The development of next-generation sequencing technologies (NGS) has made the use of whole-genome sequence data for routine genetic evaluations possible, which has triggered a considerable interest in animal and plant breeding fields. Here, we investigated whether complete or partial sequence data can improve upon existing SNP (single nucleotide polymorphism) array-based selection strategies by simulation using a mixed coalescence - gene-dropping approach.We simulated 20 or 100 causal mutations  within 65 predefined ‘gene’ regions, each 10 kb long, within a genome composed of ten 3-Mb chromosomes. We compared prediction accuracy by cross-validation using a medium-density chip (7.5 k SNPs), a high-density  and sequence data (335 k). Genetic evaluation was based on a GBLUP method. The simulations showed: (1) a law of diminishing returns with increasing number of SNPs; (2) a modest effect of SNP ascertainment bias in arrays; (3) a small advantage of using whole-genome sequence data vs. HD arrays i.e. ~4%; (4) a minor effect of NGS errors except when imputation error rates are high (≥20%); and (5) if QTN were known, prediction accuracy approached 1. Since this is obviously unrealistic, we explored milder assumptions. We showed that, if all SNPs within causal genes were included in the prediction model, accuracy could also dramatically increase by ~40%. However, this criterion was highly sensitive to either misspecification (including wrong genes) or to the use of an incomplete gene list; in these cases, accuracy fell rapidly towards that reached when all SNPs from sequence data were blindly included in the model.Our study shows that, unless an accurate prior estimate on the functionality of SNPs can be included in the predictor, there is a law of diminishing returns with increasing SNP density. As a result, use of whole-genome sequence data may not result in a highly increased selection response over high-density genotyping. The prediction of genetic merit, that is, the average phenotypic value of an infinite number of descendants from a given individual, has been the subject of active investigations in quantitative genetics for decades. Due to the rapid decrease in genotyping prices and to the development of new statistical methodologies, genomic selection is becoming the standard procedure for genetic evaluations in most relevant animal and plant species. Currently, there is ample consensus that genomic selection can be more efficient than traditional methods based only on pedigree information and phenotypes  and that this improvement more than offsets genotyping costs in many commercial breeding schemes, primarily for dairy cattle . It is eAs a result, there is currently considerable interest in the animal and plant breeding fields on the use of high-throughput sequencing technologies for genomic selection ,4. CompaAlthough sequencing prices are rapidly decreasing, routine analyses of NGS data from a massive number of individuals for genomic evaluation still face serious challenges. To begin with, raw NGS data are difficult to transfer even with high speed internet connection, and their bioinformatics’ analyses are expensive and require costly hardware equipment for the scale needed in animal or plant breeding. Often, wrong or incomplete reference genomes complicate the analyses; furthermore, a frequently overlooked issue of population studies using whole-genome sequence data is the high rate of missing SNP calls. This problem dramatically increases at low-depth sequencing. In addition, it should be noted that complete genome sequencing of whole breeding populations is unlikely in the near future, and thus, sequencing will be combined with other approaches such as array genotyping and/or genotype by sequencing. Currently, imputation is one of the most popular proposed solutions but, although accurate, imputation is not error-free either ,7.Unsurprisingly, assessing the advantages of NGS over array SNP genotypes for genomic selection is a debate. A fundamental issue is to predict whether complete or partial sequence data can improve upon existing SNP array-based selection strategies and, if so, in which cases. Here, to study the potential advantage of sequence- vs. array-based genomic selection, we adopted an optimistic view: we assumed that SNP genotypes obtained from NGS data were error-free, without missing data, and that as many individuals can be sequenced as genotyped and phenotyped. Of course these assumptions are quite unrealistic, and we attempted to model errors for some cases, even if imperfectly. Nevertheless, this initial error-free setting allowed us to study two important issues: to set up an upper bound on the limits of genomic selection and to fine-tune existing methods to improve their performance. Furthermore, given the increasing SNP density of current genotyping technologies and their allegedly low calling rates, the potential advantages of very high-density genotyping over NGS data or of genotyping by sequencing were explored. We also considered the effect of SNP ascertainment in the design of genotyping arrays.-3 per nucleotide) 2 and Va = 2 f (1-f) [a + d (1-2f)]2, f being the frequency of the allele. This implies that low-frequency alleles explain a priori the same variance as intermediate-frequency alleles, which in turn implies that their absolute effects are larger. If this is the case, whole-genome sequence data will be more accurate than SNP-array genotypes, because the alleles in commercial SNP chips do not usually include low-frequency alleles [For each QTN, additive  was adjusted such that the broad-sense heritability was Alternatively, the actual additive and dominance variances, given the observed genotypes in each base population, can be computed and Ve can be adjusted exactly for the base population. In practice, in spite of disequilibrium, we observed only small differences between both strategies and thus we fitted Ve assuming equilibrium.2 = 0.25) for evaluation instead of narrow sense heritability (h2), this had a negligible effect on results and the rationale is that true parameters are rarely known. Nevertheless, restricted maximum likelihood (REML) estimates of h2 in the simulated data were close to 0.25. For the molecular-based method, we considered the following ‘blind’ strategies:A medium-density SNP array that comprised 7500 SNPs on average.A high-density array that contained ~17 000 SNPs.RAD (Restriction site associated DNA markers) sequencing: all SNPs in 100 subsets of the genome, each 10 kb long, were selected. This approach is intended to mimic genotype by sequencing (GBS) and, in our scenario, resulted in about 11 000 SNPs. This strategy could also be assimilated to exome sequencing that consists in sequencing a small subset of the genome i.e. the coding regions.Sequence: all simulated SNPs in the population, ~335 000 SNPs.We predicted genetic merit based on pedigree BLUP and on molecular information. For BLUP, we used broad heritability .All SNPs within the defined ‘genes’, that is, the 65 10-kb regions where QTN were located ~7500 SNPs.All SNPs within a subset of genes (50%) were included, to mimic incomplete biological information, about 3800 SNPs.A subset of 50% of genes plus 30 ‘neutral’ 10-kb windows were included in the model, to mimic both incompleteness and incorrect biological information.For comparison, we also mimicked the use of biological information, which differed in its degree of accuracy (and inversely to plausibility):a priori. We shall come back to this issue later.The first strategy would correspond to the maximum and unrealistically attainable accuracy. However, the latter three strategies cannot be deemed to be completely implausible: we are assuming that only the genes that contain the causal mutations are known, but not the causal mutations themselves. Note that this information should be available To simulate SNP ascertainment bias, as in commercial arrays, a subset of 50 diploid individuals (100 sequences) was selected among those simulated by the coalescence approach; SNPs that segregated in that subsample were chosen such that their minimum allele frequency (MAF) was at least 0.15 and hence randomly selected to mimic a 50 k and 700 k SNP density. In our simulations, this implied selecting ~0.9% and ~20%, respectively, of those SNPs with a MAF greater than 0.15. In the two-breed model, the SNPs were chosen from those segregating in the second breed. In a limited number of cases, we also evaluated the consequences of choosing a random set of SNPs, irrespective of frequency (MAF > 0). With whole-genome sequence data, all SNPs were included in the model irrespective of their frequency.Genetic evaluation was based on the nonlinear A method of VanRaden . Evaluatxij is the genotype, coded as -1, 0 and 1, and aj by their estimates:where a priori variance for all SNP effects was pi being the allele frequency in the pedigree for the ith SNP and nSNP, the number of SNPs. The variance of each SNP effect Nonlinear A was solved using the solve-SNP program  that impper se, because there is only one effect per locus; it can be seen as a computational device to find nonlinear estimates of SNP effects [which in practice means that SNP effects that are smaller than two standard deviations are more shrunken, whereas SNP effects that are larger are less shrunken. Formal justification of this algorithm is in Gianola , who sug effects .To compare the performance of the three strategies , we computed the correlation between true and predicted genetic merit in a subset of individuals for which phenotypes had been removed (as a measure of predictive ability). For strategies using molecular information, Nonlinear A was used. In practice, the breeder is primarily interested in predicting the genetic merit of new individuals, and so cross-validation was performed by removing the phenotypes of the 500 last-generation individuals. One hundred replicates per case were run.For most results presented here, we assumed no missing data and no genotyping or sequencing errors. This is, as mentioned, a strong assumption. Modeling errors is, however, no easy task because they are neither unbiased nor independent in the case of NGS data. For NGS, sequencing depth is the most relevant factor but the bioinformatics pipeline and the population structure are also relevant ; further-3 and 10-4 per genotype. This range spans most results from the literature [Here, to mimic NGS errors efficiently , we used a three-step procedure: (i) a biased genotyping error  SNterature -25.TableFigure For the simulated sequence of 30 Mb, an average of 335 000 SNPs were obtained from the coalescence simulations. The unfolded site frequency spectrum (SFS) for individuals in the pedigree is in Figure To study ascertainment effect on prediction accuracy, scenarios that differ in the choice of MAF and ascertainment in one or more breeds were compared Table . The firTable The second important conclusion from Table We investigated milder and more realistic options than knowing all QTN see . The nexIn all the predictors considered here, equal priors were assigned to each SNP, and our simulations suggest that the increase in information with sequence data is offset by added noise in the prediction compared to HD genotyping. This is illustrated in Figure -3 to 10-4) is unlikely to have a strong influence on prediction accuracy. Given that the loss in prediction accuracy observed with the HD chip compared to whole-genome sequence data is ~4% . Overall, our simulations suggest that the availability of whole-genome sequence data could increase prediction accuracy, measured as correlation between predicted and true genetic value in the last generation, by 4 to 8% compared to SNP arrays. This increase is much lower than the results reported by Meuwissen and Goddard , who sugInterestingly, Hayes et al.  recentlyFurthermore, our results also show that prediction accuracy plateaus as SNP density increases. This phenomenon was already reported by Ober et al. , who didIn fact, we have shown that that there is still ample room for improvement, as evident from the large increase in prediction accuracy that could be obtained if QTN were known. In practice, decades of research have shown that this is highly unlikely to happen, and many causal variants will remain undiscovered even in the largest GWAS (genome-wide association studies) experiments  as shownA much less ambitious target is the characterization of genomic regions that are involved in the determination of a given phenotype, and here we have shown that this can result in a dramatic improvement of about 40% (in our scenario) compared to the use of whole-genome sequence data. This suggests that one main reason why whole-genome sequencing does not meet expectations – compared to high-density genotyping – is ‘simply’ the use of wrong prior information. In all the predictors considered here, equal priors are assigned to each SNP , and our simulations suggest that the increase in information provided by whole-genome sequence data is offset by added noise in the prediction.An unfortunate side effect is that even moderate errors when determining the set of true QTN can reduce this advantage Figure . For futWithout doubt, genetic architecture affects the performance of genomic selection, with and without the use of whole-genome sequence data. Unfortunately, there is much uncertainty on the precise genetic architecture of quantitative traits under selection. Therefore, all simulation studies, including ours, should be taken with some caution. Two extreme architectures have been traditionally considered: (1) a major gene model in which a few genes explain most of the genetic variance and (2) the infinitesimal model. The former does not explain the wealth of experimental data that has accumulated along the last decades whereas the latter, while not being biologically sound, can be considered as operationally correct for prediction purposes. Here, we chose an intermediate architecture that comprises a reasonable large number of loci, but each with a different effect. To study the potential effects of increasing the number of loci, even if briefly, we also simulated a model with 1000 loci that all have an equal effect and are randomly distributed among 10 chromosomes. The results Figure  were not-3 to 10-4). It is also expected that, with the ongoing development of improved technology and imputation tools, errors will tend to decrease.A persistent matter of concern in genomic data is the occurrence of errors, due to the difficulty of ensuring uniform quality control in such massive datasets. For most of the simulations investigated in this work, we assumed that errors and missing data were absent and this is, admittedly, a strong assumption. In this sense, our results should be interpreted as the uppermost attainable limits, for the genetic architecture and experimental designs considered. Nevertheless, we also evaluated in a simplified manner the potential impact of sequencing and genotyping errors and we found Figure  that, unThe issue of SNP ascertainment bias in the design of arrays has received considerable interest, in particular for its effects on the inference of genetic parameters . The maiHere, we have only considered the case of single-breed evaluation. The more distant, genetically, the breed of interest is from the breed(s) used to ascertain the SNPs in the array, the worse is the array expected to perform compared to direct sequencing . Predict, per se, is likely to be minimal, at least in a single-breed scenario. This conclusion seems relatively insensitive to the SNP ascertainment process, number of causal loci, presence of dominance and to modest genotyping or sequencing errors. In contrast, our work shows that a dramatic increase in prediction accuracy can be attained by using correct prior information. However, and importantly, this advantage can be quickly removed if the biological information used is partially incorrect or incomplete. Therefore, an improved biological understanding of the trait(s) is critical. However, in commercial breeding schemes selection targets are continuously changing to adapt to market requirements. This is equivalent to continuously modifying, even if partially, the genetic basis of the trait(s) under selection, which hinders the use of biological prior information. Furthermore, as shown, e.g., by the ENCODE project, many functional motifs in the genome are outside the coding regions, which may make it difficult to precisely delineate potential causal regions for any given trait. For all these reasons, we recommend caution on this issue that also requires further methodological developments.The availability of whole-genome sequence data can lead to increased selection response compared to current strategies based on medium-density SNP arrays but this increaseAlong this vein, although our results were relatively robust to genetic architecture, it is necessary to better understand and ascertain the genetic basis of complex traits. This implies characterizing not only the distribution of the effects of causative alleles and their frequencies, but also the influence of complex demographic scenarios that may include admixture events and multiple bottlenecks, quite frequent in livestock and plant species. For instance, Lohmueller  has pred


Malus × domestica) segregating for skin color. We show that GBS produces a relatively large, but extremely sparse, genotype matrix: over 270,000 SNPs were discovered but most SNPs have too much missing data across samples to be useful for genetic mapping. After filtering for genotype quality and missing data, only 6% of the 85 million DNA sequence reads contributed to useful genotype calls. Despite this limitation, using existing software and a set of simple heuristics, we generated a final genotype matrix containing 3967 SNPs from 89 DNA samples from a single lane of Illumina HiSeq and used it to create a saturated genetic linkage map and to identify a known QTL underlying apple skin color. We therefore demonstrate that GBS is a cost-effective method for generating genome-wide SNP data suitable for genetic mapping in a highly diverse and heterozygous agricultural species. We anticipate future improvements to the GBS analysis pipeline presented here that will enhance the utility of next-generation DNA sequence data for the purposes of genetic mapping across diverse species.Next-generation DNA sequencing (NGS) produces vast amounts of DNA sequence data, but it is not specifically designed to generate data suitable for genetic mapping. Recently developed DNA library preparation methods for NGS have helped solve this problem, however, by combining the use of reduced representation libraries with DNA sample barcoding to generate genome-wide genotype data from a common set of genetic markers across a large number of samples. Here we use such a method, called genotyping-by-sequencing (GBS), to produce a data set for genetic mapping in an F1 population of apples ( The introduction of new high-throughput DNA sequencing technologies has dramatically reduced sequencing costs and increased the pace of genomics research. One of the primary goals of genomics research is to establish relationships between genotypes and phenotypes. In agriculture, such genotype–phenotype associations form the basis of genomics-assisted breeding programs that aim to accelerate the breeding of improved varieties. Long-lived woody perennials are expensive to breed using traditional methods and therefore stand to benefit more from the genomics revolution than most other agricultural species. Offspring from breeding programs can be genetically screened and discarded at the seedling stage without incurring the enormous expense of growing them for years to fruit-bearing maturity for evaluation .Most forms of genetic mapping require the collection of genome-wide polymorphism data, where genotypes from a set of common loci are obtained from a set of samples. Although next-generation DNA sequencing technologies produce vast amounts of DNA sequence data, they were not designed to generate these types of genotype data. Recently, however, next-generation sequencing technologies have been coupled with reduced representation libraries and DNA barcoding to simultaneously identify and genotype a common set of polymorphic loci across a set of samples in a single experiment. The two most common of these methods include genotyping-by-sequencing (GBS) . For exaMalus × domestica, cultivar "Golden Delicious") reference genome  in San Michele all’Adige, Italy. Each individual progeny is represented by a single tree grafted on M9 rootstock and planted in 2003. The population has been grown and maintained following standard agronomical practice for fruit thinning, canopy pruning, chemical fertilization, and disease control. Due to the large variation in ripening time among progeny, phenotyping was repeated three times during the harvesting season. Skin color was scored as a binary trait: trees had apples with either yellow/green skin (like Golden Delicious) or red skin (like Scarlet Spur).HindIII-HF and MspI , and 0.1 μM and 10 μM of A1 and A2 adapters per well, respectively. Libraries were pooled, size-selected on a 1% agarose gel, column-cleaned using a PCR purification kit , and amplified for 12 cycles using Phusion DNA polymerase (NEB). Average fragment size was estimated on a Bioanalyzer 2100  using a DNA1000 chip following a second column-cleaning, and library quantification was performed using PicoGreen . Pooled libraries were adjusted to 10 nmol and sequenced with 100-bp, single-end reads on the HiSeq2000 .GBS libraries were constructed using the two-enzyme modification of the original GBS protocol . DNA barcode deconvolution and basic sequence quality filtering was performed using a custom Python program (barcode_splitter.py). This program splits the raw Illumina fastq file into 96 separate fastq files based on the barcode sequences associated with each sample while filtering out reads containing any ambiguous bases in the barcodes or restriction site remnants immediately after the barcode sequence. All sequences successfully passing these basic filters were then scanned for the presence of an additional restriction site remnant and, if present, the read was trimmed accordingly. Reads were also trimmed if the sequence contained the common (A2) GBS adapter, indicating that the genomic fragment sequenced was less than 90 to 100 bp in size.We created a custom bioinformatics pipeline using custom Python scripts and existing software to process raw GBS sequence data from a single lane of an Illumina HiSeq sequencer into SNP genotype tables  and when both parents are heterozygous . Only SNPs following these segregation patterns in the parents were retained. Each SNP was subsequently tested for the expected segregation ratios in the F1 progeny: heterozygous in Golden Delicious (1:1); heterozygous in Scarlet Spur (1:1); and heterozygous in both Golden Delicious and Scarlet Spur (1:2:1). SNPs deviating from these ratios according to a chi-squared test (P < 0.01) were not included in map construction. Finally, progeny genotypes inconsistent with Mendelian inheritance from the parental genotypes were set to missing. After implementing these filters, 2436 SNPs remained for linkage map construction.Linkage mapping in apple F1 populations is referred to as a double pseudo-testcross and only three genotype combinations in the parental lines are informative for the construction of a genetic map: when one parent is heterozygous and the other is homozygous  were identified and a single marker was chosen to represent the group.A single composite linkage map was constructed using JoinMap 4.0  by combiLinkage groups were constructed and ordered with a linkage LOD of at least 6.0, a minimum recombination fraction of 0.35, and a jump threshold of 5. SNPs exhibiting “suspect linkage” to several loci within their assigned group (as determined by JoinMap), poorly fitting markers within an ordered group, and markers that greatly inflated the linkage group size were dropped from the final mapping. All map distances were calculated using the Kosambi mapping function. After applying these filters, 1994 SNPs remained within the genetic linkage map.http://doi.org/10.5061/dryad.55t54).For association analysis based on the physical coordinates obtained from the reference genome, the full set of 3967 SNPs were tested for association with skin color measurements using the case/control single marker analysis in PLINK 1.04 . The PLIGBS of a single plate of 96 DNA samples yielded 85,129,960 100-bp reads using Illumina HiSeq 2000 sequencing technology. The high proportion of reads beginning with a barcode sequence (98.2%) and containing a restriction site remnant (99%) indicated that the library preparation was effective and the data were of high quality. In addition, there were very low occurrences of chimeric reads (∼1.0%) and of reads containing downstream adapter sequences (∼1.2%).After aligning each of the 96 samples’ reads to the "Golden Delicious" v1.0 reference genome, seven of the samples were found to have a relatively low numbers of reads  that uniquely aligned to the genome and these samples were subsequently dropped from further analysis. In the remaining 89 samples (87 F1 progeny and 2 parents) there was an average of 973,896  reads per sample and an average of 628,085 reads  uniquely aligned to the reference genome . DespiteMaterials and Methods), 273,835 SNPs were identified. However, the resulting genotype matrix was extremely sparse: more than 75% of the 273,835 SNPs contained >50% missing genotypes  and then re-filtering for SNPs with <20% missing genotypes. The choice of six reads per genotype as a threshold was arbitrary: it was chosen as a tradeoff between increased confidence in genotype calls and the number of SNPs retained for mapping. This set of 3967 SNPs was used to map skin color using a chi-squared test. To create the genetic map, a final set of 2436 SNPs was retained after filtering for Mendelian inconsistencies and segregation distortion .Considering only the successfully mapped reads from 89 samples, SNPs were discovered and genotypes were called by analyzing the single master alignment file with GATK  and were dropped from further mapping. The remaining 1994 SNPs were successfully grouped into 17 linkage groups , as expected for the apple genome, at a conservative LOD threshold of six. Only 35 loci were found to be unlinked to any group, suggesting a high degree of saturation of the assembled mapping groups. Once ordered, the linkage groups spanned 1272 cM, with individual linkage groups ranging from 56 cM to 96 cM. Across linkage groups, the average marker density was high with a marker found every 0.68 cM (± 0.13 SD).The final set of 2436 SNPs was separated into the three segregation types: 884 heterozygous in Golden Delicious, 1044 heterozygous in Scarlet Spur, and 508 heterozygous in both parental varieties. Once this set of SNPs was imported into JoinMap 4.0, 442 segregated identically to at least one other SNP (P < 1.26 × 10−5)  . These S × 10−5) .Interval mapping using 1994 SNPs detected one highly significant QTL for skin color on the distal portion of chromosome 9, at position 89.5 cM, near SNP 9_30303238 . The “2 ApeKI protocol described by HindIII-MspI enzyme combination followed by size selection we observed a very small proportion of sequence reads that required adapter trimming (1.2%), suggesting that most fragments were larger than 100 bp and were thus suitable for GBS. However, the number of DNA sequence reads across individual samples varied by more than an order of magnitude (0.15 million–3 million) (A major consideration when conducting GBS experiments is the choice of restriction enzyme used to generate the reduced representation library (RRL). This choice determines the tradeoff between the number of fragments in the library and the sequencing depth of the fragments. There are several GBS library preparation methods currently in use, including the original single enzyme million) , and we Because of the apple’s high heterozygosity and ancestral polyploidy, there are major challenges in the assembly of its genome. It is estimated that the assembled portion of the current apple reference assembly represents only 71% of its ∼750-Mb genome . These eThere is a larger number of possible paths one can take from a DNA sequence file to a SNP genotype table, and it is known that alignment and genotype calling parameters have a strong effect on the resulting quantity and quality of genome-wide SNP data . Moreovei.e., effectively Mendelian), and we leverage this knowledge here to verify the utility and power of GBS to identify this known locus. With 3967 SNPs genotyped in 89 samples, a simple chi-squared test for association revealed a single significant peak from position 29.3 Mb to 33.7 Mb on chromosome 9 . This map size is consistent with the sizes of apple genetic maps from previous studies, for example, 1538 cM , in agreement with the results from the chi-squared test for association. Thus, with a set of simple heuristics for calling genotypes from GBS data, a saturated genetic map can be generated and QTL mapping can robustly identify genotype–phenotype associations.Despite the high proportion of sequence reads discarded due to filtering, there remained a sufficient number of markers to perform genetic mapping of apple skin color with a modest sample size. By mapping skin color, we have intentionally focused on a trait with a simple genetic architecture, which deviates from most QTL studies that focus on more complex traits. Skin color is known to be controlled almost entirely by a single locus of large effect , the linkage group assignments conflicted with the predicted chromosomal locations according to the reference genome. Figure S2).It is worth noting that our modest mapping population size of 87 F1 offspring likely often prevented the ordering algorithm from finding the correct order for SNPs that were closer than ∼2–5 cM. Over larger distances, however, the estimated map order of SNPs was generally in agreement with physical coordinates from the reference genome (The present study demonstrates that a Mendelian trait (skin color) can be mapped in an apple F1 population using GBS data from a single lane of Illumina next-generation sequencing. Considering the currents costs of acquiring SNP data from the apple 8K genotyping microarray, we estimate that a similar quantity and quality of apple SNP data can be achieved using GBS with a 10–100× decrease in cost. Because of the rapid improvements in DNA sequencing technology, we anticipate that genotyping efforts will increasingly favor the use of GBS or similar methods over the use of microarrays. The present study uses standard software tools and simple heuristics to generate biological insights from GBS data. However, it is clear that methodological developments required for analyzing GBS data lag far behind the technology developed to generate GBS data. To maximize the utility of next-generation DNA sequence in the future, there is a clear need for improved computational and statistical tools to extract as much information as possible from the raw data, and to phase, impute, order, and genetically map large sets of genetic markers.


To facilitate downstream genotyping analyses, discoSnp ranks predictions and outputs quality and coverage per allele. Compared to finding isolated SNPs using a state-of-the-art assembly and mapping approach, discoSnp requires significantly less computational resources, shows similar precision/recall values, and highly ranked predictions are less likely to be false positives. An experimental validation was conducted on an arthropod species (the tick Ixodes ricinus) on which de novo sequencing was performed. Among the predicted SNPs that were tested, 96% were successfully genotyped and truly exhibited polymorphism.Detecting single nucleotide polymorphisms (SNPs) between genomes is becoming a routine task with next-generation sequencing. Generally, SNP detection methods use a reference genome. As non-model organisms are increasingly investigated, the need for reference-free methods has been amplified. Most of the existing reference-free methods have fundamental limitations: they can only call SNPs between exactly two datasets, and/or they require a prohibitive amount of computational resources. The method we propose,  Formally, isolated SNPs must be distant to the left and to the right by at least k nucleotides from any other polymorphism, k being one of the main parameters of a SNP detection tool.Assessing the genetic differences between individuals within a species or between chromosomes of an individual is a fundamental task in many aspects of biology. This is increasingly feasible with next-generation sequencing technologies, as individuals from virtually any species can be sequenced at a modest cost. Of specific interest, single nucleotide polymorphisms (SNPs) are variations of a single base, either between two homologous chromosomes within a single individual, or between two individuals. Finding biallelic or Mendelian SNPs is often done in many biological applications involving SNP genotyping, e.g. population genomics, health, ecology or agronomy research ,2. To beDiscoVAR, fermi . In fact, biologists are increasingly working on data for which they do not have any close reference genome. Unfortunately, while sequencing with NGS (Next Generation Sequencers) is becoming routine, assembling genomes remains a very complicated task, for which no single software performs consistently well .State-of-the-art methods to detect SNPs between individuals or strains, generally map sequenced reads  on a retly well , thus prde novo assembly to build a reference sequence. Then, these methods include, as a sub-module, a reference-based method to map back the reads of each individual to this reference sequence  contained in the reads, and there is an edge between two k-mers if they overlap on k − 1 nucleotides. KisSnp  detects and genotypes SNPs (as well as indels and larger events), between n datasets. bubbleparse .Several methods that fall into the recently –12. Thes. KisSnp  is a sofrtex_var   for large and complex genomes. Even with substantial computational resources, most of them still cannot detect variants in mammalian-size datasets. Therefore, there is still a strong need for robust tools that can detect isolated SNPs without a reference genome.de novo method called discoSnp. This method is designed to call isolated SNPs directly from sequenced reads, without a reference genome. As shown in this paper, discoSnp opens the way to the discovery of SNPs from large-scale studies, even on a standard desktop computer. discoSnp finds and ranks high quality isolated heterozygous or homozygous SNPs from any number of read sets, from 1 to n. It introduces new features to distinguish SNPs from sequencing errors or false positives due to approximate repeats. discoSnp can be used for finding high-quality isolated SNPs, either heterozygous, e.g. to build databases of high-quality markers within and across populations, or homozygous between individuals/strains, e.g. to create discriminant markers. The genotyping can be performed as a downstream analysis, based on the read coverage information it provides.For these reasons, we introduce a new discoSnp outperforms other reference-free SNP detection methods in terms of resources (faster and using at least two orders of magnitude less memory), type and number of input dataset(s) (ability to find both homozygous and heterozygous SNPs from more than two datasets), and quality of the ranking of predicted isolated SNPs. On two whole-genome mouse datasets, discoSnp detected 2 million isolated SNPs, of which 84% were also found by a manually tuned pipeline in a previous study. On a S. cerevisiae dataset discoSnp predicted 90% of the validated SNPs. On an arthropod dataset, discoSnp found 0.3 million isolated SNPs; furthermore, an experimental validation carried over a sample of these SNPs confirmed 96% of them. Finally, discoSnp is designed to reach a wide audience, as it aims to be easy to use, regardless of the size and the complexity of the input data.Results presented in this paper show that discoSnp is composed of two modules, KisSnp2 and KissReads. KisSnp2 detects putative SNPs from one or more sets of reads. KissReads evaluates the coverage and base quality of the SNPs per read set and ranks them accordingly. These two modules are independent, however, a script automatically pipelines them, so their calls are transparent to the user.As depicted in Figure cortex and KisSnp, the KisSnp2 module is based on a de Bruijn graph. A de Bruijn graph is a directed graph that contains all the k-mers present in the read dataset as vertices, and all the possible (k − 1)-overlaps between k-mers as edges. Such graphs have been widely used in de novo assembly -mers, and α and β are single nucleotides such that α ≠ β.Similarly to assembly . The ideKisSnp2, this idea is exploited by generating the de Bruijn graph of all input dataset(s) pooled together, and by searching the previously described bubbles in the graph. To avoid k-mers generated by sequencing errors, only k-mers whose support (coverage) is above or equal to a user-defined threshold, c, are taken into account in the construction of the de Bruijn graph. The de Bruijn graph is built with the minia data structure . The counterpart of this filter is that it may discard true bubbles that contain branchings only due to repeats or to some isolated non-filtered sequencing errors. To achieve a better compromise between false positive and false negative rates, especially in complex genomes, we introduce a novel concept of symmetrically branching bubbles.High copy number repeats in genomes typically yield complex bubbles, which may combinatorially increase the number of false positives. To limit this effect, a classic filter consists in removing bubbles that contain at least one branching node; this is the default behavior of non-branching if during its construction, for every extension step, there is only one k-mer that can be used (only one possible extension for each of the two paths). On the other hand, if at some point during the extension of the bubble, there is a choice between two k-mers (for at least one of the paths), the bubble is said to be branching. Now, if at some point during the extension, both paths can be extended with the same (at least) two nucleotides, then the bubble is called symmetrically branching .More precisely, a bubble is called discoSnp can be run with any of these three filtering strategies: filtering out all branching bubbles , or filtering out symmetrically branching bubbles only (−b 1), or no filtering based on the branching criteria (−b 2).The rationale for keeping simply branching bubbles is that they may correspond to true SNPs, where the branching is simply due to a sequencing error that accidentally passed the coverage filter. As shown in the ‘Results’ section, this enables to increase the recall especially in complex genomes, with high copy number repeats, while being highly efficient in removing the numerous false positive branching bubbles. KisSnp2 is composed of the 2k − 1 nucleotides, which correspond to the bubble, together with its left and right contigs that are extending the 2k − 1 sequences. For every such contig, the length of the longest unambiguous context (longest non-branching path in the graph starting from the bubble) is also indicated. Reconstructing these contexts is done with the Minia assembler (Symbiose’).Each sequence in the multi-fasta file output by ssembler  algorithssembler  .One should note that it is not possible to compute read coverages, nor read quality information, based solely on a raw s and a set of reads s, we say that s is read-coherent if, for each position of s, at least c reads are mapped, with c a user-defined threshold. Note that reads are mapped in a semi-global manner: mapped reads may have a prefix starting before the first position of s and/or may have a suffix ending after the last position of s. By default, one substitution is authorized which roughly corresponds to current error rates. Moreover, knowing that in the discoSnp framework the sequence s represents one of the two alleles of a SNP, no substitution is authorized on the polymorphic position during the mapping.Given a sequence s is mapped only by the end of reads (last k positions), and/or only by the beginning of reads (first k positions). This reveals a situation where part of the sequence s was generated by k-mers not truly belonging to the mapped reads, i.e. due to a repeat of length bigger or equal to 2k and smaller than the read size. Such a sequence is thus chimeric. An example of this situation is illustrated in Figure k-read-coherency. Given a sequence s and a set of reads s, we consider s as k-read-coherent if, for each position i of s except the last k − 1 ones, there exists at least c reads that fully map on the k-mer starting on position i. Note that this condition is symmetrical, whether s is read on both forward or reverse complement strand.Moreover, it may appear that a position of k − 1 sequences generated by KisSnp2 and the initial sets of reads, the KissReads algorithm maps the reads (using a classic seed-and-extend approach) on the sequences of k-read-coherency is computed for each read set and for each sequence of k-read-coherent, are conserved. KissReads outputs such coherent sequences together with their read depth per read set and with the average PHRED quality of the polymorphic nucleotide per read set.Given the set discoSnp scores each SNP according to the coverage repartition of its alternative paths between the conditions. The aim is to rank best the SNPs that are the most discriminant between the samples. For a given SNP, the score is the Phi coefficient of the table of read counts for each path and each dataset, computed as follows: discoSnp takes as input any number of read sets, and has two main parameters k and c, respectively the k-mer size used to build the de Bruijn graph and the minimal coverage a k-mer should reach to be inserted in the graph. When applied to two read datasets or more, discoSnp is executed only once as all datasets are pooled together. All isolated SNPs shared by any number of samples are output as single calls.In summary, discoSnp output is a sorted multi-fasta file, in which every consecutive couple of sequences corresponds to the two 2k − 1 paths of a SNP, surrounded by its left and right contigs. Headers of the sequences give information about read coverage and average PHRED quality for each input dataset, lengths of unambiguous left and right extensions, and Phi coefficient of each SNP.Finally, the We propose here an overview of the evaluation approach. Supplementary details on the protocols for generating data and for evaluating the results are provided in Supplementary Additional File 1.k = 31 and a minimal coverage of 4 (k-mers that were seen less than four times were considered to be sequencing errors) or based on the developers’ advices. When several parameter sets were tested, presented results were obtained with those giving the best results. Full details on parameters and full results are proposed in the Supplementary Additional File 1.All tools were run with at least discoSnp on large, complex genome sequences, and to compare it to the other reference-free SNP discovery tools, we propose an experiment simulating the sequencing of two diploid, human chromosome 1 individuals.In order to evaluate the behavior of To obtain a realistic distribution of SNPs and genotypes, we used SNPs predicted from real human individuals. Two vcf files were retrieved from the ‘1000 genome project’ (phase 1 release), corresponding to the human chromosome 1 of two individuals: HG00096 and HG00100. We then generated the genome sequences for the two diploids, i.e. two sequences per individual, by placing the substitutions listed in the vcf files on the human reference sequence (GRCh37/hg19 reference assembly version). In the case of a homozygous SNP, the same nucleotide was placed on the two sequences, while for a heterozygous SNP, one sequence was randomly chosen for each of the two nucleotides.A total of 316 502 positions were mutated, with 131 263 positions that were mutated in the same time in both individuals . 29 038 SNPs (9%) have a homozygous genotype in both individuals (homozygous-homozygous), 218 556 (69%) are heterozygous in only one individual (homozygous–heterozygous) and the remaining 68 908 (22%) are heterozygous in both individuals.We then simulated a 40x coverage sequencing of these two individuals, with 100-bp reads and 1% error rate, thus generating 89 753 907 reads. The sequencing procedure is further explained in Supplementary Additional File 1.k nucleotides from one another were selected (isolated SNPs) and were recorded as 2k − 1 bubbles in a reference file (same format as the output of discoSnp). This gave 150 348 heterozygous isolated SNPs for individual HG00096  and 260 539 isolated SNPs when considering both individuals . We produced two SNP reference files, discoSnp was applied) in order to compute the number of true and false positives. A predicted SNP for which the two paths match exactly the two paths of a simulated SNP present in this reference file is a true positive (TP), else it is a false positive (FP). In the same way, a SNP from the reference file not matched by any predicted SNP is a false negative (FN). The recall is given by the number of TP divided by the number of TP plus the number of FN. The precision is given by the number of TP divided by the number of TP plus the number of FP. When allowing branching bubbles (option −b 1), precision was computed by considering as false positives only bubbles that do not correspond to any simulated SNP (isolated or not). Full details on the evaluation protocol can be found in the Supplementary Additional File 1. Isolated SNPs detected with other tools than discoSnp were transformed into the discoSnp output format, in order to be evaluated with the same protocol.Among the simulated SNPs, only those distant by more than individuals) of the E. coli K-12 MG1655 strain. We then simulated SNPs with a uniform distribution such that ≈4200 SNPs (≈0.1% of the genome length) are common to any pair of individuals, half this number is common to any trio of individuals, a third to any quadruplet, and so on. With this strategy, while considering all the 30 individuals together, 69 600 SNP sites were generated, covering ≈1.5% of the genome. Similarly to the diploid dataset, we simulated a 40x sequencing of each of the 30 individuals, with 100-bp reads and 0.1% error rate. Thus, 1 855 870 reads were generated per read set.We propose an experiment simulating more than two haploid bacterial individuals. For doing this, we created 30 copies  are not. For 30 individuals, 25 993 SNPs are isolated while 333 930 (≈92%) are not. For the isolated SNPs, the two corresponding sequences of length 2k − 1 were used as a reference to assess the precision/recall of all tested tools, based on the same protocol as the one used for two diploids.Finally, we created a reference file containing the isolated-SNPs per subsets of individuals: the first two, the first three, and so on. For each of these subsets, a SNP was considered as isolated if no other SNPs were simulated in the discoSnp was applied on a real dataset to compare two mouse inbred strains, FVB/NJ and C57BL/6NJ, the latter corresponding to the mouse reference genome (NCBIM37). Reads were retrieved from the European Nucleotide Archive (www.ebi.ac.uk/ena/), 987 million reads for the C57BL/6NJ strain (ERP000041) and 1888 million reads for the FVB/NJ strain (ERP000687).discoSnp results, we retrieved the set of predicted SNPs obtained with the same data by Wong et al. . We selected SNPs having distinct genotypes in FVB/NJ and C57BL/6NJ strains. This provided 5 020 489 SNPs, among which 2 472 456 (≈49.2%) are isolated. We evaluated the number of isolated SNPs found by the two methods and those found specifically by one method or the other, by comparing the discoSnp output to the Wong et al. one. The set of SNPs that was predicted by Wong et al. was transformed in discoSnp format, as for the other experiments, in order to allow the comparisons.To compare with g et al.  . Therefore, in order to assess the performance of discoSnp on this dataset in terms of recall, these 30 SNPs were used as a reference. The 24 read sets were downloaded from the NCBI Sequence Read Archive (with the accession number SRA054922), and processed to remove barcode and adapter sequences as in the initial study.nt study . In thisdiscoSnp was run independently on populations E1, E2 and E3. For each population, discoSnp was applied on the eight read sets corresponding to the eight time points, with the default parameters and c = 11.Details about datasets and applied commands are provided in Supplementary Additional File 1.discoSnp was applied on real sets of reads as part of a population genetic study of the tick Ixodes ricinus (discoSnp. ricinus . DNA wask bp) to any other variants such as indels or other SNPs  SNPs with a coverage between 4 and 10 (126 567 SNPs) were selected to avoid sequencing errors and repeated sequences, highly frequent in ticks (66% of the genome is repeated ), (ii) Sg gassst  with a sFluidigm technology, where primers are combined with fluorochrome   . ReadingdiscoSnp results on simulated datasets, in comparison with state-of-the-art reference-free SNP detection methods (including a hybrid approach); (ii) showing how discoSnp performs on real data, with biological validation. In addition, the computational resources (time and memory) required by discoSnp are compared with those required by the other tools.We propose experiments that aim at (i) assessing the quality of discoSnp, version 1.2.1 available online, as well as with the latest versions of NIKS, bubbleparse (released on April 2013) and cortex (Cortex_var v1.0.5.21). The tests were performed on the GenOuest (genouest.org) cluster, composed by Intel Xeon© core processors with speed varying between 2 and 2.8 GHz.Results were obtained with E. coli genomes, in which SNPs were inserted in order to mimic a realistic allele frequency spectrum of several individuals . Below, we present the results obtained by different tools on (i) two haploids, and on (ii) three to 30 haploids, with respect to the set of isolated SNPs.We simulated 30 read sets from discoSnp applied with default parameters on 2 of the 30 haploid genomes serve as proof of concept of the approach. Results reach high precision and recall: 98.81% of predicted SNPs are true positives, while 97.31% of simulated isolated SNPs were recovered. Moreover, these results were obtained in 3 min 49 s, and needed no more than 7MB of RAM memory. Results presented in Table kissnp and niks, all results have precision ≈99.19 ± 0.66% and recall ≈96.56 ± 1.41%). However, discoSnp runs faster than other tools while being by far the one needing the smallest amount of memory, by at least three orders of magnitude.Results of discoSnp still produces high quality results, with precision reaching 99.83%, and recall decreasing only slightly (94.29%) for 30 individuals .When applied on more than two datasets, s Figure . NotablydiscoSnp ones, with a slightly better recall and a slightly worse precision. In this particular case, for which the reference dataset is small, complete, well covered and easy to assemble, a hybrid method can be preferred to the de novo ones, as long as the time and the memory footprints are not limiting.Furthermore, in Figure discoSnp on a more complex genome, namely diploid read datasets simulated from two human chromosome 1 individuals, to discover both heterozygous and homozygous SNPs. As expected, when the complexity and the repeat content of the input genome increase, discoSnp makes more erroneous calls and misses more real SNPs. Whereas precision remains reasonable, with <3% of false positive calls, recall is more impacted with 72% of the isolated SNPs that are recovered.In this section, we present various results obtained by Among the 72 518 SNPs that are missed, 86% fall in repeated regions of human chromosome 1 (as masked by RepeatMasker from UCSC Genome Browser). In order to increase the recall and detect some of the SNPs located in repeated regions, we can change the filtering parameter that controls the branching features of the detected bubbles. By default, only clean bubbles without any branching are kept. Allowing all kinds of branching bubbles (option −b 2) would lead to recover almost all simulated SNPs but at a cost of millions of false positive calls and a precision close to zero. As a compromise, when allowing only some of the branching bubbles, those without any symmetrical branchings (option −b 1), recall reaches 79.22%, allowing to detect some of the SNPs that were simulated inside repeated regions of the chromosome. This shows that symmetrically branching bubbles are the most common type of branching bubbles and are mainly false positives. Interestingly, among the few symmetrically branching bubbles that correspond to real SNPs, 90% are located in highly repeated regions such as transposable elements, and specifically SINE elements (59%).k − 1 contained in the chromosome 1, with both paths of the bubble mapping exactly to the non-mutated chromosome. Consequently, these bubbles would appear in any individual and could be easily filtered out when comparing coverage values between individuals. This is, in fact, the purpose of the phi coefficient, to rank the predictions according to how discriminant their coverages are between individuals, leaving almost all false positives with the lowest ranks. As shown in Figure The precision obtained when using the −b 1 parameter is slightly lower, 92.33%, with 17 153 false positives. We observed that a large majority (78.8%) of these false calls are inexact repeats of size at least 2If the user is interested specifically in non discriminant polymorphisms, or in the case when only one individual is analyzed, we recommend to use an alternative filter based on the left and right unambiguous extension lengths . Long unambiguous extensions reveal SNPs that are isolated from other polymorphisms, while short ones reveal repeated regions or regions with high densities of polymorphism. Consequently, false positives have smaller unambiguous extension sizes (median size of 24 bp versus 527 bp for true positives).de novo SNP detection tools (cortex and bubbleparse), we compared discoSnp to a hybrid strategy consisting of three steps: de novo assembly using SOAPdenovo2 (stringent parameters (d = 0 for bubbleparse and b = 0 for discoSnp), all tools have a precision of 96.50 ± 0.72% and a recall of 71.20 ± 1.50%. With more sensitive parameters (d = 1 for bubbleparse and b = 1 for discoSnp), discoSnp achieves the best precision/recall compromise, notably with the highest recall value (79.22%).In addition to state-of-the-art Pdenovo2 , mappingPdenovo2  and SNP Pdenovo2 . QualitadiscoSnp and Bubbleparse, the hybrid approach gives high ranks to many false positive predictions. We therefore conclude that ranks are not a viable indicator to filter out the false positives for this method. The discoSnp ranking approach enables to reach a precision of ∼100% for a high (≈60%) recall range, slightly outperforming Bubbleparse.Figure discoSnp stands in the computational resources that are needed. discoSnp is at least twice faster than any other compared tool, while its memory needs are lower by several orders of magnitude than all other approaches.As shown in Figure Finally, it can be of interest to detect SNPs from one unique set of reads representing an individual or a pool of individuals, as this is the case, for instance, in the tick study presented below. Therefore, we performed similar tests while searching for heterozygous SNPs from one simulated set of reads sequenced from a single diploid individual. Conclusions of this study are similar to previous ones as, with the default and most stringent filtering parameters, 67% of isolated SNPs are recovered, with roughly the same amount of false positive calls (precision of 94.1%).discoSnp on real data, we detected SNPs between two publicly available mouse datasets produced by an Illumina GA2 sequencer. Taken together, these datasets contain 2.88 billion of 100-bp reads. The first one was generated from the FVB/NJ mouse inbred strain, while the second one was generated from the C57BL/6NJ reference line. A previous study by Wong et al. . Therefore, by keeping only SNPs with a Phi coefficient greater than 0.2, we discard almost exclusively SNPs that are found only by discoSnp, and which might be false positives.IS dataset was obtained via a mapping approach, and thus it can neither be considered as an exhaustive, nor a perfect list of isolated SNPs.Note that in this experiment, we can not use the terms ‘precision’ and ‘recall’ for describing results quality. Indeed, the et al. were obtained by running a complex pipeline, involving a high-quality reference genome, 6 distinct tools, followed by a filtering step composed of 14 non-automated filters . On the other hand, discoSnp did not require any third-party tool, or any manual tuning.It is worth stressing that the results of Wong discoSnp scales remarkably well to large amounts of data. The KisSnp2 module required 34 h and 4.5GB of memory. The KissReads module, which assesses the average quality and coverage of the results, took 78.5 h and 5.7GB memory. In comparison, niks , KisSnp, cortex and bubbleparse exceeded the memory limit on a server with 512GB RAM.Finally, this experiment showed that S. cerevisiae  sequenced material exists but no reference genome is available and (ii) one is interested in detecting a small set of highly confident heterozygous SNPs. Therefore, discoSnp was applied on a 454 read set obtained from pooling and sequencing of several tick individuals isolated from natural populations . Note that as in this context there is no need to discriminate SNPs between conditions, the Phi coefficient was not used. Primers were designed for each selected SNP and 464 individuals were then genotyped for these 384 SNPs using the Fluidigm technology. Among them, 368 SNPs (95.8%) were retrieved with a minor allele frequency varying between 0.04 and 0.5, with a mean value of 0.23. Of the remaining 16 SNPs, 5 SNPs were not amplified and 11 presented only one of the two alleles.discoSnp is robust with respect to input parameters, and easy to use. The input of the software is an ordered list of any number of raw read dataset(s) , and two parameters . These two parameters have limited impact on results quality , as long as they are coherent with respect to the input data (read length and approximate coverage). The output is a fasta file composed of sequences containing ranked SNPs, together with their coverage and average PHRED quality.As there exists no exhaustive and perfect list of the isolated SNPs present in a real dataset, a large part of the results discussed in this paper were obtained on simulated data. However, we paid special attention to the simulations being realistic. The use of simulated data allowed us to evaluate the correctness of our method in a controlled environment, and to analyze false positive and false negative calls. This highlighted that the main source of wrong calls is the presence of repeated sequences in the genomes.Two distinct strategies may be adopted for distinguishing SNPs from approximate repeats. On complex genomes, which are repeat-prone, the choice of the strategy influences the results. Removing all branching bubbles (−b 0 option) is a good way to obtain high confident coverage (precision of ≈97% on human datasets) at the expense of missing some SNPs located into repeats. On the other hand, accepting simply branching bubbles, i.e. non-symmetrical (−b 1 option), enables to detect more SNPs that are located in repeated regions, but leads to an increase of the number of false positives and to the detection of non-isolated SNPs. Therefore, the choice between these two strategies is determined by the level of complexity of the genome and by the specific needs of the user.Importantly, SNPs can be ranked according to the Phi coefficient, enabling to highlight true SNPs that discriminate conditions and to discard putative false positives due to repeats. Such feature can be precious for many biological studies, where one is often interested in finding a subset of high confidence SNPs that have opposite or only different allele frequencies between individuals or pooled samples. This was the case, for instance, when comparing two inbred mouse strains. Moreover, our simulations on a human chromosome show that, by removing low-ranked SNPs, the recall of discriminant SNPs drops only by 1.3%, while the precision increases from 92.3 to 99.7%.discoSnp, using at most 5.7GB of memory. This is not at the expense of prohibitive running times, as discoSnp stays faster than all other known de novo SNP detection tools. discoSnp is usable on the standard desktop computer of any biological lab, enabling studies that were not possible with other available de novo SNP detection tools (that require at least two orders of magnitude more memory).A distinctive advantage of our method is its extremely low memory usage. For instance, in the mouse study cited above, nearly 3 billion reads (100 bp) were analyzed by discoSnp is that it cannot find the genomic locations of SNPs. However, in numerous biological applications, the localization of the polymorphisms is not required. For instance, discoSnp can be applied to identify SNPs associated to some phenotypic traits or diseases. Another limitation of discoSnp is induced by the fact that it focuses on isolated SNPs. If this kind of SNPs are well suited for designing markers, they do not represent the full SNP diversity and thus, they cannot be used directly for statistical downstream studies as phylogeny reconstruction, or estimation/comparison of the genetic diversity among or between natural populations. However, sequences obtained around those SNPs of interest can be genotyped at larger population scales with standard genotyping technologies or used for diagnostic assays. This was actually the case for the tick study, where natural populations were genotyped to characterize their reproductive mode (level of inbreeding) and to estimate the gene flow within and among populations at various spatial scales. Moreover, SNPs discovered by discoSnp and selected with respect to primer hybridization features, are currently used to build a genetic map based on the analysis of the segregation of parental alleles in the offspring of several controlled crosses.One limitation of de novo SNP, and possibly other polymorphisms such as indels and structural variants detection tools  robust detection and ranking of isolated SNPs, (ii) support for any number of read datasets, (iii) scaling to big data studies thanks to a highly memory efficient data structure, (iv) lower running times than other reference-free tools and (v) an easy to use software, capable of processing billions of reads from a mammalian genome with a single command.discoSnp provides similar results, both in terms of precision and recall, to those of other state-of-the-art reference-free SNP detection methods, while being faster and needing at least two orders of magnitude less memory. Experiments on simulated haploids show that, when analyzing together more than two individuals, discoSnp outperforms the other tools, both in terms of computational resources and results quality. Applied on real datasets, results confirm the capacity of discoSnp to scale-up to large volumes of data (less than 6GB memory on 3 billion Illumina reads), as well as its high precision, i.e. an experimental validation conducted on an arthropod species (the tick I. ricinus) on which de novo sequencing was performed, confirmed 96% of the predicted SNPs that were tested.The experiments on a dataset composed of two simulated diploids show that discoSnp source code, available under CeCILL license, can be downloaded from http://colibread.inria.fr/discoSnp/. Moreover, this web page shows how to integrate discoSnp in any Galaxy instance using the GenOuest Tool Shed.The Supplementary Data are available at NAR Online.


Bos taurus autosome (BTA) 23. We analyzed this QTL to identify which of the component traits of BI are affected and understand its molecular basis.A whole-genome association study of 4631 progeny-tested Nordic Red dairy cattle bulls using imputed next-generation sequencing data revealed a major quantitative trait locus (QTL) that affects birth index (BI) on QTL had a large negative effect on stillbirth. No animals were found to be homozygous for HAPQTL. Analysis of stillbirth records that were categorized by carrier status for HAPQTL of the sire and maternal grandsire suggested that this haplotype had a recessive mode of inheritance. Illumina BovineHD BeadChip genotypes and genotype intensity data indicated a chromosomal deletion between 12.28 and 12.81 Mbp on BTA23. An independent set of Illumina Bovine50k BeadChip genotypes identified a recessive lethal haplotype that spanned the deleted region.A genome-wide scan of BI in Nordic Red dairy cattle detected major QTL on BTA6, 14 and 23. The strongest associated single nucleotide polymorphism (SNP) on BTA23 was located at 13,313,896 bp with A deleted region of approximately 500 kb that spans three genes on BTA23 was identified and is a strong candidate QTL with a large effect on BI by increasing stillbirth.The online version of this article (doi:10.1186/s12711-016-0215-z) contains supplementary material, which is available to authorized users. Bos taurus autosome (BTA) 12 [Previously, the molecular causes of genetic defects in cattle were mainly detected by examining dead or malformed calves –9. The r(BTA) 12 . The mol(BTA) 12 . A wholeIn this study, we analyzed two component traits that contribute to BI  for the QTL on BTA23. Our objectives were: (1) to identify which component traits are affected by the BI QTL on BTA23, and (2) to understand its molecular basis.Animal Care and Use Committee approval was not required for this study because the phenotype and genotype data used were routinely collected as part of a breeding program, and no live animal experiments were performed.The study was carried out on three populations of Nordic Red dairy cattle (RDC) from Denmark (RDCDNK), Finland (RDCFIN) and Sweden (RDCSWE). A total of 4631 progeny-tested bulls with breeding values for BI and its component traits and genotype information were used for QTL mapping.http://www.nordicebv.info). However, birth weight is positively correlated with calving assistance/difficulty [http://www.nordicebv.info.As dependent traits, we analyzed the de-regressed breeding values (DRP) for calving-related traits. We scanned the breeding values for BI to identify QTL. BI is a compound index that predicts a sire’s total direct additive genetic effect on calving by combining direct  effects of stillbirth (SB) in the first (SBF) and later (SBL) calvings and calving ease (CE) in the first (CEF) and later (CEL) calvings. Calf size (CS) is not included in the prediction of BI by the Nordic Cattle Genetic Evaluation . After QC, 43,415 SNPs were retained for analyses. Genomic positions of the SNPs were based on the UMD3.1 Bovine Genome Assembly [All bulls were genotyped by using the BovineSNP50 BeadChip  version 1 or 2 and genomic DNA extracted from whole blood or semen. Quality control (QC) to select single nucleotide polymorphisms (SNPs) were as follows: a minimum call rate of 85 % for samples, and exclusion of SNPs with a call rate less than 95 %, a minor allele frequency (MAF) less than 0.5 % and a significant deviation from Hardy–Weinberg proportions  = 100 [The 50 k SNPs were imputed to full sequence in two steps. First, 50 k genotypes for bulls were imputed to HD genotypes by using the IMPUTE2 software with default parameters, except for the effective population size (Ne) = 100 . The refrun2 [In the second imputation step, using whole-genome sequencing (WGS) data from 242 dairy cattle as a reference, 12,322 bulls from three breeds  imputed to HD genotypes were further imputed to the full-sequence level by using Beagle software , 24 and run2 . Chromosrun2 .2) value less than 0.9 were removed. A total of 8,938,927 SNPs remained that were distributed across the 29 bovine autosomes [Beagle v3.3.2  was usedutosomes .First, a WGS scan was performed using a sire model without considering relationships between individuals, except for the sires and their sons. Based on the results from this scan, we selected a region on BTA23 for further analysis with an animal model that considered relationships between all individuals, using both single-marker and haplotype-based analyses.yij is the de-regressed breeding value of the jth son from the half-sib (sire) family i; μ is the general mean; b is the allelic substitution effect; and xij (ranging from 0 to 2) is the allelic dose of the jth individual for the SNP obtained by imputation. si is the random effect of the ith half-sib family, assumed to be normally distributed as σs2 is the sire variance. eij is the random residual of son j from the half-sib family i and is assumed to be normally distributed as ei); ith animal was estimated by wi = ri2/(1 − ri2), where ri2 is the reliability of the ijth animal’s DRP. Values of ri2 that were higher than 0.98 were set to 0.98 to avoid having excessively large weights for sires with large numbers of progeny records.A SNP-by-SNP analysis was carried out, in which each SNP was tested for association with the phenotype . The following linear mixed model (LMM) was used to estimate SNP effects:H0: t test. A SNP was considered to have a significant association with a trait if the All statistical analyses were conducted by using the software DMU . The nulyj is the phenotype (DRP) for the jth bull; μ is the overall mean; b is the allelic substitution effect; and xj (ranging from 0 to 2) is the allelic dose of the jth individual for the SNP. uj is the random polygenic effect with a joint multivariate normal distribution, u is the vector of polygenic effects (ui); ej is the random residual for the jth animal distributed as described in the sire model.LMM analysis was used for association analyses of the target region on BTA23 for BI and its component traits Table . AssociaH0: The model was fitted by restricted maximum likelihood (REML) using the software DMU , which pqh1j and qh2j are random effects of the two haplotypes (one each from the sire and dam) carried by the jth individual, assumed to be normally distributed as σh2 is the variance of haplotype effects. Other terms in the RHM were the same as described for the LMM.Haplotypes for the 10 SNPs that were most significantly associated in the LMM analyses and located between 13,259,463 and 13,354,932 bp on BTA23 were extracted from Beagle’s WGS imputation output  noncarrier sire mated to the daughter of a noncarrier maternal grandsire; (II) noncarrier sire mated to the daughter of a carrier maternal grandsire; (III) carrier sire mated to the daughter of a noncarrier maternal grandsire; and (IV) carrier sire mated to the daughter of a carrier maternal grandsire. Stillbirths for heifers  and cows  were analyzed separately. A total of 3,932,927 calving records from Denmark, Finland and Sweden were analyzed, with 2,785,085 records for mating type I, 536,614 for mating type II, 512,714 for mating type III and 98,514 for mating type IV. Numbers of records in each category are in Table Four classes of matings were defined according to the HAPyijkl is an indicator of calf survival ; pj is the effect of parity; tk is the effect of month and year of insemination; and ml is the effect of mating type. Vector ui is assumed to be normally distributed QTL is equal to 1/4 of the probability that both parents are carriers, corresponding to 0, 0, p is the frequency of the causative allele [The fitted mixed model included parity and month of insemination (by year) as fixed effects and the maternal grandsire as a random effect:e allele .A recessive lethal mutation can be caused by a chromosomal deletion , 30. We χ2 distribution with one degree of freedom. For SNPs that deviated from HWP, we investigated whether there was an excess or a deficit of heterozygotes.If the recessive lethal effect is due to a chromosomal deletion, then an excess of homozygotes will be called for the SNPs that are located within the deleted region . ImputedSVS8 program (Golden Helix) for genotyped bulls. Reduced intensities were observed between SNP BovineHD2300003056  and SNP BOVINEHD2300003186 . An animal was considered to carry the deletion if it met two conditions: the average signal intensity (‘Log2R ratio’) was less than 0, and 97 % of the SNPs were homozygous within the region defined by the signal intensities. Sires were categorized as carriers or noncarriers of the chromosomal deletion (CHRDEL), and the average intensity per SNP was calculated for both groups. An individual that carries CHRDEL will be erroneously genotyped as homozygous for all loci within the deleted region because only one allele is present. Thus, a small aberration (3 %) was allowed, to account for possible genotyping errors.We had access to genotype intensity data for 243 RDCFIN bulls that had been genotyped with the Illumina BovineHD Genotyping BeadChip that included 725,293 autosomal SNPs . We visup < 10−5). To avoid the influence of a deletion on phasing accuracy, we removed four SNPs that were located within the putative deleted region (between 12.2 and 12.9 Mb).Absence of homozygotes for a common haplotype among live individuals is strong evidence for the presence of a recessive lethal allele . Based oNext, we used Beagle 4.0  to imputFigure entclass1pt{minimaBI and three calving traits  were analyzed for association with SNPs in the target region on BTA23 using a LMM. The SNPs that were most strongly associated with each trait are in Table The strongest association signal among the analyzed traits was obtained for SBL Table ; Fig. 3.QTL) was highest in RDCFIN (15.5 %), followed by RDCSWE (10.1 %) and RDCDNK (3.9 %). No homozygous individuals for this haplotype were observed. When this haplotype was included in the model as a cofactor, no additional significant associations were observed  based on the carrier status of the sire and maternal grandsire. The stillbirth rate was approximately 6 % higher for matings in which both the sire and maternal grandsire carried the putative causative haplotype HAPier Fig. .Fig. 6StSignificant deviations from HWP were observed for SNPs on the HD chip in the interval between 12.1 and 12.9 Mbp  is not the only structural variation found in the region. Bickhart et al. [DEL , and Hou et al. [DEL. Both CNV are gains of DNA sequence and are much smaller than the structural variation identified here.The deletion discovered here  for eight SNPs on the 50 k chip located within the deleted region in a larger sample of 13,852 Nordic Red dairy cattle animals. The distribution of the intensities is in Fig. In addition, we checked the genotype intensity (log50k) that spanned a region between 11,818,761 and 13,122,318 bp with a population frequency of 0.043, but we found no homozygous individuals for this haplotype. Under Hardy–Weinberg equilibrium, we would expect to have 36 homozygotes for this haplotype among the 19,309 50 k-genotyped animals. The probability of observing no homozygotes by chance is 2.31 × 10−16 (assuming a Poisson distribution), if this haplotype is not a recessive lethal variant. Absence of homozygotes for HAP50k is strong evidence that Hap50k either carries or tags a recessive lethal variant.We identified a haplotype (HAPQTL). All six also showed an approximately halved sequencing depth between 12.29 and 12.84 Mbp . Among the 47 animals that carried the deletion, 42 carried the tagging haplotype. SNPs from the Illumina BovineSNP50 BeadChip located within the deleted region are in Additional file Among the 243 RDCFIN bulls with HD genotypes, 47 animals were declared carriers based on the criteria of an average genotype signal intensity less than 0 and homozygosity for more than 134 of the 139 loci within the putative deleted region between 12.2 and 12.8 Mb. Of these 47 carriers, 25 were homozygous for all 139 loci, 13 were homozygous for 138 loci, and nine were homozygous for 134–137 loci. We examined the concordance between carriers of CHRQTL). No homozygotes for HAPQTL were observed. No other QTL effects were observed in the region when HAPQTL was included in the model as a cofactor. Matings for which the sire and maternal grandsire carried HAPQTL had significantly higher frequencies of stillbirth compared to matings for which the sire and/or maternal grandsire were a noncarrier.A large-effect QTL for BI was detected on BTA23 in a genome-wide association study in Nordic Red dairy cattle. A detailed study of its component traits showed that it had a large effect on stillbirth. We constructed haplotypes from the 10 SNPs that were most strongly associated with stillbirth based on WGS data and identified animals that carried the haplotype that had a negative effect on stillbirth (HAPDEL) and identified animals that carried this deletion. Using a large number of animals with 50 k genotypes, we confirmed the existence of a recessive lethal haplotype (HAP50k) that spanned the deleted region and identified carriers of HAP50k.We used the HD SNP genotypes to search for chromosomal deletions within the QTL region. Based on deviations from HWP and reduced genotype intensity, we observed a large chromosomal deletion  that spanned this deleted region.After studying the concordance between animals carrying HAPQTL is a recessive lethal haplotype that causes stillbirth. First, no homozygotes for HAPQTL were observed. Second, the effect of HAPQTL was observed in the case of carrier-sire and carrier-maternal grandsire matings for all three Nordic Red dairy cattle populations. There was a slight increase in stillbirths among type III matings in the Finnish population. This result may be due to a higher frequency of carriers of HAPQTL, such that the dam inherits the deletion from its dam  even when the maternal grandsire is a noncarrier. Frequencies of carriers of HAPQTL were much lower in the Swedish and Danish RDC populations. Consequently, no effect on fertility was observed in type III matings in these populations.Several observations support the conclusion that HAPThe increase in the rate of stillbirths among type IV matings was smaller than the 12.5 % expected for a fully penetrant recessive lethal allele. Because stillbirths are recorded by farmers, the difference may be due to some of the stillbirths not being reported to the central registration system. It is also possible that some deaths of calves occurred during late gestation and, thus, were not recorded by farmers as stillbirths. The stillbirth is recorded as a binary trait but we analyzed it by assuming a normal distribution to test the effect of mating type on calf survival at birth. A logistic regression model might have been a better choice for the analysis of this phenotype.BTB (POZ) domain containing 9 , (2) glyoxalase I  and (3) dynein, axonemal, heavy chain 8  . The most well-known disease associated with dynein malfunction in humans is polycystic kidney disease , which is characterized by malfunctioning of the cilia and sperm, as well as respiratory problems. Around half of the PCD patients also have situs inversus. Extreme defects in laterality may be diagnosed as heterotaxy with asplenia or polysplenia [Three genes are located within the deleted region: (1) ocytosis . When glocytosis . Diseaseysplenia .Fig. 9GeMOCS1 locus. This deletion was not observed among the sequenced RDC bulls. Haplotypes constructed around the MOCS1 locus (HAPMOCS1) did not show any association with stillbirth in Nordic Red dairy cattle . The QTL for stillbirth on BTA23 was genome-wide-significant only for body conformation index, with a peak at 12,153,108 bp  under accession numbers SRX527690-SRX527732 and are part of the 1000 bull genomes project (http://www.1000bullgenomes.com).The sequence data of the animals are publicly available in the Sequence read Archive of NCBI (