In this study we present a targeted 3'UTR-primed SNP discovery strategy that utilizes sequence data from Salmo salar full length sequenced cDNAs (FLIcs). We compare the efficiency of this new strategy to the in silico SNP mining method when using both methods for targeted SNP discovery.Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA variation in vertebrates and may be used as genetic markers for a range of applications. This has led to an increased interest in identification of SNP markers in non-model species and farmed animals. The in silico SNP mining method detected novel SNPs in 15% of the target genes. Furthermore, the 3'UTR-primed SNP discovery strategy was the less labor intensive one and revealed a higher success rate than the in silico SNP mining method in the initial amplification step. When testing the methods we discovered 112 novel bi-allelic polymorphisms (type I markers) in 88 salmon genes , and three of the SNPs discovered were missense substitutions.The SNP discovery efficiency of the two methods was tested in a set of FLIc target genes. The 3'UTR-primed SNP discovery method detected novel SNPs in 35% of the target genes while the Full length insert cDNAs (FLIcs) are important genomic resources that have been developed in many farmed animals. The 3'UTR-primed SNP discovery strategy successfully utilized FLIc data to detect novel SNPs in the partially tetraploid Atlantic salmon. This strategy may therefore be useful for targeted SNP discovery in several species, and particularly useful in species that, like salmonids, have duplicated genomes. Salmo salar). If aiming at disclosing economically important traits by fine QTL mapping or to map genes in wild or farmed animals, one would need a large number of validated SNP markers [Salmo salar SNP markers have been identified and validated by genotyping in population materials [Single nucleotide polymorphisms (SNPs) represent the most widespread type of DNA variation in vertebrates. Compared to the commonly used multi-allelic microsatellite markers, the bi-allelic SNPs have much lower polymorphic information content (PIC), but this shortcoming may be compensated using a larger number of SNP markers. Recent technological developments now allow simultaneously detection of several thousands of SNPs at low costs and by use of methods that can be automated and standardized across laboratories ,2. As a  markers ,4. Despiaterials -11.Scanning for new SNPs can be conducted by use of two different approaches ,4. When in silico SNP mining method, and most known SNP markers in Salmo salar have been discovered by use of this method [Several methods have been suggested for SNP discovery in salmonids ,6,8-10. s method ,9,11. Whs method . The advin silico SNP mining approach [in silico SNP mining is the initial step of this method and the purpose of this SNP mining is to select a set of putative SNPs that can be validated by genotyping. The SNPs that may be discovered are thus the ones that were present in the individuals used for generating the cDNA libraries. The number of individuals may be relatively low, and the variation present in these individuals (the "in silico population") may not be representative of the variation present in other populations of interest. This may not affect the SNP discovery efficiency much if the intention is to discover SNPs randomly in the genome , but may be important if aiming at detecting novel SNPs in a certain set of genes and/or a certain population (targeted approach). In support of this, results from an evaluation of human SNPs detected by use of a global in silico SNP mining approach showed that a large proportion of such SNPs were monomorphic when tested in selected population panels [The approach , althougn panels .Salmo salar made publicly available [in silico SNP mining method. Most recent genomic resources made available are the high quality transcript sequence data from full length sequenced cDNAs (FLIcs) [in silico SNP mining method has exploited ESTs that consist of a combination of CDS and UTR sequences, a UTR-primed method that utilizes sequence information from annotated FLIcs could target 3'UTRs only, and thus, allow for a SNP search in gene fragments that are expected to have a higher SNP density than the ESTs utilized so far. Theoretically, both 5' and 3'UTRs could be searched for SNPs. However, the 5'UTRs in salmon cDNA inserts are considerably shorter than 3'UTRs and thus, less useful for SNP mining [The economic interest in salmon has led to development of various genomic resources including tissue and developmental stage specific cDNA libraries -19. Partvailable . These a (FLIcs) . The seq (FLIcs) . Thus, nP mining . Taken tP mining ,23-26). in silico SNP mining method for global SNP discovery we have included this method in our study, and we present results from testing the in silico SNP mining method when applied for targeted SNP discovery in FLIc genes. We also present more than 100 novel SNPs discovered in well characterized and annotated Salmo salar genes (type I markers), including some SNPs that may be of functional importance.In this study we have evaluated the performance of two SNP discovery methods applied for targeted SNP discovery. We present results from testing the UTR-primed method for SNP discovery when targeting 3'UTRs in a set of full length cDNA (FLIc) genes. Due to the successful use of the in silico SNP mining discovery methods, respectively. The small population material that was targeted for SNP discovery consisted of the four parental individuals in our reference mapping families, two individuals selected from aquaculture stocks and four individuals from Norwegian rivers . All individuals were genotyped in all candidate SNPs tested.The target genes used for testing SNP discovery efficiency in this study were selected from a white muscle tissue and pre-smolt developmental stage specific cDNA library . The genThe UTR-primed SNP discovery method consisted of two main steps Figure , and theIn the first step a pair of PCR primers targeting sequences within the 3'UTR (UTR-primed PCR) was designed for each of the target genes to amplify a fragment of about 400 bp (see methods). Amplification of such fragments was performed by use of genomic DNA from two individuals followed by agarose gel analyses of the PCR products. If the PCR amplification resulted in a single clear band in the agarose gel test, the PCR products were further analysed by DNA sequencing. All target genes where the initial PCR amplification resulted in either no PCR products (negative or weak bands on agarose gel testing), multiple PCR products (multiple bands on agarose gel) or revealed multiple heterozygote sites (five or more) when analysed by DNA sequencing were not included in the next main step. Twenty-three of the 208 selected primer pairs failed on the agarose gel test while an additional twenty of the selected primer pairs failed on the sequencing test. Thus, in this initial PCR and sequencing step 43 UTR fragments from selected target genes where not amplified properly or lacked the desired sequence quality while in 165 genes (79%) the primer pairs produced PCR fragments that could be further used for SNP discovery.All the 165 target genes that passed the first step were included in the second step aiming at detecting novel SNPs in the UTR-primed PCR products. In this step individuals from the small population material were genotyped by sequencing the amplified target gene fragments. PCR products were generated by amplification of the individuals genomic DNA and the chromatograms from sequencing of individual samples were inspected manually to assure that heterozygote positions were detected. Finally, all chromatograms in the population panel were aligned and compared. A given site within the sequence was accepted as a true polymorphism only if the rare variant was observed in at least two individuals. These comparisons resulted in the discovery of at least one SNP or DIP in 73 of the 165 genes. This gives a SNP discovery rate of 44% in this second step of the UTR-primed method. A summary of the success rate in each of the main steps is given in table The comparisons of individual genotypes also served as a validation step where paralogous sequence variants (PSVs) could be excluded. A PSV located in highly similar paralogous sequences can not be distinguished from a true SNP based on results from one or a few individuals. However, while a true SNP segregates according to Hardy/Weinberg expectations in a population panel, a PSV would appear as a heterozygote in all individuals . Any "SNin silico SNP-mining method in 158 target genes, sixty of these also tested by use of the UTR-primed SNP discovery method. The in silico SNP mining method included the two steps of the UTR-primed method  were performed manually by comparing the FLIcs to the ESTs available from GenBank that were likely to represent target gene transcript sequences (see methods). The chromatograms from such ESTs were aligned to the corresponding FLIcs and the alignments were manually searched for putative SNPs. Any sites that revealed sequence differences at the 50 first or 50 last nucleotides in a given EST when compared to the corresponding FLIc were not considered as putative SNPs. Also, if a given EST revealed multiple sequence differences within the sequence (e.g. >3 differences for each 100 bp) when compared to the corresponding FLIc the EST was not considered as originating from an allelic sequence, but rather to represent paralogous sequence. Consequently, any sequence differences in such ESTs were not included as putative SNPs in this initial step. The in silico SNP mining step resulted in discovery of 131 putative SNPs in the 158 target genes (table The Primers were then designed to amplify a short sequence (in average 150 bp) that included the candidate SNP. The PCR products from these amplifications were tested by agarose gel analysis and subsequently sequenced. The results from the initial PCR and sequencing step showed that 70 of the 131 PCR products from target genes failed in either the PCR amplification or sequencing quality test of PCR products. Thus, only 61 PCR products (47%) from 57 different target genes could be further used for SNP search in the population material.in silico based approach for targeted SNP discovery. A SNP was discovered in 6 cases (10%) when applying the in silico SNP mining method for SNP discovery on the 60 target genes tested by both methods.The final SNP discovery step was, like in the UTR-primed method, performed by genotyping individuals in a small population material. All 61 PCR products passing the previous quality step was tested in this final step, and a SNP was identified in 25 out of the 61 PCR products. This gives a SNP discovery rate of 41%  and the IPEC (intron-primed exon crossing) strategies ,13. We tin silico SNP mining method had a much lower success rate at this step . Other reasons that the initial PCR amplification failed more often when testing the in silico SNP mining method could be that primers made using transcript sequences by chance were located in exon-intron boundaries, or that large introns were included in the amplicon leading to a poor amplification efficiency.All the target genes were selected from FLIcs and have been characterized by full length sequencing of the corresponding cDNAs. However, the complete genomic sequence of the target genes had not been characterized, and the intron sequence data needed to design primers for testing the IPEC (intron-primed-exon-crossing) method  was therIn a study by Smith et al.  a methodin silico SNP mining of putative SNPs (first step of the in silico method). The conversion rate refers to the number of polymorphic SNPs confirmed by genotyping divided by the total number of putative SNPs discovered in the SNP mining [in silico SNP mining as well as on the species that are investigated [in silico data is not representative of the population used in the genotyping, the putative SNPs selected may be monomorphic in the population used for validation. Vice versa, SNPs present in the genotyped population, but not in the in silico data will not be discovered since these SNPs don't appear as putative SNPs in the first place. All the mentioned factors may lead to substantial decrease in the conversion rates. When applying the in silico SNP mining method for global SNP discovery in Atlantic salmon a dedicated pipeline for selection of putative SNPs have been introduced to maximize the conversion rate [in silico SNP mining was performed manually in our study while a dedicated pipeline was used to select the putative SNPs in Hayes et al., and only the SNPs with best PolyBayes ranking were genotyped [in silico data may have been detected in Hayes et al, such SNPs may have appeared as monomorphic in our validation. Taken together, the low conversion rate leading to fewer putative SNPs that were validated as true SNPs in our study than in Hayes et al. could be a consequence of using a targeted SNP discovery approach .A conversion rate is often used for measuring the performance of the P mining . Severalstigated ,28,29. Iion rate . Genotypenotyped . It is lin silico SNP mining method detected a novel SNP in 15% of target genes  discovered, the dbSNP NCBI assay ID (ss) accession numbers as well as the corresponding FLIc accession numbers from GenBank is given in additional file Salmo salar FLIcs and thus, are an important contribution to the number of validated SNP markers in Atlantic salmon. All polymorphisms discovered by the UTR-primed method were located within the 3'UTRs of the target genes. The SNPs discovered by use of the in silico SNP mining method were located both in exons, introns and in 3'UTRs while the few SNPs discovered by use of the EPIC method were located in introns. table A total of 112 SNPs or DIPs were discovered in 88 different genes in this study. A single SNP (or DIP) was discovered in 70 of the genes while two and three SNPs were discovered in 12 and 6 genes, respectively. All SNPs have been submitted to dbSNP Applying the UTR-primed method we have scanned a total of 66000 bp by sequence analysis of 165 UTR derived PCR products and discovered a total of 63 SNPs and 16 DIPs. This indicates that there is about one bi-allelic polymorphism in every 840 bp of the 3'UTRs. The population material screened when searching for SNPs in the 165 PCR fragments consisted of relatively few individuals. Thus, it is likely that most rare SNPs or population specific SNPs in the target genes were not discovered in this study. In Ryynänen and Primmer a population material consisting of a similar small number of individuals were screened when searching for SNPs [A total of 62 SNPs that were shown to be polymorphic in one or more of the SALMAP parents where genotyped in the reference families . The 62 Salmo salar genes. All cDNA clones used for developing EST resources have inserts consisting of complete 3'UTRs. The sequence of the 3'UTR in any target gene identified by its EST sequence may therefore be relatively easily accessed by sequencing the cDNA insert from the 3' end. The UTR-primed SNP discovery approach may therefore be the better strategy for targeted SNP discovery in non-model species, and may be particularly useful in species, that like salmonids, has duplicated genomes. Detection of SNPs by use of a global approach and the in silico SNP mining method has proven to be a successful strategy for discovery of novel SNPs in many species. However, if using small populations or fine mapping certain regions or genes, a targeted approach may more efficiently provide the novel SNPs necessary for that particular project. Thus, we believe that the targeted UTR-primed method may be a useful complementary method for SNP detection in farmed animals and non-model species.FLIcs are important genomic resources that have been developed in many farmed animals. The UTR-primed SNP discovery strategy successfully utilized data from full length insert cDNAs (FLIcs) for targeted SNP discovery in http://www.ncbi.nlm.nih.gov/snp. In all cases where a novel SNP was discovered in the parental individuals in the SALMAP reference families genotyping of the whole family was performed (two parents and 46 offspring in each of two families).A material consisting of the four parental individuals in the SALMAP reference families, two individuals from aquaculture stocks and four individuals from Norwegian rivers, a total of 10 individuals, were used as a panel for genotype validation of SNPs. All loci were typed in these 10 individuals. If a SNP was revealed in only one of the ten individuals, an additional typing of this marker was performed in five individuals. A novel candidate SNP was only approved as a true SNP if the rare variant was observed in at least two individuals. All new SNPs discovered in this study were submitted to the dbSNP database in silico SNP discovery method while 208 target genes were used for testing the UTR-primed SNP discovery method. A total of 60 genes were tested using both methods. The SNPs identified in this study were used for gene mapping if informative in our family materials. To make an additional independent comparison of the methods and at same time minimize cost and labor when discovering suitable SNPs for mapping target genes, the additional genes included in this study were randomly divided in two groups where each group were tested by one method only.The genes selected for testing the SNP discovery methods were those expressed in white muscle tissue at the pre-smolt developmental stage. They were chosen as target genes because they have all been characterized by high quality full length sequencing of their transcript sequences including the complete 3' UTRs . Apart fhttp://frodo.wi.mit.edu/primer3/[in silico SNP mining method the primers for the initial PCR were designed with short product size range settings (130-170 bp) in an attempt to limit PCR failure due to co-amplification of large introns. When testing the UTR-primed SNP discovery method the primers for the initial PCR were designed with product size range settings 350-450 bp to amplify fragments that could be completely sequenced in one direction . When designing primers to test the EPIC method the product size range settings were 200-250 bp. The primers for the EPIC method were designed with such a size range setting in an attempt to include introns (intron crossing) when using genomic DNA as template. Except the modification of the PCR product size range settings, all other settings, including Tm optimum of 60°C, were the default conditions given by primer3 software. The -21m13 forward (5'TGTAAAACGACGGCCAGT3') and -21m13 reverse (5'CAGGAAACAGCTATGACC2') universal primer sequences were added to the 5' end of forward and reverse PCR primers, respectively. The PCR reactions were performed using standard conditions: 50 ng genomic DNA, 1.5 mM MgCl, 0.2 mM dNTP's, 0.5 μM of forward and reverse primer, 5% DMSO and 0.5 U Taq polymerase (Quiagen) in total volumes of 25 μl. A Tetrad Thermal Cycler  was used for the PCR amplification with the following programme: 94°C for 3 min, then 28 cycles with initial denaturation at 94°C 30 s, annealing 56°C 30 s, extension 72°C 2 min and a final extension step at 72°C for 5 min.All primers were designed by use of primer 3 software /primer3/. When teDNA sequencing was performed by use of forward and reverse -21m13 universal primers and the MegaBACE 1000 automatic sequencer (Amersham Pharmacia) as described in Adzhubei et al. . The indin silico SNP mining method was the selection of Atlantic salmon transcript sequences (ESTs) from GenBank that were likely to originate from the selected target genes. An initial BLASTN homology search against salmonid EST data in GenBank using each of the selected FLIcs as input was performed to identify such candidate sequences. The Atlantic salmon EST transcript sequences that was finally selected for further comparison to the FLIcs were those matches that returned E-values less than 10-20 and with at least 96% sequence identity to the target genes. Only ESTs where chromatograms were available was used in the in silico comparison ([http://web.uvic.ca/grasp/). The selected ESTs were compared to the corresponding FLIcs using Sequencher 4.7 software. These alignments were manually inspected and obvious sequence errors caused by low quality data, sequence differences located within poly G or C sequences or other simple repeats were not considered as putative SNPs. The quality of EST data is generally low at the very beginning and the very end of the chromatograms. In order to process all ESTs in the same manner and to avoid too many sequence errors included as putative SNPs any difference within the 50 first and 50 last base pairs in the EST sequences were not considered as putative SNPs. Similar settings were used in Hayes et al. [in silico SNP mining primers were designed to amplify a fragment of the target gene that included this candidate SNP as described in the PCR amplification and DNA sequencing section of methods.The inital step of the parison , or if only comparing SNPs detected in the 60 target genes tested by use of both methods (p = 0.028).in silico SNPs, loaded all SNP data to GenBank, carried out bioinformatics and statistics, and drafted the manuscript.RA participated in PCR amplification and sequencing when testing the methods, performed the search for putative BH conceived and coordinated the study, performed the linkage analysis and helped to draft the final manuscript.SL performed major part of the PCR and sequencing and helped in the manual processing of the data.All authors have read and approved the final manuscript.A complete overview of all SNPs and DIPs discovered, the dbSNP submitter (ss) accession numbers as well as the corresponding FLIc accession numbers from GenBank.Click here for file


Genome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). However, no systematic examination of connections between genomic regions and predictive ability of complex phenotypes has been carried out.In this study, we partitioned SNPs based on their annotation to characterize genomic regions that deliver low and high predictive power for three broiler traits in chickens using a whole-genome approach. Additive genomic relationship kernels were constructed for each of the genic regions considered, and a kernel-based Bayesian ridge regression was employed as prediction machine. We found that the predictive performance for ultrasound area of breast meat from using genic regions marked by SNPs was consistently better than that from SNPs in IGR, while IGR tagged by SNPs were better than the genic regions for body weight and hen house egg production. We also noted that predictive ability delivered by the whole battery of markers was close to the best prediction achieved by one of the genomic regions.Whole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions. Our results suggest that, while differences among genomic regions in terms of predictive ability were observed, the whole-genome approach remains as a promising tool if interest is on prediction of complex traits. High-throughput genotyping technology has increasingly produced more dense sets of genetic markers, e.g., from tens to hundreds of thousands of SNP variables. Availability of high-density DNA genotyping chips, such as 770K and 600K SNP arrays in cattle  and chiccis-acting regulatory elements of genes residing nearby. Several authors have found a significant overlap between expression quantitative trait loci studies and genomic regions having effects in GWAS )To explore links between the aforementioned genomic regions and predictive power, the following comparisons were carried out. Predictive abilities of SNPs in each of the four genomic regions  were compared with that from randomly sampled SNPs in IGR with an equal number of SNPs to those in the four regions. If a large number of regulatory elements is placed distantly from the genes that they regulate, or if influential regions span the entire genome but are not limited to particular segments, then IGR, devoid of protein-coding sequences, may have comparable or perhaps even better predictive power. On the other hand, if this does not hold, the collection of functionally enriched regions  would be expected to yield a better prediction than that delivered by SNPs in IGR. As a benchmark, a model using all available SNPs was tested as well.i is given byUse of a semi-parametric kernel method for genome-enabled prediction was suggested first by Gianola et al.  and Gianxi is a vector of SNP genotypes observed on i. We assume g is represented as Kα, where K is an n×n kernel matrix indexed by the observed SNP covariates. This specification mitigates the “curse of dimensionality”, so that with g=Kα, the original 600K SNP predictors are reduced to the number of observations, that is 1,351, 1,336 or 823 animals. If we choose the residual sum of squares and the square of the norm of the coefficient α as a loss function and penalty, respectively, this is simply Bayesian ridge regression employing the kernel matrix K instead of the commonly used n×p genotypes matrix X, where p is the number of SNPs. We can now rewrite Equation (1) in matrix form, such that y=μ+Kα+ε. In order to implement the procedure under a Bayesian framework, a flat prior was assigned to μ, and λ||α||2, contrary to K is not included in the penalty function and optimization is not carried out under a Hilbert space. Our approach shares the spirit of that of Long et al. [K by imposing an L1 regularization.where s et al.  and Moros et al. . In our g et al. , where tK∝XXT∝G, where X is a SNP genotype matrix as before, and G resulted from a centered and standardized X, followed by division by the number of SNP, as proposed by VanRaden [The kernel used was VanRaden . This keith row of K.The Bayesian model was implemented by Gibbs sampling. For each genomic region, a MCMC chain was run and the first 20,000 samples were discarded as burn-in. Subsequently, 40,000 samples were obtained and thinned at a rate of 10, leaving 4,000 mildly correlated samples for posterior inference. Convergence of the chain was checked by visual inspection of trace plots of the parameters. The predictive ability of our Bayesian ridge regression model was assessed by a cross-validation (CV). Specifically, a 10 fold CV scheme was applied by assigning animals randomly to one of 10 disjoint subsets. Of these 10 subsets, 9 were combined to form a training set, and the remaining was used as testing set. Each of the 10 subsets was used as a testing set only once. Since the CV distribution was dispersed because of small sample size, the above 10 fold CV was replicated 15 times, at random. Predictive abilities were evaluated via the Pearson product-moment correlation between preadjusted phenotypes and predicted additive genetic values, that is Dissimilarities among various genomic regions were assessed using a hierarchical clustering method. For each trait, a matrix containing the pairwise Euclidean norms between predicted genetic values  between training and testing animals for each CV fold were negative or close to 0, regardless of a genomic region. Figures Mean and median values of genomic relatedness  in chicken will become available in the near future, but their contribution towards a better predictive performance may be limited for a trait like HHP, because such variations are observed only at particular regions in the genome. All genic regions considered delivered a better predictive performance for BM and an inferior prediction for BW and HHP.Similarities between the genomic regions considered were investigated further using a hierarchical clustering method. Dendrogram topologies with genomic regions treated as clusters were consistent with results obtained in CV correlations. Seemingly, the hierarchical clustering agreed with the ability of genomic regions to deliver predictions for complex traits.Previous studies have shown that many QTN (quantitative trait nucleotides) of large effects in animals tend to reside in coding regions, e.g., DGAT1 in cattle , but QTNWe conclude that examining sources of predictive performance aids in interpretation of results. Although genomic annotations of livestock species are more scarce than in humans, our approach may be adaptable to other traits and species as well. A recent study found that the GC content of CDS and introns was negatively correlated with gene expression levels in chicken, while 5’ UTR presented a positive association . It is oPotential limitations of this study include that chromosome 16 was severely underrepresented due to scarce information on the current reference genome, and SNPs from chromosomes 29–38 were not available in the current SNP panels. Chromosome 16 contains the major histocompatibility complex, known to influence immune function . Hence, Whole-genome prediction methods allow predicting complex traits, irrespective of knowledge of their molecular basis. Although this is typically regarded as a black box approach , dissecThe authors declare that they have no competing interests.GM conceived, carried out the study, and drafted the manuscript; RAA and AK provided critical insights and revised the manuscript; DG supervised the study and revised the manuscript. All authors read and approved the final manuscript.


Microlaena stipoides (Poaceae), was targeted using chemical mutagenesis in conjunction with high throughput sequencing of genes for key domestication traits. While M. stipoides has previously been identified as having potential as a new grain crop for human consumption, only a limited understanding of its genetic diversity and breeding system was available to aid the domestication process. Next generation sequencing of deeply-pooled target amplicons estimated allelic diversity of a selected base population at 14.3 SNP/Mb and identified novel, putatively mutation-induced polymorphisms at about 2.4 mutations/Mb. A 97% lethal dose (LD97) of ethyl methanesulfonate treatment was applied without inducing sterility in this polyploid species. Forward and reverse genetic screens identified beneficial alleles for the domestication trait, seed-shattering. Unique phenotypes observed in the M2 population suggest the potential for rapid accumulation of beneficial traits without recourse to a traditional cross-breeding strategy. This approach may be applicable to other wild species, unlocking their potential as new food, fibre and fuel crops.Global food demand, climatic variability and reduced land availability are driving the need for domestication of new crop species. The accelerated domestication of a rice-like Australian dryland polyploid grass,  Cereal production is the major source of carbohydrate in human diets, with most provided by eight genera of the Poaceae Australia is a unique source of under-utilised germplasm, due to its short agricultural history, geographic isolation and relative lack of arable land, plant domestication. The Australian Poaceae have evolved independently of other world environments. Moreover, they are adapted to a wide range of resource-limited environments, providing a novel genetic resource for plant pre-breeding. To date, no commercial cereal crops have been developed from this gene pool, although there has been a history of traditional use by indigenous Australians Microlaena stipoides (weeping rice grass) shared a common ancestor with cultivated rice, Oryza sativa  and its tetraploid genome size (880 Mbp) is approximately double that of diploid O. sativa (394 Mbp) Approximately 35 million years ago a sativa . M. stipqSH1sh4Increased availability of genomic data has contributed to a better understanding of the genetic basis of the so-called “domestication syndrome” in commercially cultivated Poaceae O. sativa lines had an estimated global value of over US$20 billion By 2004, mutant-derived M. stipoides.We aimed to accelerate the process of crop domestication by identifying variation in specific traits and their underlying component genes. We harnessed the sensitivity of NGS to characterise both natural and EMS-induced variation within bulked amplicon pools, identifying candidate alleles for improved breeding of the semi-domesticated species, M. stipoides, cv AR1, was supplied by Native Seeds Pty Ltd  as our base material. Due to poor germination rates in M. stipoides when dehusked, seeds where treated with husks intact and all florets included in the trial were manually checked to ensure the husk contained a filled seed. Imbibition was conducted at a concentration of approximately 10 g of seed per 40 ml of solution  and grown under glass house conditions with average minimum and maximum daily temperatures of 15°C and 24°C respectively.A seventh generation, predominantly inbred, breeding line of M. stipoides with a control and nine treatments of 60, 80, 100, 115,130,145,160,175 and 200 mM EMS (150 seeds/treatment). Germination was monitored and recorded every 2–3 days for a month for the mutated, M1d, and control seedlings, S1 d, . In order to maximise mutation density in the polyploid background, an LD95 was targeted with final percentage germination calculated at 6 weeks post treatment resulting in an LD97 being achieved. Seedlings were then monitored on an ongoing basis for any notable phenotypic variations. Seedlings were transplanted into individual 6 cm×6 cm×15 cm forestry tubes of Searle's premium potting mix and grown to maturity on a semi shaded roof top with water applied as required to keep the potting mix moist. M2d and S2 d seed was collected individually from all non-sterile mutants. Due to the perennial habit of M. stipoides both M1 d and M2d individuals from the dosage trial which displayed a promising or novel phenotype could be retained and were transplanted to a field site for final evaluation.Based on effective doses used in other Poaceae, an initial dose response curve with a control and four treatments of 40, 60, 80 and 100 mM EMS (200 seeds/treatment) was generated. A second dose response experiment was then conducted to ascertain the efficacy endpoints for M. stipoides has a predominantly cleistogamous (selfing) reproductive system, though it can also exhibit opportunistic chasmogamous (outcrossing) breeding cycles For the 145 mM EMS screening population, both the control (200) and treatment  seeds were screened for grain fill prior to the water or 145 mM EMS treatment. The M2 and S2 populations were used as the basis for genotypic screening. Leaves were collected from 754 juvenile M2 seedlings and 109 S2 individuals. DNA was extracted from fresh leaf tissue using a modified MagAttract 96 DNA Plant Protocol  with one additional reverse osmosis purified water wash prior to being quantified using UV spectrophotometry at a wavelength of 260 nm and 280 nm . The DNA was normalised using Gibco Nuclease Free water to a concentration of 2 ng/µl using the MWG Theonyx . Prior to amplification DNA was pooled from five individuals and 10 ng of pooled template was used per PCR. Stringent quality controls were applied during sample preparation. DNA was quantified, normalised and pooled in equimolar proportions at each step in an attempt to maintain relative allele frequencies in the subsequent GAII sequence data.Waxy gene Isa gene sh4/SHA1 and qSH1M.stipoides were targeted. PCR products were quantified by gel electrophoresis using Scion image . Amplification products were combined in equimolar amounts to form homologue-specific pools of 109 and 754 M2 (mutant) individuals, in addition to a pool of 109 S2 (control) individuals. The homologue-specific pools were then quantified by pico-green and combined in equimolar amounts to form megapools representing two mutant and one control population. These three megapools were run as individual lanes on the Illumina GAIIx platform  using a paired-end strategy with a fragment size of 400 bp and a read length of 75 bp.Four candidate domestication related homologues were targeted for PCR amplification . Reads with a quality score of less than 0.001 were discarded and paired-end reads were trimmed to a minimum of 30 bp. Reference assembly against M. stipoides sequence  was undertaken with a mismatch cost of 2, insertion and deletion costs of 3, length fraction of 0.8 and similarity of 0.8, minimum distance for paired end reads of 180 bp with a maximum of 340 bp, and non-specific matches ignored. SNP detection parameters of; window length 21, maximum number of gaps or mismatches 2, SNP minimum quality score 30 and quality score for the surrounding bases 30, minimum coverage required 1×, with a minimum variant frequency of 0.000001%, was designed to capture all high read quality polymorphisms.Sequence data were trimmed using CLC Genomics Workbench version 4.0.3 . The genotyping was performed according to the iPLEX Gold SNP protocol on the Sequenom MassARRAY Compact platform and analysed using Typer 4.0.M. stipoides using a two stage experiment based on final germination frequency . To ensure that sterility had not been induced d and S1d (where M# (mutant) and S# (selfed control) refer to the generation and d denotes the dosage trial) populations were harvested. Germination tests confirmed the viability of the M2 seed at this dose.We established an optimal EMS treatment for requency . Figure Survival at 42 days post treatment was 9% in the mutant population , compared with 82.5% in the control population (200 seeds). Novel phenotypic variations amongst the surviving mutant seedlings were observed throughout development . At harvField based evaluation of M2 mutants identified 50 plants  with comisa, qSH1, sh4 and waxy) were selected for their impact on domestication in other cereals and sequence availability in M. stipoides. Preliminary Sanger sequencing (data not shown) of wild type individuals identified within-individual polymorphisms assumed to result from multi-locus variation due to either tetraploidy, and/or heterozygosity. In addition, SNP variation was found between individuals in the base population suggesting a degree of out-crossing.The four target genes, ). Subsequent to reference assembly and application of a minimum coverage threshhold of 400×, an average coverage was calculated for each pool; 109 control plants - 27118×, 109 mutants -32289× and 754 mutants -10171×. Coverage was both gene and pool dependent, as previously reported for the Illumina GA platform A PCR pooling strategy was used for NGS analysis, creating cost effective, single lane experiments characterising SNP type and frequency for each gene. Three amplicon pools  were sequenced on the Illumina GAII. After stringent trimming the three pools retained the following read numbers and average read lengths (ARL); Control pool , 109 mutants pool  and 754 mutants pool . Reads which assembled to the reference genes were submitted to the NCBI database  and sh4 (111 SNPs) had a greater number of SNPs identified than the waxy gene (49 SNPs) or the isa gene (5 SNPs). Each of the control/mutant pools had a distinct distribution of SNPs either unique to a single pool, or shared between multiple pools  will be independently mutagenised The range of phenotypic variation observed allowed the selection of 50 enhanced mutant plants to be used as a pre-breeding population. The selected plants exhibited a unique composite of improvements to plant architecture, reduction of shattering at grain maturity and an increased grain size and/or yield not observed in individuals within the wild-type AR1 base material.M. stipoides AR1 base material is a semi-domesticated facultative cleisotogamous polyploid with the capacity to outcross, though this was rarely observed in this study. Cloning of another undomesticated, yet cultivated polyploid Poaceae, Echinochloa ssp., identified multiple homeologues of sh4, with sequence polymorphism confirmed between the individual's genomic copies The The use of a pooled amplicon-based NGS approach was effective for detecting wild-type polymorphisms and putatively, EMS-induced mutations. However, there are limitations to using the Illumina platform for this purpose. These include the need to account for and robustly identify low frequency alleles, the non-uniformity of coverage and end bias et al. (2011) reliably identified heterozygous mutations in pools of 96 diploid individuals  and confirmed that utilizing a 0.5% minimum allele frequency greatly reduced the risk of false positives. Hence the lowest frequency SNPs identifiable from our 109 pools would be an individual with a homozygous SNP in a single genome or wild-type heterozygous loci on both , which is then expected to be slightly over-represented in the Illumina data Site-specific analysis determined that SNPs identified from the Illumina data with an allele frequency greater than 0.5% were above the error threshold for this analysis. Subsequent Sequenom analysis confirmed the presence of SNP with an allele frequency as low as 0.7%. Similarly, Tsai 97 may have created a genetic bottleneck amongst the mutant population. It is therefore to be expected that some non-EMS induced SNPs were unique to the control populations.As expected, the frequency and distribution of the 234 SNPs was gene dependent . This vaM. stipoides population is accurate (2.4 mutations/Mb), it is lower than mutation densities previously reported for hexaploid (42 mutations/Mb) and tetraploid (25 mutations/Mb) wheat Brassica species (17 mutations/Mb) 97) without inducing significant levels of M2 lethality or sterility.The wild-type polymorphism in the AR1 line (14.3 SNP/Mb) indicates that there is considerable diversity within the base material which has potential to be captured, though the greater proportion of these polymorphisms appear to be functionally neutral. If the estimated EMS induced mutation density generated in the Many traits associated with the domestication syndrome are often the result of a loss of function of a recessive gene, such as seed shattering, which in the wild is advantageous and maintained by heterozygosity and natural selection qSH1 gene, controlling the formation of the grain abscission layer, or the sh4 gene, a putative transcription factor qSH1, putatively causing a premature stop codon. The sh4 amplicon screened was 52% intronic DNA, and the majority of wild-type SNPs occurred in this non-coding region. However two SNPs (a C/A and a G/T) identified as causing putative premature stop codons were identified at the 5′ end of exon two. These SNPs may be responsible for the low numbers of non-shattering plants observed in the control lines. Subsequent screening using Sequenom MassARRAY analysis for the C/A SNP confirmed its presence in an individual recorded to have a non-shattering phenotype at harvest.The non-shattering phenotype was observed in the control lines, but was more prevalent in the mutant population. A non-shattering phenotype in rice can result from loss of function of either the waxy gene, encoding Granule Bound Starch Synthase I, causes high amylose (waxy) starch to form in grain endosperm in cultivated hexaploid wheat, where a gene dosage effect has been identified. This gene has now been shown to be the major determinant of endosperm starch composition in rice M. stipoides which had species-specific UTR based primers. As starch composition of the endosperm is important for seed germination, SNPs affecting this gene's function may be under strong selective pressure. Of the 49 SNPs identified in this gene the majority occurred in non-coding regions. Only two potentially non-synonymous SNPs were found, one early in the transit peptide and the other at the end of exon 13.Loss of function of the Isa gene, first characterised in barley, encodes bi-functional amylase/subtilisin inhibitor which acts as part of a seed's defense mechanism against fungal and bacterial pathogens Hordeum) species. Sequence diversity within this gene has been positively correlated with increasing environmental variability Isa gene of the wild populations were putatively functional, and neither would necessarily cause a loss of function The In species where the breeding system is well understood, reverse genetic information provides the opportunity for a renaissance in mutation breeding, precisely because it can pin-point and isolate an independent series of component traits and their alleles. This information may then be used to guide breeding programs over a relatively short number of generations. Where pooled samples are analyzed using short read NGS technologies, it is critical that rare natural or mutant alleles are distinguishable from sequencing error M. stipoides and demonstrated value in both forward and reverse genetic screening of the population. The reverse genetic screen has added valuable knowledge about the extent of the diversity within this base population, while screening only a very limited proportion of such a large genome (∼0.001%). Continued technological developments in DNA sequencing will allow greater efficacy of deep pooled screening, and genome-wide screening to include a comprehensive set of domestication genes. Although mutation breeding has been widely used over the past 80+ years to introduce novel alleles into many major crops, it has only had limited use in accelerated domestication. Here we have been able to select mutants with a set of component phenotypes representing multiple beneficial traits without the use of a cross breeding strategy. These phenotypes were identified during the forward screening of the mutant population and included beneficial combinations of plant size, improved architecture, compact panicle structure, increased seed size, non-shattering habit, rhizome production, increased tillering, shorter awn length, increased dry matter yield and greater seed production.We have successfully accelerated the process of domestication in With the rapidly advancing field of molecular genomics and a growing understanding of the genetic events behind domestication, the utilization of molecular techniques in conjunction with mutation breeding should make it possible to accelerate the domestication of other wild plants as new environmentally sustainable crop species.Figure S1EMS dosage effect on frequency of seed germination. Note: a single fertile seedling was produced by the 175 mM EMS treatment, though there was complete lethality at both 160 mM and 200 mM EMS treatments.(TIF)Click here for additional data file.Figure S2Sequence variability as measured by the method of Shenkin et al. (1991), for the Microlaena stipoides waxy gene. The information-theoretical complexity measure S is plotted for each nucleotide position. A. Control pool; B. pool of 109 mutant individuals; C. pool of 754 mutant individuals.(TIF)Click here for additional data file.File S1Table S1: Primers and PCR conditions for gene homologues amplified for next generation SNP discovery. Table S2: Complete details of SNP loci identified in the four target genes in Microlaena stipoides, within the Illumina sequence data above the error threshold .(DOCX)Click here for additional data file.


To bring down the per-sample cost, one approach is to pool barcoded NGS libraries before sample enrichment. Still, we lack a complete understanding of how this multiplexed tNGS approach and the varying performance of the ever-evolving analytical tools can affect the quality of variant discovery. Therefore, we evaluated the impact of different software tools and analytical approaches on the discovery of single nucleotide polymorphisms (SNPs) in multiplexed tNGS data. To generate our own test model, we combined a sequence capture method with NGS in three experimental stages of increasing complexity .Compared to classical genotyping, targeted next-generation sequencing  across multiplexed samples, with <10% strand bias. However, the SNP calling quality was substantially affected by the choice of tools and mapping strategy. With the aim of reducing computational requirements, we compared conventional whole-genome mapping and SNP-calling with a new faster approach: target-region mapping with subsequent ‘read-backmapping’ to the whole genome to reduce the false detection rate. Consequently, we developed a combined mapping pipeline, which includes standard tools , and tested it on public HiSeq2000 exome data from the 1000 Genomes Project. Our pipeline saved 12 hours of run time per Hiseq2000 exome sample and detected ~5% more SNPs than the conventional whole genome approach. This suggests that more potential novel SNPs may be discovered using both approaches than with just the conventional approach.tNGS. Our study has also shown the benefit of computing inter-sample SNP-concordances and inspecting read alignments in order to attain more confident results.We recommend applying our general ‘two-step’ mapping approach for more efficient SNP discovery in  Correlations between genotype and phenotype variations have traditionally been studied by determining the genotype of known markers. For example, genome-wide association studies (GWAS) have revealed associations of known common variants with several of the common diseases. But these associations typically explain less than 25% of the heritable risk estimated for each of those diseases ,2. This tNGS) data? What is the effect of the varying performance of the rapidly evolving alignment and analytical tools? Can a sample multiplexing approach reduce the analysis and study costs? To address these questions, as a test model, we combined a hybridization-based sample enrichment method  with an NGS platform . With sample mix-up control and enrichment cost reduction in mind, we evaluated the enrichment of barcoded SOLiD libraries , rather than preparing NGS libraries after enrichment [tNGS method on E. coli, as a simple model to optimize the process, and human genomic samples in three experimental stages of increasing scope and complexity, culminating in a SNP concordance evaluation for the BRCA1 and BRCA2 cancer genes.Target enrichment methods rely either on PCR or sequence-specific nucleic acid hybridization, and each method has unique advantages and disadvantages -7. The crichment -10. Indirichment ,8,11-14.tNGS data, we carefully analyzed the data using different software and tools. We found that SNP detection depends strongly on the chosen analytical tools and settings, rather than on key enrichment measures such as high and uniform coverage, the percentage of reads mapped on target, or an adequate enrichment fold. Unexpectedly, all evaluated tools failed to identify a large proportion of true sequence variations . Whole-genome (WG) mapping/SNP-calling was time-consuming, but with the benefit of few false positive SNPs. Target region (TR) mapping/SNP-calling was faster and yielded more true SNPs than WG mapping, but led to a high proportion (up to 50%) of likely false positive SNPs. Target region mapping can force-map reads from other genomic loci into the target, leading to false positive SNPs and requiring a postprocessing cleanup. To benefit from both methods, we here report a novel ‘two-step’ mapping approach that starts with TR mapping/SNP-calling, followed by backmapping only the SNP-supporting reads to the WG. Because of the recent wide interest in whole exome sequencing, we also applied our approach to public human exome enrichment data generated by Illumina HiSeq instruments. Moreover, our report includes detailed practical instructions, such as validating SNPs by computing inter-sample SNP-concordances between multiplexed technical replicates, filtering for novel SNPs, or establishing and evaluating a tNGS method.To identify a suitable analytical approach in terms of computational time and accuracy of sequence variant detection in tNGS model) and a fourth, purely analytical stage using human exome data. The study design, the established wet-lab workflow, and the bioinformatics workflows for our test tNGS model are presented in Figure E. coli for 68 genes and sequenced them on a SOLiD NGS platform. In the second stage, we enriched three pools of barcoded libraries of E. coli for the same 68 genes and sequenced these on the same NGS platform. The results of this stage showed reproducible uniform coverages and enrichment folds for most barcodes and multiplexes tested. Full details on the target enrichment experiments and results, especially the wet-lab procedure, can be found in the Additional file 1. In the following results section we concentrate on the respective data analysis and tool development.We developed a novel, fast two-stage backmapping method in the course of three experimental and analytical stages (our test BRCA1 and BRCA2). We enriched these genomic regions at different multiplexing levels . We also included two non-barcoded controls. The sequencing results and enrichment measures are summarized in Additional file In the third stage, we focused on enriching pools of barcoded libraries of HapMap individuals for non-repeat-masked regions of two clinically relevant human cancer genes  is a typical objective of resequencing. To test how well variants are discovered using our multiplexed For the Yoruban sample we observed a good SNP overlap (15/15) and reached a concordance of 14/15 through visual inspection in IGV. Bioscope called only 13/15 SNPs with the chosen settings and also with many alternative settings (Bioscope_settings.xls at ). Our maFigure BRCA1/2 experiment  to two human exome Illumina HiSeq2000 data sets: a female CEU HapMap individual and her father  SOLiD libraries instead of gDNA to one selected microarray-based sequence capture method. We tested this pre-enrichment sample multiplex approach by sequencing 68 E. coli genes as well as two human cancer genes (BRCA1 and BRCA2) in three independent stages and different multiplexing folds on the SOLiD system. We achieved good and reproducible coverage profiles for the TRs across most of the different multiplexed samples, enriching human exonic as well as intronic regions with less than 10% strand bias . We further recommend, if cost allows, to duplicate samples (which is easily performed in a multiplexed experiment) and validate computed SNPs by inter-sample concordance checks between these technical replicates.SNP-callers were originally optimised for low or moderate variations in coverage. In other words, extremely high coverages or high coverage gradients are currently challenging from the bioinformatics point of view. Even for non-enriched samples, some degree of automatic or manual postprocessing is required specifically to distinguish SNPs from misalignments . A previBRCA1/2 regions, or 61 MB of exome regions, compared to 3 Gb of human genome) and cleaning up force-mapped reads. For exomes, we cut run time by more than half, enabling overnight runs on an 8-core node, or turnaround within a working day (4–5 hours) on a 16-core node. This run time cut is necessary to match the speed of new high-throughput platforms such as the Illumina HiSeq2500, which takes 27 hours for producing 120 Gb , the genome-sized data traffic may overload the compute cluster when too many samples are processed in parallel. Compared to BioScope, other pipelines such as BWA alleviate this problem, but a complete exome run using BWA, SAMtools and Picard nevertheless takes about a full day. Our aim was to cut the computational time significantly by mapping to the small TR  for a reliable and efficient analysis of exome and targeted enrichment data. To attain confident results, we specifically recommend the SNP-validation between duplicated samples and a final tNGS test model is shown in Figure The study design for our We performed read mapping and SNP calling with SOLiD Corona Lite and later switched to Bioscope (a pre-release version of v1.2), after the manufacturer advised that the Corona Lite SNP-caller (pre-release diBayes) was not designed for enriched samples . Our Bioscope mapping and SNP-calling settings (Bioscope plan and ini files) are fully documented on the project homepage . For SNPE. coli WG mapping we used the E. coli K-12 M1655 genome  as our mapping reference. For human WG mapping we used the NCBI36/hg18 assembly as our mapping reference. For TR mapping we constructed a single FASTA reference sequence consisting of greater TRs, each separated by a block of 50 Ns (i.e. taking into account the read length of 50). Greater TRs consisted of the actual TRs, extended by 49 bases and merged with adjacent TRs if an overlap occurred. The reason for defining greater TRs was to map reads which partially overlap actual TRs. For E. coli we targeted 68 genes with a total length of 90 kb. For the human samples we targeted 198 small regions with a total length of 90 kb in two genes (a reduction of the size of the intended TRs from 165 kb to 90 kb because of the repeat-masker to enable designing efficient capture probes).We mapped the 50-mer reads using two separate mapping strategies for each sample: WG mapping  and TR mapping . For We wrote R-scripts  for the As validated references for our own SNP-calling, we created consensus SNP lists for the Yoruban HapMap individual NA18507. We created our ‘gold’ consensus from the HapMap3 data , WG IllTo clean up SNPs obtained after our fast TR mapping approach, we initially tested a ‘SNP-backmapping’ approach . This did not eliminate all potential false positive SNPs Figure . We thenTo evaluate the performance of our backmapping pipeline we used publicly available sequence data of a HapMap CEU family trio (ID 1463) who had been repeatedly sequenced within the 1000 Genomes Project . As reprWe aligned the reads to the human genome reference NCBI GRCh37  using BWThe exome-based reference sequence for the TR mapping was created as follows: We downloaded a file of exonic regions, represented by the compatible Consensus Coding Sequence (CCDS) file ‘CCDS.20110907.txt’ at . We thenWe uniquely extracted reads covering heterozygous SNPs, from the BAM-file. As the reads had been sequenced from paired-end libraries, we also extracted the according mate from the BAM-file and created new FASTQ files containing these reads and their (possibly unmapped) mates. We implemented the read-extraction process in a self-written shell script which used VCF-Tools , SAMtoolUsing a self-written Python script and the coordinate transformation file (see above), we transformed the coordinates in the SNP-list from the exome reference coordinate system to the genomic coordinate system. We divided the SNP-list into heterozygous SNPs and homozygous SNPs. Because the heterozygous SNPs can be artifacts resulting from the alignment of non-exonic reads to the exome reference, we extracted the corrected genotype at the SNP-coordinates from the backmapped WG BAM file using the pibase software.None of the co-authors provided competing of interests.AE, AF designed the study; NS, AK, PFS performed the sample enrichment; AE designed the next-generation sequencing experiments; MF, AK, IT, B-SP, BS, NS performed data analysis; IT, B-SP, MF created the tNGS Backmapping pipeline; AE, MF interpreted the data and wrote the manuscript; NS, PR, IT, SS, AF proof-read and edited the manuscript; AE, MF, IT, B-SP, PR, AF revised the manuscript; AE, PFS, PR, AF coordinated the project. All authors read and approved the final manuscript. Supporting Methods and Results. It includes additional details of the Methods, Results and Discussion. - It lists all the Supplementary Tables and Figures .- It also includes Tables A1-A3 and Figures A1-A5.Click here for file Supporting Tables. Tables S1-S26.Click here for file Supporting Figures. Figures S1-S9.Click here for file


Due to the rapid progress of next-generation sequencing (NGS) facilities, an explosion of human whole genome data will become available in the coming years. These data can be used to optimize and to increase the resolution of the phylogenetic Y chromosomal tree. Moreover, the exponential growth of known Y chromosomal lineages will require an automatic determination of the phylogenetic position of an individual based on whole genome SNP calling data and an up to date Y chromosomal tree.We present an automated approach, ‘AMY-tree’, which is able to determine the phylogenetic position of a Y chromosome using a whole genome SNP profile, independently from the NGS platform and SNP calling program, whereby mistakes in the SNP calling or phylogenetic Y chromosomal tree are taken into account. Moreover, AMY-tree indicates ambiguities within the present phylogenetic tree and points out new Y-SNPs which may be phylogenetically relevant. The AMY-tree software package was validated successfully on 118 whole genome SNP profiles of 109 males with different origins. Moreover, support was found for an unknown recurrent mutation, wrong reported mutation conversions and a large amount of new interesting Y-SNPs.Therefore, AMY-tree is a useful tool to determine the Y lineage of a sample based on SNP calling, to identify Y-SNPs with yet unknown phylogenetic position and to optimize the Y chromosomal phylogenetic tree in the future. AMY-tree will not add lineages to the existing phylogenetic tree of the Y-chromosome but it is the first step to analyse whole genome SNP profiles in a phylogenetic framework. A large part of the human Y chromosome, named the nonrecombining Y chromosomal portion or NRY, is strictly paternally inherited. Due to the absence of recombination during meiosis for this Y chromosomal portion, it is possible to define the hierarchic descent order of all human NRY variants and to infer the order and time of their descent in the phylogenetic tree, as the coalescence theory describes . The phyThe last published version of the full phylogenetic tree with all human NRY lineages, named haplogroups, was published by . This trIt is expected that an explosion of Y chromosomal data will become available in the near future considering the increasing number of next-generation sequencing (NGS) studies. DNA sequencing efficiency has increased by approximately 100,000-fold in the last decade; next-generation sequencing machines may now resequence the entire human genome in only a few days . This whAutomated determination of the Y chromosomal lineage. Currently, to determine the Y chromosomal haplogroup of a sample for a broad population study, all required binary markers are still genotyped individually or in limited multiplexes by RFLP, SNaPshot, TaqMan or even by direct sequencing [1) quencing ,19,20. HDetection of new Y-SNPs. Currently, new Y-SNPs are mainly found by sequencing a small portion of the Y chromosome for a limited number of individuals of a certain haplogroup, introducing ascertainment bias [2) ent bias ,21,22. TControl of the currently used Y chromosomal tree. There are two possible ways to update the currently used Y chromosomal phylogenetic tree: first, making Tabula rasa of the current phylogenetic tree and obtaining a new phylogeny based on only the high quality whole sequenced Y-chromosomes, or secondly, controlling the current phylogeny and including new validated Y-SNPs based on whole sequenced Y-chromosomes. The tabula rasa option has several drawbacks mainly since there are not yet enough qualitative samples from all regions and of all sub-haplogroups and since continuity between present and future Y-chromosomal studies is required. The currently used Y-chromosomal tree seems to be very robust and the backbone seems to be solid [3) be solid . Therefobe solid . MoreoveUpdating of the Y chromosomal tree. The state of the art Y chromosomal phylogeny is difficult to follow up based on the literature, even for scientists working exclusively on this chromosome. Moreover, it will be extremely complicated when more SNPs will be reported in the post-genomic period. Often it is the case that a publication shows new Y-SNPs within a particular haplogroup, however, it does not mention the phylogenetic position of previous reported Y-SNPs in the revised Y chromosomal tree which makes it very difficult to integrate the new with old data. For example, a new set of Y-SNPs reshaped recently the phylogeny of haplogroup G in [4) oup G in . howeveroup G in  were notoup G in  phylogenHere, we present an automated approach as a first solution to these four challenges of using whole genome SNP calling data in future Y chromosomal research. This methodology has been implemented in a software package, called ‘AMY-tree’, and validated on data available from several already published whole genome SNP calling projects. Therefore, AMY-tree is the first step to analyse whole genome SNP profiles in the existing phylogenetic framework.The AMY-tree software package is written in Perl; its workflow is shown in Figure The SNP calling data file that will be used as one of the input files for AMY-tree contains all Y-SNPs of a single individual which are called by comparing the whole genome sequences with the reference genome. Since SV calling is still difficult and inefficient in whole genome sequencing applications , we rejehttp://www.isogg.com) and the Y Chromosome Browser of Family Tree DNA (ymap.ftdna.com). See Additional file Another input file is the Y-SNP conversion file containing all the essential data of the Y-SNPs for which mutation conversion is scientifically reported. The mutation conversion of a Y-SNP is always reported by its ancestral and mutation allelic states, e.g. M173: A->C with A the ancestral and C the mutant allele state of M173. As a single (new reported) SNP has often several names when the SNP is detected in different labs independently, the synonyms of the SNP are also given, e.g. M173 = P241 = Page29. Besides the name, mutation conversion and possible synonyms of the SNP, the position of the SNP on the Y chromosome (on both NCBI build 36/hg18 and GRCh37/hg19) is also present in this file which is based on the information given in , all pubThe Y chromosomal phylogenetic tree file contains the Y chromosomal phylogenetic data. It will not be possible in the future to draw all Y chromosomal lineages in a clear graphical way once there will be an exponentially growth of Y-SNP data. Therefore, we choose here for a simple tab separated values format that is easy to understand and created by the user. We have made a Y chromosomal phylogenetic tree file based on the  tree andfasta file of the reference genome that was used to call the SNPs because the ancestral or mutant state of all SNPs in the phylogenetic tree needs to be determined. This is most of the times NCBI build 36/hg18 or GRCh37/hg19.AMY-tree also requires a The strength of the AMY-tree algorithm is that it is a combination of several strategies which use all information from the SNP calling in the analysis while also taking into account mistakes in the SNP calling and/or phylogenetic tree. A fictive example of AMY-tree’s functioning is given in Figure First of all, the algorithm will determine the status of each Y-SNP in the Y chromosomal phylogenetic tree. Each Y-SNP will receive an allelic state code: 0, 1 or −1 indicating the ancestral, mutant or other allelic state respectively. These statuses depend on both the called Y-SNPs and the reference genome: if a SNP is not called, the allelic state of the reference genome is used. This could be initially wrong but in the next step the quality of the SNP calls are checked and if the quality is insufficient the allelic states of the reference genome are not taken into account for further analysis.The first requirement to determine a (sub-)haplogroup is to check the quality of the SNP calls. There are two categories: excellent data with a ‘sufficient’ SNP calling quality and not excellent data with an ‘insufficient’ SNP calling quality. Each sample will be divided in one of these two categories based on their quality of the SNP calls. The SNP calling quality of the Y chromosome of a sample can be estimated because the reference is a composed Y chromosome of several sub-haplogroups including haplogroups G and R  althoughIn the next step, the status of each node in the phylogenetic tree is determined; the determination of this Boolean status  depends on the Y-SNP calling quality. When the ‘Call quality test’ reports a sufficient Y-SNP calling quality, all statuses of Y-SNPs  will be used to calculate the percentage mutant SNPs. For sufficient SNP calling quality samples, a percentage of more than 85% is needed to call a node ‘true’. When the ‘Call quality test’ reports an insufficient Y-SNP calling quality, all mutant alleles of the reference genome are ignored when calculating the percentage mutant SNPs and a percentage of more than 5% is needed to call a node ‘true’. In Figure The AMY-tree search algorithm which uses the phylogenetic tree and the statuses of all nodes to determine the (sub-)haplogroup of the sample can be divided in several sub-algorithms. First, the vertical sub-algorithm checks which leaf nodes of the phylogenetic tree are ‘true’. When considering a phylogenetic tree with the root on the left site and the leaves on right site, all leaves are positioned vertically hence the name of this method. The rectangles filled in grey in the example of Figure The analysis file reports the ‘Call quality test’ score, the results of the vertical, horizontal, combinatorial and specific sub-algorithms and the determined sub-haplogroup of each sample. The name of the sub-haplogroups is given according to both nomenclatures proposed by , namely The SNP status file shows the state of all Y-SNPs which are given in the Y-SNP mutation conversion file. In other words, for each already reported Y-SNP the user will find out if the sample has the ancestral (0), mutant (1) or other (−1) state. Moreover, it is also reported in this list if the mentioned state is obtained by calling or by derivation from the reference. The ConvNotTree file contains all Y-SNPs which have the mutant state and which are mentioned in the Y-SNP conversion file but are not present in the phylogenetic tree that was used. All synonyms of these SNPs are as well mentioned in this file. In the New SNPs file all the Y-SNPs are listed for which the phylogenetic position is not yet determined and which are not yet reported in the Y-SNP conversion file. This list is useful to detect novel yupp-SNPs on different phylogenetic levels.Most SNP calling programs for whole genome sequencing data such as GATK unified genotyper , RTG invThe last published version of the full phylogenetic tree with all human NRY lineages was published by , furtherTo test the AMY-tree software package, we collected 118 full genome SNP calls of in total 109 males available from four different projects . The samples were sequenced with the DNA nanoball sequencing  with an et al.[Next, one genome was available from the Irish genome project described in Tong et al.; the samet al.[Moreover, five genome wide SNP calling profiles from whole genome and exome sequencing of samples with African origin were used. This Khoisan and Bantu genomes project was described in Schuster et al.. For twoFinally, whole genome data of 77 individuals was collected from the first pilot study of the 1000 Genomes project which were sequenced on several platforms  with an on average 4× sequence coverage . The SNPSufficient Y-SNP calling quality was found for all the high sequence coverage data of Complete Genomics, the medium sequence coverage data of the Irish Genome project and two medium coverage samples of the Khoisan and Bantu genomes project. Insufficient Y-SNP calling quality was observed for all the 1000 Genomes samples which had low sequence coverage and the three other samples of the Khoisan and Bantu genomes project which had also low sequence coverage or of which only the exome was sequenced  versus Complete Genomics (average coverage is 80×). Under low coverage sequencing circumstances, on average less than 5x coverage per site per individual, accurate SNP calling is indeed difficult, and there is often considerable uncertainty associated with the results. To reduce the uncertainty associated with SNP calling it is necessary to sequence deeply with at least 20x coverage [When comparing the results of the nine genomes which were sequenced as well by Complete Genomics as by the 1000 Genomes project, the difference between both projects is clear. Four samples give the same results for both methods based on the Karafet tree and five samples based on the Updated tree. For the other couples, it is clear that the results of the 1000 Genomes project always shows a less in-depth phylogenetic level than the results of Complete Genomics; e.g. individual NA07357 which belongs to R1* or R-M173* based on the 1000 Genomes data coverage . This isFinally, the results of the sub-haplogroup assignments were all expected based on the ancestral origin of the sampled populations as shown in Figure Based on the 109 test samples, AMY-tree did not report any inconsistency within the Karafet and Updated tree, exclusively some small ambiguities. These results confirm the well-established backbone and the sub-haplogroup determination of the currently used Y chromosomal phylogenies. Although the low number of test samples, some issues are however found dealing with recurrent mutations and wrong reported mutation conversions. First, Y-SNP V218 was mutant for all the three Complete Genomics samples and the four 1000 Genomes samples belonging to I1* (I-M253*), in contrast to all other tested samples. Nevertheless, the mutant allele of V218 is characteristic for sub-haplogroup A2, reported for the first time by [Next, some problems were noticed with the reported mutation conversions. The ancestral and mutant alleles are not always well reported in manuscripts or databases. After analysis, we changed the conversion of V161 and V100  but not yet scientifically verified. For the Complete Genomics samples, we found a mutant allele for on average 12.8 known SNPs per sample if we take already all Y-SNPs of the Updated tree out of the analysis; haplogroup R1b samples have on average 3.1 Y-SNPs, haplogroup E samples have on average of 23.8 Y-SNPs and haplogroup I samples have on average 20.7 Y-SNPs. For the 1000 Genomes samples, we found a mutant allele on average 5.4 known SNPs per sample if we take already all Y-SNPs of the Updated tree out of the analysis; haplogroup R1 sample have on average 1.2 Y-SNPs, haplogroup NO have on average 1.3 Y-SNPs, haplogroup E have on average 12.1 Y-SNPs and haplogroup I have on average 9.1 Y-SNPs. For samples that were analysed with both platforms, the ‘ConvNotTree Y-SNPs’ which were found by 1000 Genomes were always a subset of those found by Complete Genomics.‘ConvNotTree Y-SNPs’ are interesting to detect new interesting Y-SNPs for which the mutation conversion is already known but which are not included yet in the phylogenetic framework. For some of these Y-SNPs, a suggestion for their phylogenetic position is also known on the ISOGG website  like Z1101, Z1107, and Z1116. However, ‘ConvNotTree Y-SNPs’ could perfectly diverge the three sub-haplogroup I1* (I-M253*)-samples  as well as the three sub-haplogroup R1b1b2a1a2g* (R-U152*)-samples  from each other, as the ISOGG website already reported. Therefore, the ISOGG-tree may in addition with AMY-tree be useful to revise scientifically some (sub-)haplogroups as here for I1 (I-M253) and R1b1b2a1a2g (R-U152).The list of ‘ConvNotTree Y-SNPs’ was identical for all eight individuals which were paternally related. However, one individual (NA12883) revealed one extra mutant SNP, namely L374. According to ISOGG, SNP L374 should be only mutant within haplogroup G instead of in sub-haplogroup R-P312* where to all relatives belongs to. Therefore, this SNP call is a false positive or a recurrent – Numerous Y-SNPs with unknown mutation conversion or phylogenetic position were identified in the test samples. For the Complete Genomics samples, on average 2899.6 yupp-SNPs were found. Sub-haplogroup R1b samples with on average 2066 yupp-SNPs reported, showed less yupp-SNPs than haplogroup E and I with an average of 3688 and 3301 yupp-SNPs respectively. For the 1000 Genomes samples, on average 286 yupp-SNPs were found. Haplogroup R1b samples with on average 65 yupp-SNPs reported a lower number than haplogroup E, NO and I with an average of 418, 235 and 401 yupp-SNPs respectively. The 1000 Genomes samples mentioned on average only 10% of the yupp-SNPs reported based on Complete Genomics because of the strong difference in sequence coverage. The reason that haplogroup R samples always show less yupp-SNP calls is that the reference genome is composed of multiple individuals belonging to different haplogroup R samples . The numde novo Y-SNPs, due to the low observed Y-SNP mutation rates in deep rooted pedigrees [Nevertheless, the results for the eight individuals which are paternally related with each other, revealed that there is always a chance for false positive SNP calls, also for samples sequenced with high coverage. The numbers of yupp-SNPs of these eight samples differ from each other: large numbers of yupp-SNPs are only reported in one individual or are missing in one individual. The reason is most likely false positive calls, more than edigrees .The AMY-tree software was validated successfully based on 118 full genomes from 109 males with different geographical origins. The samples were analysed on several NGS platforms for whole genome sequencing with different sequence coverage depths. Therefore, AMY-tree may partly solve four issues of future Y chromosomal research:1) It is possible to automatically determine the phylogenetical lineage to which a sample belongs to based on whole genome sequencing data without massive time-consuming efforts and therefore also avoiding laboratory and analysis errors. At this moment, SV calling is still difficult and inefficient in whole genome sequencing applications . As suchin silico.2) Y-SNPs which are not yet reported in scientific publications but which may be crucial for some clarification of the Y chromosomal tree may now be detected efficiently. It is of course necessary to take into account that false positive SNP calls are always possible. Confirmation of those SNPs is possible by laboratory work or by future bio-informatic tools which may combine several AMY-tree results to verify it 3) It is possible to verify present and future Y chromosomal phylogenies, in addition to the detection of Y-SNP recurrent mutations and wrongly reported Y-SNP conversions based on whole genome SNP calling data. As long as the results are observed in at least two independent samples, it may confirm the observations.http://www.phylotree.com[4) The state of the art Y chromosome phylogeny will be easier to keep up to date when there will be an exponentially amount of Y chromosomal data in the future. This is possible due to the fact that data of the whole Y chromosome is collected for each individual such that the position of certain future detected SNPs will be scored as well in old sequenced samples. Moreover, the proposed tab separated value format in AMY-tree is useful to describe the up to date phylogenetic tree, especially for the massive expected Y chromosomal tree. This approach is already for a long time present for the mtDNA phylogenetic tree via otree.com.The software AMY-Tree  is freely available on the website bio.kuleuven.be/eeb/lbeg. On this website, at least every six months (unless no new data has appeared), an update will be given of the SNP conversion file and of the Updated Y chromosomal phylogenetic tree in tab separated value format based on scientific publications.The authors declare no conflict of interest.Research design & supervision: MHDL; Programming: AVG; Writing: AVG & MHDL; Commenting on manuscript: AVG, RD & MHDL. All authors read and approved the final manuscript.Table S2. Table-format of the latest full published Y-chromosomal phylogenetic tree of [Table S3. Table format of the updated Y-chromosomal phylogenetic tree based on the most recent scientific publications (May 2012). For each reported Y-chromosomal (sub-)haplogroup, the phylogenetic lineage whereto the (sub-)haplogroup belongs, called the ‘parental lineage’, and the defining Y-SNPs of the lineage are given. Table S4. Characteristics and AMY-tree results of the test panel with samples from the Complete Genomics (CG), Irish genome (IrG), Khoisan and Bantu Genomes (KBG) and 1000 Genomes (1000G) projects. For each sample the population as well as the ancestral continent of the population is given, next to the relationship with other samples of the test panel. Abbreviations of the populations are ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern & Western European ancestry from the CEPH collection; CHB: Han Chinese in Beijing, China; GIH: Gujarati Indian in Houston, Texas, USA; JPT: Japanese in Tokyo, Japan; LWK: Luhya in Webuye, Kenya; MKK: Maasai in Kinyawa, Kenya; MXL: Mexican ancestry in Los Angeles, California, USA; NK: Khoisan from Northern Kalahari; SA: Bantu from South Africa; SK: Khoisan from Southern Kalahari; TSI: Toscans in Italy; PUR: Puerto Rican in Puerto Rico; YRI: Yoruba in Ibadan, Nigeria. Table S5. Characteristics and AMY-tree results of samples of which the sub-haplogroups was previously reported in literature. Samples are taken from the Complete Genomics (CG), Irish genome (IrG), Khoisan and Bantu Genomes (KBG) and 1000 Genomes (1000G) project. For each sample the population as well as the ancestral continent of the population is given, next to the relationship with other samples of the test panel. Abbreviations of the populations are CEU: Utah residents with Northern & Western European ancestry from the CEPH collection; MXL: Mexican ancestry in Los Angeles, California, USA; NK: Khoisan from Northern Kalahari; SA: Bantu from South Africa; SK: Khoisan from Southern Kalahari; TSI: Toscans in Italy; PUR: Puerto Rican in Puerto Rico; YRI: Yoruba in Ibadan, Nigeria. The sub-haplogroup as it is given in the literature is compared to the determined sub-haplogroups in Karafet and Update tree.The latest version of the Y-SNP conversion file (May 2012). This list contains the name, the synonyms, the RefSNP ID, the position on the Y-chromosome according to references NCBI36 (Hg18) and GRCh37 (Hg 19) and the mutant conversion state  of all reported Y-SNPs.  tree of . For eacClick here for fileFigure S2. Relationship between call quality test score and number of Y-SNPs called against hg18 for 118 samples from different genome sequencing projects.Call quality test scores for 118 samples from different genome sequencing projects created within the AMY-tree algorithm. All samples are ordered according to their project and their number of called Y-SNPs. Click here for fileSupplementary Method.Click here for file


Human Papillomavirus type 16 (HPV16) causes over half of all cervical cancer and some HPV16 variants are more oncogenic than others. The genetic basis for the extraordinary oncogenic properties of HPV16 compared to other HPVs is unknown. In addition, we neither know which nucleotides vary across and within HPV types and lineages, nor which of the single nucleotide polymorphisms (SNPs) determine oncogenicity.A reference set of 62 HPV16 complete genome sequences was established and used to examine patterns of evolutionary relatedness amongst variants using a pairwise identity heatmap and HPV16 phylogeny. A BLAST-based algorithm was developed to impute complete genome data from partial sequence information using the reference database. To interrogate the oncogenic risk of determined and imputed HPV16 SNPs, odds-ratios for each SNP were calculated in a case-control viral genome-wide association study (VWAS) using biopsy confirmed high-grade cervix neoplasia and self-limited HPV16 infections from Guanacaste, Costa Rica.HPV16 variants display evolutionarily stable lineages that contain conserved diagnostic SNPs. The imputation algorithm indicated that an average of 97.5±1.03% of SNPs could be accurately imputed. The VWAS revealed specific HPV16 viral SNPs associated with variant lineages and elevated odds ratios; however, individual causal SNPs could not be distinguished with certainty due to the nature of HPV evolution.Conserved and lineage-specific SNPs can be imputed with a high degree of accuracy from limited viral polymorphic data due to the lack of recombination and the stochastic mechanism of variation accumulation in the HPV genome. However, to determine the role of novel variants or non-lineage-specific SNPs by VWAS will require direct sequence analysis. The investigation of patterns of genetic variation and the identification of diagnostic SNPs for lineages of HPV16 variants provides a valuable resource for future studies of HPV16 pathogenicity. Human papillomaviruses (HPVs) are a highly prevalent, globally distributed group of DNA viruses infecting cutaneous and mucosal epithelia throughout the human body −8 per nucleotide per year Genetically, papillomaviruses (PVs) evolve slowly, with a mutation rate of approximately 2±0.5×10Genotype imputation relies on a high correlation between genetic variants at sites across the genome of an organism (for review see Although the extraordinarily high association (odds ratio >300) between HPV16 and cervical cancer A global alignment (available from authors upon request) of HPV16 complete genomes was performed to examine patterns of nucleotide variation. The panel consisted of 62 complete genome sequences  containiPrior phylogenetic analyses of HPV16 variants To evaluate the ability to use sequence information from limited, specific regions of the HPV16 genome for imputation, we had to determine the correlation of SNPs between regions. Due to the non-recombinant nature of HPV, diagnostic  SNPs could be identified and plotted against position in the genome for each separate phylogenetic lineage . Of noteThe L1, E6 and/or the URR regions of the HPV16 genome are frequently utilized for variant studies. Typically, epidemiological studies assign HPV16 lineage based upon the nucleotide sequence of one or more of these regions The L1 ORF was found to allow discrimination between all but the E(As)/E(p) sublineages. By contrast, the E6 ORF was able to resolve the two European sublineages (E(p) vs. E(As)), and the African sublineages (Af-1 vs. Af-2), but unable to distinguish the Asian-American sublineages (NA1 vs. AA1 vs. AA2). The E6 and L1 ORFs, individually, provide limited information on viral lineage. In summary, the URR and E2/E4 overlap region contain diagnostic SNPs that can be genotyped to determine HPV16 viral lineage. All diagnostic SNPs  from each of the 62 complete genomes in the reference database with and without removal of the test genome; and, complete-genome sequencing of 8 previously unsequenced, randomly selected samples from amongst the imputed study samples  in HPV16 and means that, as performed here, phylogenetic information must be taken into account when analyzing SNPs with elevated odds ratios in HPV. SNPs within high LD (non-recombining) regions of the human and other diploid genomes will have similar properties. In addition, the set of non-lineage-specific SNPs, those that are not correlated with the set of measured SNPs, will have to be determined directly before their contribution to risk and pathogenesis can be assessed.The analysis of genetic relatedness  between Imputation error estimation showed that imputation produced only a small number of erroneously assigned SNPs per sequence . Simulation of imputation without removal of the test genome was likely an overestimate of the accuracy, but was used to account for multiple closely related matches. Simulation of imputation with removal of the test genome, by contrast, may have underestimated the mean accuracy, since a proportion of the query samples came from lineages over-represented in the reference panel. Errors, under the former circumstances, occur when multiple sequences in the reference panel are identical over the region covered by the partial sequence information but different across the remaining genome. As the database of complete HPV16 genomes expands to cover essentially all recurring variations throughout the world, it can be estimated that imputation accuracy will reach a maximum, close to but not equal to 100%. There will always be a set of SNPs (n ≈ 20) that are variable within and between lineages  that do not allow absolute imputation, these HPV16 SNPs will require direct determination to evaluate their risk for cancer.We used the genome sequences of 412 HPV16 infected cases and controls (396 imputed and 16 sequenced) to perform a VWAS to identify SNPs that confer additional risk for developing high-grade cervical intraepithelial neoplasia. Although this low-power ‘proof of principle’ analysis found no statistically significant individual SNPs, we identified intriguing trends at the sublineage level. In this dataset, there is a higher association with CIN3+ for Non-E lineages than for E lineages Case and control state was obtained from the large population-based cohort study  conducted in Guanacaste, Costa Rica, previously described DNA isolated from exfoliated cervical cell samples was used for all HPV analyses, including initial HPV detection, typing and variant sequencing, as previously described Complete-genome sequences of HPV16  sequences in the reference panel was taken as input; an n × n matrix of all pairwise identities was then produced. The matrix was plotted as a heatmap and scaled such that the maximum observed pairwise identity (100%) was represented by blue and the minimum (97.6%) was represented by red  with the highest BLAST scores were selected. In cases where the query sequence was the concatenated sequence of more than one region , returning separate hits for each region, the complete-genome sequence(s) with the highest combined BLAST score(s) were selected.The missing nucleotides of the partial query sequences were completed using the selected best complete-genome sequence.In the case of multiple, equally high scoring BLAST hits for a single query sequence, random sampling of the best hits was used by generating a 100 sets of imputed nucleotide sequences. This was done to allow calculation of unbiased confidence intervals for the subsequent odds ratio analysis. Random sampling was performed using the built-in random choice function in Mathematica 7.0 under default options .To estimate the precision of imputation with the set of 62 HPV16 reference genomes and the algorithm used herewith, the following analyses were performed. Dummy imputations were conducted using the isolated URR region from each complete-genome sequence in the HPV16 reference panel as a query sequence. The reference panel from which the URR sequences were drawn was used as the BLAST database, under two conditions: with and without removal of the complete HPV16 sequence from which the query sequence was taken. Imputed sequences were then compared to the known complete-genome information at all SNP positions for both methods. For each query sequence, the mean and standard deviation of the percentage of correct SNPs across the best matches for each test sequence was calculated. Averaging the individual means and taking their standard deviation produced overall estimates for each method.An empirical estimate of imputation error was performed by randomly selecting 8 of the 396 samples (≈2%) that were subject to genotype imputation followed by direct sequencing of their complete genomes. Random selection was performed using the built-in random sample function in Mathematica 7.0 as described above. For each of the 8 samples, the true sequence was compared to 100 imputed sequences. The mean and standard deviation of % error were calculated for each sequenced sample and the overall estimate was calculated by taking the average and standard deviation of the individual means.-6. Odds ratios and 95% confidence intervals were calculated by taking exponentials of the summary estimates. Results were plotted as odds ratios around 1.0, to inspect qualitative trends. All analyses were run using SAS version 9.1.3 .Of the 35 complete-genome sequences determined from the Guanacaste study and included in the reference database, 16 were also either cases or controls and were combined with the 396 imputed sequences in the epidemiological analyses. Logistic regression was performed on the SNP positions using all imputed sequences and complete genomes in order to compare subjects with CIN3+ to the HPV16-positive “control” population. Only SNP positions with adequate sample size  were analyzed. In the logistic regression calculations, the reference group at each position was determined by setting the most prevalent nucleotide as the referent. Logistic regression was run for each of the 100 imputed datasets, parameter estimates and estimated covariances were written to separate datasets. The MIANALYZE procedure was used to combine output data from all analyses. The procedure reads the parameter estimate and covariance computed in each individual logistic regression, and creates a summary estimate and a p-value for each nucleotide position. After Bonferroni correction for 7916 repeated comparisons of SNPs, p-values were considered significant if they were ≤6.32×10Table S1Designated names, NCBI accession numbers and lineage assignments for HPV16 isolates.(PDF)Click here for additional data file.Table S2Diagnostic single nucleotide polymorphisms (SNPs) for HPV16 lineages.(PDF)Click here for additional data file.Data S1Mathematica 7 code for performing imputation based on BLAST results. A partial sequence of the HPV16 genome was used to search a database of complete genomes using BLAST. The results of the BLAST search were then used to identify the full length genomes that had the highest score and one complete genome sequence was added to a new file that was used for further analyses. Code annotations are enclosed by parentheses and stars, i.e., (*…*). Commands can be pasted directly into a Mathematica notebook and executed. Test data is available from the authors by request.(PDF)Click here for additional data file.Data S2Mathematica 7 HPV sequence analysis code. Mathematica 7 notebook containing code used for performing sequence analysis on the nucleotide sequences of a reference HPV complete genome sequence library. Analyses include calculating pairwise differences and plotting a heatmap of values, as well as identifying lineage-specific (diagnostic) single nucleotide polymorphisms. Code annotations are enclosed by parentheses and stars i.e., (*…*). Commands can be pasted directly into a Mathematica notebook and executed. Test data is available from the authors by request.(PDF)Click here for additional data file.