus the expected number called in all t-copy regions is N(t) = ptltP(λt). The specificity of our SNP calling can therefore be defined and calculated as:Assume that the average density of "SNPs" in a Among all of the calls, the proportion of correct SNPs (proportion of true positives) is approximately:The genome of Dr. James Watson was the first full genome to be sequenced using a next-generation sequencing technology . SeveralWe proceeded as follows. Of the 106.5 million high-quality reads that were generated for the Watson project, 76.6 million are available in the Short Read Archive at NCBI. The available reads total 18,952,140,632 bases and provide approximately 6.15-fold coverage of the genome. We downloaded the corresponding SFF files . DIAL processed this data in about 350 CPU hours on a workstation. Starting at 0.4× coverage, and then at regular increments, DIAL predicted heterozygous positions. Along with each SNP prediction, DIAL reports an interval of assembled sequence that contains the putative variant position. Our subsequent analysis focused on high-confidence SNP calls where no other SNPs were predicted nearby; this was done because our main intended application for DIAL is to make predictions used for subsequent experimental studies, which frequently require variant-free sequences on both sides, e.g., for PCR primers. However, it also helps to eliminate low-copy-number duplicated regions that split more than a few million years ago, because older duplicates will probably have accumulated multiple nucleotide substitutions. Figures We judged that a heterozygous SNP predicted by our method is correct if (1) the accompanying assembly of flanking sequence aligned to precisely one position in the human reference genome (NCBI Build 36) at appropriately high thresholds (98% identity over 200 bp) and (2) it agreed with any of the Watson heterozygosity calls that we downloaded from three sources . Figures We also considered the number of false negatives, i.e., heterozygous positions in the Watson genome that seem to meet the conditions we impose but are not identified by DIAL. We estimate that there are about 1 million heterozygous positions that do not lie in a repetitive region or near another heterozygous position, but DIAL finds only 299,064 from 6.15× of sequence data. The reasons for this discrepancy include the difficulty of predicting heterozygous positions with only low-coverage data and the variety of additional conditions (see Implementation) that we require before calling a SNP.Pongo pygmaeus which contained 51,923,090 single reads to call heterozygous sites and evaluate our approach on Illumina reads. We adjusted the alignment parameters in the pipeline to make them more amenable to the short reads and were able to identify 5,389 high-confidence SNPs for this dataset. A consequence of the short read lengths is that the assembled contigs for the orangutan reads were about 90 bp, whereas the flanking regions for the Watson genome dataset were around 350 bp. As for the Watson dataset, we plot the number of SNPs called by DIAL and the SNP locations which could be verified in Figure Illumina sequencing technology generates short reads only 75 bp long, but the amount of data generated in a single run far exceeds that from the Roche/454 technology. We used a single run of 75 bp reads from a Bornean orangutan We had SNP calls from various outside sources for the Watson genome dataset; however no such calls exist for this one. Hence, we verified our calls against a mapping of reads on the orangutan draft assembly (WUSTL version Pongo_abelii-2.0), which we made using MAQ . An alleCompared to generating sequence data from an entire genome, sequencing transcribed DNA (cDNA derived from messenger RNA) has several potential advantages. For a typical mammalian genome, the total length of all gene transcripts is approximately 1% of the genome's length; this means roughly 30 million bases instead of 3 billion. Thus, much less sequence data is required to reach depth-4 coverage for a substantial fraction of the target. And for some applications, the much higher fraction of functional sites is useful. However, there are disadvantages as well. While blood is a typical source of DNA for whole-genome sequencing, the number of genes transcribed in blood cells is rather low. If possible, it may be preferable to sequence DNA from another tissue or combination of tissues. Another complication is that the relative amounts of transcript DNA vary widely among genes, which lowers the efficiency of transcript sequencing (because of redundant data from highly expressed genes), and forces us to remove our constraint on the maximum number of fragments that overlap a SNP.Odocoileus hemionus). Despite the reduced number of genes transcribed in lymph nodes compared to certain other tissues , we identified 269 high-confidence SNPs, which is sufficient to populate a small genotyping array with SNPs having a good chance of relating to differences in immune-system genes with phenotypic effects. Note that a similar amount of whole-genome DNA would provide only about 0.03-fold coverage, which according to our results discussed above is expected to reveal less than 1 SNP, assuming a total of about 1 million nucleotide differences in the sampled mule deer.To experiment with this approach, we applied DIAL to 93 Mbp  of Roche/454 transcript data obtained from a pooled  lymph-node cDNA library from mule deer . This paper provides a theoretical model of the numbers of correct and false positive calls, as well as experimental data that suggest parameter values for that model. These will help researchers to estimate both the amount of DNA sequence data required to populate a custom-built SNP array and the rate of inevitable incorrect calls caused by low-copy segmental duplications. This information will be useful in practice to balance sequencing costs with SNP-call reliability as the relative costs of sequencing and genotyping fluctuate.The number of SNPs that one can afford to genotype is typically far less than the total number of common SNPs in the species of interest. Hence, it may be cost-effective to limit sequencing to DNA from specific parts of the genome, so that a given amount of sequence data will produce far deeper coverage. One approach, mentioned above, is to sequence gene transcripts, and thereby target protein-coding regions. Another strategy is to sequence "reduced representation libraries" -22, creaSarcophilus harrisii), the largest living carnivorous marsupial. The species is currently found in the wild only in the state of Tasmania, Australia, and is under threat of extinction by an aggressive transmittable cancer [Monodelphis domestica). The two lineages diverged about 100 million years ago, making it impossible to use the opossum sequence as a reference genome for mapping the devil reads. Using Roche/454 machines, we generated low-coverage sequence for two Tasmanian devils from geographically dispersed locations. DIAL then called SNPs, which were used to design a genotyping array. The results of that study will be presented in a separate report.We are applying this strategy to the Tasmanian devil (e cancer . The phy• Project Name: DIALhttp://www.bx.psu.edu/miller_lab/• Availability: • Operating system(s): Linux• Programming language(s): C/Python• Other requirements: Newbler, Velvet for assembling the flanking regions of the called SNPs• License: MIT License• Any restrictions to use by non-academics: NoneAR and WM designed the project. YZ performed the probabilistic analysis. AR designed and implemented the DIAL software. VMH and SCS founded the Tasmanian devil genome project, which motivated calling SNPs without a reference. AR and WM wrote the paper with input from the co-authors. All authors read and approved the final manuscript.


Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted. Rheumatoid arthritis (RA) is an autoimmune disease causing inflammation and soft-tissue swelling of mainly diarthrodial joints. The disease can lead to considerable loss of mobility due to pain and joint destruction. Over the past decade, an improved understanding of the pathophysiology of the disease has had a big impact on RA therapy, mainly through the use of so-called disease-modifying antirheumatic drugs .Complex human diseases such as RA have complicated genetic architectures . Single-We explored the predictive ability of RFs analysis to identify genetic associations with RA and their interactions with other genetic loci in the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide linkage data sets. Furthermore, we performed cluster analysis using the phenotypic data to identify clinically distinct subgroups of RA patients, which may represent a pathophysiologically heterogeneous patient population. Subsequently, such phenotypic subgroups were used to identify additional susceptibility genes.PTPN22, CTLA4, HAVCR1, IBD5, SLC22A4, IL3, IL4, SUMO4, MAP3K71P2, DLG5, CARD15, RUNX1, and MIF) being considered. The missing genotypes were estimated using the proximity calculated by RFs on the training subjects [The phenotype (NAPHENO) and candidate gene (CANDGENE.DAT) data were provided by the North American Rheumatoid Arthritis Consortium  and consIn this study, we generated two replicate data sets in which the subjects were independent within samples and were dependent across samples. This approach ignores dependencies between the data sets that in general lead to inflation of type I error in the replication set. However, we feel that this approach is justified to simplify the confirmatory analyses using the available data. The data set was split into two replication data sets by randomly selecting a case from each affected sib pair for one of the data sets and the remaining case contributing to the second data set, with the controls almost evenly divided into the two data sets. Data set 1 includes 398 cases and 427 controls, while data set 2 includes 387 cases and 428 controls. It should be noted that due to the relatedness between the replication and the analysis data sets, the confirmation from the second data set should be interpreted with caution.The data from the genome-wide linkage panel  were available for 1998 individuals from families with RA patient (NAILL01–NAILL25). For families with two or more siblings, one was randomly selected from each family for data set 1, and the second subject was then randomly selected from the remaining of samples for data set 2. The singletons were randomly divided into the two data sets. Each of the data sets consists of 740 unrelated subjects. The analysis of the replication data set should be considered exploratory.The RF method is not particularly efficient at imputing missing data for large number of SNPs. Therefore, we applied an extension of the expectation-maximization (EM) algorithm implemented in HelixTree  to impute the missing genotypes for the genome-wide linkage data set. In a simulation study, this method achieved outstanding performance of imputation accuracy above 95% with missing rate ranging from 1% to 10% .p < 0.05 were also analyzed in a multiple SNP model.The association between each candidate gene SNP and RA status was tested using all samples. Two methods were used for the candidate gene data set to adjust for the degree of relatedness among the affected siblings, a generalized estimating equations (GEE) method with likelihood ratio statistics  and Risch and Teng's statistic . Results2 test when all three genotypes were observed and the least frequent genotype observation was more than five. In situations in which these criteria were not met, Fisher's exact test was used to test the association. The p-values were then used to rank the significance of all SNPs.For the genome-wide data set, single-SNP associations with RA were assessed using a χth percentile for AgeOnset (≥50), TenderJtCt (≥13), SwollenJtCt (≥13), JAMScore (≥52), SeverityLH (≥5), and SeverityRH (≥5). DRB1_1 and DRB1_2 marker variables were combined and dichotomized on the basis of the presence or absence of RA-associated epitopes . Patient self-reported American Rheumatism Association (ARA) sub-scores and the overall ARA score were not included in the analysis. All other clinical measurements, except BMI (51% missing), were used in the analysis.An unsupervised clustering analysis was carried out on the RA cases using the detailed phenotypic data  to identify clinically distinct subgroups of RA patients. For the purpose of the analysis, the phenotypic variables were dichotomized using a threshold of the 75Clustering analysis was carried out using modified 'Jaccard' coefficient (proportion of non-zero pairs that are similar between subjects) as the distance measure . MultidiIn addition to the conventional association analysis methods that were used in the study to identify RA susceptibility loci, we also used RFs . The metAll of the 17 candidate gene SNPs and the 5742 SNPs from the genome-wide linkage panel were included in the RFs model. RFs were also applied using selected subsets of SNPs as predictor variables. These subsets were selected on the basis of their ranking on the importance measurement implemented in the RFs package. The SNPs that were highly ranked were selected for the RFs model.. The R package for Random Forests (randomForest 4.5.-15) was installed and utilized following the instructions.All statistical analyses were performed using the R statistical software package (version 2.1.0) from R Project p < 0.05 are summarized in Table PTPN22*rs2476601 and SUM04*rs237025 loci showed the strongest association to RA among the significant ones. In the multivariate model, which included all five associated SNPs, PTPN22 and CTLA4 were found to be independently associated (p < 0.05) with RA with a modest effect.The SNPs associated with RA status at PTPN22*rs2476601 and SUM04*rs237025 are consistently ranked the top two on the variable importance score in both data sets. Interestingly, both SNPs show strong associations in the univariate GEE analysis  indicated poor predictive ability in both data sets. This did not improve by using the three most important SNPs ranked by the RFs model (AUCs = 0.59 for both data sets). Furthermore, SNPs We investigated whether there are phenotypically distinct subgroups among RA patients. Such subgroups may represent etiologically or genetically distinct entities among RA patients, and therefore consideration of these subgroups in a genetic association study may lead to an increase in the power to identify susceptibility genes.p-value 0.01. These phenotypic clusters are correlated with a set of clinical measures, such as ARA criteria score [Figure ia score , which rPTPN22*rs2476601, MAP3K71P2*rs577001, and RUNX1*rs2268277. RUNX1*rs2268277 was not identified in our association analysis and our previous classification, and therefore could be an additional putative association.Using the regression analysis method, we did not identify significant associations between SNPs and cluster status among RA patients, or additional SNPs when only severe cases and controls were analyzed. The RFs model predicted the two distinct clusters among RA cases with an overall predictive ability of 0.56 as measured by the AUC of the ROC curve Table . In addiWe examined the predictive ability of RFs using the 5742 genome-wide linkage SNPs. Using the classic training-testing strategy with five-fold CV and the AUC of the ROC curves, we found that the RFs model did not build a reliable prediction model for RA status in either data set 1 or 2. However, when we only used SNPs that were highly ranked on the importance score measure , high sensitivities and specificities were consistently obtained in all five CVs for both data set 1 Figure . When wep-value was calculated by comparing to the profile of shared SNP numbers which was generated by 10,000 random draws from 5742 SNPs), albeit a small number. There are a number of reasons for the lack of replicability in variable selection. For example, the limited sample size, the weakness of individual SNP effects, the insufficient SNP coverage in the genome, the sampling strategy itself, and over-fitting can all contribute to the inconsistency across samples.We generated two replicate data sets in this study to compare the replicability of variable selection using the RFs method and the regular SNP association test. In Table As an example of application, we used RFs for the analysis of the NARAC data sets and found that the candidate gene SNPs did not predict RA patients and control subjects accurately. This is despite including SNPs (either as a subset or together with the other candidate gene SNPs) that have been found to be associated in multiple studies ,8. HowevThe RFs algorithm is known to be more resistant to "over-fitting" and the noise variables  than conAlthough we have developed a strategy to generate two replicate samples that were independent within single samples but related across the samples, the genetic effects were not replicated by comparing the most important SNPs in either RFs or by univariate SNP association tests using the genome-wide SNPs. Such lack of replicability in predictive SNPs suggests that the genetic effects may not be robust when the effect size of each SNP is minimal and/or the environmental factors or other covariates were not taken into account in the model. Therefore, in analyzing high dimensional genomic data, it may be essential to incorporate the non-genetic factors that contribute to the risk of the disease. In the analyses of predicting common disease status using the rich genome-wide SNP data, we believe the machine learning methods such as RFs can play an important role in understanding the complicated genetic structures. In addition, further investigations are needed to optimize the algorithms of the RF approach as well as to understand the limitations in attempting to achieve reliable predictive models.The author(s) declare that they have no competing interests.


Anopheles gambiae we conducted a major resequencing programme, primarily targeting regions within or close to candidate genes for insecticide resistance.Association mapping approaches are dependent upon discovery and validation of single nucleotide polymorphisms (SNPs). To further association studies in An. gambiae genome. Comparison of SNPs identified from pooled templates with those from individual sequences revealed a very low false positive rate. False negative rates were much higher and mostly resulted from SNPs with a low minor allele frequency. Pooled-template sequencing also provided good estimates of SNP allele frequencies. Allele frequency estimation success, along with false positive and negative call rates, improved significantly when using a qualitative measure of SNP call quality. We identified a total of 7062 polymorphic features comprising 6995 SNPs and 67 indels, with, on average, a SNP every 34 bp; a high rate of polymorphism that is comparable to other studies of mosquitoes. SNPs were significantly more frequent in members of the cytochrome p450 mono-oxygenases and carboxy/cholinesterase gene-families than in glutathione-S-transferases, other detoxification genes, and control genomic regions. Polymorphic sites showed a significantly clustered distribution, but the degree of SNP clustering (independent of SNP frequency) did not vary among gene families, suggesting that clustering of polymorphisms is a general property of the An. gambiae genome.Using two pools of mosquito template DNA we sequenced over 300 kbp across 660 distinct amplicons of the The high frequency and clustering of SNPs has important ramifications for the design of high-throughput genotyping assays based on allele specific primer extension or probe hybridisation. We illustrate these issues in the context of the design of Illumina GoldenGate assays. Anopheles gambiae is dependent upon the availability of suitable genomic markers. Quantitative trait locus (QTL) mapping analyses in An. gambiae have successfully employed polymorphic microsatellites [Plasmodium refractoriness and encapsulation [An. gambiae.Mapping of loci controlling traits of interest in the malaria vector mosquito tellites  – the uttellites ,3, Plasmsulation -6 or hybsulation ,8. HowevAn. gambiae in 2002 [An. gambiae): a long-term M-form laboratory strain originating from Nigeria and field-collected S-forms from Western Kenya, crossed with additional Kenyan S-forms. As such, the SNPs identified in the PEST sequence are expected to be biased towards those that segregate between the M and S molecular forms, rather than SNPs likely to be polymorphic within and among natural populations. In addition, SNPs are at relatively low frequency in the PEST genome (approximately 1 segregating site every 620 bp) and have an uneven distribution across the genome, resulting in a paucity of SNPs in many chromosomal divisions . To datimmunity -12.An. gambiae. Gene expression studies using the An. gambiae Detox-chip [An. gambiae Detox chip [We are interested in the factors controlling resistance to insecticides in tox-chip  – a micrtox-chip -16. Howetox chip  has probAn. gambiae. Our resequencing used pooled genomic DNA (gDNA) as template, and we also evaluate the performance of the pooling technique with respect to accuracy in allele frequency detection and Type I and II error rates for SNP discovery. SNPs to be screened in highly multiplexed approaches, such as the Illumina GoldenGate assay [An. gambiae genome, and how this impacts the design of highly-multiplexed arrays. Information on all SNPs discovered in the present study are freely available in public access databases.The primary aim of our study was to resequence the suite of genes present on the Detox chip microarray to provide data for development of a highly multiplexed SNP array for association mapping of insecticide resistance in te assay  and Affyte assay , must noAn. gambiae M- and S-forms of diverse geographical origin. The M pool consisted of samples from Odumasy, coastal Ghana (N = 3), Bonia, northern Ghana (N = 3) and Koubri, southern Burkina Faso (N = 4) and the S pool consisted of samples from Odumasy, Ghana (N = 3), Mampong, central Ghana (N = 2), Asembo Bay, Kenya (N = 2) and Thyolo, Malawi (N = 3). DNA from each sample was extracted using the Ballinger-Crabtree method [et al. [+/2La inversion kayotype was determined using the PCR diagnostic developed by White et al. 2007 [In order to incorporate high diversity and reduce sequencing time and costs, two pools of gDNAs were created from e method  and mole [et al. . The 2Laal. 2007 . Frequenal. 2007 , pools cAn. gambiae detox chip [cis regulatory elements. Where genes were > 5 kbp in length, primers were designed to amplify regions approximately every 5 kbp. In total, 973 primer pairs were designed (including redesigned primer pairs to replace those which could not be optimised). Reactions were optimised to yield a single product, which was sequenced in both forward and reverse directions, using the amplicon-specific primers, by Macrogen .Target loci were primarily selected to be coincident with the genes on the tox chip  with addtox chip , which mtox chip  and chece.g. only unidirectional sequence with a relatively high background signal is available. Since sequencing was undertaken on PCR products of pooled DNAs, estimates of SNP frequency were based on a visual estimate of relative peak height in ambiguous positions.Sequence traces were aligned with CodonCode Aligner  Traces, or portions thereof, with low Phred quality scores were automatically discarded. Nucleotide positions exhibiting polymorphism within or between template DNA pools, were identified with the aid of the mutation detection tool in CodonCode Aligner, and through manual inspection of all sequencing traces. We assigned a confidence score to each SNP identified: 1 = a SNP is identified with full confidence, being clearly apparent in both forward and reverse sequencing traces; 2 = a SNP is identified with confidence but with the caveat that only unidirectional sequence is available; 3 = a SNP is observed but with some cause for doubt, SS numbers; rs numbers are scheduled to be available in build 129 or 130 of dbSNP).All SNPs have been submitted to dbSNP  of field collected specimens from a single southern Ghanaian population  and two pools (N = 5 each) from Cameroon  using the same 13 primer pairs. Data from the two pools of N = 5 were combined for analysis.Sequencing of the individual samples used to make up the pooled DNA was undertaken for thirteen amplicons , CYP6M1_2). Although some of these loci were chosen on the basis of biological interest, the only other criterion was that primers generated single, strong PCR amplicons. Thus, the loci should comprise a representative sample of our pooled sequences. Individual DNA samples were amplified using the identical primers to those used on pooled DNA templates. As before, individual sequences were aligned with CodonCode Aligner and mutations identified using the CodonCode Aligner mutation detection tool, with all calls checked by manual inspection. SNP frequency from individual DNAs was calculated and compared to the SNP frequency estimates obtained via sequencing of pooled DNA samples. In addition, to investigate how polymorphism in the mixed-template pools corresponded with polymorphism in natural populations, we also sequenced two pools (S) and the nucleotide diversity (π). Although allele frequencies were determined through pooling, π can be estimated [S) and nucleotide diversity figures to account for the false positive (FPR) and false negative rates (FNR) identified through comparison of individual and pooled sequences (S = Sestimated/1(1-FNR+FPR); π = πestimated/(1-FNR+FPR)). Variability was determined across the total dataset, and was also analysed following subdivision into five categories of loci: cytochrome p450 mono-oxygenases; glutathione-S-transferases; carboxy/cholinesterases; other detoxification loci; and other loci with no known detoxification function. Bootstrapped confidence limits of S were calculated using the Poptools add-in for Microsoft Excel [Variability was calculated as the number of segregating sites (stimated  followinstimated . We adjuft Excel . In ordeAll SNPs identified were submitted to the Illumina Assay Design Tool (ADT) to determine their suitability for genotyping with the GoldenGate assay. The ADT assesses whether an Illumina assay can be used to interrogate the SNP, checking for duplicated regions, SNPs in flanking sequence, and whether probe melting temperatures are within assay limits.i.e. SNPs identified through sequencing of pooled templates that were not confirmed by sequencing of individual samples, was low at 4.1%. Sequencing of pooled DNA generated a high rate of false negatives, with 30% of SNPs identified by individual template sequencing missed, but the median frequency of these SNPs was low  and moderately well with those in a pool of M-form samples collected from a single population in Cameroon (median r = 0.51). Allele frequencies in the diverse M-form pool were moderately correlated with those in the Cameroon single population pool (r = 0.42), and only weakly related to those in the Ghanaian single population pool (r = 0.21). Correlations between the allele frequencies obtained from sequencing individuals comprising the M and S-pools and the Cameroon and Ghanaian pools were similar to those obtained when comparing pooled estimates. Thus SNPs identified in the diverse S-form pool could be of greater general utility than those in the diverse M-form pool, though those present in both M- and S-form pools are likely to be of most widespread value.Allele frequencies estimated from the diverse S-form pool correlated well with those in pools of samples from a single population collection of S-form samples from Ghana (median Pearson correlation across sequenced amplicons N = 10) and S (N = 10) individuals revealed a total of 7062 polymorphic features. Sixty-seven (0.95%) were indels, of which we could not determine the exact position for 36. Additional indels were inferred from rapid reductions in quality of sequencing traces, but neither the causative polymorphism nor its exact position could be determined. These are not included in the polymorphic feature count and thus we will have underestimated the true indel frequency. The remaining 6995 polymorphic features were SNPs. 702 of the 7026 features (10%) already have dbSNP numbers from sequencing of the PEST strain, whereas 6324 are novel. Sixty-seven triallelic and three tetrallelic SNPs were identified directly from sequencing traces. An additional 15 SNPs were inferred to be triallelic through discrepancies between the nucleotide variation we identified and that identified at the same SNP position via sequencing of the PEST strain. Thus, we estimate that approximately 1% of all SNPs are multiallelic.In total, sequencing was undertaken successfully on 660 loci , comprising 323,114 bp. Other PCR reactions failed to optimise, gave unusable sequence or were affected by multiple indel events which prevented analysis. Sequencing of geographically diverse pools of M (π = 0.0079) and S pools (π = 0.0082) but there was significant variation among gene classes  with the cytochrome P450s and carboxylesterases much more polymorphic than other classes , although lower frequencies of segregating sites were observed on the X chromosome and around centromeres , indicating significant clustering of SNPs, with many exhibiting very small gap distances  to the average for each sequence we found that 60% of gap sizes fell below the average, with little variation among the locus classes . The distribution of gap widths was significantly less symmetric about the averages than expected by chance (Sign test To determine if the SNPs could be used for association mapping studies on the Illumina Goldengate platform we used Illumina's assay design tool (ADT) algorithm . Of the SNPs we identified, only 10% had been identified previously from sequencing of the PEST strain genome. This suggests that the sequencing of this strain has dramatically underestimated the true SNP frequency in An. gambiae. Similarly, Morlais et al., in sequencing of 3 lab strains , found 324 SNPs in 26 loci  [et al. [The development and application of high-throughput genotyping methodologies for the malaria mosquito  17 kbp) . Only 39 [et al. )By sequencing the gDNA of pooled individuals we substantially reduced the cost of the resequencing programme. Through comparison of allele frequencies estimated from pooled DNAs with those obtained from sequencing of individual templates it is apparent that pooling of template DNAs yields relatively accurate allele frequency estimates and a very low rate of false positives. Many low frequency SNPs that were identified through sequencing of individual DNA samples were missed in the sequencing of pooled templates. However, since low frequency SNPs perform poorly in detection of linkage disequilibrium  this is An. gambiae [Nucleotide diversity estimates in our study are comparable to those obtained in other studies of  gambiae ,11,30,31 gambiae   result in high rates of mutability of p450s [Drosophila, enzymes involved in xenobiotic metabolism exhibit a higher nonsynonymous: synonymous (dN/dS) ratio than the average over the dataset . Nucleotide diversity is a product of mutation rate and Ne and the highest recorded levels of polymorphism, for the urochordate Ciona savignyi, are thought to be due to its high Ne [Ne available for An. gambiae suggest levels of Ne equal to a few thousand [Ne is notoriously difficult to estimate accurately, particularly for species exhibiting often limited genetic population structure over wide geographic scales, such as An. gambiae. Improved Ne estimates would help elucidate the role of Ne in explaining the high nucleotide diversity that we, and other authors, have observed.The high diversity in  high Ne . The estthousand ,48. HoweDrosophila spp. recombination rates are positively correlated with nucleotide diversity [An. gambiae, the first major study to estimate recombination rate indicated a small recombination map length of 215 cM over the 278 Mb genome, or 0.78 cM/Mb [An. gambiae do not support a relationship between diversity and recombination rate. However, more recently, a survey of recombination rate along the X-chromosome, recorded an overall average recombination rate of 1 cM/Mb, but with dramatic variation in local rates between 0.2 and 7 cM/Mb [An. gambiae. Fine-scale estimates of recombination rate are now required to permit investigation of how the interplay between recombination and selection determines diversity.In iversity -51, espeiversity -51. In A78 cM/Mb . Thus, b 7 cM/Mb  dependenAnopheles gambiae, primarily in or around detoxification-related genes. SNP frequencies varied among gene families, being particularly high in members of the P450 monooxygenase and carboxyl/cholinesterase enzyme superfamilies. The SNPs identified represent a valuable resource for mapping studies, but a high SNP frequency and clustered distribution in An. gambiae, which may be general features of mosquito genomes, present a significant challenge for the design of genotyping arrays.By sequencing pooled template DNA, we have identified nearly 7000 SNPs in CSW designed PCR assays, performed sequence alignments, analysed the data and wrote the manuscript. DW designed PCR assays, performed sequence alignments, analysed the data and co-wrote the manuscript. KS carried out molecular laboratory work. MJD participated in the design and coordination of the study and contributed to the manuscript. All authors read and approved the manuscript.Loci studied and snps identifiedClick here for file