The recent discovery of widespread copy number variation in humans has forced a shift away from the assumption of two copies per locus per cell throughout the autosomal genome. In particular, a SNP site can no longer always be accurately assigned one of three genotypes in an individual. In the presence of copy number variability, the individual may theoretically harbor any number of copies of each of the two SNP alleles.To address this issue, we have developed a method to infer a "generalized genotype" from raw SNP microarray data. Here we apply our approach to data from 48 individuals and uncover thousands of aberrant SNPs, most in regions that were previously unreported as copy number variants. We show that our allele-specific copy numbers follow Mendelian inheritance patterns that would be obscured in the absence of SNP allele information. The interplay between duplication and point mutation in our data shed light on the relative frequencies of these events in human history, showing that at least some of the duplication events were recurrent.This new multi-allelic view of SNPs has a complicated role in disease association studies, and further work will be necessary in order to accurately assess its importance. Software to perform generalized genotyping from SNP array data is freely available online . GG, GT, or TT) at each SNP site. If this assumption of two copies at each locus were relaxed, one could consider a generalized genotype whereby the SNP is multi-allelic when considering both base residue and copy number. An individual could carry, for example, a GGT (duplication), G – (hemizygous deletion), or – (homozygous deletion) genotype at a SNP locus. As most recent estimates put the proportion of the genome harboring CNVs at at least 12% [A copy number variant (CNV) is defined as a chromosomal segment, at least 1 kb in length, whose (germline) copy number varies across individuals in the human population . As the east 12% , allowinThe GeneChip Human Mapping Array Set  is a popWe analyzed SNP array data from a collection of 48 individuals of various ethnic backgrounds. For a reference panel, we used 16 unrelated individuals of African, African-American, Asian, European, and Hispanic ethnicities (see Methods). An ethnically diverse panel of reference samples minimizes the likelihood of a recurrent CNV skewing the "copy number two" reference signal. Applying our algorithm to all automsomal SNPs on the array, we found 21,568 SNP loci that demonstrated aberrant genotypes , and since CNVs can be quite focal, it is possible for the PCR-amplified region to miss the aberrant locus entirely. A detected deletion could also be an artifact of the SNP array assay, since the deletion or duplication elsewhere in the restriction fragment may result in its length moving outside the range of lengths that the assay's PCR step would amplify [in silico genotypes was similar to that of the qPCR results.In order to validate our discoveries using independent experimental means, we performed quantitative real-time PCR (qPCR) experiments for 30 of the regions containing putatively aberrant SNPs, using DNA from individuals in our sample set. A comparison of the qPCR results with our genotype inferences is given in Table  amplify . As ment amplify  experimee.g. de novo events  copy number two, there are theoretically five possible haploid genotypes for that SNP: AA, AB, BB, A, and B, where A and B are the two base residues for the SNP site. The presence or absence of each of these five haploid genotypes sheds light on recurrence and temporal order of both the duplication event at that locus as well as the point mutation that resulted in the SNP. For example, the presence of both AA and BB would imply a recurrent duplication, occurring on both the A and B SNP background. To investigate empirically, we examined 496 aberrant loci in detail (see Methods). We found no evidence of a single chromosome with different base residues on each copy of a duplication (i.e. an AB chromosome) Figure . It is tSince our detected aberrant SNPs automatically indicate the presence of CNVs, we compared the genomic coordinates of these loci to CNVs previously reported in the literature. Of our 21,568 SNP loci, 5537 25.7%) are contained in regions catalogued as CNVs in the Database of Genomic Variants, while only 11.9% of the 490,032 autosomal SNPs on the SNP array are contained in these regions. Though this demonstrates more overlap than would be expected by chance, the majority of loci we have uncovered are novel. However, one would expect that recurrently variant SNPs in our data set would often exist at a higher frequency in the general population, and would thus be more likely to have been previously discovered. Indeed, if we restrict our attention to SNPs that are aberrant in at least two unrelated individuals in our sample set, we find that 1062 of 1905 (55.7%) of these lie in previously reported CNVs. As the aberrance rate of a SNP in our sample increases, its likelihood of having been previously reported continues to rise – over 95% of the 351 SNPs that are aberrant in at least 10% of our unrelated sample set are contained in previously reported CNVs. Therefore, although we report thousands of new CNVs, our results are in some sense concordant with what has been revealed on a population level. Since it has been shown that sequence between intrachromosomal segmental duplications are predisposed to CNVs .7% are c in our sMany genes previously confirmed as polymorphic have functions in metabolism and immunity, and likely act as mediators of normal human variation as well as genomic disease. We compiled a list of transcribed CNVs in our data, along with the Gene Ontology  (GO) terWe have presented the first computational method for genotyping SNPs from microarray data in the general case where the individual is not restricted to two copies of the SNP locus per cell. Our work highlights, with several examples, the relevance of considering both copy number and SNP allelic information simultaneously. We have uncovered tens of thousands of SNPs with aberrant genotypes in humans of various ethnicities, corresponding to thousands of novel CNVs. It is likely that our results actually drastically underrepresent the prevalence of aberrant SNPs in the population, as the array's manufacturer deliberately excluded SNPs that violated Hardy-Weinberg equilibrium, Mendelian inheritance, and other quality control requirements  that wouThe raw .cel files from the 48 individuals – NA10851, NA10855, NA10863, NA11831, NA11832, NA12056, NA12057, NA12234, NA12264, NA12707, NA12716, NA12717, NA12801, NA12812, NA12813, NA18503, NA18504, NA18505, NA18506, NA18507, NA18508, NA18515, NA18516, NA18517, NA18532, NA18545, NA18558, NA18605, NA18612, NA18959, NA18967, NA18969, NA18997, NA19137, NA19138, NA19139, NA19152, NA19153, NA19154, NE00088, NE00090, NE00091, NE00375, NE00403, NE00598, NE00963, NE01118, and NE01119 – in the Mapping 500 K Sample Data Set were downloaded from the Affymetrix web site [We used 16 individuals – NA11831, NA12057, NA18505, NA18507, NA18517, NA18532, NA18545, NA18558, NA18959, NA18967, NA18969, NA19138, NA19152, NE00090, NE00403, and NE01119 – as our reference panel, selected because they are unrelated and are from a variety of ethnic backgrounds. Using the data from these 16, we trained the PLASQ  would prμl (384 well) reactions with 2 ng of template DNA. A QuantiTect SYBR Green PCR kit  was used for the PCR reaction. PCR conditions were as follows: 2 min at 50°C, 15 min at 95°C, followed by 40 three-step cycles of .Relative gene copy numbers were determined by quantitative real-time PCR using a PRISM 7900HT Sequence Detection System (384 well) . Real-time PCR was performed in 12.5-Primers were designed using Primer 3  and syntCustom MLPA probes were designed to match suitable sequences within 300 bp of the original SNP location. Control probes were drawn from other chromosomal locations and have previously been used to analyze more than 100 individuals without evidence for copy number variation . OligonuDetermining which SNP alleles are harbored in a duplication is subject to the same phasing difficulties as SNP haplotype determination. In order to maximize our ability to determine correct phasing, we considered only SNP sites that were duplicated to total copy number three in at least one trio offspring, with one parent having copy number three and the other either two or three. To avoid the possibility of a deletion on one chromosome, we omitted from consideration all loci contained in a deleted regions in any individual (either in our data or in the Database of Genomic Variants). This left us with 496 SNPs for which we sought to detect the presence of the AA, AB, BB, A, and B chromosomes in our sample set, through a combination of phasing and examination of individual genotypes  in the Database of Genomic Variants (Build 35 coordinates). The "rearrangement hotspots"  are regiWe mapped all SNPs on the array to their genomic positions using the UCSC Genome Browser (Build 35). The 11,944 genes whose transcribed regions contain at least one of the  SNPs on the array comprised our "gene universe". Our duplicated genes and deleted genes were those with transcripts containing SNP sites that were duplicated or deleted, respectively, in at least one of our 48 samples. We made use of the R  softwareLEM carried out all PCR experiments, helped refine the computational method, and drafted parts of the manuscript. MAA carried out all MLPA experiments and drafted parts of the manuscript. XL contributed expertise in the handling and extraction of DNA. TL conceived of the study, designed the statistical methodology, and wrote the manuscript. All authors read and approved the final manuscript.All 21568 duplicated and deleted SNPs. This table lists all 21568 detected SNPs. Included in the table are SNP ID, genomic coordinates, frequency of deletion/duplication, and the gene harboring the SNP.Click here for fileComparison of in silico results with MLPA results. This table lists sample, SNP ID, genomic coordinates, MLPA copy number, and in silico copy number and generalized genotype for all 17 SNP loci that were compared.Click here for fileMendelian and non-Mendelian inheritance patterns identified by generalized genotype. This figure shows two hypothetical cases in which the generalized genotype accurately assesses Mendelian inheritance. a) Under the assumption of a diallelic genotype, the inheritance appears to be non-Mendelian. When copy number variation is taken into account, Mendelian inheritance is revealed. b) The de novo duplication is obscured when total copy number alone is considered. However, the true genotype uncovers this event, since allelic information is taken into account.Click here for fileSupplementary Methods. This file describes how we were able to "phase" the alleles in duplicated SNPs.Click here for file


IFNW1), interferon beta-1 (IFNB1), interferon gamma (IFNG) and the interferon consensus-sequence binding protein 1 (ICSBP1).The aetiology of the autoimmune disease type 1 diabetes (T1D) involves many genetic and environmental factors. Evidence suggests that innate immune responses, including the action of interferons, may also play a role in the initiation and/or pathogenic process of autoimmunity. In the present report, we have adopted a linkage disequilibrium (LD) mapping approach to test for an association between T1D and three regions encompassing 13 interferon alpha (IFNA) genes, interferon omega-1 , by sequencing We have developed informative sets of SNPs for the interferon and interferon related genes. No statistical evidence of a major association between T1D and any of the interferon and interferon related genes tested was found. IL2RA (CD25), encoding the α-subunit of the IL-2 receptor on chromosome 10p15[IFNA1 [OMIM: 147660], IFNA2 [OMIM: 147562], IFNA4 [OMIM: 147564], IFNA5 [OMIM: 147565], IFNA6 [OMIM: 147566], IFNA7 [OMIM: 147567], IFNA8 [OMIM: 147568], IFNA10 [OMIM: 147577], IFNA13 [OMIM: 147578], IFNA14 [OMIM: 147579], IFNA16 [OMIM: 147580], IFNA17 [OMIM: 147583] and IFNA21 [OMIM: 147584]), IFNW1 [OMIM: 147553], IFNB1 [OMIM: 147640], IFNG [OMIM: 147570] and ICSBP1 [OMIM: 601565], using tag SNPs , four susceptibility loci have been identified and convincingly replicated: the HLA class II genes on chromosome 6p2122100, fo, the CTLome 10p15. Here, wtag SNPs  in a colThe type I interferons, including the IFNAs, IFNB1 and IFNW1, are a large, evolutionarily-conserved family of homologous pro-inflammatory antiviral, immune-regulatory, cytokines, encoded by a cluster of single exon genes in a 400 kb region of human chromosome 9p21.3, and the orthologous ~400 kb region of mouse chromosome 4. The type II interferon, IFNG, encoded by a four-exon gene on chromosome 12, also exhibits antiviral activity but in contrast to the type I interferons, its main biological activity appears to be immunomodulatory. Type I interferons have increased prior probability in terms of being associated with susceptibility to human immune-mediated disease because this region has been linked with susceptibility to a number of mouse models of autoimmune diseases and related traits -16, althICSBP1, the product of which, a transcription factor of the interferon regulatory factor (IRF) family, plays a major role in interferon signalling. Although nine distinct IRFs have been described[ICSBP1, specifically because chromosome 16q24.1, the region containing the nine-exon gene encoding ICSBP1, has shown some evidence of linkage to T1D previously [We also assessed the related described, we analeviously -20.R2 = 0.81) and genotyped in the family collection. All tag SNP genotypes in parents and T1D affected offspring were in Hardy-Weinberg equilibrium (HWE). The multilocus test[P-value was 0.35 , 18 of which were SNPs and three were DIPs; of these, five SNPs and three DIPs were novel (see R2 = 0.83) and genotyped in the family collection. All tag SNP genotypes in parents and T1D affected offspring were in HWE, except for rs10811465, which deviated from HWE in the parents . As there were no apparent errors with the genotype scoring, this SNP was re-typed using an alternative Taqman assay to check for a genotyping error. We found a high correlation of genotypes between the two assays (correlation coefficient = 0.99) and consequently, that the SNP genotypes still deviated from HWE in parents . Blast searches of the primer and probe sequences suggested that the assays should be specific, reducing the likelihood of deviation due to gene duplication. As the multilocus test does not assume HWE[P-value was 0.12  and genotyped in the family collection  in 23 European Americans identified 13 polymorphisms in their resequencing of tion see . All tagIFNG SNP was obtained from the literature, rs2430561[P-value was 0.091  and genotyped in the family collection. All tag SNP genotypes in parents and T1D affected offspring were in HWE, except for rs12554686, which deviated from HWE in the parents . As there appear to be no obvious errors with the original Invader genotype scoring, this SNP was also re-typed using an alternative Taqman assay to check for a genotyping error. We found a high correlation of genotypes between the two assays (correlation coefficient = 0.96) and consequently, that the SNP genotypes still deviated from HWE in parents . As with rs10811465 in IFNB1, blast searches suggested that the assays should be specific, reducing the likelihood of deviation due to gene duplication. The multilocus test P-value was 0.90  and genotyped in the family collection. All tag SNP genotypes in parents and T1D affected offspring were in HWE. The multilocus test P-value was 0.58 , which measures the ability to predict each known SNP genotype by linear regression on the tag SNP genotypes[IFNG polymorphisms were extracted from the SeattleSNP database of 23 Caucasian individuals, we increased the MAF ≥ 0.1[IFNA region and the large number of SNPs with a MAF ≥ 0.05, we used a MAF ≥ 0.1 when selecting tag SNPs for the IFNA region. We required the subset of tags SNPs to predict the remaining SNPs with a minimum R2 of 0.8Tag SNPs were selected for genotypes,11. GeneTag SNPs for all genes were genotyped in a family collection consisting of 472 multiplex families from the Diabetes UK Warren 1 collection and 268 multiplex families from the (USA) Human Biological Data Interchange, providing up to 1,427 parent-child trios. Ethical approval by the relevant research ethics committees was obtained for all DNA samples collected, and written informed consent was obtained from the participants.i.e. the standard protocol was followed). Those Taqman kits that failed to show 100% concordance with the sequencing data were either: 1) replaced by an alternative tag SNP; or, 2) reassessed through genotyping on the 96 panel after an initial nested PCR amplification step (to the isolated genomic region containing the specific SNP of interest), only those kits that showed 100% concordance with results obtained in the 96 patient DNA genotyping and 32 patient DNA sequencing panels after the nested PCR step were used for genotyping in the T1D family panel. All eight kits requiring nested PCR prior to genotyping showed 100% concordance with sequencing data from the 32 DNA resequencing set.Genotyping of the tag SNPs was performed using either Taqman (ABI) or Invader (Third Wave Technologies) assays with the exception of rs35085912, a DIP, for which fluorescent primers were designed (Primer1:CGCCTCTTATGTACCCACAAA-FAM Primer2:TTTTTCTGATTGAATCTCCCATT) and size differences discriminated using an ABI3700 DNA analyser. Owing to the exceptionally high degree of sequence homology within the IFNA genes and their 3' and 5' flanking regions it was necessary to modify the standard Taqman genotyping protocol for over half the tag SNPs in this region. This was achieved through a PCR amplification of a sequence specific to the region containing the polymorphism of interest, which was subsequently used as the template for Taqman assay. An initial quality assurance pilot study was carried out on each of the SNP-specific Taqman kits before genotyping on the full T1D family set commenced. A panel of 96 T1D patients were genotyped using each respective Taqman kit. This 96 patient panel included the 32 patients used for the resequencing efforts described above for initial identification of SNPs and allele frequencies. All kits that showed 100% correlation between Taqman kit genotyping results and resequencing results were used to genotype native genomic DNA from the families (Stata statistical system[Tag SNPs were analysed using a multilocus test, which essentially tests for an association between T1D and the tag SNPs due to LD with one or more causal variants,11. The al system and may al system.GAJM and CEL participated in the design of the study, carried out gene annotation, sequencing, genotyping, data analysis and manuscript preparation. JDC participated in the design of the study, performed data analysis and participated in manuscript preparation. AV carried out gene annotation, sequencing and genotyping. FP, LG and JSH carried out genotyping. NMW coordinated data management.BCH and ACL participated in genome informatics. PAL participated in the conception and design of the study. JAT participated in the conception, design and coordination of the study and participated in manuscript preparation. All Authors read and approved the final manuscript.IFNA region. SNPs identified in the Novel SNPs are denoted by "ss" numbers and previously published SNPs are denoted by "rs" numbers. The gene refers to the IFNA gene in closest proximity to the SNP. Minor allele frequencies are based on the sequencing panel as listed in Table R2 values for non-typed SNPs. Tag SNPs are marked in bold. UTR, untranslated region.Click here for fileIFNA tag SNPs. Genotyping summary for Genotype summary for the IFNA tag SNPs. Minor allele frequencies are based on parental genotypes. Number of parent-child trios obtained by population.Click here for fileIFNB1 region. SNPs identified in the Novel SNPs are denoted by "ss" numbers and previously published SNPs are denoted by "rs" numbers. R2 values for non-typed SNPs. Tag SNPs are marked in bold. UTR, untranslated region.Click here for fileIFNB1 tag SNPs. Genotyping summary for Genotype summary for the IFNB1 tag SNPs. Minor allele frequencies are based on parental genotypes. Number of parent-child trios obtained by population.Click here for fileIFNG region. SNPs identified in the Novel SNPs are denoted by "ss" numbers and previously published SNPs are denoted by "rs" numbers. R2 values for non-typed SNPs. Tag SNPs are marked in bold. UTR, untranslated region.Click here for fileIFNG tag SNPs and rs2430561. Genotyping summary for Genotype summary for the IFNG tag SNPs and rs2430561. Minor allele frequencies are based on parental genotypes. Number of parent-child trios obtained by population.Click here for fileIFNW1 region. SNPs identified in the Novel SNPs are denoted by "ss" numbers and previously published SNPs are denoted by "rs" numbers. R2 values for non-typed SNPs. Tag SNPs are marked in bold. UTR, untranslated region.Click here for fileIFNW1 tag SNPs. Genotyping summary for Genotype summary for the IFNW1 tag SNPs. Minor allele frequencies are based on parental genotypes. Number of parent-child trios obtained by population.Click here for fileICSBP1 region. SNPs identified in the Novel SNPs are denoted by "ss" numbers and previously published SNPs are denoted by "rs" numbers. R2 values for non-typed SNPs. Tag SNPs are marked in bold. UTR, untranslated region.Click here for fileICSBP1 tag SNPs. Genotyping summary for Genotype summary for the ICSBP1 tag SNPs. Minor allele frequencies are based on parental genotypes. Number of parent-child trios obtained by population.Click here for file


An efficient, cost-effective and open-source approach is described for high-throughput genotyping of large fixed panels of diploid individuals. Association mapping aimed at identifying DNA polymorphisms that contribute to variation in complex traits entails genotyping a large number of single-nucleotide polymorphisms (SNPs) in a very large panel of individuals. Few technologies, however, provide inexpensive high-throughput genotyping. Here, we present an efficient approach developed specifically for genotyping large fixed panels of diploid individuals. The cost-effective, open-source nature of our methodology may make it particularly attractive to those working in nonmodel systems. It is also important that SNPs are genotyped in a very large panel of individuals to provide sufficient power to detect variants that may have only subtle phenotypic effects. Studies suggest panel sizes of much larger than 1,000 individuals are required to achieve modest power to detect associations if they are present [Understanding the genetic architecture of complex polygenic traits is a fundamental goal of modern biological and medical research, and the currently favored experimental paradigm is association mapping . Associet al. -4. As a et al. . Thus, e present ,7.A plethora of SNP genotyping platforms is currently available  PCR amplicons, which in turn were used as template for multiplexed OLA reactions. Twenty 8-plex OLA reactions were performed on single 2 to 3 kb amplicons as template, and one 8-plex reaction used two pooled PCR amplicons as template. Following amplification of the products of ligation, each sample was printed in duplicate onto nylon membranes. This resulted in a set of 10 membranes holding SNPs incorporating barcode pairs 01 to 08, and a set of 11 membranes holding SNPs incorporating barcode pairs 09 to 16. Within each set, membranes were combined and sequentially hybridized with the appropriate 16 labeled barcodes to generate the genotype data. The background-subtracted array intensity data are provided in Additional data files 9 (replicate spot 1) and 10 (replicate spot 2), and the genotypes assigned to the individuals are given in Additional data file 11.We designed SNP genotyping assays for 156 biallelic polymorphisms in the Enhancer of split [hairy [All OLA-based genotyping approaches rely on oligos binding to a small region flanking the query SNP. If this flanking region harbors a minor allele at a SNP other than the query SNP, binding and subsequent ligation efficiency could be hindered if designed OLA oligonucleotides only match the major allele at this secondary SNP. Thus, a secondary SNP could cause the entire genotyping assay to fail, or in double heterozygotes for the query and secondary SNPs, result in incorrect genotype assignment. Because full resequencing data were available around each of the query SNPs , we werWhen the resequencing data indicate that there are no secondary SNPs flanking a query SNP, 86% (104/121) of the assays we designed converted. In contrast, just 65% (22/34) of the assays converted when a secondary SNP was present, and OLA oligos were designed to match only the major allele at that secondary SNP. It is of interest that the likelihood of an assay with a secondary SNP failing did not seem to depend on whether the secondary SNP was in the upstream or downstream oligo binding region, or on the distance of the secondary SNP from the query SNP. If we controlled for secondary SNPs by incorporating degenerate bases into the OLA oligos, then the success rate was equivalent  to that observed for query SNPs without secondary SNPs. Thus, our data suggest that if SNPs are identified via resequencing, employing degenerate bases in the OLA oligos can control for secondary SNPs.Enhancer of split did not control for a pair of secondary SNPs (one 8 base pairs (bp) upstream and one 9 bp downstream, both at a frequency 1/16 in the resequenced alleles) and converted to an apparently working assay. To check the accuracy of the OLA genotypes for es09.C20633T we sequenced 354 diploid individuals (GenBank accession numbers AY905900 to AY906258), and 3.1% (11/354) gave discordant genotypes. In each case a true C/T heterozygote was incorrectly called a T/T homozygote due to heterozygosity at a secondary SNP: in 10/11 individuals one of the previously identified segregating sites was to blame, while the remaining error was due to a previously unidentified SNP 1 bp downstream of the query SNP. Secondary SNPs may present a general problem for OLA-based genotyping methodologies, although their impact is dependent on the likelihood of there being a segregating site within the 16 base pairs upstream and downstream of the query SNP. Thus, for species with high levels of nucleotide diversity, such as Drosophila, the effect of secondary SNPs on OLA-based genotyping is expected to be more pronounced than for species with lower levels of diversity, such as humans.For OLA assays that convert, but have an uncontrolled secondary SNP, the miscall rate can be appreciably higher than for sites without a secondary SNP. The OLA assay for site es09.C20633T in P < 0.05. This is more than expected by chance, although the deviations from HWE were generally slight (the absolute mean disequilibrium for these 34 sites is D = 0.012). We hypothesized that the large panel size employed in our study  enabled detection of subtle violations of the HWE assumptions, which would not have been observed in a smaller panel. To test this hypothesis, we sampled 96 genotyped individuals at random from the population, and estimated the deviation from HWE for the same 115 SNPs. Over 1,000 sampled replicates, the average number of assays deviating from HWE was 8, similar to the 6 expected by chance alone.Adherence to Hardy-Weinberg equilibrium (HWE) is a common criterion with which to assess the quality of a genotyping assay, as a deviation can suggest incorrect genotype assignments . HoweverEnhancer of split gene complex were selected in/near exons in an attempt to reduce the number of sequencing reads interrupted by heterozygous insertion/deletion polymorphisms, which are common in Drosophila noncoding DNA. The five sequenced regions collectively harbored 19 frequent  genotyped SNPs  using the same PCR products used as template in the OLA reactions to provide a direct estimate of the accuracy of our genotyping assay. For four of the sequenced regions we sequenced 94 diploids , and for the fifth sequenced 375 diploids , with no individual being sequenced for more than one region. Between 44 and 322 individuals gave genotypes for both the OLA and sequencing over the 19 SNPs . The genotype intensity plots for the 19 tested SNPs are provided in Additional data file 12. From Table et al. [et al. [To verify the accuracy of genotype calls from our OLA genotyping method, we performed a resequencing survey. Five regions from the e et al.  for a coe et al. ,40,41. W [et al. , would cIn the SNP genotyping literature, repeatability, or how often a technology gives concordant genotypes across replicates, is sometimes used as a surrogate for accuracy, or how often a technology yields the correct genotype. We suspect that the cases of incorrect genotype calls caused by uncontrolled secondary SNPs that we mention above are highly repeatable. Thus, for ligation-based genotyping of material not subject to resequencing multiple alleles, measures of repeatability will overestimate the genotyping accuracy for some SNPs.We attempted to genotype 168 SNPs and biallelic insertion/deletion polymorphisms. If we ignore the 34 assays developed without regard to secondary SNPs in OLA genotyping oligo binding regions, 86% (115/134) of the assays convert. This conversion rate is particularly notable because it is derived from the actual production genotyping pipeline rather than independent proof-of-principal experiments. Furthermore, subsequent work has demonstrated very similar conversion rates for OLA genotyping assays conducted at 12- and 16-plex (data not shown). The call rate  for the 115 converting assays here averages 93.9%, and we estimate the miscall rate to be <0.35%. Over the 115 converting assays, on average 1.1% of the individuals were assigned a genotype for only one of the two replicate spots on the membrane, and just 0.06% were assigned different genotypes for each replicate spot. Thus, for a very slight reduction in assay robustness, one could effectively double membrane density, and therefore throughput, by spotting samples only once.It has been pointed out by Syvänen ,10 that et al. [et al. [Originally developed by Landegren et al. , many SNet al. ,36,44-55et al. ,44,53-57 [et al. , is to p [et al. ,54,55,57 [et al. ,58. SmalArray-based technologies are in widespread use. Arrays are used for applications as diverse as whole-genome expression profiling, polymorphism identification , and seqThe full cost of any method is difficult to measure, and also may not translate well among institutions. We estimate that the cost in consumables , including the cost of failing assays, for the work presented in this study is less than $0.03/genotype. Across genotyping technologies, this is at the lower end of the cost per genotype scale. In common with every other genotyping method, some form of robotic liquid-handling system is required for our approach, as is a reasonable thermocycling capacity. Unlike some other methods however, the platform-specific requirements of the method we outline are few , and we contend that much of this equipment is available to the majority of academic researchers, or in the case of the arraying robot, can be inexpensively built .et al. [An ideal genotyping system, capable of genotyping millions of SNPs for thousands of individuals at low cost, does not exist. Therefore, the best genotyping method must be chosen on the basis of the specific requirements of the envisioned genotyping project, and the resources available. Our method adds to the diversity of the available technology, in particular because it fits into a multiplexing niche  not well covered by existing technologies, and because of its open-source nature. Our method has been developed specifically for the high-depth association mapping applications we carry out in our laboratory , and thD. melanogaster individuals, and collected over a quarter of a million genotypes at a cost of less than $0.03/genotype. The assay conversion rate was 86%, and for converting assays 94% of the individuals were assigned a genotype with 99.65% accuracy, as determined by dideoxy sequencing. The methods we describe do not require a great deal of specialized equipment, and may be of great utility for carrying out high-power association mapping of candidate gene regions in individual laboratories. The methodology may help bridge the gap between highly multiplexed technologies capable of genotyping thousands of sites simultaneously, but which can be very costly for large samples of individuals, and methods that are easily extended to large populations, but can be difficult to multiplex beyond a small number of SNPs.We describe a genotyping pipeline that uses a multiplexed OLA applied to PCR amplified DNA, followed by amplification of ligation products using common primers, and array-based detection. We tested 168 genotyping assays in parallel for a panel of 2,000 A broad outline of the method for a single SNP is shown in Figure Drosophila melanogaster flies were collected from a single outbred population, and genomic DNA extracted from each using the PureGene cell and tissue DNA isolation kit . The DNA from each fly was diluted to 200 μl in 0.1 × TE , and 1 μl aliquoted directly into a series of 384-well plates and dried down. The resulting DNA panel consisted of six 384-well plates , and each set of DNA was used as template in standard 5 μl PCR reactions. We amplified twelve 2 to 3 kb amplicons for the complete panel of D. melanogaster DNA: eleven amplicons were developed across the Enhancer of split locus, and a single amplicon was developed upstream of the hairy locus. Oligo sequences are listed in Additional data file 2.Over 2,000 Enhancer of split locus  [hairy locus (GenBank accession numbers AY055833 to AY055842) [hairy gene. Genotyping oligo sequences are listed in Additional data file 2, and details of the polymorphisms are provided in Additional data file 4.We identified polymorphisms using an alignment of 16 resequenced alleles for the  file 3) , and desY055842)  was usedet al. [Three OLA genotyping oligos are required for each query SNP: two allele-specific upstream oligos (5'-M13F+C+BARCODE+U.FLANK-3') and a single common, 5'-phosphorylated downstream oligo (5'-D.FLANK+G+M13R.RC -3'). M13F (5'-GACGTTGTAAAACG-3') and M13R.RC (5'-CCTGTGTGAAATTG-3') are 14 nucleotide (nt) sequences matching the M13 forward amplification primer (5'-CCCAGTCACGACGTTGTAAAACG-3'), and the reverse complement of the M13 reverse primer (5'-AGCGGATAACAATTTCACACAGG-3'), respectively. A single 'C' ('G') nucleotide incorporated into the upstream (downstream) oligos after the M13 sequence may homogenize amplification across multiple products . A 16 ntet al. , and thoUnmodified genotyping oligos were purchased at the lowest synthesis scale from Illumina Inc.  and from Sigma-Genosys , and resuspended at a concentration of 100 μM in 1 × low EDTA TE . Downstream genotyping oligos were individually phosphorylated at the 5' end in 12.5 μl reactions containing 1 × T4 polynucleotide kinase buffer , 1 mM ATP, 10 units T4 polynucleotide kinase (NEB), and 200 pmol oligo. These reactions were incubated for 60 minutes at 37°C and 20 minutes at 65°C. We found it difficult to reliably phosphorylate several oligonucleotides simultaneously (data not shown).2, 1 mM NAD), 2.5 mM dithiothreitol, 1.6 units Taq (Thermus aquaticus) DNA ligase (NEB), and 0.03 pmol of each genotyping oligo. Each OLA reaction mix is spiked with 0.2 μl of PCR product using a HydraII 96-syringe pipetting unit . The small reaction sizes ensure that reagent costs are kept to a minimum. Ligation is performed using the following cycling profile: initial denaturation for 5 minutes at 95°C, followed by 3 cycles of 30 s at 95°C and 25 minutes at 45°C, followed by storage at 4°C. When perfectly matched up- and downstream oligos are juxtaposed to form a duplex with the amplified DNA they are ligated together , and is incubated for 40 minutes at 37°C and 15 minutes at 80°C. After hybridization, the membranes are washed five times for 20 minutes at 40°C in 50 ml washing buffer , and exposed against phosphor screens , heated for 15 minutes at 65°C and 5 minutes at 95°C, and immediately arrayed onto uncharged nylon membranes  without cleanup. Following UV cross-linking at 50 mJ, membranes are bathed in neutralization buffer  for 30 minutes, and stored at 4°C in neutralization buffer until required. Our home-built Cartesian arraying robot uses 384 solid pins , can be inexpensively constructed , and is controlled by our custom Arrayatron perlscript  from a regular PC. Our standard production macroarray membranes are 120 mm × 75 mm, and hold 4,608 features. Each sample was printed in duplicate, resulting in a set of 10 membranes holding SNPs incorporating barcode pairs 01 to 08, and a set of 11 membranes holding SNPs incorporating barcode pairs 09 to 16. Each set of membranes were combined in single hybridization tubes, and pre-hybridized for 3 hours (overnight for first use of membranes) at 42°C in 5 ml hybridization buffer  containing 0.1 mg/ml denatured sonicated herring sperm DNA. Following pre-hybridization, the membranes were hybridized for 4 hours at 42°C in 5 ml hybridization buffer with 0.1 mg/ml denatured sonicated herring sperm DNA and a [γ-s Figure . After sIn concert with recycling barcodes across different SNPs, hybridizing multiple membranes allows simultaneous scoring of many SNPs. Radioactive detection is cost-effective, robust, and does not require a great deal of equipment  not already available to many investigators. We have found, however, that the same arrays simultaneously probed with two infrared-labeled probes (IR-700 and IR-800) and detected using an Odyssey imaging system  yield equivalent genotypes. This non-radioactive detection system has several advantages and may prove a worthwhile extension to our method.AA, Aa, and aa), then implements a likelihood function to assign a genotype, or a no-call, to each individual  to quantify the intensity of each spot from the images obtained by scanning the phosphor screen. The resulting intensity data are passed to a custom script  written in the freely available statistical programming language R . This scl et al.  for a deD. melanogaster alleles sequenced for the Enhancer of split gene region. Additional data file The following additional files are available with the online version of this article. Additional data file Detailed protocols for the presented SNP genotyping platform.Click here for fileOligonucleotide sequences for PCR, OLA genotyping, and sequencing.Click here for fileDrosophila melanogaster alleles for the complete Enhancer of split gene complex.Alignment of 16 resequenced Click here for fileDetails of the 168 SNPs and insertion/deletion polymorphisms genotyped.Click here for filePassing a sequence alignment in FASTA format to the script will return tables of the SNPs and insertion/deletion polymorphisms present in the alignment, and a consensus sequence.Click here for fileThis includes a parts list, diagrams, and photographs of the system.Click here for fileThis script allows the user to control the movement of our custom-built Cartesian arraying robot with a regular PC.Click here for fileThis script is written in the freely available statistical programming language R, and allows the user to take the intensity data for each allele of each spot from the hybridized membranes, and assign genotypes to each spot.Click here for fileD. melanogaster individual, or a blank. The first column is the name of the individual (or blank), the second column identifies the replicate spot (spot 1), and the remaining columns hold the intensity data, with alleles from the same polymorphism in consecutive columns. The column names for the intensity data are constructed from the amplicon within which the site resides, its position (in base pairs) in a sequence alignment, the SNP allele, and the barcode marking the allele.Each row represents a Click here for fileD. melanogaster individual, or a blank. The first column is the name of the individual (or blank), the second column identifies the replicate spot (spot 2), and the remaining columns hold the intensity data, with alleles from the same polymorphism in consecutive columns. The column names for the intensity data are constructed from the amplicon within which the site resides, its position (in base pairs) in a sequence alignment, the SNP allele, and the barcode marking the allele.Each row represents a Click here for fileThe first column is the individual name, and the remaining columns hold genotype data . The column names for the genotype data are constructed from the amplicon within which the site resides, the major allele, the position (in base pairs) of the site in a sequence alignment, and the minor allele.Click here for fileD. melanogaster individuals. The points representing individuals assigned genotypes by an OLA assay and by sequencing are colored and large, while the remaining individuals are shown as smaller gray points. Red, major allele homozygote in both OLA and sequencing; black, heterozygote in both OLA and sequencing; green, minor allele homozygote in both OLA and sequencing; yellow, OLA and sequencing yield different genotypes.Each plot displays approximately 2,000 points, representing single Click here for file


Neurons in the murine vomeronasal organ (VNO) express a family of class Ib major histocompatibility complex (MHC) proteins (M10s) that interact with the V2R class of VNO receptors. This interaction may play a direct role in the detection of pheromonal cues that initiate reproductive and territorial behaviors. The crystal structure of M10.5, an M10 family member, is similar to that of classical MHC molecules. However, the M10.5 counterpart of the MHC peptide-binding groove is open and unoccupied, revealing the first structure of an empty class I MHC molecule. Similar to empty MHC molecules, but unlike peptide-filled MHC proteins and non-peptide–binding MHC homologs, M10.5 is thermally unstable, suggesting that its groove is normally occupied. However, M10.5 does not bind endogenous peptides when expressed in mammalian cells or when offered a mixture of class I–binding peptides. The F pocket side of the M10.5 groove is open, suggesting that ligands larger than 8–10-mer class I–binding peptides could fit by extending out of the groove. Moreover, variable residues point up from the groove helices, rather than toward the groove as in classical MHC structures. These data suggest that M10s are unlikely to provide specific recognition of class I MHC–binding peptides, but are consistent with binding to other ligands, including proteins such as the V2Rs. MHC-like protein M10.5 is expressed in the vomeronasal organ. The structure does not bind endogenous class I-binding peptides, but is thought to interact with a larger V2R pheromone receptor.  In most mammals, chemical communication between conspecific animals is involved in initiation of reproductive and territorial behaviors. The detection of these species- and gender-specific chemical cues, also called pheromones, is thought to involve receptors of the vomeronasal organ (VNO), a small neuronal epithelium located between the nasal cavity and the palate . Althougi2, and exhibit sequence similarity to the T2R family of bitter taste receptors  deviation of 1.22 Å for 164 of 181 α-carbon atoms), with the largest differences located in the region of the α2 domain helix immediately preceding six residues that are disordered in M10.5 (residues 145 to 150). The overall similarity of the M10.5 and H-2Dd α1–α2 platforms, which are contained within class I heavy chains that are paired with human (M10.5) or mouse (H-2Dd) β2m light chains, demonstrates that the observed structure of the M10.5 α1–α2 platform does not result from an artifactual change induced by pairing with human, rather than mouse, β2m. The more open character of the M10.5 groove as compared with the grooves of non–peptide-binding MHC homologs is seen when the calculated accessible surface areas are compared: approximately 730 Å2 for M10.5 compared with approximately 760 Å2 for typical class I MHC molecules –3937–39]d, the ninth and tenth residue of the bound peptide (P9 and P10) form hydrogen-bonding and van der Waals contacts with Val76, Asp77, Tyr84, Thr143, Lys146, and Trp147 , which hydrogen bond directly or through a water molecule with main chain atoms of the peptide N-terminal residue ,39, are d Trp147 B. In M10An interesting feature of possible functional relevance is the relatively open nature of the F pocket side of the M10.5 groove C, suggesd. Residues that line the M10.5 and H-2Dd grooves were defined as having more than 5.0 Å2 accessible surface area to a 1.4 Å probe and less than 5.0 Å2 to a 5.0 Å probe  in the H-2Dd structure ), followed by elution in 10 ml of a linear gradient of buffer A to buffer B (10 mM phosphate buffer [pH 7.0]/300 mM NaCl) using a Vision Workstation HPLC system (Applied Biosystems). The concentration of MUPs in the HPLC fractions were estimated to be approximately 0.5 mg/ml based on Coomassie-stained SDS-PAGE gels and absorbance at 280 nm. Unfractionated urine contained approximately 100-fold higher concentrations of MUPs based on the gel staining. Whole and fractionated urine samples were diluted 10-fold into a buffer containing 50 mM HEPES (pH 7.4)/150 mM NaCl and injected at room temperature over the M10.5-, FcRn-, and mock-coupled flow cells. None of the samples showed any measurable responses aside from nonspecific refractive index changes, and no significant differences were observed between M10.5- and FcRn-coupled cells (data not shown). Although these results rule out binding between M10.5 and protein components in urine , detection of molecules smaller than 1 kDa using a Biacore 2000 is problematic due to noise and buffer effects. As a positive control for the integrity of the coupled M10.5 proteins, a rabbit polyclonal antiserum raised against insect cell-purified M10.5 was injected over all four flow cells giving a high-affinity response only on the M10.5-coupled cells (data not shown).Figure S1o-Fc maps contoured at 3.0 σ showing electron density within the α1–α2 domain. Electron density is shown for each of the five molecules in the asymmetric unit. There is no density indicative of bound peptide(s) or another type of groove occupant, except for a small peak in 5-fold averaged maps, which may result from approximately six carbons of a weakly interacting polyethylene glycol molecule in a location similar to that seen for a hexaethylene glycol molecule in the groove of Zn-α2-glycoprotein [2-glycoprotein groove, the extra electron density within the M10.5 groove is weak and discontinuous. Thus if M10.5 does bind polyethylene glycol, it does not bind it in a single, ordered conformation, as observed in the structure of Zn-α2-glycoprotein [(A) Sigma-A–weighted Foprotein , which, oprotein , and proo-Fc map using the M10.5 model refined with 5-fold NCS constraints.(B) Real space–averaged F(2.0 MB JPG).Click here for additional data file.http://www.pir.uniprot.org) for proteins discussed in this paper are as follows: β2m, human (P61769), β2m, mouse (Q91XJ8), C receptor (Q9UNN8), CD1 (P11609), CD8 (P01731), FcRn (P13599), GABAB (Q9WV18), Gαi2 (P08752), Gαo (P18872), H-2Dd (P01900), H-2Kb (P01901), H-2Kd (P01902), H2-M3 (Q31093), HFE (Q6B0J5), HLA-E (P13747), M10.5 (Q85ZW7), MIC-A (Q5C9P8), NKG2D receptor (P26718), Qa-2 (P79567), Rae-1β (O08603), T22 (Q9BCZ1), TAP1 (Q62427), TAP2 (P36371), UL18 (P08560), and Zn-α2-glycoprotein (P25311).Uniprot accession numbers (http://www.rcsb.org/pdb/) for the M10.5 structure is 1ZS8. The accession numbers for the other proteins discussed in this paper are as follows: FcRn (3FRU), H-2Dd (1BII), and Qa-2 (1K8D).The Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank accession number (


We have identified 645,088 candidate polymorphisms in zebrafish and observe a single nucleotide polymorphism (SNP) validation rate of 71% to 86%, improving with polymorphism confidence score. Variant sites are non-random, with an excess of specific novel T- and A-rich motifs. We positioned half of the polymorphisms on zebrafish genetic and physical maps as a resource for positional cloning. We further demonstrate bulked segregant analysis using the anchored SNPs as a method for high-throughput genetic mapping in zebrafish. Danio rerio) is increasingly used to genetically map traits relevant to vertebrate development, pathophysiology, and behavior. Forward genetic screens can generate thousands of mutations with phenotypes that are salient to human development and pathophysiology. These generate novel opportunities to delineate the molecular processes underlying important traits. The Zebrafish Information Network (ZFIN) currently annotates 2,744 characterized mutants, many generated in large-scale mutagenesis screens [The zebrafish model organism  clones that are currently positioned on the extensive genetic maps of zebrafish, thus forming an immediate resource for SNP-based genetic mapping. Each of these BACs is positioned on a genetic map by anchorage of previously mapped markers, which in turn provides the location of many newly identified SNPs within the BAC. The great number of newly discovered SNPs of each BAC increases the likelihood that one at a map position will be informative for a given experimental cross. This facilitates high-throughput genetic mapping across diverse experimental designs and laboratories. In order to demonstrate that the newly discovered SNP maps are effective in positional cloning experiments, we employed them in bulked segregant analysis of the previously cloned position . We addiWe screened finished BAC sequence for evidence of polymorphism in shotgun sequence generated predominantly from the Tuebingen (TU) mapping strain. These BACs represent 39% of the Zv6 draft genome assembly. We deemed 627,185 variants to be candidate polymorphisms, including 548,508 bi-allelic SNPs and 78,327 insertion-deletions, based upon a study-assigned polymorphism confidence score ≥4. The scoring algorithm counts observations of differing alleles and biases against sequencing errors as well as non-binary SNPs, which allows selection of variants with the most compelling evidence of polymorphism . We additionally identified 17,903 simple tandem repeats with sequence evidence of length polymorphism . To test the accuracy of SNP identification and to correlate confidence score with experimental validation rate, we randomly selected a set of 375 candidate SNPs among those with a confidence score ≥4 and genotyped a wild-type sample of 40 TU [Our study-assigned confidence score correlated well with the likelihood of validation for sampled candidate SNPs. Increasing the confidence score threshold for SNP selection to 5, 6, or 7 improved the validation rate to 80%, 83%, and 86%, respectively. By comparison, we previously observed polymorphism for 77.1% of zebrafish SNPs in dbSNP build 124  when evaluated in the partially inbred strains in which the SNPs were originally discovered   meiotic map   were informative and equivalently distinguished the two parental haplotypes at the locus. Five different mutant pools, each composed of DNA from ten mutant embryos, appeared homozygous at the linked SNPs. In contrast, the genotype of a DNA pool of ten wild-type embryos was an intermediate between a heterozygote and the opposite homozygote at each linked SNP ; Figure trilobite locus. In contrast, no wild-type embryos were homozygous for the mutation-linked allele. These results support the effectiveness of the anchored SNP genetic maps for successful application in bulked segregant mapping in zebrafish.To test the utility of the novel SNP maps for linkage analysis we investigated the previously cloned zebrafish y cloned . The trass4878492; Figure ss4878492; Figure The principal goal of this study was to identify SNPs of finished zebrafish sequence and to position them on current genetic maps as a resource for SNP-based linkage mapping in zebrafish. We identified 548,508 candidate bi-allelic SNPs in zebrafish and demonstrated correlation of their rate of validation with an assigned confidence score for each polymorphism. This rate increases from 71% for SNPs with a confidence score ≥4 to 86% for SNPs with a score ≥7. We also identified 78,327 insertion-deletion polymorphisms and 17,903 SSLPs. We anchored 269,812 of the candidate SNPs to 2,126 loci on 4 principal zebrafish genetic maps . Each of the loci positioned in our study harbor an average of 127 candidate SNPs (and 4 SSLPs) to provide an informative marker in any given cross. These loci are distributed across four genetic maps; the density of the composite SNP-based resource exceeds that of each individual map. Future genetic positioning of the remaining 52% of the 4,410 BACs that harbor candidate SNPs will improve current map coverage. Consolidation to a single reference genetic or physical map and advance characterization of allele frequency in common mapping strains would also aid positional cloning in zebrafish.trilobite locus through bulked segregant analysis using SNPs anchored to these genetic maps. In our test of SNPs near the trilobite locus (the vangl2 gene), the relative dosage of A and B allele frequencies of a given SNP within mutant and wild-type DNA pools clearly distinguished the linked and unlinked markers. For a 10 cM map, a SNP will be no more than 5 cM from the trait locus, thus a mutant pool may harbor 5% (1 in 20) recombinant chromosomes, dependent upon sampling. Several mutant pools tested did harbor 1 in 20 recombinant chromosomes in the vicinity of the linked marker. In our genotyping assays of SNPs on the linked BAC, mutant pools with a recombinant chromosome were, at most, only slightly distinguishable from those mutant pools without a recombinant chromosome, while remaining easily distinguishable from wild-type pools. These results suggest that an informative 10 cM map is sufficient for bulked segregant mapping employing SNPs.To explore the utility of the new resource, we confirmed the known location of the trilobite mapping cross [Genetic diversity of the common zebrafish mapping strains is relatively uncharacterized. The identified SNPs illustrate that the TU strain is not inbred for a large portion of its genome and is likely to be a laboratory founder population. Sequence evidence of polymorphism for 98.8% of candidate SNPs with a confidence score ≥4 is restricted to TU data. However, we observe extensive polymorphism in other strains for these SNPs, potentially marking haplotypes of a common ancestral origin. Many of the validated SNPs were informative within and across the three tested partially inbred lines (with origins in the AB and DAR strains) ,18, as wMany zebrafish traits have been produced in large-scale mutagenesis screens, and most remain to be positionally cloned. Current SNP genotyping technology is capable of bulked segregant mapping of large numbers of these traits concurrently. Subsequent fine mapping of recombinant progeny in the vicinity of each trait locus may be accomplished with additional regional markers, ideally with advance knowledge of marker information content in common mapping strains. Accomplishing these tasks using SNPs anchored to accurate genetic or physical maps has the potential to be less labor-intensive, faster, and more precise than traditional mapping employing SSLPs. The density of SNPs in the zebrafish genome greatly exceeds that of SSLPs, offering a higher potential mapping resolution. We observed one SNP per 1.8 kb and one SSLP per 40 kb of finished clone sequence. Genotyping of SNP markers may be accomplished using standard manual gel electrophoresis-based methods such as single-stranded conformation polymorphism, restriction fragment length polymorphism, or direct sequencing. However, robust automated commercial SNP genotyping systems are capable of higher throughput at a lower cost. Continued development of accurate SNP-based genetic maps will empower disease gene identification and complex trait mapping employing the zebrafish model organism.This study identifies a dense polymorphism resource for genetic mapping in the zebrafish model organism, including SNPs, SSLPs, and insertion-deletions. Intriguingly, we observe that the neighboring sequence context of zebrafish SNPs is non-random. Half of the newly discovered polymorphisms are anchored to genetic maps as well as to the current draft physical map to improve utility for positional cloning. We illustrate an efficient bulked segregant mapping strategy using the resource to rapidly identify linkage through assay of only two samples: a wild-type and a mutant DNA pool. Dense polymorphism maps and high-throughput genotyping technologies will accelerate genetic mapping in a powerful vertebrate model organism that has proven instrumental in understanding human development and disease.-100, filter = false, number of alignments = 1,000,000). The comparisons were allowed to run until approximately 3GB of result files were generated. These results were then used to generate a FASTA database of repeated sequence. These repeats were then located on each genomic scaffold by BLAST . Found repetitive sequences were masked on each scaffold. The masked scaffolds were then used for comparison against the entire genome, as above, until another approximately 3GB of result files were generated. This process looped until every scaffold had been compared against the genome, with the repeated sequences found in each round being added to the previously created database. Once all scaffolds of 200 kb or less were compared, those larger than 200 kb were processed for completion of the repeat database. In total, 56% of the Zv5 assembly and 36% of finished clones were observed to be non-unique by these criteria.The Zv5 genome assembly was downloaded from ENSEMBL . The genDanio rerio Sequencing Group at the Sanger Institute was obtained from online databases [-35). Each masked finished clone was then compared via BLAST to independent unfinished clones , and to shotgun sequence . Results of the BLAST runs were examined to find sequence mismatches, which were stored in a database. Where multiple adjacent base deletions (or insertions) were observed, results consistent with a single insertion-deletion record were summarized. Each variant was then given a confidence score to highlight those candidate SNPs with only two alleles that were seen in similar proportions, and at sequence regions sampled with greater frequency. This score was calculated as follows: conf_score = /)2 × . The ceiling(x) function gives the smallest integer ≥x. Base quality scores were not employed given available computational resources, and given that simple sequencing errors received low study-assigned confidence scores. We directly correlated study-assigned confidence score with experimental evidence of validation rather than compare to alternative SNP prediction methods.Clone sequence and whole genome shotgun sequence produced by the atabases ,23. Thesconf_score/) to exclude potential non-unique sequences. The depth of 25 was empirically chosen based upon available experimental evidence during SNP validation. Candidate SNPs with multiple alignments on an unassembled clone or multiple alignments on a single shotgun sequence (sequence not used for repeat database creation) were assigned a confidence score of zero. The mean sequence depth for a SNP with a confidence score ≥4 was 9.73-fold.Where a region of the genome is repetitive but represented uniquely in a draft assembly, our repeat database will be insufficient to mask sequences in the process of SNP discovery. With discovery of candidate polymorphisms experimentally consistent with non-unique loci, the scoring algorithm was further modified. If the candidate SNP had a sequence depth of >25, the confidence score was penalized (-90). These sequences were then examined to calculate the length of each instance of the SSLP.Finished clones were analyzed to find simple tandem repeats with repeat units of between 2 bp (dimers) and 10 bp (decamers), and at least 6 repeats. A custom algorithm scanned the target sequence for a given repeat type and paused with two occurrences. Next it filtered out repeats composed of monomers. The bases following each repeat were then examined for continuation of the repeat unit. If the number of repeat units exceeded six, then the discovered repeat was reported. Concatenated repeats were identified by examining the list of reported repeats, sorting by location, and assessing whether the end of one repeat was less than one repeat unit from the start of an adjacent repeat. Simple tandem repeats residing in non-unique sequence were omitted from further consideration. Each simple tandem repeat with a total of 400 bp of flanking sequence was then compared to the previously created clone and shotgun sequence databases using BLAST . Annotations with a unique position, >70% sequence alignment and >95% sequence identity by BLAST were assigned genomic positions. In total, 619 SSLPs, 500 SNPs, 2,004 genes, 132 cDNAs, and 7,100 ESTs that were annotated on the HS, MGH, T51, and LN54 maps were located on finished clones employed for polymorphism discovery.In order to assign BACs (and SNPs anchored to them) to positions in existing genetic maps, we identified annotated markers of these genetic maps contained in the sequenced BACs. Annotations on meiotic (HS and MGH) and RH (T51 and LN54) maps were downloaded from ZFIN . Additioet al.  and not et al. ,26 and fet al. . AnnotatFrozen TU population fish  were kindly provided by Dr Zoltan Varga and staff of the Zebrafish International Resource Center . They were offspring from 25 or more group matings of TU wild-type fish. Each group mating consisted of two females and three males. Two frozen adult male fish of each of the partially inbred strains SJD (derived from the Darjeeling line), SJC (derived from C32), and SJA (derived from AB) were kindly provided by Dr Stephen Johnson . Genomic DNA was extracted using the PureGene Genomic DNA Purification Kit . DNA was quantified by PicoGreen assay .2, 0.25 mM dNTPs, 335 nM of each primer, and 2 ng DNA template. We detected incorporation of R110- and TAMRA-labeled terminators by fluorescence polarization on a Molecular Devices/LJL Analyst HT.We randomly selected 392 candidate SNPs, each with a confidence score ≥4, for assessment of polymorphism within the TU population and partially inbred lines . We genotyped SNPs by single nucleotide primer extension and fluorescence polarization in 384-well format . ReactioAll assays were conducted using both forward and reverse strand extension primers: both assays worked for 304 (77.6%), 1 of the 2 assays worked for 71 (18.1%), and both assays failed for 17 of 392 candidate SNPs (4.3%). In total, 375 yielded an informative assay, an overall conversion rate of 95.7%; for these 375 SNPs we obtained 95.8% of genotypes sought among the 46 screened DNAs. The concordance rate among paired forward and reverse strand genotypes of unique loci was 99.6%. Amplimer and extension primer sequences are provided in Additional data file 4. Primer design was accomplished using custom software to minimize primer-dimer and hairpin loop artifacts, to avoid non-unique sequence and variant sites, and to target a Tm of 56°C (average length of 20 bases).tri locus, zSNPs 9697125, 9697126, 9697739, 9697740, 9697743, 9698041, 9698042, 9698043, 9698044, 9698045, 9698049, 9698050, 9698051, 9698140, 9698141, 9698142, 9698151, 9698155, 9698159, 9698161, 9698162, 9698163, 9698166, 9698167, 9698309, 9698311, and 9698312. The SNPs were assayed in DNA from 50 tri mutant and 10 wild-type progeny of a cross of F1 wild-type trim209 carriers. Five equal-part DNA pools of ten mutant embryos each, and one pool of ten wild-type embryos were used for bulked segregant analysis. Individual mutant embryos were genotyped to assess the genetic distance between the markers and the trait locus. An unlinked SNP of linkage group 10 (zSNP6329949) was additionally genotyped for data comparison. Amplimer and extension primer sequences are provided in Additional data file 4.We designed fluorescence polarization assays for 27 candidate SNPs from BAC zC258D20 that is positioned on linkage group 7 near the Zebrafish polymorphism data have been submitted to dbSNP  NCBI:ss, and to D. rerio sequence, categorized by assigned confidence score and by substitution type. Additional data file The following additional data are available with the online version of this paper. Additional data file SNP positions within the Zv6 sequence assembly (chromosome-base number) are provided. Where not present in the Zv6 assembly, a single asterisk indicates SNP position within the Zv5 assembly or a double asterisk indicates SNP position within the Zv4 assembly.Click here for fileD. rerio sequence, categorized by assigned confidence score and by substitution typeProportions of single nucleotide variants observed in Click here for fileA five-part table (a-e) in text file format amenable to database importClick here for fileDesigned genotyping assays for validated SNPsClick here for fileZv5 repetitive sequencesClick here for file


Despite their unprecedented density, current SNP genotyping arrays contain large amounts of redundancy, with up to 40 oligonucleotide features used to query each SNP. By using publicly available reference genotype data from the International HapMap, we show that 93.6% sensitivity at <5% false positive rate can be obtained with only four probes per SNP, compared with 98.3% with the full data set. Removal of this redundancy will allow for more comprehensive whole-genome association studies with increased SNP density and larger sample sizes. High density genotyping arrays have been heralded as one of the technologies that will enable whole-genome association tests to make good on the promise that sequencing the human genome will reveal the genetic causes of complex diseases http://www.affymetrix.com/support/technical/sample_data/hapmap_trio_data.affx) contains raw, feature-level data for 90 CEPH individuals genotyped across these 116,204 SNPs. Our analyses are based on the Xba subset, which contains 58,960 SNPs. Most of these  were also included in the HapMap Project The physical Affymetrix GeneChip Human Mapping 100K chip contains 116,204 biallelic SNPs interrogated in two subsets that differ in which restriction enzyme was used in their preparation, either XbaI or HindIII http://www.affymetrix.com/support/technical/product_updates/brlmm_algorithm.affx), and an improved method, CRLMM, was just released as an R/oligo developmental package We used the Bioconductor http://www.naturalvariation.org/snpredundancy).The Affymetrix arrays contain probe sets for each SNP with up to 20 perfect match and 20 mismatch features . RLMM, BWe first tested for internal concordance in genotype classification, comparing genotype calls made with reduced probesets to the full probeset of 20 features . For each SNP, the number of discordant genotypes was counted. That is, for each of the 90 individuals in the data set, their reduced probeset-derived genotype was compared to the full probeset-derived genotype. We next tested specificity and sensitivity with independent genotype classification by comparing RLMM genotype calls made with various feature subsets to HapMap genotypes. Again, for each SNP, specificity was measured as the number of discrepancies across samples . SensitiWe next considered the manner in which discrepancies occurred by comparing how genotypes should have been called  to how they were actually called  for each of the reduced probesets. As has been previously reported Finally, we tested if the genotyping errors were non-randomly distributed, perhaps due to bad samples or to bad SNPs. No DNA sample gave an abnormally large number of discrepant genotypes as would be the case with a poor sample or array; however some SNPs performed poorly, independent of the redundancy in the probeset. For example, we compared genotyping discrepancies for genotypes generated using a reduced probeset  to the full probeset  in In general, poorly performing SNPs reduce the overall performance of the array and should be eliminated in future array designs. For example, by removing SNPs that had more than 1 discrepancy when using the full feature set , the overall proportion of SNPs with 4 or fewer discrepancies  when typed with 2 strands 1 and position increased by 3.15% to 96.7%.We have shown that genotypes classified with as few as 4 features are nearly as sensitive as those classified using sets with 20 features (93.6% vs 98.3% at a 95% specificity threshold). While additional features generally improve sensitivity and specificity, they do so at a diminishing rate. The discrepancy rate for SNP genotypes was correlated between full and reduced probeset calls, indicating poorly performing SNPs are only slightly improved with additional features. By contrast, 58% of SNPs are perfectly genotyped with only 4 features improving to 87% with the full set. This improvement comes at a cost of 5–10× in SNP real estate. By thoughtfully choosing which SNPs to include, and drastically reducing the number of features to use for each SNP, large increases in total SNPs can be realized in new array designs. At the current 5 µm feature density, GeneChip arrays contain 6.55 million features. At 4 features per SNP >1.5 million SNPs can be achieved. In addition, as more robust genotype calling methods are developed  Figure S1Cumulative distribution of internal discordances across probeset combinations Each curve shows the cumulative proportion of discrepant genotypes obtained when genotypes are called using the full probe set compared to genotyped called using the indicated subset of probes. While the total number of discrepancies is out of a possible 90, only the first 30 are shown, which is the point at which the 5 curves converge.(0.63 MB TIF)Click here for additional data file.Table S1Cumulative distribution showing the proportion of SNPs typed with a maximum number of discrepencies when compared to genotypes generated with all 5 probes and both strands.By default RLMM uses the information from both the sense and antisense strands, but we further modified it in two ways. Method 1 selected the strand with the strongest signal averaged across A and B probe sets. Method 2 selected the strand with the larger difference between average A and average B allele intensities. Performance for method 2 was inferior across all parameter combinations, therefore method 1 results are shown in subsequent figures/tables.(0.02 MB XLS)Click here for additional data file.Table S2Cumulative distribution showing the proportion of SNPs typed with a maximum number of discrepencies when compared to HapMap genotypes.By default RLMM uses the information from both the sense and antisense strands, but we further modified it in two ways. Method 1 selected the strand with the strongest signal averaged across A and B probe sets. Method 2 selected the strand with the larger difference between average A and average B allele intensities. Performance for method 2 was inferior across all parameter combinations, therefore method 1 results are shown in subsequent figures/tables.(0.02 MB XLS)Click here for additional data file.Table S3Misclassified genotypes accumulate in heterozygotes.The number of probes per strand and strands used is indicated in the upper left-hand corner of each box. The numbers represent counts of genotypes binned by their “true” genotypes  compared to how RLMM classified them . When only one probe and one strand are used, a large excess of misclassified heterozygotes is seen. False positive rate corresponds to the proportion of misclassified genotypes in each column or row. Relative amount indicates the proportion of each type of false positive within the total number of misclassified genotypes.(0.02 MB XLS)Click here for additional data file.


The local environment of single nucleotide polymorphisms (SNPs) contains abundant genetic information for the study of mechanisms of mutation, genome evolution, and causes of diseases. Recent studies revealed that neighboring-nucleotide biases on SNPs were strong and the genome-wide bias patterns could be represented by a small subset of the total SNPs. It remains unsolved for the estimation of the effective SNP size, the number of SNPs that are sufficient to represent the bias patterns observed from the whole SNP data.To estimate the effective SNP size, we developed a novel statistical method, SNPKS, which considers both the statistical and biological significances. SNPKS consists of two major steps: to obtain an initial effective size by the Kolmogorov-Smirnov test (KS test) and to find an intermediate effective size by interval evaluation. The SNPKS algorithm was implemented in computer programs and applied to the real SNP data. The effective SNP size was estimated to be 38,200, 39,300, 38,000, and 38,700 in the human, chimpanzee, dog, and mouse genomes, respectively, and 39,100, 39,600, 39,200, and 42,200 in human intergenic, genic, intronic, and CpG island regions, respectively.SNPKS is the first statistical method to estimate the effective SNP size. It runs efficiently and greatly outperforms the algorithm implemented in SNPNB. The application of SNPKS to the real SNP data revealed the similar small effective SNP size  in the human, chimpanzee, dog, and mouse genomes as well as in human genomic regions. The findings suggest strong influence of genetic factors across vertebrate genomes. Single nucleotide polymorphisms (SNPs) are the most abundant genetic variation in vertebrate genomes. They have been important tools in many biological fields, including mutation mechanisms, genome evolution, disease studies, pharmacogenomics, and fine mapping -4. StronMutation at the nucleotide level does not occur randomly. Recent studies of mutational mechanisms revealed that the influence of neighboring nucleotides on SNPs was strong in the human and mouse genomes ,6,7. SpeSo far, how to efficiently estimate the effective SNP size remains unsolved. The SNPNB, an user-friendly application implemented by Java and Perl, can assist the user to evaluate and obtain a number which is close to the effective SNP size . HoweverTo estimate the effective SNP size, we designed and integrated a two-step procedure in our system Figure . In the N, we randomly generate a sub-sample of SNPs with size n0 (n0<<N). First, we calculate the frequency of each nucleotide at each neighboring site in the whole SNP data and sample SNP data, respectively. According to our previous analyses [fi,j and gi,j be the frequency of sample SNP data and whole SNP data, respectively, and Fi,j and Gi,j be cumulative frequency of sample SNPs and whole SNPs, respectively. Here i denotes one of the four nucleotides  and j denotes a neighboring site in the SNP flanking sequences (-10 to -1 at the 5' side and +1 to +10 at the 3' side). The fi,j and Fi,j of the sample SNPs are defined by:For the whole SNP data with size analyses ,7, we exgi,j and Gi,j are defined similarly except for that N, the size of the whole SNP data, instead of n0 is used. The maximum difference of cumulative frequency for each nucleotide is defined by:The Di fails to be less than 0.2%, the size of the sample set is increased by 10,000. The procedure above will run it again until the criterion of Di < 0.2% is satisfied  for the next procedure.Next, we compare the maximum difference of cumulative relative frequency of each nucleotide with the threshold value of biological significance instead of test statistic given by the KS test, because the tolerable difference in the KS test is too generous to find a reasonable sample size . Here wen, the second step is to test whether the bias patterns obtained from the sample with this size can effectively represent the bias patterns observed from the whole SNP data. This is performed by an interval evaluation using 30 different SNP subsets with size n randomly sampled from the whole SNP dataset. We choose 30 different subsets because when the sample size approaches 30, we can safely assume the distribution of the bias patterns to be normal for inference purpose by central limit theorem. For each nucleotide at each neighboring site, we calculate the bias relative to the genome sequence average  in each of the 30 sample sets, and then get its average bias  is less than its standard deviation for all nucleotides at all neighboring sites, an intermediate effective SNP size (Ne0) is found. That is, the proposed method iteratively evaluates the following difference:After getting an initial effective SNP size Bi,j - si,j, ∀ i, j     (4)|si,j is the standard deviation from the 30 bias patterns. Otherwise, we increase the sample size by 10,000 and run this step again. The procedure runs iteratively until the criterion is satisfied.where Ne0 estimates. The effective SNP size is thus the mean of these 100 estimates.The two steps above run repetitively 100 times. This leads to 100 To implement the SNPKS method, we developed computer programs in C and Perl. In the SNPKS algorithm, we need to regularly generate random numbers and then extract random SNPs from the whole SNP dataset based on the generated random numbers. This routine is computationally intensive; therefore, we wrote a computer program in C. The KS test and interval estimation were implemented in a Perl script, which calls the C program automatically. The application has been tested on both Microsoft Windows and Linux operating systems. The programs, instructions, and test data are available at the website .n0) and run the SNPKS programs iteratively. We got an initial effective size (n) 38,000. Then, we took 30 random subsets with size 38,000 and performed an interval evaluation. As shown in Figure We applied SNPKS to estimate the effective SNP size in four vertebrate genomes: human, chimpanzee, dog, and mouse. The genome-wide SNP data were retrieved from the dbSNP database of the National Center for Biotechnology Information (NCBI) (see Methods). The number of the test SNPs are shown in Table N). The comparative results suggest strong genetic influences such as CpG effects across vertebrate genomes [The effective SNP size was estimated to be 38,200 ± 2,500, 39,300 ± 2,100, 38,000 ± 2,300, and 38,700 ± 2,300 for the human, chimpanzee, dog, and mouse genomes, respectively. The 95% confidence intervals were in a narrow range in these four genomes  because the number of SNPs mapped in exons or UTRs was not sufficient. Although the numbers of SNPs and sequence compositions in the genomic regions varied greatly, their effective SNP sizes were estimated to be similar: 39,100 ± 2,300 (intergenic regions), 39,600 ± 2,200 (genes), 39,200 ± 2,200 (introns), and 42,200 ± 2,700 (CpG islands). The effective SNP size in the CpG islands is the largest. This reflects the lack of methylation and suppression of 5 islands .We compared the performance of our method versus the empirical iterative procedures in SNPNB . We testNe) is used to measure the size of an idealized population having the same effect of random sampling on gene frequency as that in the actual population. It can be estimated by Ne = θ/4 μ, where θ is the population parameter and μis the mutation rate per sequence per generation [The effective SNP size in this study is defined as the minimum number of SNPs that are sufficient to represent the bias patterns observed from the whole SNP data. It measures the sequence context patterns observed in the SNPs. To our knowledge, this term has not been used in any other report except for our previous study . This teneration . One exaneration .It is important to know how many SNPs are sufficient to represent the bias patterns observed from the whole SNP data. First, this evaluates whether the observed patterns are representative or random in the genome ,19. The P = 0.83). While this may suggest the strong influence of genetic factors in these genomes, further investigations on these factors and SNP ascertainment biases are warranted. Note that, because the size was increased by 10,000 each time in the iterative procedure in SNPKS, it is unlikely the method led to the similar effective sizes. Further, the effective sizes of the SNPs among the human genomic regions were overall slightly higher than the genome-wide whole SNPs . If we apply the Bonferroni corrections for 80 multiple comparison tests, we have the significance level α/80 for each test . That men Figure . HoweverWe proposed an integrated statistical method (SNPKS) to estimate the effective SNP size. SNPKS consists of two major steps: evaluation of an initial effective size and iterative tests of the size by interval evaluation. SNPKS considers both the biological significance and the statistical significance. SNPKS is the first method to estimate the effective size based on statistical tests and greatly outperforms SNPNB. The application of SNPKS to real SNP data in the human, chimpanzee, dog, and mouse genomes revealed the similar small effective SNP size  in these four genomes and in human genomic regions, suggesting strong influence of genetic factors across vertebrate genomes.We downloaded SNPs in four vertebrate genomes  from the dbSNP database . We retrWe selected only those SNPs that were biallelic, mapped in the non-repetitive sequences, and at least 20 nucleotides long at each side of flanking sequences. A SNP was assigned in the non-repetitive sequences if the 20 nucleotides at each side of the SNP did not overlap any repeats. A total of 5,200,425 (human), 1,470,501 (chimpanzee), 2,690,084 (dog), 7,832,159 (mouse Build 126), 376,146 (mouse Build 123), 861,498 (HapMap phase I), and 2,435,362 (HapMap phase II) SNPs were extracted after this data process (Table CpG/ExpCpG ≥ 0.65, and length ≥ 500 bp to screen CpG islands in the human genome sequences. The criteria above can effectively exclude the universal Alu repeats, which typically have a sequence length of 300 bp, GC content of 53%, and ObsCpG/ExpCpG ratio of 0.62 [We next identified SNPs in human genomic regions using the procedures described in Jiang and Zhao . First,  of 0.62 ,34. We iDS participated in the data preparation and the method development, carried out computational work, and helped to draft the manuscript. CJ participated in the data preparation and analysis. ZZ conceived of the study, participated in its design, method development, and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.Bias comparison using genome-wide dog SNPs. Supplementary Table S1 – Bias comparison using genome-wide dog SNPs.Click here for file