Arabidopsis thaliana ortholog using HAP2(GCS1) variants with modifications to regions amino(N) and carboxy(C) to its single transmembrane domain. These quantitative in vivo complementation studies show that the N-terminal region tolerates exchange with a closely related sequence, but not with a more distantly related plant sequence. In contrast, a distantly related C-terminus is functional in Arabidopsis, indicating that the primary sequence of the C-terminus is not critical. However, mutations that neutralized the charge of the C-terminus impair HAP2(GCS1)-dependent gamete fusion. Our results provide data identifying the essential functional features of this highly conserved sperm fusion protein. They suggest that the N-terminus functions by interacting with female gamete-expressed proteins and that the positively charged C-terminus may function through electrostatic interactions with the sperm plasma membrane.HAP2(GCS1) is a deeply conserved sperm protein that is essential for gamete fusion. Here we use complementation assays to define major functional regions of the  hap2-1 fertilization defect in Arabidopsis. We found that the N-terminus does not tolerate sequence divergence, but the histidine-rich C-terminus does. We propose that the N-terminus of HAP2(GCS1) functions in part by interacting with proteins on the surface of female gametes. The key feature of the C-terminus is positive charge, a characteristic that could favor interactions with the plasma membrane that promote membrane fusion. Our studies provide a description of HAP2(GCS1) functional domains and provide an important framework for defining the role of this essential component of a conserved reproductive mechanism.Recent studies suggest that HAP2(GCS1) is a deeply conserved protein required for gamete membrane fusion, a critical yet poorly understood step in sexual reproduction. HAP2(GCS1) is present in many plant, protist, and animal genomes, and has been shown to be essential for fertilization in Arabidopsis, Chlamydomonas, and Plasmodium. The loss-of-function phenotype in Chlamydomonas suggests a direct role in gamete plasma membrane fusion. HAP2(GCS1) has no known functional domains, making it difficult to predict how it contributes to gamete fusion. We set out to map the critical features of this protein by testing a series of deletions, substitutions, and interspecific chimeras for their ability to rescue the  The fusion of gamete plasma membranes is a critical event in fertilization, but despite the ubiquity of the process among sexually reproducing eukaryotes, no conserved mechanism for gamete fusion has been described. At least two factors contribute to our lack of mechanistic insight. First, many proteins that mediate binding and fusion of complementary gametes evolve rapidly, thereby reinforcing barriers to interspecific hybridization Arabidopsis thaliana, At) identified HAP2(GCS1), a sperm-expressed gene that is essential for fertilization HAP2(GCS1), for HAPLESS2GENERATIVE CELL SPECIFIC1Genetic analysis in Arabidopsis (HAP2(GCS1) in fertilization may be widespread in eukaryotes as orthologs are present in several protist, animal, and plant genomes HAP2(GCS1) function in male gametes also blocks fertilization in Plasmodium berghei  loss-of-function mutation prevents fertilization, despite the ability of gametes to bind one another and bring opposing membranes into close proximity and (ii) CrHAP2(GCS1) is enriched at the tip of the minus mating projection just prior to fusion.The role of All predicted HAP2(GCS1) orthologs share a common primary architecture. Each is divided into two regions by a single pass transmembrane domain and contains a HAP2-GCS1 domain of about 50 amino acids in the large region amino(N)-terminal to the transmembrane domain . The carWe use quantitative molecular-genetic assays in Arabidopsis to characterize the major features of HAP2(GCS1). We find that both regions of the protein are essential for function, and that these regions are under different selective pressures: Primary sequence from a closely related plant, not a distant relative, can replace the Arabidopsis N-terminus. Thus, while HAP2(GCS1) does not define species-level interactions between gametes, function of the N-terminus may be constrained by co-evolution with partner proteins expressed by female gametes. On the other hand, the Arabidopsis HAP2(GCS1) C-terminus retains function when replaced with sequence from a distantly related plant or mutated sequences, as long as positive charge is retained. Thus, net charge over the C-terminus is the critical feature of this region. These experiments thus establish essential characteristics of an ancient protein required for gamete fusion.hap2-1 blocks the ability of sperm to participate in fertilization, but does not affect female reproduction R) and (ii) β-glucuronidase (GUS) driven by the pollen-specific LAT52 promoter (LAT52:GUS) hap2-1 was identified in the quartet (qrt1-2) background, a mutation that maintains male meiotic products in tetrads LAT52:GUS expression in pollen, allows one to distinguish heterozygous hap2-1 (hap2-1/+) pollen from wild-type  and 50% wild-type plants (Basta sensitive (BastaS), GUS- pollen); homozygous hap2-1 (hap2-1/-) plants are not recovered hap2-1 transmission is distorted (not the expected 1∶2∶1 segregation of wild type: heterozygous: homozygous mutant). Furthermore, pollination of wild-type females with hap2-1/+ pollen yields no progeny with the hap2-1 allele, e.g. all progeny are sired by wild-type pollen produced by the heterozygous father. Thus, hap2-1 cannot be transmitted though the male germline.ild-type . Self-feHAP2(GCS1) could restore male transmission of the hap2-1 allele (hap2-1/+ mutants with a wild-type HAP2(GCS1) genomic clone [including 1.5 kb of HAP2(GCS1) promoter sequence] complemented the fertilization defect, and self-fertilization of hap2-1/+ mutants carrying this transgene produced hap2-1/- progeny  hap2-1 to progeny when crossed to wild-type females, producing BastaRhap2-1/+ seedlings hap2-1/+ plants with a series of HAP2(GCS1) CDS variants under the control of the same 1.5 kb HAP2(GCS1) promoter sequence; each variant T-DNA construct carried a kanamycin resistance (KanR) gene. To track expression of CDS constructs and to differentiate between endogenous and introduced HAP2(GCS1), we included sequences encoding short epitope tags (V5 We developed a system to test if coding sequence (CDS) variants of 1 allele . Transfo tags V5  at the 3 tags V5 .HAP2(GCS1) variants were capable of mediating gamete fusion, we generated hap2-1/+ transgenic lines that were homozygous for the CDS variant , CDS  or (ii) hap2-1, CDS . As with the genomic construct hap2-1, should be restored to hap2-1 sperm.To determine if CDS/CDS, . These phap2-1  females, hap2-1 was inherited  CDS. These control experiments demonstrated that the addition of C-terminal epitope tags did not disrupt the function of the HAP2(GCS1) CDS and that expressing the HAP2(GCS1) CDS from the HAP2(GCS1) promoter resulted in expression of functional HAP2(GCS1) protein.Introduction of either the native CDS (data not shown), or an epitope tagged version rescued fertility of hap2-1 . In bothnherited , indicatR seedlings, produced hap2-1/- plants, or restored normal segregation among the progeny of self-fertilization  transmembrane domain were essential for HAP2(GCS1) function. The amino acids encoded by exons 2-15  mRNA was higher in these flowers than in hap2-1/+ flowers  that lie on either side of the transmembrane domain are essential for function.Epitope tag sequences were detected in floral mRNA extracted from AtN• or •AtC lines, and the abundance of  flowers , suggesthap2-1. We chose rice  as a representative monocot sequence; monocots and dicots diverged at least 200 million years ago hap2-1 fertilization defect (not shown). To ensure that the rice protein was properly expressed and localized in Arabidopsis sperm, we replaced Arabidopsis exons 2-15 and the Arabidopsis C-terminus with the orthologous rice sequences to maintain the Arabidopsis signal sequence and transmembrane domain  with sequences from plant orthologs rescued OsN•OsC, . This ex1 defect .Sisymbrium irio  can be made by neutralizing the C-terminus with nonpolar residues.We dissected the Arabidopsis HAP2(GCS1) C-terminus to determine what features were required for function. Alignments of flowering plant C-termini representing dicots and monocots revealed a run of 13 conserved amino acids immediately after the predicted transmembrane domain followed by three histidine-rich domains. The longest histidine-rich stretch is adjacent to the transmembrane domain, and has an average  of 12.5 . A varia of 12.5 . However of 12.5 . Replacehap2-1/- progeny from self-fertilization and 9–15% transmission of the hap2-1 allele in crosses to ms1 females  or histidine-rich region (AtN•+13) abrogates HAP2(GCS1) function. C-termini consisting of the endogenous H1 or positively charged H1 variants are fully functional. However, substituting nonpolar residues for histidine in H1 domain generates a minimally functional protein, yielding only 2–4%  females .hap2-1/- plants to understand the effect of the nonpolar C-terminus on HAP2(GCS1) function. Two sperm were present in AtN•AtC mut øΔ, hap2-1 pollen grains , indicating gametophyte development was normal. Furthermore, AtN•AtC mut øΔ, hap2-1 pollen tubes were able to target ovules and deliver sperm, as judged by counting the number of ovules that received LAT52:GUS activity 7.5 hours after manual self-pollination  hypomorph specifically disrupted fertilization. Analysis of embryo and endosperm development four days after self-pollination of AtN•AtC mut øΔ, hap2-1/- plants revealed normal development in 2–8% of ovules  C-terminal charge crippled function. In the majority of cases, neither sperm was capable of fusing with the egg or central cell  variants has defined two regions that play distinct roles during HAP2(GSC1)-mediated double fertilization. The protein can be divided into N- and C-terminal regions based on the position of the transmembrane domain. Both regions are essential for function, but different evolutionary constraints are driving their roles in fertilization.www.cbs.dtu.dk/services/TMHMM-2.0/); with the invariant conservation of cysteine residues within the HAP2-GCS1 domain that are predicted to participate in disulfide bonding in an extracellular environment; and with the successful use of N-terminal epitopes to produce antibodies that block Plasmodium reproduction We propose that an extracellular orientation of the N-terminus allows this region to regulate gamete fusion by its interaction with factors on the egg or central cell. This organization is consistent with Hidden Markof Modeling  does not contribute to a species level barrier to hybridization. Wind and/or animals indiscriminately pollinate many flowering plant species, so it is important to consider mechanisms that limit hybridization. In some organisms, protein:protein interactions essential for complementary gamete binding and fusion are rapidly co-evolving to enhance reproductive isolation of one species from another Positive charge, not primary amino acid sequence, is the C-terminal characteristic conserved among HAP2(GCS1) orthologs and our data show that positive charge is required for function. Unlike the protein:protein interactions proposed for the N-terminus, the intracellular C-terminus may be functioning through electrostatic interactions with negatively charged molecules (e.g. the inner face of the plasma membrane) that favor membrane fusion. Positively charged domains located on the intracellular domain of fusion-associated small transmembrane (FAST) proteins have been implicated in fusion of host cells by non-enveloped viruses hap2-1 pollen tubes have a reduced ability to target ovules compared to wild type, suggesting that in flowering plants, HAP2(GCS1) may have a role in pollen tube guidance that is distinct from its essential role in gamete fusion hap2-1 pollen tubes compete with wild-type pollen tubes for access to ovules. Pollen tubes burst upon arrival at the ovule, exposing sperm to the extracellular environment. It will be interesting to determine if this change in environment results in a drop in sperm pH that activates HAP2(GCS1) function.Flowering plant C-termini are enriched in histidine whereas other positively charged amino acids (arginine and lysine) are prevalent in other orthologs hap2-1 sperm expressing a HAP2(GCS1) variant with a neutralized C-terminus (AtN•AtC mut øΔ) had significantly reduced fertility. Double fertilization occurred in only ∼7% of the ovules we analyzed, while many ovules remained unfertilized (∼40%). A large portion of the ovules contained products of single fertilization events  that fail to complete seed development. These results highlight a unique advantage of flowering plants for the study of gamete fusion: the outcomes of two distinct fertilization events, both requiring HAP2(GCS1) function, can be observed independently. This situation provides a sensitive means to detect reduced fusion efficiency. We consistently observed fertilization of only one female gamete when two hap2-1 sperm expressing the AtN•AtC mut øΔ HAP2(GCS1) variant were delivered to an ovule, specifically detecting more single fertilizations with the egg (embryo-only) than the central cell (endosperm-only). This suggests that sperm:central cell fusion may require more HAP2(GCS1) activity or that central cell fusion is particularly sensitive to the C-terminal charge of HAP2(GCS1).HAP2(GCS1) loss-of-function minus gametes, yet membranes fail to fuse All evidence to date indicates that HAP2(GCS1) has an essential role in fertilization We have mapped the key domains of HAP2(GCS1) and propose a model in which the N-terminus functions by interacting with female gamete-expressed proteins and the C-terminus is required to interact with the plasma membrane through its positive charge. By analogy to known fusogenic proteins, we propose that these interactions bring gamete membranes into close proximity, destabilize the phospholipid bilayer, and generate membrane structures favoring their fusion All seeds were plated onto solid Murashige and Skoog (MS) medium  supplemented with 0.5% sucrose containing 25 µg/mL glufosinate ammonium  and/or 50 µg/mL kanamycin sulfate . Seedlings were transplanted to sterile 2MIX potting medium  and grown at 20°C, 16 day/8 night hour light cycle in a GCW30 walk-in Arabidopsis chamber  at 50–60% humidity. Plants were bottom-watered with 0.5X 15-5-15 (N-P-K) Peters Professional fertilizer  as needed.HAP2(GCS1) CDS. Mutations were made in the CDS to eliminate the endogenous EcoRI site at position 71 , to create a second BamH I site at position 1618 that complements the endogenous BamH I site at 178, and to create a Bmt I site at position 1740. Each directed mutation was made using the QuickChange mutagenesis protocol . Additional changes and sequence swaps were introduced through linker primers that contain both appropriate restriction sites and new sequences.Chimeric constructs were made using a modified Arabidopsis HAP2(GCS1) promoter All CDS variants were subcloned into a custom vector based on the pTAT backbone 1 plants were selected on MS plates containing both Basta and kanamyacin, but subsequent generations were selected on either Basta or kanamyacin . In cases where complementation failed and no lines resulted in >50% BastaR, lines with the highest percentage BastaR were analyzed. Transgenic lines with a single insertion of the CDS construct were selected based on kanR data (expect 75–83% kanR).Each recombinant pCamHap2 T-DNA plasmid was transformed into Agrobacterium strain GV3101 namyacin . FifteenStage 12 flowers or pistils dissected 7.5 hour after manual pollination were fixed and stained for GUS according to previously published methods HAP2(GCS1) transmembrane domain , H3.3  and the exogenous V5C4 dual epitope  were used at a final concentration of 100 nM.Stage 12–14 flowers were collected, frozen in liquid nitrogen, and stored at −80°C until needed. RNA was isolated from 25–50 flowers per line using Qiagen RNA Mini columns , including the optional on-column DNase treatment. Complementary first strand DNA was synthesized from 1 µg of total RNA with random hexamers and poly dT primers using the TaqMan kit . One fortieth of each reverse transcription reaction was used per 25 µL quantitative real-time PCR (qPCR) replicate. qPCR was completed on a 7300 Real-Time PCR System  using Platinum Taq SYBR Green Mix with ROX . Amplification was quantified over 40 cycles of 95°C, 0∶15 denaturation and 60°C, 2∶30 extension, and amplicons were evaluated with a standard dissociation step. Primers for the t values of triplicate reactions were averaged per sample, normalized to sperm-expressed histone H3.3 transcript hap2-1/+ flowers (transmembrane primers) or presented without fold-change ratios (V5C4 epitope primers). This difference in data presentation is due to the nature of the targets of each primer set: The transmembrane primers (TM) amplify the same sequence from both endogenous HAP2(GCS1) and CDS transcripts. This primer set thus allows us to quantify how much more HAP2(GCS1) variant mRNA is present compared to hap2-1/+. The epitope tag primers (V5C4) only recognize the CDS transgene  or 96 hours (self-pollinated pistils) after manual pollination, and prepared for analysis by chloral hydrate clearing Pistils were dissected 48  variant. Schematic of each variant (left) is paired with the data from each transgenic line and is compared to hap2-1/+  percentage of T3 progeny with specific hap2-1 genotypes Click here for additional data file.Figure S2HAP2(GCS1) variants in hap2-1/+, CDS/CDS transgenic lines. (A-C) mRNA abundance of the CDS variants. Quantitative real-time PCR data was normalized to values for sperm-expressed histone H3.3 t value for the V5C4 epitope tag mRNA sequence; values greater than 12, based on additional negative controls (data not shown), indicates an absence of mRNA. Note lower ΔCt values denote the presence of more mRNA in each sample. (C) Relative abundance of each CDS variant, compared to hap2-1/+. The quantity of mRNA encoding the transmembrane domain, which is shared by endogenous HAP2(GCS1) and all variants, was measured and expressed relative to hap2-1/+. Expression levels higher than one correspond to transcript quantities of CDS variants greater than found in hap2-1/+ flowers. (D) Representative ethidium bromide-stained agarose gel of qPCR amplification of the V5C4 epitope tag from the control line (AtN•AtC) or constructs that failed to complement hap2-1 Click here for additional data file.Figure S3A. thaliana HAP2(GCS1). Vertical lines in the CDS represent exon:exon junctions, these positions are marked by carets in B. (B) Primary sequence alignment of the N-terminal region for the three HAP2(GCS1) orthologs used in this study in the context of the entire Arabidopsis CDS. Amino acid identity at respective positions in the Arabidopsis sequence is shown with a dot (•); gaps in alignments are shown with a dash (-). Key Arabidopsis amino acid position numbers are given above the sequence.Alignment of HAP2(GCS1) orthologs used. (A) Schematic of the relationship between mRNA and CDS of (0.30 MB TIF)Click here for additional data file.


Genome-wide association studies have successfully identified several loci underlying complex diseases in humans. The development of high density SNP maps in domestic animal species should allow the detection of QTLs for economically important traits through association studies with much higher accuracy than traditional linkage analysis. Here we report the association analysis of the dataset simulated for the XII QTL-MAS meeting . We used two strategies, single marker association and haplotype-based association (Blossoc) that were applied to i) the raw data, and ii) the data corrected for infinitesimal, sex and generation effects.Both methods performed similarly in detecting the most strongly associated SNPs, about ten loci in total. The most significant ones were located in chromosomes 1, 4 and 5. Overall, the largest differences were found between corrected and raw data, rather than between single and multiple marker analysis. The use of raw data increased greatly the number of significant loci, but possibly also the rate of false positives. Bootstrap model aggregation removed most of discrepancies between adjusted and raw data when SMA was employed.Model choice should be carefully considered in genome-wide association studies. Genome-wide association studies (GWAS) have successfully identified loci underlying several complex diseases ,2 and quA total of 4665 animals with genotypes and phenotypes, the first 4 generations of the data set provided by the QTL-MAS Workshop [Initially, association analyses were performed using the raw data (Y = μ + SNP + e) fitted for each SNP. Next, the data were analyzed with a mixed model including the infinitesimal (a), sex (S) and generation (G) effects. Residuals from this mixed model were then used as input data in the haplotype-based analysis. For the corrected SMA, we used the same mixed model except that the SNP effect was estimated simultaneously: Y = μ + S + G + a + SNP + e. Mixed model analyses were carried out with QxPak, which employs a maximum likelihood approach .-8 (F-test), with the restriction that minimum distance between selected SNPs was 5 cM. When two significant SNPs were found within a 5 cM region, the most significant one was retained. However, not all of these putative QTLs are necessarily genuine QTLs. Recently, bootstrap model aggregation . To speFor the corrected-data SMA, an additive model was used with the same p-value threshold and minimum distance to select associated SNPs as with SMA on raw data. Significance tests in QxPak are based on likelihood ratio test. Intensive computational procedures to further control for false positives were not feasible with this mixed model approach, nor with the haplotype-based analyses.Blossoc, a linkage disequilibrium (LD) association mapping tool, was used for the haplotype-based analyses. This method attempts to build 'perfect' phylogenetic trees for each marker and scores these according to non-random clustering of affected individuals, judging high-scoring areas as likely candidates for containing disease affecting variation . AlthougThe agreement between methods and between data corrections was based on the percentage of coincident associated SNPs. A coincident associated SNP pair or match was defined as a pair of associated SNPs in two analyses whose distance was shorter than 5 cM. The percentage of SNP matches was calculated as the ratio between the number of matches and the sum of the matches and the number of SNPs uniquely detected by either of the two analyses to be compared. The degree of concordance of matching SNPs was the absolute difference in cM of the estimated location of coincident SNPs between analyses.Analyses were performed on a Linux server with dual Xeon processors and 8 Gb RAM. The total CPU time required for the analyses is shown in the Additional file In general, both SMA and Blossoc performed similarly in identifying the most strongly associated SNPs Figure , indepenIncorporating known population structure into the phenotype modeling reduced the noisiness of the association profile Figure . Peaks wThree to five-fold more SNPs were selected using raw data than with corrected data Table . Nonethe2 = 0.3), which was highly associated with the phenotype. The equivalent SNP in the SMA raw (323) showed low BPP (0.1). Therefore, SNP 331 is probably a false positive. SNP 3952 was detected with Blossoc and also with SMA raw (BPP = 0.99), suggesting that it is a true QTL. The SNP identified in the beginning of Chromosome 3 (2133) by the SMA method (BPP = 0.89) was also detected with Blossoc on raw data, but was not selected with Blossoc on corrected data. This SNP showed a well defined peak with corrected Blossoc, close to the significance threshold (HQ = 13). Therefore, SNP 2133 is probably also a true QTL. These results indicate that the thresholds defined here for both SMA and Blossoc on corrected data might be too conservative. Another SNP towards the middle end of this chromosome (2601) was detected only with Blossoc. This was the most distinct result found between methods . This SNP showed high LD with SNP 402 , and increased greatly when corrected data was used (maximum distance of 0.4 cM). Nevertheless, the establishment of a threshold is a complicated issue and has a profound influence on the results, especially when methods with different nature of scores are compared. Our results indicate that 8 SNPs are confidently associated to the trait, as found by all methods evaluated here with a small variation in cM among coincident SNPs.-5) of interaction with sex. The additive effects of males and females were similar in magnitude but of opposite sign.We also fit more complex models incorporating dominance and epistasis  ≥ 8. * – Significant associations, considering a BPP > 0.25 for SMA raw, decreased the number of associated SNPs to 15. Considering an adjusted threshold for Blossoc raw ≥ 65, to account for the inflation caused by the population structure, the number of significant associated SNPs decreased to 19.Click here for fileTotal CPU time required for the analyses, including the significance tests. 1 – a)1st step: 600 bins ; b) 2nd step: top interactions with -log10(p) >3 for 675 pairwise locations to refine . 2 – Correction with mixed model including sex, generation and infinitesimal effects using QxPak. Note: Model aggregation using 1000 bootstrap samples on the 33 putative QTLs of the SMA raw took 4 h 37 m 46 s , between loci (epistasis) and with sex were performed using only the corrected SMA procedure implemented on an R script. We used residual data from a mixed model including sex, generation and pedigree. This approximation is necessary given the computational burden of between loci interactions. For dominance and sex, we tested the effects of dominance or of the interaction between additive and sex against a model incorporating only an additive effect. No procedure for controlling false positives was done and only best results with strong -log10(p) are reported here. For the epistasis, a two step strategy based on LD between SNPs was used. First, we subdivided the genome in 1 cM bins, and tested epistasis between the bins. In each bin, the SNP with the highest average LD (r2) with all the other SNPs in the bin was chosen as the representative SNP of the bin (tagSNP) and used for the interaction testing. Cockerham parameterization of the model was used [10(p) ≥ 3 were selected in order to refine the location of the interacting loci. We refined these locations by evaluating interactions between all SNPs contained in the two bins initially detected to have some significant interactions. Further, we ranked the results for each type of interactions according to their significance value. Nonetheless, we control potential LD effect by removing SNPs in a minimal distance of 10 cM from the top ranked list (-log10(p) ≥ 6).was used  and all Click here for file


Meleagris gallopavo (turkey) individuals.The development of second generation sequencing methods has enabled large scale DNA variation studies at moderate cost. For the high throughput discovery of single nucleotide polymorphisms (SNPs) in species lacking a sequenced reference genome, we set-up an analysis pipeline based on a short read de novo sequence assembler and a program designed to identify variation within short reads. To illustrate the potential of this technique, we present the results obtained with a randomly sheared, enzymatically generated, 2-3 kbp genome fraction of six pooled A total of 100 million 36 bp reads were generated, representing approximately 5-6% (~62 Mbp) of the turkey genome, with an estimated sequence depth of 58. Reads consisting of bases called with less than 1% error probability were selected and assembled into contigs. Subsequently, high throughput discovery of nucleotide variation was performed using sequences with more than 90% reliability by using the assembled contigs that were 50 bp or longer as the reference sequence. We identified more than 7,500 SNPs with a high probability of representing true nucleotide variation in turkeys. Increasing the reference genome by adding publicly available turkey BAC-end sequences increased the number of SNPs to over 11,000. A comparison with the sequenced chicken genome indicated that the assembled turkey contigs were distributed uniformly across the turkey genome. Genotyping of a representative sample of 340 SNPs resulted in a SNP conversion rate of 95%. The correlation of the minor allele count (MAC) and observed minor allele frequency (MAF) for the validated SNPs was 0.69.We provide an efficient and cost-effective approach for the identification of thousands of high quality SNPs in species currently lacking a sequenced genome and applied this to turkey. The methodology addresses a random fraction of the genome, resulting in an even distribution of SNPs across the targeted genome. The scalability and availability of highly automated genotyping assays for single nucleotide polymorphisms (SNPs) has made the SNP a popular marker in genetic linkage and association studies in a variety of species. In humans, large-scale identification and characterization has resulted in a repository of over 14 million SNPs -6. The aHowever, many species, such as turkey, are still lacking a completely sequenced genome. Although high-throughput sequencing technologies are rapidly evolving and have drastically lowered the cost of whole genome DNA sequencing, the de novo assembly of a mammalian-sized genome remains a challenge . DespiteThe aim of this study was to develop an extremely cost effective method to detect high quality SNPs in unsequenced genomes. We applied this method to turkey, a species of considerable economic importance, and used the genome of a closely related species, chicken -22, to bSau3A and isolating the fragments in the size range of 2-3 kbp. This fraction consists of an estimated 5-6% of the turkey genome. The turkey genome has a high similarity to the chicken genome and is approximately the same size (~1.2 Gbp). Therefore, the isolated 5-6% fraction of the turkey genome represents approximately 62 Mbp. This estimate was confirmed by selecting all 2-3 kb fragments of an in silico Sau3A digest of the chicken genome build WASHUC2, which resulted in a total of 27,025 fragments representing 63.4 million bases. The turkey RRL was sequenced using the Illumina sequencing technique [Sau3A fragments. The resulting data set of short sequence reads forms the basis for contig assembly, providing sufficient sequence context flanking the SNPs to allow for the subsequent development of SNP genotyping assays.We prepared a pooled DNA sample consisting of DNA samples from six turkey individuals. A RRL was prepared by digesting the pooled DNA sample with echnique  after rand base. Subsequent removal of sequence reads with non-called bases resulted in almost 108 million reads, providing an estimated 56-fold coverage of 5-6% of the turkey genome. We used Sau3A to generate the RRL and, as expected, we observed that a fraction of the sheared DNA fragments started with the GATC restriction tag  have undergone relatively very few chromosomal rearrangements during evolution compared to mammals . MoreoveIr values suggest that the turkey genome is slightly less repetitive (0.6247) than the chicken genome (0.7126). The average Ir for the chicken genome was 0.3905 and ranged from 0.0793 in chromosome 19 to 1.3419 in chromosome 16. Compared to other eukaryotes like Human, Mouse and Arabidopsis [An assessment of the quality of our assembled turkey contigs was done by mapping the contigs to the chicken genome and comparing the results with the alignment statistics of turkey BES of the same size range. The results indicate that the contigs up to 300 bp, in general, are of good quality and that turkey BES share high sequence identity with the chicken genome. The comparison between the assembled contigs and BES indicate that most of these contigs represent valid sequences of the turkey genome. At increasing contig length, the number of sequences that align uninterrupted to a unique location in the chicken genome declines, dropping below 10% for contigs in the size range of 1000 bp. The fact that this decline is not observed for the turkey BES indicates that it is not due to small indels between the chicken and turkey sequences, but that this is an artifact caused by the assembly. These results indicate that at increasing contig lengths, the chance of mis-assemblage by SSAKE increases exponentially. However, because most SNP typing assays make use of the sequences directly flanking the SNP, this will only have a small effect on the success rate of the genotyping assays. At total of 7609 SNPs were identified on the assembled short read contigs of which 84% was flanked by sufficient sequence to allow probe design in a genotyping assay. To make the turkey reference more contiguous we used the chicken genome to identify contig pairs that uniquely mapped adjacent to each other, showing a small overlap. In 87% of these cases, overlapping contigs appeared to have identical sequences within the overlapping region. Although biased by the alignment algorithms used, which remove unaligned tailing ends of contigs, our comparative assembly results suggest that the mapped contigs are of a constant high quality and can be mapped with high accuracy. Therefore, these results allow the merging of the smaller contigs, resulting in a significant increase in the average length of the assembled turkey contigs. The resulting reference sequence appeared to be beneficial in the identification of SNPs and, in particular, increased the number of SNPs with sufficient flanking sequence for designing a genotyping assay. This benefit is clearly illustrated by the 4% increase in the total number of SNPs identified and 22% increase in SNPs with at least 40 bp of flanking sequence on both sides. The alignment of the turkey contigs with the highly similar chicken genome also turned out to be a good predictor of genotyping success rates for the SNPs Table . The SNPbidopsis  the chicbidopsis . This loTo further maximize the number of identified SNPs, the available turkey BES were added to the reference genome. Again, these additional sequences not only resulted in the identification of an additional 3357 additional SNPs, they also increased the number of SNPs with a sufficient amount of flanking sequence. The assembly of short read contigs and BES resulted in, at least, a 25% reduction of sequence redundancy in the assembled short read contigs. Removal of sequence redundancy in the reference genome is beneficial for downstream SNP detection because of the reduction in the number of sequence reads being assigned ambiguously to multiple locations on the reference genome during the alignment. SNPs predicted within sequence clusters containing these ambiguously mapped reads are indistinguishable from falsely predicted SNPs due to the clustering of paralogous sequences and thus discarded.Our conservative approach requiring a minimal MAC of three was designed to minimize false positive SNP discovery and, consequently, ignored large numbers of less abundant true nucleotide variations. The five additional SNPs we identified by PCR and sequencing that were not previously detected in silico are a typical consequence of applying a minimum redundancy cut-off. However, the selection for SNPs with a MAC of at least three drastically reduces the chance that sequencing errors are considered an SNP. Keeping the number of false positives as low as possible in general is more important than maximizing the number of SNPs. True nucleotide variation might also be lost during sequence assembly in which contigs are extended by a read only if the consensus base ratio is 0.6 or more. Single nucleotide polymorphisms with a MAF higher than 0.4 very likely break the contig extension; for this reason, they will only be detected on the tailing ends of assembled contigs. The absence of sequence context on one side of these polymorphisms further hampers the alignment of additional reads to form deep sequence clusters meeting the minimum allele count constraint applied during SNP detection. This concept explains the increase in the number of SNPs discovered on the extended reference genome though the number and total number of base pairs covered decreased. The occurrence of a few SNPs with an estimated MAC higher than 0.4 can be explained by a lower MAC in the assembly data subset compared to the MAC in the SNP detection data subset.Our strategy of assembling a reference genome from short next-generation sequences of a randomly sheared RRL of pooled genomes, followed by subsequent SNP detection by aligning the same short reads against this reference genome, is a cost-effective and efficient method for the high rate discovery of SNPs in species with unsequenced genomes. The availability of a closely related sequenced genome is not a requirement but comparative mapping facilitates the selection for high quality SNPs. Our comparison with the chicken genome further suggests that the high quality SNPs identified in this report most likely cover the complete turkey genome and provide the first large SNP resource for genetic studies in turkey.0 individuals from a male and a female turkey line, selected for growth and reproduction characteristics respectively, three samples from each line. The selection of the restriction enzyme was based on the 10 to 20-fold reduction of genome complexity in the 2-3 kb size region run on a 1.5% agarose gel. Ten enzymes were tested ; of which, Sau3A was finally selected to make the Turkey RRL because of good digestion performance and a desired 5-6% fraction of the genome in the 2-3 KB size range. In total, 100 μg of the pooled DNA was digested using 1,000 units of the restriction enzyme Sau3A in a total volume of 240 μl. The digested pooled DNA sample was fractionated on 1.5% low melting point agarose gel at 100 V for 3 hours and stained with ethidium bromide. The 2-3 kb sized fraction was sliced out of the gel, melted, and loaded on a new 1.5% low melting agarose gel for another fractionation at 100 V for 1 hour. The 2-3 kb fraction was sliced out of the gel and the DNA was recovered by β-Agarase-I treatment, purified by phenol-chloroform extraction, and precipitated with 2-propanol. DNA was dissolved in TE with a concentration of 50 ng/μl. The isolated DNA was randomly sheared, end-repaired, and prepared using the Illumina Sample preparation kit [Genomic DNA was extracted from the blood of six unrelated Ftion kit .Five picomole aliquots of the library were processed with the Illumina Cluster Generation Station  following the manufacturer's recommendations. The Illumina IG Genome analyzer  was programmed to produce a theoretical fixed read length of 36 bp. Images were collected over 4,040 tiles, each of which contained 685-41,954 clusters.Reads were trimmed to 32 bp, and reads with an occurrence of more than four times the theoretical coverage were discarded. Two data sets were created; one was the assembly data set and the other the SNP detection data set. In the SNP detection data set, we required a per base quality score of at least 10 if the read was singly represented. For the assembly data set, we required that a particular 32 bp sequence be represented two times or that every base in the 32 bp sequence have a quality score of at least 20.Furthermore, the assembly data set was analyzed for repetitive elements using RepeatMasker  with defThe short read contigs (c50) were mapped on the chicken genome with the selection criteria that a contig had to align along 80% of its length with at least 60% identity. Short read contigs in the size range of 50-100 bp were mapped using Megablast , and shoThe turkey genome reference sequence was further extended by adding 20,388 publicly available BES of the CHORI-260 turkey BAC library  to all sThe SNP detection was performed with MAQ  , 60 ng template DNA, and 4 pmol of each of the two primers. The PCR cycling conditions were 95°C for 5 min, 35 cycles of 30 s at 95°C, 45 s at 55°C, and 90 s at 72°C, followed by a final elongation step of 72°C for 2 min.The PCR products of the six animals from the discovery panel were purified using millipore PCR cleanup filter plates (MSNU03050) and sequenced using the DETT sequencing kit according to the manufacturer's specifications .Unincorporated dye terminator was removed by ethanol precipitation and analyzed on a 48-capillary ABI 3730 DNA analyzer (Applied Biosystems). Sequencing results were further analyzed with the STADEN Package.® Genotyping assay on an Illumina® BeadXpress with veraCode™ technology. Selection criteria for the SNPs were based on the Illumine design score (above 0.8) and MAC ranging from .5 to .15 detected by MAQ [0 animals from the discovery panel and 29 additional F0 animals and further consisted of 47 F1 animals and 14 unrelated animals derived from 2 inbred lines. Genotyping results were analyzed in Beadstudio.The second method of validation was genotyping the SNPs using the Illumina GoldenGated by MAQ . For theThe correlation between allele frequency estimated by sequencing and genotyping was calculated over 310 observations  SNP database (dbSNP) under submitter handle WU_ABGC. NCBI_ss 142460378-142468928 excluding  represents predicted SNPs that were not tested in our animal panel. Predicted SNPs that were confirmed are listed in Additional file HHDK designed and developed the genome reference construction method and SNP prediction method and wrote the manuscript. AV, BWD, and RPMAC collected and prepared the samples and performed the initial validation and genotyping analysis. TFCC, JHD, and RPMAC coordinated and supervised the DNA sequencing. MAMG and RPMAC coordinated and supervised the experiment implementation and assisted in the manuscript preparation. All authors read and approved the final manuscript.Distribution of short read turkey contigs, turkey BES, and SNPs on chicken chromosomes. In black, short read contigs <100 bp; in blue, short read contigs ≥ 100 bp; in red, BES; in yellow, BES-short-read contigs; and in green, SNPs. On the X-axis, the chicken genome in 200 kb intervals. On the Y-axis, the frequency of mapped turkey features for a specific chicken genome interval.Click here for filePrimer sequences, PCR product sizes, and number of SNPs confirmed per amplicon for the 25 loci evaluated for genome similarity and SNPs. 1C = chicken, T = turkey. First, 13 loci were used in the determination of genome conservation between chicken and turkey. Next, 12 loci were used to validate the contig assembly and SNP detection procedure.Click here for fileValidation of candidate SNPs by genotyping a panel consisting of 96 turkeys. The 96 animals included the six turkeys from which the pooled DNA sample for SNP discovery was prepared. Listed are the 324 polymorphic SNPs out of 340 predicted SNPs that resulted in a working assay. For all SNPs, the minor allele frequencies (MAF) were calculated for the group of six turkeys from which the pooled DNA sample was prepared and for the total panel. Also, the fraction of individuals heterozygous for an SNP locus was calculated.Click here for file


As we move forward from the current generation of genome-wide association (GWA) studies, additional cohorts of different ancestries will be studied to increase power, fine map association signals, and generalize association results to additional populations. Knowledge of genetic ancestry as well as population substructure will become increasingly important for GWA studies in populations of unknown ancestry. Here we propose genotyping pooled DNA samples using genome-wide SNP arrays as a viable option to efficiently and inexpensively estimate admixture proportion and identify ancestry informative markers (AIMs) in populations of unknown origin. We constructed DNA pools from African American, Native Hawaiian, Latina, and Jamaican samples and genotyped them using the Affymetrix 6.0 array. Aided by individual genotype data from the African American cohort, we established quality control filters to remove poorly performing SNPs and estimated allele frequencies for the remaining SNPs in each panel. We then applied a regression-based method to estimate the proportion of admixture in each cohort using the allele frequencies estimated from pooling and populations from the International HapMap Consortium as reference panels, and identified AIMs unique to each population. In this study, we demonstrated that genotyping pooled DNA samples yields estimates of admixture proportion that are both consistent with our knowledge of population history and similar to those obtained by genotyping known AIMs. Furthermore, through validation by individual genotyping, we demonstrated that pooling is quite effective for identifying SNPs with large allele frequency differences  and that these AIMs are able to differentiate two closely related populations (HapMap JPT and CHB). Many association studies have been published looking for genetic variants contributing to a variety of human traits such as obesity, diabetes, and height. Because the frequency of genetic variants can differ across populations, it is important to have estimates of genetic ancestry in the individuals being studied. In this study, we were able to measure genetic ancestry in populations of mixed ancestry by genotyping pooled, rather than individual, DNA samples. This represents a rapid and inexpensive means for modeling genetic ancestry and thus could facilitate future association or population-genetic studies in populations of unknown ancestry for which whole-genome data do not already exist. The alternative approach is to apply PCA to individual-level genetic data for a large number of loci, typically obtained from GWA studies, to infer global ancestry. The limitation of this approach is the high cost of obtaining genome-wide genotype data from a sizable cohort, particularly when studying a less well-funded phenotype. Therefore, the need to efficiently (both in terms of cost and time) assess the biogeographical ancestry in the study population and to rapidly screen hundreds of thousands of genetic makers for AIMs will be valuable for future genetic association and demographic studies. This is particularly true for populations of relatively complicated admixture or of origins dissimilar to standard reference populations such as those catalogued by the International HapMap Consortium Currently, two main approaches exist for inferring genetic ancestry. If the ancestral populations of the population being studied are known, ancestry informative markers (AIMs) numbering in the hundreds can be genotyped to infer global ancestry via principal components analysis (PCA) or a clustering-based algorithm ; two pools of 321 African American women (MEC-AA), two pools of 252 Native Hawaiian women (MEC-H), two pools of 332 Latina women (MEC-L), and two pools of 202 Japanese American women (MEC-J) from Los Angeles, CA and Honolulu, HI; six pools of 688 Jamaican samples from Kingston, Jamaica (GXE); and four pools of 480 Jamaican samples from Spanishtown, Jamaica (SPT) see . Second,i.e., SNPs with pooled AF estimates significantly different from those predicted by the pseudopopulation. Finally, we validated putative AIMs by genotyping the individuals who comprised the pools.To both assess genetic ancestry and identify new AIMs particular to the admixed populations, we first used our QC-filtered pooled genotype data to estimate the relative contributions of different continental ancestries to each of our admixed populations . Second, for each of our admixed panels we constructed a corresponding weighted reference panel (pseudopopulation) based on the estimated admixture proportion, and identified putative AIMs, i.e., the admixture proportion), we applied a linear regression-based approach to the QC-filtered data, overcoming the uncertainty in pooled AF estimates with the high density of SNPs. For each SNP, we modeled the estimated allele frequency of the pooled sample as a linear combination of the known allele frequencies in the HapMap YRI (West African), CEU (European), and/or CHB/JPT (East Asian) reference panels. The associated regression coefficients can be thought of as estimates of the proportional contribution from each of the reference panels  that had been already genotyped in the laboratory. Given the success of genotyping pooled DNA in identifying disease variants with large AF differences between cases and controls see , we expeST = 0.0067). We first removed population outliers along any of the top 10 principal components by EIGENSTRAT ST between JPT and CHB is 0.026, with a correlation of 0.946 with the true axis of variation , and will likely be effective in distinguishing populations from neighboring countries  and CHB (Han Chinese) . Therefore, the extent of the summed proportion of admixture of a pooled panel will not necessarily correlate with the expected number of AIMs with large AF differences.In contrast to the MEC-H and MEC-L panels, the Jamaican pools (GXE and SPT) appeared to be much better modeled using just the YRI and CEU reference panels when we compared the distribution of AF differences among the top putative AIMs  and the pulation , to thosIn light of the results presented here, we envision that studies using pooled DNA have great potential utility for future association studies. Given the success of identifying variants with large effect sizes using pooled DNA Although we utilized the availability of individual genotypes in informing our QC filters, individual level genotypes are not required to establish filter parameters. Given a population of individuals randomly pooled into multiple pools in order to assess the genetic ancestry of the population, one can compare pools in a pair-wise case/control-like fashion where no associations would be expected. Then, by assessing changes in the genomic control inflation factor Finally, it should be noted that the recommendations for use of genotyping pooled DNA on a genome-wide array – to determine genetic ancestry, to screen for disease variants with large AF differences, and to study population demographics – are made based on the current state of the technology and methodology. Given our experience with the Affymetrix 6.0 platform, we have focused on applications that require the detection of moderate to large allele frequency differences. We anticipate that advances in the genotyping platform and improvements in sample handling may enhance the overall data quality and accuracy of allele frequency estimates, and that the same filter parameters may retain more SNPs for analysis than did the conservative approach taken here. Thus, given a sufficiently robust platform, it may be increasingly possible to efficiently search genome-wide for variants that have small allele frequency differences between samples using pooled DNA.The cohorts used in this study consisted of 775 African American individuals from Maywood, IL (MAY); 1,039 and 1,467 Jamaican individuals from Kingston (GXE) and Spanishtown (SPT), Jamaica, respectively; and women from the Hawai'i and Los Angeles Multi-Ethnic Cohort (MEC) AAθ measuring the degree of rotation of the AA genotype cluster with respect to the horizontal axis  for each autosomal SNP as the following , with respect to which new axes x' and y' were established. We then defined poolθ, the angle of the replicate pool intensity with respect to the x' axis as:poolx' and pooly' represent the x'- and y'-coordinates of the replicate pool intensity, and NF is the normalization factor to adjust for differential allelic signal intensities using the location of the AB genotype cluster, akin to the various forms of k-correction proposed  for the A allele for each replicate given 2 test statistic  as the proportion of SNPs with an expected  P-value of >0.05 that were ranked among the top 0.05% SNPs by estimated pooling P-value. PFPs were calculated for the pre- and post-filtered list of SNPs at various filter cut-offs to establish the final values used for each filter.Informed by the genotype data from the individuals comprising the MAY pool, we explored several possible filtering methods to identify those that most efficiently eliminated SNPs that genotyped inconsistently in pooled DNA. We first compared the distributions of the 200 worst and best performing SNPs with respect to parameters of various potential filters to determine both which filtering methods were most effective and to approximate values for filter cut-offs. The worst and best performing SNPs were defined as follows: for each SNP, we calculated the corrected χtatistic  , 397 SNPs (or at least one proxy with r2>0.8) passed the three QC filters in pooling, for a false negative rate of 0.509 due to QC filtering.  The relatively elevated inflation factor after applying the three filters likely represents error in the pooled AF estimates we were unable to account for in our study design. As one is often searching for variants with small AF differences between case and control groups in a disease association, we also recommend fitting the distribution of the pooled AF estimates from the case and control pools to the overall pooled AF distribution to ensure a similar distribution of AF estimates between the case and control pools. In our experience this further lowers the inflation factor (from 1.38 to 1.08 in our data) and improves the PFP .In the manner described above, we established three filters that were effective in eliminating SNPs that genotyped poorly or inconsistently: 1) separation of individual genotype clusters based on Fisher's linear discriminant, a measure of distance between two clusters (FLD-filter), 2) radius of intensity of the signal from pooled DNA (r-filter), and 3) population minor allele frequency estimated from pooled DNA (MAF-filter) see . ApplyinBy taking advantage of the individual genotypes from the MAY pools, we also established a filter to measure the consistency of the AF estimates for each SNP. Over the four MAY pools, we calculated the difference in the AF estimates between the pooled sample and the individual samples, and the variance across the four pools was used as a measure of consistency of the AF estimates  population j for SNP i with respect to allele A according to the Affymetrix 6.0 array annotation . jβ is the regression coefficient and is an estimate of the proportion of contribution from population j, and c is the constant combining error and unexplained ancestry . Because allele A assignment on Affymetrix 6.0 array is independent of the minor allele at the locus, E(jiP) = E(uiP) = 0.5, which is necessary for the accurate estimation of jβ using regression , using all SNPs that passed our QC filters (see above) and had genotyping success rates >0.8 in all three HapMap populations . We used the three HapMap populations genotyped on the Affymetrix 6.0 array as our reference ancestral populations c.To estimate the proportion of admixture in the pooled populations, we employed a linear regression model where the estimated allele frequency for SNP gression . βj's an2 statistic will have a greatly inflated value. Here we corrected for this χ2 statistic inflation using a method proposed by Visscher and Le Hellard 2 statistic based on estimated allele counts derived from the estimated pooled allele frequency.  V is the sum of the sampling variance for the case and control pools, given by:pcasee) and var(pcontrole) are the squared standard errors among the pooled AF estimates from all of the replicates for the case and control pools, respectively. V, var(pcasee) and var(pcontrole) were calculated for each SNP tested for association. When multiple case or control pools were available, the total pooled allele frequency used was the weighted average  of the pooled allele frequencies estimated for each pool. The pooled variance, var(p_totcasee), is then given by:k case pools each with in replicates. iSE is the standard error of the estimated AF for the ith pool. The pooled variance for the control pool was calculated similarly.One factor that influences the analysis of pooled but not individual genotype data is that when DNA pools are genotyped, an estimated rather than observed number of allele counts is obtained. The variance around the estimated allele frequency obtained from pooled genotyping includes variance that arises specifically due to pooling in addition to the sampling variance. If the additional variance is not taken into account, a standard χWhen identifying AIMs informative for ancestry over and above that explained by available reference panels, all pools from the population being studied were designated the “case” pools, and the pseudopopulation was used as the “control” pool. In this case, the sampling variance for the pseudopopulation was based on a population size of either 120 individuals (if only YRI and CEU were used) or 210 individuals . Pooling specific variance for the pseudopopulation was assumed to be 0.http://www.hapmap.org). Pseudopopulations were constructed based on the estimates of admixture proportion using the HapMap populations as proxies for the ancestral populations.Ancestry informative markers were selected for the GXE, SPT, MEC-H, and MEC-L panels by comparing the estimated allele frequency in each population by pooling to its respective weighted reference panel (pseudopopulation), or for the MEC-J panel by comparison to the AF from the HapMap phase 3 CHD  population  with the SNP of interest. Here, P-values measure the extent to which pooled allele frequencies differ from those expected using the pseudopopulation. LD was determined using the set of pre- and post-QC filtered sets of SNPs, based on the HapMap YRI population; SNPs within 20 Mb of the SNP of interest were considered to be in LD if they had r2>0.5 in HapMap YRI with the SNP of interest. “Encouraging” SNPs had at least one SNP in LD with a GC-corrected P-value<0.05 and had at least half of the surrounding SNPs (those in LD) with GC-corrected P-values<0.1. “Discouraging” SNPs had none of the SNPs in LD with GC-corrected P-values<0.1. The remaining SNPs were categorized as “inconclusive,” a category also encompassing SNPs with no other SNPs in LD. Non-discouraging  AIMs were then further pruned to remove any AIMs within 4 Mb of each other to obtain a panel of independent AIMs.For the Jamaican pools, SNPs were divided into three categories, based on the P-value, excluding any SNPs categorized as discouraging when either the filtered or unfiltered set of SNPs in LD was examined for categorization. Second, we selected an additional 25 SNPs with GC-corrected P-values <1×10−3, at least 2 SNPs in LD with the SNP of interest from the unfiltered dataset, and a categorization of encouraging when using both the filtered and unfiltered datasets for a set of SNPs in LD. For this second list, we chose the SNPs with the largest number of SNPs that were in LD that also had P-values <0.05.We chose a set of 50 candidate AIMs each in GXE and SPT to be validated by individual genotyping, using two complementary approaches. First, we selected the top 25 SNPs based on GC-corrected i.e., JPT/CHB for MEC-H and MEC-J, and CEU for MEC-L).Identification of AIMs in the MEC-H, MEC-L, and MEC-J panels was performed similarly, with the exception that AIMs in MEC-H were not pruned by distance in order to allow validation using SNPs previously genotyped in those samples. The HapMap reference panel representing the major ancestry in each of the MEC pools was used as the reference panel for LD determination Click here for additional data file.Figure S2Distribution of allele frequency differences among the top 200 AIMs for the GXE and SPT pools. The top 200 putative AIMs in the GXE and SPT pools were identified as described in the text and in (0.21 MB TIF)Click here for additional data file.Figure S3O, is defined, conceptually, as the intersection between the two lines that run through the center of the two homozygous clusters angled in the same direction as the clusters Click here for additional data file.Figure S4P-value of >0.05 ranked among the top 0.05% SNPs by estimated pooling P-value  FLD-filter, (B) MAF-filter, and (C) hist-filter are plotted against the number of SNPs remaining after filtering and the effectiveness of the QC filter, as measured by proportion of false positives (PFP) in A and B, or by genomic control inflation factor in C. The solid lines correspond to the number of SNPs that passed the QC filter in the MAY pools (A and B) and in the MEC-AA pool (C), while the dotted lines correspond to the PFP. PFP is defined as the proportion of SNPs with an expected  stic see ). For th(0.33 MB TIF)Click here for additional data file.Figure S5r/r' ratio. We calculated the radius r as the distance from the origin O . r was then normalized using the expected value for an average individual DNA sample on the same plate, given by r'. r' was defined as the distance from the origin O to I, the expected intensity signal of the individual DNA sample given the same estimated allele frequency. There is one r/r' ratio for each pool replicate at each SNP. Red circles represent the centers of the genotype clusters.Determination of the (0.60 MB TIF)Click here for additional data file.Figure S6r/r' ratio greater than or equal to the cut-off value to be retained for downstream analysis; light blue lines required 12 of 13 replicates; dark green lines required 11 of 13 replicates; light green lines required 10 of 13. Solid lines correspond to the number of SNPs passing the QC filter at the particular cut-off value; dotted lines correspond to the PFP. For all values of the filter cut-off, requiring fewer replicates to pass retained a greater number of SNPs. At cut-off values of 0.9 to 0.95 the PFP increased, perhaps reflecting the removal of real associations. To optimize both PFP and SNP coverage, either a cut-off value of 0.8, requiring 11 of 13 passing replicates, or a cut-off value of 0.85, requiring 12 of 13 passing replicates, may be appropriate. Here we adopted a cut-off threshold of 0.8 and required a pass rate of 80% among the replicates.Proportion of false positives and genomic coverage at various r-filter cut-offs. We evaluated the effect of using various cut-offs for the r-filter and requiring SNPs from a variable number of replicates (cases and controls combined) to pass the filter. Dark blue lines required all 13 case and control replicates of the MAY pools to have an (0.64 MB TIF)Click here for additional data file.Table S1ST between pooled panels and their respective pseudopopulations.F(0.03 MB DOC)Click here for additional data file.Table S2List of ancestry informative markers identified from pooled Native Hawaiian and pooled Latina populations.(0.09 MB XLS)Click here for additional data file.Text S1Supplemental methods.(0.07 MB DOC)Click here for additional data file.Text S2Formula derivations.(0.06 MB DOC)Click here for additional data file.


While Simple Sequence Repeats (SSRs) are extremely useful genetic markers, recent advances in technology have produced a shift toward use of single nucleotide polymorphisms (SNPs). The different mutational properties of these two classes of markers result in differences in heterozygosities and allele frequencies that may have implications for their use in assessing relatedness and evaluation of genetic diversity. We compared analyses based on 89 SSRs (primarily dinucleotide repeats) to analyses based on 847 SNPs in individuals from the same 259 inbred maize lines, which had been chosen to represent the diversity available among current and historic lines used in breeding. The SSRs performed better at clustering germplasm into populations than did a set of 847 SNPs or 554 SNP haplotypes, and SSRs provided more resolution in measuring genetic distance based on allele-sharing. Except for closely related pairs of individuals, measures of distance based on SSRs were only weakly correlated with measures of distance based on SNPs. Our results suggest that 1) large numbers of SNP loci will be required to replace highly polymorphic SSRs in studies of diversity and relatedness and 2) relatedness among highly-diverged maize lines is difficult to measure accurately regardless of the marker system. Until recently, Simple Sequence Repeats (SSRs), also called microsatellites, have been the genetic markers of choice, because they are economical to score, have high allelic diversity, and are usually selectively neutral −8/bp/generation), the vast majority of SNPs are biallelic, and thus have a maximum heterozygosity of 0.5. Not only is the SSR mutational rate much higher  to analyses based on 847 SNPs in individuals from the same 259 inbred maize lines. These lines represent the widest genetic diversity available among current and historic lines used in breeding As expected, the allelic diversities and frequency spectra were very different for these data sets . Due to To make greater use of the information content of the SNP data, we constructed haplotypes for those loci where more than one SNP had been scored from the same amplicon see . The 554et al.et al.Using SSR variation, Liu For all values of k tested, it is clear that the SNP data did not contain sufficient information to resolve all the relationships that were detected by SSR variation, resulting in a lower percentage of individuals that could be assigned to a population. This was not due simply to the higher allele number, since a reduced data set of 80 SSRs with 1694 alleles (the same number as the SNP data set) resulted in the same percent assignment as the full data set (data not shown).et al.2 = 0.88 and 0.93, respectively), but there was clearly much more spread along the y axes (SNPs) than along the x axes (SSRs): 116 lines had NSS membership between 0.2 and 0.8 based on SNPs, as opposed to only 58 lines that fell in that range based on SSRs. In no case was an individual assigned to a different population based on different marker information. In a small number of cases, SNP data resulted in assignments for individuals that were unassigned using SSR data. One such example is line F2834T, indicated by an arrow in each panel of The assignment of individuals to populations was very consistent among marker types, for those individuals that were assigned. Assuming three populations as described by Liu 2 = 0.73, 2 = 0.11). The lower panel of Distance matrices based on allele sharing were constructed for all pairs of individuals using either SSR data or total SNP data, and the relationship between the distance matrices was plotted . For theGiven that the SSR data set had a much larger proportion of low frequency alleles, we tested whether this difference was responsible for the decrease in resolution provided by the SNP data set. To do this, we divided the SNP data into five frequency classes, calculated all pairwise distances based on each class, and calculated the correlation between those distances and the SSR-based distances. Although the results were slightly confounded by sample size, et al.Liu This comparison is somewhat misleading, however, because of the ascertainment of the SNPs in our data set. If all 259 lines had been fully re-sequenced, there would be more rare alleles to be captured. We therefore simulated a set of 847 unlinked SNPs in 259 individuals under the standard neutral model (SNM), producing the full frequency spectrum of variation. As expected, the percent of alleles captured in a small core set dropped substantially (from 98% to 83% for a subset of 20), but was still much higher than the percent of SSR alleles captured in a core set of the same size . FurtherstF was very similar regardless of marker set used (0.08–0.10), with stF based on SSRs being in the lower end of the range, as expected because of their higher heterozygosity. stF based on individual SNP loci, however, varied from 0 to 0.568; some of these differences in stF may be due to the action of natural or artificial selection at particular loci. To test this hypothesis, we used the FDIST2 program stF and eH. Loci that have an unusually large value of stF, given their heterozygosity, are candidates for having experienced selection.Overall 2 = 1), showed a pattern that was very common among the SNPs with higher stF: the NSS and TS populations had the same allele at very high frequency, while the SS population had the alternate allele at a very high frequency. Usually the allele at high frequency in NSS and TS was also at high frequency in Mexican samples that included teosintes (these frequencies can be viewed at www.panzea.org).We found that 32 SNPs, about 3.8% of the 847 loci tested, had p-values <0.05 (uncorrected for multiple tests), so there was little evidence for selection. P-values for two SNPs, both from the same locus , were the only ones that were significant using a Bonferroni-corrected critical value of 0.05/847 = 0.000059. These two SNPs, whose alleles are in complete linkage disequilbrium  times more biallelic markers are needed to achieve the same genetic distance accuracy as a set of microsatellites with k alleles. In our case, the average number of alleles per SSR locus was about 20; thus we should need [(20–1) * 89] = 1691 SNP markers to achieve the same accuracy; we had almost exactly half that many SNP markers.These results are consistent with theoretical predictions The difference in information content of SSRs and biallelic markers may also be due to differences in the frequency spectrum: SSR loci have many more low-frequency alleles than SNP loci, since at least half of all SNP alleles must be at high or intermediate frequency. This frequency difference is even greater when ascertained SNPs are genotyped.et al.et al.et al., although, regardless of the number of populations in the model, far more individuals (44.8–47.5%) were classified as mixed using SNP data. It was surprising that, as k was increased, the likelihood increased but the percent assignment declined.Population genetics theory predicts that “preferentially discovering SNPs with high heterozygosity leads to an underestimation of the magnitude of structure” et al.Our analysis showed that most maize lines in a diverse sample are separated by a large genetic distance and, consistent with the results of Jones et al.Yu et al.The development of core sets is often described in terms of the fraction of SSR alleles retained in subsets of the collection. An implicit assumption is that the presence of unique SSR alleles in an individual or population is an indicator of the presence of unique functional variation. As stated in Laborda We have shown that 98% of the (ascertained) SNP allelic variation in a sample of 259 diverse lines can be captured in a set of 20 lines, as opposed to only 48% of the SSR alleles. How much of this difference is due to the loss of rare SNP alleles due to ascertainment? Data sets simulating fully re-sequenced data showed that rare SNP alleles are indeed missed, however, because maize is not a population at equilibrium, it is not clear how many singletons would have been found by complete re-sequencing of these inbred lines. In a mix of landraces and inbreds, Tenaillon et al all functional variation, since different combinations of SNP alleles (both within and between loci) will give rise to haplotypes that may differ in their contributions to phenotypes of interest. SSR allelic diversity should be positively correlated with SNP haplotypic diversity for the simple reason that both attributes are functions of effective population size.  As a proxy for larger effective population size, maximal SSR diversity ensures that novel combinations of functional SNP alleles are more likely to be captured in a sample. Note that this applies only at the genome-wide level: Payseur and Cutter However, even if every SNP were observed and captured, SNP diversity at individual sites is unlikely to capture While SNPs represent the latest technology, and have a number of technical advantages, there may be biological questions for which SSRs provide higher quality information. However, conclusions about the relative usefulness of SSRs and SNPs will vary depending on the specifics of the particular study. Because the SSR mutational process is very heterogeneous among loci Individuals from 259 maize inbred lines were used in this study: 33-16, 38-11, 4226, A188, A214N, A239, A272, A441-5, A554, A556, A6, A619, A632, A634, A635, A641, A654, A659, A661, A679, A680, A682, B10, B103, B104, B105, B109, B115, B14A, B164, B2, B37, B46, B52, B57, B64, B68, B73, B73Htrhm, B75, B76, B77, B79, B84, B97, C103, C123, C49A, CH701-30, CH9, CI187-2, CI21E, CI28A, CI31A, CI3A, CI64, CI66, CI.7, CI90C, CI91B, CM174, CM37, CM7, CML10, CML103, CML108, CML11, CML14, CML154Q, CML157Q, CML158Q, CML218, CML220, CML228, CML238, CML247, CML254, CML258, CML261, CML264, CML277, CML281, CML287, CML311, CML314, CML321, CML322, CML323, CML328, CML331, CML332, CML333, CML341, CML38, CML45, CML5, CML52, CML61, CML69, CML77, CML91, CML92, CMV3, CO106, CO125, CO255, DE1, DE_2, DE_3, DE811, E2558W, EP1, F2834T, F44, F6, F7, GA209, GT112, H105W, H49, H84, H91, H95, H99, Hi27, Hy, I137TN, I205, Ia5125, IDS91, K148, K4, K55, K64, Ki11, Ki14, Ki21, Ki3, Ki43, Ki44, Ky21, Ky226, Ky228, L317, L578, M14, M162W, M37W, MEF156-55-2, Mo17, Mo18W, Mo1W, Mo24W, Mo44, Mo45, Mo46, Mo47, MoG, Mp339, MS1334, MS153, MS71, Mt42, N192, N28Ht, N6, N7A, NC222, NC230, NC232, NC236, NC238, NC250, NC258, NC260, NC262, NC264, NC290A, NC294, NC296, NC296A, NC298, NC300, NC302, NC304, NC306, NC310, NC314, NC318, NC320, NC324, NC326, NC328, NC33, NC336, NC338, NC340, NC342, NC344, NC346, NC348, NC350, NC352, NC354, NC356, NC358, NC360, NC362, NC364, NC366, NC368, ND246, Oh40B, Oh43, Oh43E, Oh603, Oh7B, Os420, Pa762, Pa875, Pa880, Pa91, R109B, R168, R177, R229, R4, SC213R, SC357, SD44, T232, T234, T8, Tx303, Tx601, Tzi10, Tzi16, Tzi18, Tzi25, Tzi8, Tzi9, U267Y, Va102, Va14, Va17, Va22, Va26, Va35, Va59, Va85, Va99, VaW6, W117Ht, W153R, W182B, W22, W64A, WD, Wf9. These lines have been previously described et al.http://www.maizegdb.org/).Of the 89 SSRs used in this study, 76 are dinucleotide repeats, 5 are trinucleotide repeats, 7 are tetranucleotide repeats, and one is a pentanucleotide repeat. Analysis of a largely overlapping data set has been published before A collection of 913 SNPs that had been found to be polymorphic in a set of 14 maize inbred lines and 16 inbred teosintes http://www.panzea.org/db/searches/webform/moldiversity_search.After exclusion of lines not common to the two data sets and markers with >20% missing data, the final data set consisted of 259 individuals scored for 847 SNPs and 89 SSRs. The SNPs were located in 563 PCR products throughout the genome, thus representing 563 loci. A list of the SNPs can be found in Haplotypes were constructed when more than one SNP had been scored from a single amplicon. Because the individuals sampled came from inbred lines, the vast majority of SNP genotypes were homozygous, making haplotypes unambiguous. In cases where more than one SNP was heterozygous in an amplicon from one individual, haplotypes were inferred if only two haplotypes were observed in homozygous individuals , otherwise they were treated as missing data.Summary statistics, genetic distances , core sets, and input files for STRUCTURE were obtained using PowerMarker 1) 89 SSRs.2) 847 SNPs.3) 554 SNPs or SNP haplotypes. SNPs from the same amplicon were grouped into haplotypes that were recoded as alleles. There were 251 2-SNP loci and 21 3-SNP loci, as well as 282 isolated SNPs. If the genotype of any SNP at a locus was missing in an individual, the entire locus was treated as missing data for that individual.4) Data sets 1 and 2 combined.For each value of k, STRUCTURE was run three times with burn-in and runs of 50,000 each. The admixture model was used, allele frequencies were assumed to be correlated, and alleles were assumed to be unlinked (an assumption that is violated by data sets 2 and 4). An individual was assigned to a population if it had ≥0.8 membership in that population.stF calculation, individuals were designated as SS, NSS, TS, or mixed, according to the results of STRUCTURE using the SSR data set with k = 3. The three groups identified by STRUCTURE were classified as Stiff Stalk (SS), Non-Stiff Stalk (NSS), or Tropical-Semitropical (TS) based on the membership of individuals that typify those groups For purposes of stF observed for individual SNPs were unusually large, given average stF and locus-specific heterozygosity. Parameters for the simulations were: demes = 3, sampled populations = 3, sample size = 50 (following the recommendation of the FDIST2 documentation). Average stF in the simulations = 0.1, the average observed for the SNP data set.We used the program FDIST2 et al.Three data sets of 847 SNPs in 259 individuals were simulated using the program ms Dataset S1SNP loci used in this study.(0.03 MB DOC)Click here for additional data file.


Streptococcus agalactiae (Group B Streptococcus (GBS)) is an important human pathogen, particularly of newborns. Emerging evidence for a relationship between genotype and virulence has accentuated the need for efficient and well-defined typing methods. The objective of this study was to develop a single nucleotide polymorphism (SNP) based method for assigning GBS isolates to multilocus sequence typing (MLST)-defined clonal complexes.It was found that a SNP set derived from the MLST database on the basis of maximisation of Simpsons Index of Diversity provided poor resolution and did not define groups concordant with the population structure as defined by eBURST analysis of the MLST database. This was interpreted as being a consequence of low diversity and high frequency horizontal gene transfer. Accordingly, a different approach to SNP identification was developed. This entailed use of the "Not-N" bioinformatic algorithm that identifies SNPs diagnostic for groups of known sequence variants, together with an empirical process of SNP testing. This yielded a four member SNP set that divides GBS into 10 groups that are concordant with the population structure. A fifth SNP was identified that increased the sensitivity for the clinically significant clonal complex 17 to 100%. Kinetic PCR methods for the interrogation of these SNPs were developed, and used to genotype 116 well characterized isolates.A five SNP method for dividing GBS into biologically valid groups has been developed. These SNPs are ideal for high throughput surveillance activities, and combining with more rapidly evolving loci when additional resolution is required. Streptococcus agalactiae  is a significant human pathogen, particularly of newborn infants. The population structure of this species has been examined using restriction digestions of the genome ) is the probability that an isolate belonging to the CC of interest will be genotyped as such.Specificity (TN/[TN +FP]) is the probability that an isolate that does not belong to the CC of interest will be genotyped as such.PPV (TP/[TP + FP]) represents the proportion of isolates that type as a CC member that are correctly identified.eBURST ,23 is a Isolates were subcultured onto brain heart infusion agar  and incubated at 37°C for 24 hrs. Genomic DNA was obtained by a new mutanolysin-based extraction method. Two loopfuls of cells were resuspended in 500 μL of water containing 50 U of mutanolysin (Sigma Aldrich) incubated at 56°C for one hour, boiled for ten minutes at 100°C, and placed on ice to cool. Tubes were centrifuged at full speed in a microfuge for three minutes, and the supernatant collected and stored at 4°C.Real-time PCR was carried out on the ABI7300 device (Applied Biosystems). Each reaction contained 5 pmol of each primer, 1 μL of genomic DNA, 10 μL of Applied Biosystems SYBR Green MasterMix, and molecular grade water to a final volume of 20 μL. For the glnA429 reactions, double the amount of primer was used (10 pmol each primer) to obtain reliable amplification.The PCR protocol was as follows: 50°C for two minutes, followed by a stage at 95°C for 10 minutes; 95°C for 15 seconds, and 60°C for one minute for 40 cycles.The dissociation stage was 60°C–95°C.The primer sequences for the kinetic PCR reactions are shown in Table This experimental work has been approved by the Sydney West Area Health Service Human Research Ethics Committee .D-maximisation SNP selection method that, in the case of S. aureus, yielded a SNP set with good resolving power and good correlation between SNP profile and clonal complex structure. However, for GBS the rate of increase of D per SNP added to the set was lower than any other bacterial species tested so far  . Eleven SNPs were required to reach D = 0.95, while the previous worst performer in the accumulation of D per SNP was S. aureus, which required seven SNPs to reach D = 0.95 [The aim of this study was to identify a small SNP set that can resolve the major GBS clonal complexes as defined by eBURST analysis of the GBS MLST database. Firstly we assessed the utility of the D maximised SNP set was investigated. GBS displays very little diversity among MLST allele sequences; Jones et al (2003) reported that sequence variation ranges from 1.2% to 2.5% at different MLST loci. Moreover, examination of the MLST database reveals that approximately 45% of eBURST-defined single locus variants are generated through the acquisition of a pre-existing allele, rather than by the generation of a new allele through presumed mutation (data not shown). This indicates that the species' horizontal gene transfer rate is at least comparable to point mutation. Minimum SNPs-based derivation of D-maximised SNP sets from non-clonal microbial species usually provides an almost exponential increase in D as SNPs are added to the SNP set [D-maximised set. Thus our current understanding of D-maximised SNP sets derived from GBS MLST data is that significant horizontal gene transfer disrupts that relationship between SNP profile and population structure, and a paucity of SNPs to choose from means that the resolving power of D-maximised SNP sets is poor with respect to comparably sized SNP sets derived from the MLST databases of other non-clonal bacterial species.The reason for the poor performance of the  SNP set . This isD maximisation is not the optimum approach for identifying a useful SNP set for GBS, and that a new approach was needed. It was also concluded that while sets of 7–8 SNPs provide a very good compromise between number of SNPs and resolving power for other bacterial species [D-maximisation experiments suggest that the optimum size of a generally applicable GBS-genotyping SNP set will be smaller than 7–8, irrespective of the SNP identification method used.Accordingly it was concluded that  species , the lacglnA36, glnA429, glcK180, and adhP111. The positions of these in the concatenated MLST database are 1536, 1929, 2697 and 111 respectively.The alternative strategy that was developed involved the use of the Minimum SNPs Not-N algorithm  to find A complete description of the relationship between the SNP profiles and the GBS MLST-defined population structure was determined, using the MLST database as downloaded on April 4, 2007. This was accomplished using the "working backwards" mode of Minimum SNPs. An overview of the results is shown in Table The correspondence between the generally applicable SNP profiles, and the GBS population structure as revealed by eBURST analysis is good, but imperfect. A significant contributor to this is the inability of eBURST analysis to generate a reliable population structure from a high frequency recombination species. This is a limitation determined by differences in evolutionary history between different parts of the genome, rather than of the eBURST algorithm itself. This phenomenon is particularly evident with the large highly interlinked eBURST group that contains CC1, CC19 and CC10, and a number of smaller/derived CCs. eBURST analysis cannot reliably define precise evolutionary pathways with this population structure because there are so many essentially equivalent topologies. A consequence of this is that any given topology will inevitably result in very closely related STs sometimes being separated by many steps in the eBURST diagram. Therefore, assigning isolates to particular clones, with a high level of confidence, is not possible within such a highly interlinked population.Nevertheless, if CC membership is clinically relevant, false negatives may be a serious problem. Accordingly we developed a strategy to minimise false negatives for defined groups of STs. During the course of this study we observed that if the "defined variant" algorithm in Minimum SNPs is used to identify SNPs diagnostic for a specific CC founder, the first one or two SNPs in the series will differentiate the CC from the rest of the population, and the later SNPs in the series will distinguish the CC founder from its double and single locus variants. This approach alone produces similar false positive and negative rates to those produced with the four generally applicable SNPs described above and so is therefore less efficient, because different SNP sets are required for each CC. However, given that a generally applicable SNP set has been already been developed using other means, we postulated that for any given CC, the false negatives would be different for a "CC founder specific SNP", and the four generally applicable SNPs. Therefore, a strategy in which a single CC-specific SNP was interrogated in isolates that are negative for that CC on the basis of the generally applicable SNPs would ensure that no single locus variants were missed. We have termed such a SNP a "safety net SNP". It was also postulated that the safety net SNP would be effective in reducing false positives from the generally applicable SNP set. As with the "false negative" problem, we hypothesised that the false positives from the safety net SNP, and the generally applicable SNPs would not overlap.This strategy was tested using CC17 as a model system, because this CC is generally regarded as causing a high proportion of paediatric infections, and is potentially a valuable diagnostic target.If CC17 is defined as consisting of ST-17 plus its single locus variants, the generally applicable SNPs provide very good sensitivity and specificity Table . The ST-atr351 (position 1350 in the concatenated database), with ST-17 possessing the "A" allele. This allele eliminates the four false negatives, so we term atr351 the "safety-net" SNP. Eighty-five (27.1%) of known GBS STs possess the A allele, so at first analysis, this SNP appears to generate a large number of false positives for CC17. However, the great majority of these STs are members of CC17, or the closely related CCs, CC67 and CC22. Remarkably, only five STs that are "A" at the safety net SNP are not members of the eBURST group that contains CC17, CC67 and CC22.Minimum SNPs "Defined variant" analysis for ST-17 revealed that the single most discriminatory SNP (i.e SNP1 in the program's output) is An unanticipated benefit of interrogating the safety net SNP is that it is very effective at resolving CC22 and CC23, which are not resolved by the generally applicable SNPs. The only CC22 members which do not type as CC17 on the basis of the safety net SNP are ST-168 and ST-169  In summary, the safety net SNP may be qualitatively regarded as being very effective at discriminating CC17, together with the closely related CC67 and CC22, from other CCs within the species.Quantifying the performance of the safety net SNP is not straightforward, because it is designed to provide an alternative or back-up to the generally applicable SNP set, as opposed to simply adding another allele to the SNP profile of the diagnostic target. The most informative approach is to calculate its performance separately against groups of STs that return particular profiles when interrogated at the generally applicable SNPs. This is because the different generally applicable SNPs-defined groups of STs differ greatly with respect to the proportion that possess the CC17 "A" allele at the safety net SNP. A corollary of this is that the profile obtained from the generally applicable SNPs may potentially indicate whether it is worthwhile interrogating the safety-net SNP at all.As stated above, the three CC17 false negatives as defined by the generally applicable SNPs return two different profiles – those associated with CC67, and CC23. The specificity of the safety-net SNP differs greatly as a function of which generally applicable SNP profile has been obtained , ST-86 (CC-19), ST-226 (not part of a major CC), ST-271 (CC67) and ST305 (ST23). None of these possess an A allele at the safety net SNP apart from ST-271 which, is arguably part of CC-17 anyway. Therefore, the safety net SNP is very effective eliminating CC17 false positives.The power of the safety net SNP on its own to serve as a CC17 diagnostic is also of interest. As stated above, the general property of this SNP is that it discriminates the closely related CCs CC17, CC22 and CC67 from the rest of the species. eBURST analysis puts these CCs into a single group. The specificity, sensitivity and PPV provided by this SNP in diagnosing either ST17 plus single locus variants (narrow definition of CC17), or the CC17–CC22–CC67 eBURST group (wide definition of CC17) were calculated. As expected, this SNP provides high sensitivity against both targets, and high specificity and PPV against the CC-17–CC22–CC67 eBURST group  which is a current employer of FH and a previous employer of PMG. QUT, and authors PMG and FH would potentially benefit from any commercial arrangement regarding this patent application.EH optimised and validated the SNP interrogation procedures and carried out the majority of the SNP-based genotyping and associated data analysis. TF identified the generally applicable SNP set and developed the prototype SNP interrogation assays. AJS developed the safety net SNP interrogation assay and carried out a significant proportion of the genotyping and associated data analysis. DK, FK and GLG participated in the study design, selected and assembled the isolates and participated in data analysis. FH participated in the study design and the optimisation of the SNP interrogation assays. PMG conceived of and coordinated the study, carried out the bioinformatics analyses apart from the initial SNP selection, and wrote the majority of the manuscript. All authors approved the final manuscript.Complete isolate information.Click here for fileGBS st all match profiles.Click here for fileGBS SNPs Vs CCsrev.Click here for fileatr351.STs that possess the "A" allele at the CC17 safety net SNP, Click here for file