It has been hypothesized that multivariate analysis and systematic detection of epistatic interactions between explanatory genotyping variables may help resolve the problem of "missing heritability" currently observed in genome-wide association studies (GWAS). However, even the simplest bivariate analysis is still held back by significant statistical and computational challenges that are often addressed by reducing the set of analysed markers. Theoretically, it has been shown that combinations of loci may exist that show weak or no effects individually, but show significant (even complete) explanatory power over phenotype when combined. Reducing the set of analysed SNPs before bivariate analysis could easily omit such critical loci.We have developed an exhaustive bivariate GWAS analysis methodology that yields a manageable subset of candidate marker pairs for subsequent analysis using other, often more computationally expensive techniques. Our model-free filtering approach is based on classification using ROC curve analysis, an alternative to much slower regression-based modelling techniques. Exhaustive analysis of studies containing approximately 450,000 SNPs and 5,000 samples requires only 2 hours using a desktop CPU or 13 minutes using a GPU (Graphics Processing Unit). We validate our methodology with analysis of simulated datasets as well as the seven Wellcome Trust Case-Control Consortium datasets that represent a wide range of real life GWAS challenges. We have identified SNP pairs that have considerably stronger association with disease than their individual component SNPs that often show negligible effect univariately. When compared against previously reported results in the literature, our methods re-detect most significant SNP-pairs and additionally detect many pairs absent from the literature that show strong association with disease. The high overlap suggests that our fast analysis could substitute for some slower alternatives.We demonstrate that the proposed methodology is robust, fast and capable of exhaustive search for epistatic interactions using a standard desktop computer. First, our implementation is significantly faster than timings for comparable algorithms reported in the literature, especially as our method allows simultaneous use of multiple statistical filters with low computing time overhead. Second, for some diseases, we have identified hundreds of SNP pairs that pass formal multiple test (Bonferroni) correction and could form a rich source of hypotheses for follow-up analysis.http://bioinformatics.research.nicta.com.au/gwis.A web-based version of the software used for this analysis is available at  Genome-wide association studies (GWAS) have discovered many underlying genetic causes of disease, but have also raised important questions about standard approaches to modelling complex traits . While cTo date there exists little experimentally-validated evidence of SNP interactions in humans, largely due to the complexity of multivariate GWAS analysis. Even in only bivariate analysis, the number of possible SNP interactions that need to be searched is extremely large, as there are 125 billion possible SNP pairs in a GWAS of 500,000 SNPs. The scale of the problem produces significant computational and statistical challenges. Numerous approaches proposed to address these challenges are unable to scale to this large number of tests, due to both performance and accuracy . This has led to claims that finding epistatic interactions via exhaustive search is infeasible ,5. Whilesignificant epistatic interactions. As discussed in [epistatic interaction if using both of them allows discrimination between Cases and Controls with significantly higher sensitivity and specificity than is possible using any one of them individually. The significance is quantified as a p-value for rejection of a well specified null hypothesis (see Methods for details). This rejection implies in particular, that the improvement cannot be explained by biased sampling from a population pre-classified by any one of the SNPs in the pair. In the Discussion section we argue that our generic formal definition of epistasis captures some biological aspects of epistasis that Fisher's popular definition of interaction [Our goal is to present a system capable of exhaustive search through all SNP pairs in an entire GWAS, detecting all ussed in , both theraction  misses.The definitions given above can be directly converted into computational methods, suitable for scanning trillions of SNP pairs in a modern GWAS and providing an alternative to widely-used regression based approaches. In this work, we present a platform called Genome Wide Interaction Search (GWIS), that is based on classification, and novel rigorous statistical tests based on receiver operating characteristic (ROC) curve analysis . Our proWe demonstrate that exhaustive search of all possible pairs in standard GWAS is feasible and fast on a desktop computer and that our proposed technique is faster than currently available exhaustive techniques. Aside from the computational challenges mentioned above, there are a number of statistical challenges that also need to be resolved. Principled methods are needed that allow for significance-correction of the billions of SNP-pair and genotype combinations, and that are able to cope with characteristics of real-world data, e.g. confounding factors due to strong univariate signals, examining significance in the far tail of distributions where the central limit approximation does not hold, and SNPs with low minor allele frequency giving rise to very low genotype counts.gain in sensitivity and specificity (GSS), is designed to detect epistatic SNP interactions, and computes exact p-values, without using large sample normal approximations. Each application of the GSS test to a pair of SNPs involves solving a number of min-max optimisations, which are pair specific and are therefore impractical for scanning trillions of putative SNP-pairs. Hence, we introduce two extra tests, referred to as sensitivity and specificity (SS) and difference in sensitivity and specificity (DSS), that act as practical fast proxies for the GSS test.We introduce a novel and theoretically well-founded, model-free hypothesis test specifically designed for multivariate GWAS analysis. It is based on relating the sensitivity and specificity observed in the sampled data to the sensitivity and specificity that could be achieved in the 'true' population. The test, named Algorithms for detecting epistatic SNP interactions are typically evaluated using simulated data, for reasons of both scalability and interpretation -19. HoweThis paper makes several contributions. First, we use an operational definition of epistasis based on classification of individuals into Cases or Controls to develop a set of robust, principled methods for explicitly detecting significant epistatic interactions in GWAS data. Second, we demonstrate that our proposed methods scale well and are fast enough to permit exhaustive analysis of current and near-future GWAS data. Third, we have applied GWIS to a diverse range of both simulated and real life benchmark data, and detected many significant associations in addition to confirming many associations previously reported. Finally, our analysis of real data indicates the limitations of conventional statistical methods such as Pearson's An exhaustive evaluation of all possible SNP pairings is the most powerful strategy to detect epistatic interactions  but to dGWIS is able to exhaustively search whole GWAS on a desktop PC with no special hardware, and can also take advantage of available retail Graphics Processing Units (GPUs) to further reduce execution time. The implementation of GWIS allows multiple filters to simultaneously evaluate SNP pairs with low impact on speed. Table Table For GWIS, we report runtime using one filter and three filters, namely If we consider arrays of 5M SNPs, the estimated difference in times shows the necessity of faster exhaustive methods. Many algorithms that had acceptable runtime on current size GWAS will take weeks or months to compute on the larger number of SNPs as the total number of pairs to be evaluated grows quadratically. While the CPU implementation of GWIS would require about 3 months, the GPU implementation requires 3 days, a feasible wait for research results. Both CPU and GPU implementations could be deployed on a computing cluster to easily reduce this runtime down to a few minutes.We expected the runtime of our methods to increase linearly with the number of samples and quadratically with increasing SNPs (i.e. linear in terms of SNP-pairs). To verify this, we examined program runtime on simulated datasets varying both the number of samples and the number of SNPs. These datasets contained between 125K and 1M SNPs and between 1250 and 10K samples. Due to the independence of computations on each SNP-pair, both CPU and GPU implementations show the expected relationships between samples, SNPs and runtime. Note that actual timings will be affected by machine architecture; in addition to obvious factors such as clock speed, we exploit low-level functions that are found in most modern CPUs. Older CPUs without high performance functions will not execute GWIS as quickly.The efficiency of GWIS enables exhaustive pairwise analysis of multiple studies using multiple statistical filters. We present an initial analysis of the seven WTCCC datasets listed in Table ociation  that hasociation ,29,30 buociation , a GPU mociation ,31 and wociation . Table 3We found that the evaluated methods varied greatly in the number of interactions detected. un RAPID , which iThe vast number of positive results that a conventional Figure In Figure The confounding by strong univariate signals similar to the results of In Table Interestingly, repeating the same approach over the pairs detected by GBOOST removes very few pairs for most datasets. This is likely because GBOOST looks for significant interactions by examining the improvement of fit in log-linear regression models with and without an interaction term, in essence searching for SNP pairs with no strong univariate effects. The downside of such a technique is that a number of assumptions must be made, in this case requiring that the epistatic SNP pair must fall under an additive model. Such assumptions are not made by the GSS test.Current implementations of the GSS statistic are too computationally expensive to use on all possible SNP pairs but can easily be run over a few million candidate pairs  are commonly used to measure effect size  and have2 OR vs. critical sens/spec for each of the SNP pairs reported as significantly interacting by GWIS, reported in previous studies or reported by both. SNP pairs identified by GBOOST when run using default parameters have been separately marked.In Figure Although a substantial number of pairs were detected by both GWIS and literature methods, there are some discrepancies. Many pairs detected by GWIS alone often have greater odds ratios and critical sens/spec than pairs detected by the literature only. This suggests that GWIS can detect many potentially interacting pairs that are missed by methods in the literature.All diseases show some literature pairs that have not been detected by GWIS . The supplementary Figures 22-24 show how this category changes, and the overlap with literature improves, once the Bonferroni threshold requirement is relaxed.The second category, marked by cyan diamonds, predominantly consist of literature pairs where the level of improvement of the pair over its individual SNPs is insufficient to be deemed epistatic according to our stringent requirement of improvement above Bonferroni threshold, i.e. Recall that GWIS is intended to detect potential epistatic interactions. It is very encouraging that although GWIS' epistasis definition does not explicitly maximize odds-ratios or critical sens/spec, literature pairs with high odds-ratios and critical sens/spec are reliably detected by GWIS.2 OR| and SNP pairs with low critical sens/spec (≤ 7%) often having very large |log2 OR|. These pairs are often closely located (≤ 1Mb) and a number have been detected by previous studies. Some exceptions do exist to these trends with T1D showing a SNP pair detected only by GWIS that has OR above 4 and critical sens/spec above 30%.The analysis across datasets shows the expected trend of lower critical sens/spec having increased |logAs discussed earlier, GBOOST results are largely significant according to the GSS filter. There were a few points detected by GBOOST but not by GSS filtering. These pairs tended to have relatively small OR and were only just under the strict Bonferroni threshold being used.We can also use the previous literature to provide evidence that the DSS statistic is acting as a reasonable proxy for the GSS filter. If pairs from previous literature that are significant according to GSS but were not detected by DSS, then the DSS filter has failed to detected some relevant pairs. In Table ≈ 4% to ≈ 66%. This large variance is likely related to the fact that different methods chose to focus on one or two WTCCC datasets rather than all seven. Datasets that show a large reduction in the number of reported pairs after GSS filtering have tended to be driven by methods searching for strong phenotype association rather than purely epistatic effects.The results indicate that the number of previously reported pairs that remain significant under GSS varies dramatically ranging from The "Overlap" column shows that aside from nine pairs in four diseases, all previously reported pairs that are significant under GSS were also detected by GWIS using the combination of DSS and filters. This provides additional evidence that the DSS primary filter is sensitive enough to detect pairs that are likely to be significant under GSS.We also note that many 'novel' SNP pairs were also detected by GWIS. While re-iterating that further quality control and inspection would need to be performed to validate such pairs, it is indicative that exhaustive search combined with the statistics we propose here is likely able to detect a greater quantity of novel epistatic interactions. Such further analysis may also involve re-adjustment of the cutoff threshold to values below Bonferroni threshold used in Figure To further validate our proposed statistic GSS and heuristic DSS, we evaluate their power and false positive rates over a set of synthetic benchmark datasets. The datasets chosen were generated for  and simuFor each parameter combination, a single "epistatic" interaction has been embedded into each of the datasets. This allows us to calculate power (i.e. the fraction of times our method detects the "true" pair) and false positive rate . These results are shown in Figure Over all the parameter combinations, DSS provided higher power than The number of false-positives from DSS was also very low, and appeared to grow linearly with increases in the number of samples. The maximum false-positive rate observed for DSS on any dataset was 0.003 and the average false positive rate over all parameter combinations was 0.001.Although with the Bonferroni-corrected p-value threshold DSS performed better than While these figures validate our proposed DSS filter, it is worth noting that the simple scenario of a single epistatic interaction is unlikely to emulate that of real datasets, and as such, the conclusions that can be drawn from current synthetic benchmarks, including that used here, are limited. For instance, the QQ plots in Additional File Figure In recent years, there have been several proposals that exploit the inherently parallelisable structure of GWAS data to provide reasonably fast solutions capable of processing a WTCCC dataset in several hours. However, SNP arrays currently being used in GWAS studies are an order of magnitude larger , resultiOur method provides faster discovery of epistatic interactions, which enables more effective, interactive usage. The tool provides an efficient and fast screening capabilities that can be run locally on researchers' desktop computers rather than expensive computing clusters. The reported results can then be refined with more computationally expensive methods such as logistic regression or permutation testing, or in combination with additional biological reference material.As indicated by several previous publications ,24,40, tA popular strategy is to consider only pairs containing univariately strong SNPs ,40 or paAn alternative strategy is the use of known biological data. Here, the number of SNPs examined is reduced to those with prior evidence of possible epistatic effects  or that Distance constraints, in which SNP pairs are discarded if they are too close together ,24,32, aThe feasibility of exhaustive search as demonstrated in this work removes the need for such constraints. Exhaustive search can examine all possible SNP pairings and, if a robust statistical filter is used, will greatly reduce the set of epistatic interactions requiring follow-up analysis. Further filtering can then be applied to remove those SNP pairs that are not relevant for a given experiment.Our prime goal in this paper is to present a practical system capable of exhaustive search through all SNP pairs in real, full scale GWAS, detecting all pairs evidencing significant epistatic effects. This requires a robust definition of epistasis which can be translated into an actionable mathematical algorithm .Operationally, epistatic interaction means in this paper two things:(i) that there exists a scoring function of genotype calls for the pair of SNPs and a decision threshold such that a substantial subset of subjects scoring above the threshold is significantly enriched (biased) in either Cases or Controls, and the split of the sample according to this threshold results in OR significantly different from 1;(ii) for any scoring function depending on a single SNP of the pair only, such an enrichment is highly unlikely to be achievable by re-sampling data from the population.In particular, our definition captures three examples of penetrance tables for "non-standard" epistatically interacting loci discussed by Cordell [11, Tables Despite advances in speed, the most common benchmark for epistasis remains simulated data, where a single epistatic interaction embedded in a small number of SNPs is used to judge a method's power and false positive rate under various parameter settings. In this work, we evaluate the power of a standard Using the improved association with phenotype.Studying pairwise associations in GWAS data is necessarily a filtering process, reducing the billions of possible interactions by several (5 or 6) orders of magnitude down to a small number that can be analysed in detail. In order to have any chance of discovering epistatic interactions, the majority of pairs of SNPs that show little improvement over their univariate associations must be explicitly discarded; in other words, we must specifically look for pairs of SNPs that together show Empirical evidence showing the impact this confounding has on the directly ,29,30 ordirectly ,32,48,49GWIS is a model-free method for detecting epistatic SNPs, designed to be sensitive to any associations in the data that separate Cases and Controls. However, this separation may be due to signals other than that caused by phenotype. It has been noted that pairwise SNP analysis may be more susceptible to noise caused by genotyping errors, population structure or batch effects ,43 compaIn this section we outline various filtering procedures used in this paper for detection of putative epistasis loci. We shall focus particularly on the receiver operating characteristic (ROC) analysis method, which is part of the novelty of this paper. More details and formal descriptions have been shifted to the Additional File Here we outline three particular "model free" statistical filtering methods implemented in GWIS and explicitly used in this presentation.Our filtering approach quantifies the ability of a pair of SNP-probes to segregate Cases from Controls in available data sample compared to the segregation ability of the two SNP-probes taken individually. There are a number of methods in the literature that attempt to measure this type of improvement for epistasis detection, e.g. BOOST uses the decrease in residual error between additive and full interaction regression models  while thThe key distinct features of our method can be summarised follows:• It is based on ROC curve analysis, focussing on classification rather than regression;• The filters use an exact quantification of underlying probability distributions rather than relying on asymptotic normality;• The approach permits a natural interpretation that links the properties of the sample data back to population data.sample prevalence mapping, allocating to each individual the ratio of the number of Cases to the sum of Cases and Controls in the dataset which carry exactly the same genotype as this individual. For any pair of SNP-probes we have three such prevalence mappings, one for the pair and two for the individual probes. Each mapping can be used to construct a ROC curve: the plot of the true positive rate (TPR) versus the false positive rate (FPR). These are piecewise linear curves. Specifically, a 9-piece ROC curve for the pair, ROC , which dominates both 3-piece curves ROC(ig), i = 1, 2, for the individual SNP-probes. This domination results from the increased number of genotype calls for a pair of SNPs which allows for finer stratification of the data. For most probe pairs, this stratification will have little effect on the ability to separate Cases from Controls but for some the difference will be significant.With each SNP-probe or pair of SNP-probes, we associate a se, sp), achieved by the pair of SNP-probes, we have to determine the probability of observing equal or higher specificity and sensitivity due to biased sampling from the population for which true specificity and sensitivity falls in the region below either of the ROC curves for the individual SNPs. When the ROC curve for the pair overlaps any of the individual SNP curves, this probability will be close to 1, hence not significant. However, as a measure of potentially improved capability of the pair, it is natural to use the most significant improvement, i.e. the smallest such p-value, corresponding to the circled dot in Figure ROC(ig) curves for individual probes. This expansion is conservative in the sense that it produces less significant, i.e. increased p-values. We shall refer to this smallest probability value as PGSS, the p-value for gain in sensitivity and specificity, and introduce the following notation for their negative log10:For any specific sensitivity and specificity value, say  and ROC(g2), see the shaded region in Figure t0, t1 are the total numbers of Controls and Cases in the sample dataset, respectively. In this case π0 and π1 represent the (unknown) population proportion of deleterious alleles in Controls and Cases respectively. For a given point on the ROC curve (defined by x0 and x1), maximizing over the unknown population probabilities corresponds to a worst case scenario for rejection of the null hypothesis p-values quantifying the largest probability of observing a sensitivity greater than x1/t1 and a specificity greater than 1 - x0/t0 by biased sampling. The true p-value, for the actual  for the population, must obviously be less than this. Minimizing over the pairs of points of ROC curve gives the set of alleles with the "best" capability to discriminate Cases from Controls.while the "max" is over the smallest convex region The optimisation itself is relatively easily computable on modern hardware with carefully crafted algorithms. More details are given in the Supplement and .PGSS has to be solved separately for each pair of probes which will create a pair-specific null hypothesis H0. It is convenient and meaningful to consider the special case of (2) for I2 below the main diagonal. It can be shown that in such a case the whole optimisation (2) reduces to optimisation against the diagonal p-value for sensitivity and specificity test, and can be computed asThe above optimisation pendence . The res0 and π1 again represent the (unknown) population proportions of deleterious alleles in Controls and Cases, respectively, but since the null hypothesis is in fact restricted to the main diagonal, the optimisation over the population parameters reduces to maximisation over a single variable π0. The interpretation is as before with the "max" part corresponding to an upper limit on the true p-value and minimisation over the pairs of points corresponding to selection of the smallest such upper limit, thereby giving the most significant improvement of the pair is a classification of individuals into Controls and Cases using the pair's genotype calls over bias sampling from hypothetically inseparable population.In this case πx1, x2)), and therefore PSS is relatively easy to implement in practice for exhaustive scanning of probe-pairs as a primary filter.The crucial, "max" part of this statistic can be easily tabulated  is defined by (4) if we replace ROC by ROC(ig). This brings us to the introduction of the following proxy for fltGSS filter ; see Additional File Figures We shall call fltcontributing or high risk scenario, (cntr ≡ OR ≫ 1), is illustrated in subplot (c). Here the red star corresponds to set of genotype calls with the highest fltGSS for contributing gain, which happen to be a singleton set {}. The carriers of this genotype constitute ξ1:= x1/t1 = 5.79% of Cases and ξ0:= x0/t0 = 0.24% Controls, resulting in extremely high odds ratio OR = 25.73 and significant fltGSS = 33.83. The opposite, protective scenario, (prtv ≡ OR ≪ 1), illustrated in Figure ξ1 = 0.42% and relatively high fraction of Controls ξ0 = 7.39% resulting in OR ≈ 0.05. In the contributing scenario we would like to increase ξ1 = SEN and decrease ξ0 to ≈ 0; in the protective situation, we would like to maximize ξ0 = SPE with simultaneous reduction of ξ1 to ≈ 0.GWAS studies aim in particular at identification of genomic rare variants in the population which are associated with increased or decreased risk of developing a disease. At data filtering stage, the main focus in this paper, we would like to identify SNP-pairs and sets of genotyping calls which allow us to identify subsets of the dataset with an OR for developing disease significantly different from 1. There are two possibilities illustrated by example in Figure The above section introduces principles on which our custom filtering algorithms are built. In this subsection we describe some additional enhancements and heuristics which were added to practical implementations used.k-tuple of genotyping features we may find subsets of their values displaying different degree of protection or contribution to the phenotype in question. One obvious modification to the above GSS test is to extend it to two separate tests, one for protective the other for contributing capabilities. The heuristic which we have followed in this regard consisted in restricting the "max" in computing PGSS once to a subset ROCcntr contributing alleles, and another to a subset ROCprtv of protective alleles. The demarcation is defined as follows. Let i = 0, 1, ..., 9 denote the (ordered) sequence of 10 points of ROC. ThenAs we have discussed above, for any x0, x1 such that min <0.02.Both SS and GSS tests are capable of identification of genotype probes which allow for strong separation in relatively small fractions of the population. This is a desired property for detection of rare variants. However, in practice the limited sample size imposes practical limitations on minimal size which could be of practical interest and is immune to noise or numerical instability of the optimisation procedures used. In our analysis we demanded that in computation of the outer minimum in either (2) or (4) we disregarded all contributions from t0 and t1 have sizes measured in thousands. This means in practice that in evaluating (2) and (4) we need to deal with multiplications, divisions and summations of thousands of numbers either so small or so large that they cannot be represented directly in computer hardware. For the description of the specific procedures developed to deal with this task and presentation of related formal proofs of their correctness we refer to a dedicated methods paper [The solution of this optimisation is not straightforward, for an average GWAS, ds paper . Here we• First, we prove that the functions under "max" in (2) and (4) have no local maxima;• For (2) the maximum is achieved on the boundary of • Due to that uniqueness, we can efficiently use any iterative procedure for finding the maximum. In particular we have used the bisection method, which converges to the solution along the boundary.• Finally, for numerical efficiency we have developed specific numerical simplifications which effectively reduce computation of the sums in (2) and (4) down to additions of small numbers of terms of order of one, with provably negligible penalty errors.SS Pfor tens of thousands of possible values of counts x0 and x1, hence for the multiple millions of pairs , the computing burden could become significant, warranting additional simplifications and reductions. In the case of GSS the computational burden is even harder, direct scan with this statistical filter becomes impractical , and so arises the need for developing more efficient proxies such as DSS (5).With the simplifications outlined above, the computation of values for individual probes and probe-pairs becomes a tractable numerical task. However when it comes to an exhaustive tabulation of the whole 2-dimensional distribution underpinning computation of We have used a number of other techniques than those described above for filtering putative interactions in GWAS. We outline them here for completeness.This is one of the most popular methods for interactions detection in GWAS. It has two distinct components:Computation of statistics. This is a well defined statistic which could be used directly for ranking of hits;• Computation of p-value for determination of significance. This part is is more complex and the usual solution is to apply a formula which is rigorously derived for sampling from a normal distribution [• ribution .We have used such formulae with 8 and 2 degrees of freedom when dealing with bivariate or univariate analysis, respectively. Additionally, we have applied the hod (see ). In allWe compute the following standard approximately distribution with V - 1 degrees of freedom [H0 : iυ N= iυ Efor all i, υ holds, then X2 = 0.This statistic is known to have  freedom ,52, whicx0, x1). Such p-values turn out to be in fact very close numerically to the Pss test, see Additional File Figure 21. For that reason we did not scan data with Fisher Exact test based filters, but the SS filter is a good indicator of its performance.Fisher Exact test is often used for evaluation of 2×2 contingency tables  and as sWe have used BOOST and GPU version GBOOST algorithms which we have downloaded from the web, and for details we refer to ,10. Thesest (see ).DoO: Difference of Odds; DSS: Difference in Sensitivity and Specificity; FE: Fishers Exact; FPR: False positive rate; GPU: Graphics Processing Unit; GWAS: Genome wide association studies; GWIS: Genome Wide Interaction Search; GSS: Gain in Sensitivity and Specificity; OR: Odds Ratio; ROC: Receiver operating characteristic; SNP: Single nucleotide polymorphism; SS: Sensitivity and Specificity; TPR: True positive rate; WTCCC: Wellcome Trust Case-Control Consortium.WTCCC DatasetsBD: Bipoloar Disorder; CAD: Coronary Artery Disease; CD: Crohn's Disease; HT: Hypertension; RA: Rheumatoid Arthritis; T1D: Type I Diabetes; T2D: Type II Diabetes.BG contributed to development and initial and final implementation of algorithms used, carried out most numerical experiments, and drafted the manuscript. DR implemented and optimised CPU implementation, performed simulations and assisted in writing and revising manuscript. QW implemented and optimized GPU version of algorithms, performed benchmarking including third party software, and collated results for literature comparison. FS assisted in development of software for analysis of results, and collated results for literature comparison. HF developed software for analysis of results, contributed to analysis of data, and assisted in critical revisions of manuscript. RC has critically revised and analysed results and manuscript. LS assisted in critical revisions of manuscript. MI helped get access to data and critical revised manuscript. CSO contributed to writing of manuscript, specifically for discussion, and contributed to comparison against literature. AK conceived of the study and participated in its design, designed and developed prototypes of methods used, implemented software for analysis of results, coordinated the project and helped to draft the manuscript.All authors read and approved the final manuscript.The authors declare that they have no competing interests.Supplementary MaterialsClick here for fileLists of detected SNP pairsClick here for file


Staphylococcus aureus genotyping. Eight single nucleotide polymorphisms (SNPs) were derived from the S. aureus multi-locus sequence typing (MLST) database on the basis of maximized Simpson's Index of Diversity. Only G↔A, G↔T, C↔A, C↔T SNPs were considered for inclusion, to facilitate allele discrimination by HRM. In silico experiments revealed that DNA fragments incorporating the SNPs give much higher resolving power than randomly selected fragments. It was shown that the predicted optimum fragment size for HRM analysis was 200 bp, and that other SNPs within the fragments contribute to the resolving power. Six DNA fragments ranging from 83 bp to 219 bp, incorporating the resolution optimized SNPs were designed. HRM analysis of these fragments using 94 diverse S. aureus isolates of known sequence type or clonal complex (CC) revealed that sequence variants are resolved largely in accordance with G+C content. A combination of experimental results and in silico prediction indicates that HRM analysis resolves S. aureus into 268 “melt types” (MelTs), and provides a Simpson's Index of Diversity of 0.978 with respect to MLST. There is a high concordance between HRM analysis and the MLST defined CCs. We have generated a Microsoft Excel key which facilitates data interpretation and translation between MelT and MLST data. The potential of this approach for genotyping other bacterial pathogens was investigated using a computerized approach to estimate the densities of SNPs with unlinked allelic states. The MLST databases for all species tested contained abundant unlinked SNPs, thus suggesting that high resolving power is not dependent upon large numbers of SNPs.High resolution melting (HRM) analysis is gaining prominence as a method for discriminating DNA sequence variants. Its advantage is that it is performed in a real-time PCR device, and the PCR amplification and HRM analysis are closed tube, and effectively single step. We have developed an HRM-based method for  The identification and monitoring of strains of pathogenic bacteria by genotyping is central to public health, infection control, and veterinary and food microbiology. Electrophoretic techniques such as pulsed-field gel electrophoresis and amplified fragment length polymorphism have been used extensively for many years. More recently, methods that involve the interrogation of known polymorphic regions, such as multi-locus variable number tandem repeat analysis and multilocus sequence typing (MLST), have become prominent. Rapid advances in whole genome sequencing using “next generation” technology means that time consuming and/or expensive genotyping methods will likely become non-competitive in the short to medium term. However, methods based on single-nucleotide polymorphisms (SNPs) will likely remain attractive due to the many efficient and adaptable methods for SNP interrogation, the increasing amount of publicly available comparative sequence data, and increasing understanding of the informative power of SNPs and SNP combinations.D), and the interrogation of these SNPs using allele specific real-time PCR (kinetic PCR). In this context, D is the probability that two sequences in the alignment, selected at random without replacement, will be discriminated by the SNPs. This approach has been applied to several species, and in general has been based upon SNPs derived from MLST datasets A previously described approach to SNP-based bacterial genotyping involved the computerized derivation from sequence alignments of sets of SNPs that are optimized with respect to the Simpson's Index of Diversity  In the case of Recently, a variant of this approach has been described, in which DNA fragments encompassing the resolution-optimized SNPs are interrogated by high-resolution melting (HRM) analysis S. aureus, and also explore the properties of the Minim typing approach and it's potential to be applied to other bacterial species. This study showed that Minim typing subdivides S. aureus in a manner concordant with its population structure. It also revealed that prominent species of bacterial pathogens contain large numbers of SNPs that have been subjected to HGT and so have the potential to underpin highly discriminatory genotyping methods. As larger datasets of whole genome data become available this approach will facilitate an optimized but parsimonious use of genome wide SNPs.Here we describe a method for Minim typing of S. aureus strains from each other, and designed HRM fragments of the appropriate size that contained these SNPs. The high resolving power of the six chosen fragments in relation to MLST was confirmed by comparing the chosen fragments with random fragments of the same size and also demonstrating that the fragments covered regions of the concatenated MLST sequence that are associated with a high Simpson's Index of Diversity. Following these in silico analyzes, we tested the method on 95 clinical S. aureus isolates and found that the HRM curves were reproducible and the order of curves essentially as predicted with minor exceptions. We generated a key that facilitates translation between HRM based genotypes and MLST and demonstrated that the clustering of HRM genotypes is concordant with the S. aureus population structure as defined by MLST.To investigate the properties of the Minim typing approach we began by determining that fragment sizes of 150–200 bp represented a good compromise between sequence diversity, and the ability to reliably resolve distinct. We then identified a set of SNPs optimised for resolving m) is 0.2°C. The predicted ΔTm caused by a single change in the number of G or C residues decreases as the length of the fragment increases. Assuming a constant ionic environment, this is described by ΔTm = 41((GC2–GC1)/N), where GC is the number of G+C residues and N is the length of the fragments m conferred by a single change in the number of G or C residues in a 200 bp fragment is approximately 0.2°C. It was concluded that variants of fragments >200 bp may not always be discriminated by HRM analysis, even when they differ in G+C content.The predicted effective fragment sizes for Minim typing were calculated. We conservatively assumed that sequence variations that do not affect the G+C content are not revealed by HRM, even though this is not always the case. First, we addressed the question as to the upper limit on the size of fragments for which sequence variants may be reliably discriminated by HRM analysis. Our experience with the Corbett Rotorgene is that the limit for the reliable detection of differences in melting temperatures  of these involved a change in G+C content that would likely facilitate allele discrimination by HRM analysis. To design the Minim typing method, the program Minimum SNPs was used to identify highly informative Simpson's Index of Diversity (D) optimized SNPs that change the G+C content. Six fragments were identified that encompassed SNPs at positions 78 and 210 of the arcC locus, positions 88 and 155 of the aroE locus, position 286 of the gmk locus, position 294 of the pta locus, position 36, and positions 241 and 243 of the tpi locus. There were considerable constraints on primer design, and as result, even though the aim was for the fragments to be close to 200 bp, they ranged in size from 83 bp to 219 bp  and the mean–log(1-D) value was 1.19 (SD 0.17), with none of the randomly selected combinations providing better resolution than the fragments we originally selected that provided 275 MelT profiles and a–log(1-D) of 1.68 .D values . The HRM curves can also be represented as difference graphs by using one of the curves as a baseline comparator. For the aroE88/155 fragment in particular, the difference graph using the ST239 control (curve 23) as the baseline, better demonstrated the separation between curves  with respect to the 1420 MLST sequence types. A Microsoft Excel “MelT key” that facilitates Minim data interpretation and translation between MelT and MLST data was assembled . Additional replicate analyses of these isolates were carried out at subsets of the loci. The total number of individual HRM assays carried out was 640, which may be regarded as equivalent to 107 complete Minim typing procedures, with a mean of 13.4 replicates per isolate. Another indication of robustness was that 155 HRM analyses, spread approximately equally across the loci, were performed on known sequences. The HRM data was all in accordance with the sequence data.S. aureus Minim typing be concordant with the population structure defined by MLST. In particular, this would allow the inference of the CC from the MelT data. We tested this by two means. First, we calculated Wallace's coefficient to compare MelT with clonal complexes and sub-clonal complexes (subCC) as defined by eBURST analysis for the 1420 STs, including 201 singletons. This yielded Wallace (MelT → CC) = 0.85 (95% CI 0.82–0.88) and Wallace (MelT→subCC) = 0.73 (95% CI 0.69–0.77). This indicates that if two strains are in the same cluster by MelT they have an 85% chance of having the same CC and 73% chance of having the same subCC. This analysis assumes an equal abundance of all STs, so the calculations were repeated using MLST data that better reflects the differing abundances of STs. In the MLST database, 3318 submitted isolates had STs that were present in the 1420 STs used to generate the MelT key. The ST data from these yielded Wallace (MelT→CC) = 0.949 (95% CI 0.938–0.961) and Wallace (MelT→subCC) = 0.841 (95% CI 0.0.823–0.860). These higher values reflect the abundance in the database of isolates with CC and subCC founder STs, and the effectiveness of Minim typing at discriminating these founder STs. The discriminatory power of Minim typing with this data set of 3318 isolates is D = 0.961 (95% CI 0.959–0.963), which is comparable to that of MLST at D = 0.987 (95% CI 0.983–0.989). The subCC data using MLST provides D = 0.957, which is less than that provided by Minim typing. Therefore Minim typing is concordant with MLST and provides discriminatory power significantly beyond assignment into CCs and subCCs.It is desirable that Second, the STs defined by MelTs were examined individually. In general, for CCs of significant size, a single MelT encompasses a CC founder and large proportion of its SLVs, and this MelT does not encompass the founder of any other CCs. Many MelTs encompass no major CC founder, but rather encompass a small number of closely related STs, such as SLVs of the same CC founder. These findings may be illustrated using the large and diverse CC8. The sensitivity, specificity and positive predictive values (PPV) of Minim typing for CCs and subCCs were calculated, using the simplifying assumption that all STs are equally abundant . MelTs dThe value of the Minim approach is in part a function of the density of informative SNPs. If a sequence alignment can be converted to a completely consistent dendrogram, then there is no homoplasy, no evidence for recombination involving the SNPs and the resolving power of SNPs defined by the sequence variants can only increase arithmetically as SNPs are added to the typing set. Conversely, if recombination is extensive, the resolving power can increase exponentially as SNPs are added.in silico “SNP depletion” experiment was carried out using the different MLST datasets. Highly resolving SNPs were least dense in S. aureus, confirming a previous analysis Haemophilus influenzae and Streptococcus pneumoniae have particularly high densities of informative SNPs . In addition, there is much that is arbitrary about MLST itself. The choice of different numbers of fragments, sizes of fragments, or genes used for MLST would have resulted in many differences in detail concerning which isolates are resolved by MLST and which are not–but this would not have affected greatly the general model of the population structure, and the assignment of isolates to particular lineages. Exact concordance with MLST typing has little or no biological meaning.There are several limitations to this Minim typing approach. First, given the available HRM technology, it is not usually possible to discriminate fragments with the same G+C content but of a different sequence. Therefore, in generating the translation key we conservatively assumed that fragments with the same G+C content will produce the same curve. As a consequence, the discriminatory power of HRM typing will always be less than that of full MLST. Also, new MLST alleles that have the same G+C content as previous alleles in the interrogated fragment will not be revealed through Minim typing. Although it would have been ideal to have had full MLST sequences for all tested isolates, to reduce the amount of sequencing required, we made initial comparison with the kinetic SNP PCR typing method and any discrepancies with Minim typing were resolved by sequencing of the relevant MLST alleles. Finally, as the MLST database is updated with new STs and CCs being deposited, the translation key will concordantly need to be updated, therefore we have established a website where updated keys can be accessed  and the speed with which the SNP is recombined throughout the population, which will be a function of rate of HGT. B. pseudomallei is notable because its SNP diversity is low, so the pool of informative SNPs is shallow, but the most informative SNPs provide very good resolving power. This suggests a high rate of HGT, which indeed has been previously reported Streptococcus pyogenes has a similar population structure. The results from C. jejuni/C. coli were of interest because they showed a low density of recombined SNPs, which appears inconsistent with the reported high frequency of recombination The Minim approach can be applied to other important bacterial species and we provide the theoretical basis for choosing SNPs and indications as to the resolving power achievable for several different species. Determination of the density of highly informative SNPs in different species showed that such SNPs can be very abundant, with in-silico generation and optimization of the typing scheme. The protocol we have developed for identifying regions optimized for genotyping based upon G+C content and interpreting the data with reference to databases of sequence variation could easily be adapted to select fragments and generate a key for other platforms such as with base composition analysis using PCR/ESI-MS The use of HRMType allows the accurate S. aureus could usefully be incorporated into a progressive hierarchical typing scheme S. aureus this could be used in conjunction with interrogation of hypervariable regions  in November 2009. Four STs  with an incorrect concatenated sequence length and one ST (ST1110) with a likely frame shift at position 81 of the glpF locus were subsequently removed from the alignment. Ten STs  were removed on the basis that their tpi alleles and in some cases pta alleles were >13% diverged from the other S. aureus alleles and all but one had a best BLASTn hit to a Staphylococcus species other than S. aureus. These alleles would likely fail to amplify or amplify late during PCR using the primers we designed using the S. aureus allele sequences, due to polymorphisms in the primer binding regions. These STs were appended to the MelT key (see below) with a note indicating that one or more of the regions may fail to amplify, and thus they will not produce a complete MelT profile. A further nine STs which are likely members of clonal complex 75  were also removed as this clonal complex shows significant divergence from other S. aureus and may represent a distinct species or sub-species The concatenated MLST sequences for 1444 STs (ie up to ST1508) were downloaded from the D) of 0.95 for the MLST dataset D optimized SNPs which were likely to contribute to a change in the melting temperature of the DNA  for interrogation by HRM. From the original SNP set, we used SNPs which were transitions for most STs  as forced inclusions and SNPs which were transversions  as forced exclusions in Minimum SNPs We had previously identified a set of eight SNPs with a Simpsons Index of Diversity  to analyze regions internal to the primers. We have called the program HRMType and it generates a prediction of the number and order of HRM curves for specified regions from an input sequence alignment . For simD value for randomly generated fragments from the concatenated sequence. Individual fragments were not allowed to span more than one MLST loci in the concatenated sequence. Initially regions with fixed sizes ranging from 20 to 200 bp were used and subsequently regions with the same size as our 6 chosen regions were used. Multiple iterations allowed us to determine the distributions of D and the transformation 10(1-D)–log for these randomly generated fragments. Additionally, for each position in the MLST concatenated sequence we performed a multivariate regression analysis with the outcome variable 10(1-D)–log and the independent variable the absence or presence of that position within the selected fragments. Adjustment was made for fragment size. The coefficient from the regression analysis provided a measure of association between each position of the concatenated sequence and the overall resolving power obtained. These coefficients of association were plotted against the concatenated position to generate a SNP association map , 0.4 uM each primer and 1 ul of the diluted DNA extraction in a total volume of 10 ul. The reactions were performed on a on a Rotorgene 6000 device (Corbett Life Science). Cycling conditions were 50°C for 2 minutes, 95°C for 2 minutes followed by 40 cycles of 95°C for 3 seconds, 56°C for 18 seconds and 72°C for 12 seconds. HRM was carried out from 66°C to 84°C in 0.1°C increments for 2 seconds each. Raw HRM curves were normalized by the Rotorgene 6000 software v 1.7 . DiffereS. aureusSequencing of selected MLST loci of some isolates was carried out using standard MLST primers and methodology for S. aureus was carried out in silico. An eBURST analysis S. aureus MLST database. The group definition was set to “six alleles in common”, and each group was defined as a clonal complex (CC). “SubCCs” within CCs were identified on the basis of the presence of a progenitor with at least five SLVs. In the case of large CCs with several subCCs and chains of STs with SLV relationships, the boundary between sub-CCs was placed at the half way point between sub-CC progenitors on the eBURST diagram. The sensitivity ), specificity ) and positive predictive value ) of MelTs for a CC or sub-CC of interest were calculated.Wallace's Coefficient was calculated using Comparing Partitions S. aureus, Streptococcus pyogenes, Campylobacter jejuni, Burkeholderia pseudomallei, Streptococcus pneumoniae, Enterococcus faecium and Haemophilus influenzae, the following procedure was followed. Firstly a set of six SNPs that provided an optimized D value was derived using the software Minimum SNPs. The derivation was then iterated, with the first five SNPs in the set held constant, and the increase in resolving power provided by the 6th SNP recorded. This 6th SNP was then excluded from analysis in subsequent iterations. This was continued until the increase in resolving power was five times the maximum increase that can be conferred by a non-recombined SNP. Six member SNP sets were used because this provides a comprehensive test of SNP performance, and also yields manageable numbers of alterative SNPs that provide a >5X the maximum increase in resolving power possible for non-recombined SNPs. The “5X” parameter was chosen arbitrarily as representing a figure considerably greater than that possible from a non-recombined SNP.For the MLST datasets of D). Therefore, the higher the value of –log(1-D) conferred by the first five SNPs in the set, the smaller the maximum increase in this parameter that can be conferred by the addition of a non-recombined SNP to the set. This may be represented by an exponential decay function: if PR is the resolving power conferred by a set of SNPs, and MI is the maximum increase in resolving power that can be conferred by the addition of a non-recombined SNP to the set, then: MI = 0.3389ePR−2.116 (PR and MI are both expressed in terms of –log(1-D)). For each of the MLST datasets, this function was used to derive MI from the PR value conferred by the first five SNPs in the set.Normalization was required to determine the maximum increase in resolving power that can be conferred by a non-recombined SNP for each of the different MLST datasets. This is because the different datasets provided different resolving powers from the first five SNPs. Maximum resolving power from recombined SNPs, expressed in terms of number of genotypes, increases logarithmically as SNPs are added to the set. Conversely, maximum resolving power from non-recombined SNPs increases linearly. The log(number of genotypes), assuming all genotypes all contain the same number of STs, is equal to –log(1-Data S1Staphylococcus aureus MelT key.(XLSX)Click here for additional data file.Data S2Comparison of MelT and kinetic PCR SNP typing results.(XLS)Click here for additional data file.Text S1Suggested protocol for typing of clinical isolates.(DOC)Click here for additional data file.Text S2HRMType program.(TXT)Click here for additional data file.Text S3HRMType Manual.(PDF)Click here for additional data file.


Identification of single nucleotide polymorphisms (SNPs) for specific genes involved in reproduction might improve reliability of genomic estimates for these low-heritability traits. Semen from 550 Holstein bulls of high  or low  daughter pregnancy rate (DPR) was genotyped for 434 candidate SNPs using the Sequenom MassARRAY® system. Three types of SNPs were evaluated: SNPs previously reported to be associated with reproductive traits or physically close to genetic markers for reproduction, SNPs in genes that are well known to be involved in reproductive processes, and SNPs in genes that are differentially expressed between physiological conditions in a variety of tissues associated in reproductive function. Eleven reproduction and production traits were analyzed.P < 0.05) with DPR. Among these were genes involved in the endocrine system, cell signaling, immune function and inhibition of apoptosis. A total of 10 genes were regulated by estradiol. In addition, 22 SNPs were associated with heifer conception rate, 33 with cow conception rate, 36 with productive life, 34 with net merit, 23 with milk yield, 19 with fat yield, 13 with fat percent, 19 with protein yield, 22 with protein percent, and 13 with somatic cell score. The allele substitution effect for SNPs associated with heifer conception rate, cow conception rate, productive life and net merit were in the same direction as for DPR. Allele substitution effects for several SNPs associated with production traits were in the opposite direction as DPR. Nonetheless, there were 29 SNPs associated with DPR that were not negatively associated with production traits.A total of 40 SNPs were associated (SNPs in a total of 40 genes associated with DPR were identified as well as SNPs for other traits. It might be feasible to include these SNPs into genomic tests of reproduction and other traits. The genes associated with DPR are likely to be important for understanding the physiology of reproduction. Given the large number of SNPs associated with DPR that were not negatively associated with production traits, it should be possible to select for DPR without compromising production. There is a negative genetic correlation between milk yield and fertility in dairy cattle -3. PartlOne possible way to improve the accuracy of genomic estimates of fertility is to incorporate SNPs for specific genes involved in reproduction into SNP panels. The bovine genome contains over 20,000 genes, and over 14,000 of those do not contain a single SNP on the BovineSNP50 chip . Incorpoin vitro fertilization or development, such as STAT5A. SNPs in which the linear or dominance effect was age in R . The acchttp://www.ingenuity.com). The reference set was the Ingenuity Knowledge Base (genes only) and both direct and indirect relationships that were experimentally observed were included. Three analyses were conducted. The first was to identify canonical pathways in which 2 or more genes were overrepresented. The program was also used to build customized networks of genes based on direct and indirect relationships. Finally, upstream regulators in which genes related to DPR were overrepresented were identified. A P value of 0.05 or less was considered significant for all analyses.The list of genes significantly related to DPR was subjected to pathway analysis using Ingenuity Pathway Analysis (IPA) software  and were removed from all further analyses. The probability that the MAF was ≥ 5% was dependent upon the type of SNP. Four of the 5 genes in which the SNP was in the non-coding regions or was synonymous had a MAF ≥ 5% (80%) whereas only 20% (2/10) of the nonsense, 25% of the missense (99/397), and 9% (2/22) of the frameshift mutations had ≥ 5% MAF .Of the 434 SNPs, only 107 had MAF ≥ 5% and only 98 of those that had MAF ≥ 5% and had a call rate > 70%. Nine SNPs had MAF ≥ 5% but failed the genotyping process . All but one of these SNPs caused a missense mutation. The exception was for UHRF1, which was a frameshift mutation where the mutation causing the frameshift had a frequency of 91.7%. The genes most out of equilibrium were CCT8, MARVELD1 and SYTL2, in which the number of minor allele homozygotes was lower than expected, CD2, DTX2, NEU3, and RALGPS1, in which the number of heterozygotes was lower than expected, and TAF9 and TSPYL1, in which the number of heterozygotes was greater than expected.Characteristics of the 98 SNPs in which MAF ≥ 5% and call rate was > 70% are shown in Additional file P values and Q values corrected for multiple testing were determined. Since the Q value correction for multiple testing is highly conservative in cases where few tests are significant, both the P value and the Q value were used to identify SNPs associated with genetic traits.Each of the 98 SNPs with MAF ≥ 5% and a call rate > 70% were analyzed for effects on DPR and other genetic traits. Two types of analyses were performed: a regression analysis to determine the allele substitution effect of each SNP  and use of an orthogonal contrast to determine the dominance effect (heterozygote vs. the average of the two homozygotes). Both ACAT2, AP3B1, APBB1, BSP3, C17H22orf25 (interim symbol TANGO2), C1QB, C7H19orf60, CACNA1D, CAST, CCDC86, CD14, CD40, CFDP2, COQ9, CPSF1, CSNK1E, CSPP1, DEPDC7, DSC2, DYRK3, FUT1, GPLD1, HSD17B12, HSD17B7, LDB3, MARVELD1, MON1B, MRGPRF, MS4A8B, NEU3, NFKBIL1, NLRP9, OCLN, PARM1, PCCB, PMM2, RABEP2, TBC1D24, TDRKH, and ZP2]. These effects were significant based on both P and Q values. In addition, there were 4 genes exhibiting dominance based on P values, including two in which the allele substitution effect was significant (CD14 and FUT1) and two in which the allele substitution was not significant (ARL6IP1 and TSHB). After correcting for multiple testing, none of the dominance effects achieved significance.Results for DPR are shown in Table AP3B1, APBB1, C1QB, CACNA1D, CD14, CPSF1, CSNK1E, DEPDC7, DSC2, FSHR, FYB, GPLD1, HSD17B7, LDB3, MS4A8B, NFKBIL1, PARM1, TDRKH, and ZP2) and dominance effects for 5 SNPs  and dominance effects for 4 SNPs  and dominance effects for 5 SNPs  and dominance effects for 6 SNPs  were in the opposite direction as for DPR . There were 7 SNPs associated with FY and 5 of these were in the opposite direction as for DPR. There were 7 SNPs associated with PY and 5 of these were in the opposite direction as for DPR. For other production traits, however, there were fewer negative relationships between allele substitution effects on DPR. For FPC, there were 5 SNPs but only 1 was in the opposite direction as DPR. For PPC, there were 13 SNPs but only 1 was in the opposite direction as DPR. For SCS, there were 5 SNPs with three in the same direction as DPR and two in the opposite direction.Fewer SNPs associated with DPR were also associated with production traits. Furthermore, when occurring, the direction of the effect was often in the opposite direction as for DPR, especially for yield traits. There were 10 SNPs associated with MY and all but one . Thus, it should be possible to select for specific SNPs affecting DPR without compromising yield traits.CAST, FGF2, FSHR, HSPA1A, IRF9, NLRP9, PGR and STAT5A) and only 2 (CAST and NLRP9) were significantly associated with DPR .An analysis was performed to determine whether the tissue type used to identify genes for SNP discovery affected the probability that a gene was related to DPR . These were Estrogen Biosynthesis (HSD17B7 and HSD17B12), Estrogen-Dependent Breast Cancer Signaling (HSD17B7 and HSD17B12), Hepatic Fibrosis/Hepatic Stellate Activation (CD14 and CD40), Tight Junction Signaling (CDSF1 and OCLN), and Dopamine-DARPP32 Feedback in cAMP Signaling (CACNAID and CSNK1E). The IPA software also built 4 networks of genes related to DPR. The most revealing was one that included 16 genes  which interacted directly or indirectly with UBC  overrepresented. A total of 5 transcription factors were identified  and one growth factor (TGFB1). Estradiol regulates 10 DPR genes, TGFB1 regulates 6 genes, and prostaglandin E1 regulates 2 genes  and where effects were Polymorphisms in the current study were chosen for having the greatest probability of changing protein function. In order to maximize the possibility of finding causative SNPs, we prioritized the selection of SNPs within a gene to favor those causing the greatest change in protein function. This decision may have been one reason why there was a high rate (75%) of SNPs with MAF < 5% because the SNP would be subjected to purifying selection. Only 20% of the nonsense, 25% of the missense and 9% of the frameshift mutations had MAF ≥ 5% whereas this frequency was 80% of the 5 SNPs that were in a non-coding region or did not result in an amino acid substitution. Many of the SNPs were not in Hardy-Weinberg equilibrium and this, too, may reflect the effect of the SNPs on protein function.CCT8, MARVELD1 and SYTL2) had less than expected frequencies of minor allele homozygotes. The interpretation is that few of the mutations in which MAF was ≥ 5% were lethal. Interestingly, for six genes, the heterozygote was more or less frequent than expected. Some of the decrease in heterozygosity could be due to inbreeding, which is high in Holstein cattle [Of the 9 SNPs most out of equilibrium, only 3 (n cattle . Other cn cattle ), heteron cattle , or in tThe antagonistic genetic relationship between fertility traits and milk production -3 was veSNPs that affected DPR were also positively related with other fertility traits . Other studies have also shown a positive genetic correlation among fertility traits ,28,63. IBAIAP2, GHR, LEP, IGF1, IGFBP7, ITGB5, PAPPA2, SCRN1, and SERPINA14) and were not analyzed. Of the other 8, 2 were significantly associated with DPR (CAST and NLRP9) and one tended to be (FGF2). The exact SNP in CAST analyzed here was previously associated with DPR, PL, and NM [NLRP9 than the one studied here was associated with incidence of still birth [FGF2, tended to have an association with DPR (P = 0.08), with the AA genotype being superior to the GG genotype. Previously, the AA genotype of FGF2 was associated with higher estimated relative conception rate in bulls [in vitro embryo development [PGR, was previously associated with in vitro fertilization rate and development [in vivo fertilization [P = 0.16), the GG genotype was superior to the CC genotype for DPR in agreement with the superior genotype seen earlier [FSHR was previously associated with superovulation response [HSPA1A, associated with calving rate in beef cattle [IRF9, which was physically close to a SNP for interval to insemination [STAT5A, associated with in vitro embryo development [HSPA1A was significantly associated with PL and NM . The COQ9 protein is necessary for the synthesis of CoQ10 [CSPP1 gene plays a role in spindle formation and cytokinesis [MARVELD1 inhibits cell cycle progression and migration [LDB3 helps organize actin and α-actinin binding in sarcomeres [CPSF1 is involved in 3′ end-processing of pre-messenger RNAs into messenger RNAs [A variety of other roles are also represented by the genes with SNPs associated with DPR. Two genes are involved in energy pathways . It is not surprising that the proteins encoded for by so many genes bind to UBC because ubiquitin is involved in a large number of intracellular functions -144. FivIn conclusion, SNPs in a total of 40 genes associated with DPR were identified as well as SNPs for other traits. It might be feasible to include these SNPs into genomic tests of reproduction and other traits. The genes associated with DPR are likely to be important for understanding the physiology of reproduction and manipulating reproduction function in cattle. Given the large number of SNPs associated with DPR that were not negatively associated with production traits, it should be possible to select for DPR without compromising production.CCR: Cow conception rate; DPR: Daughter pregnancy rate; ERCR: Estimated relative conception rate; FPC: Fat percent, FY, Fat yield; HCR: Heifer conception rate; MAF: Minor allele frequency; MY: Milk yield; NM: Net merit; PL: Productive life; PPC: Protein percent; PTA: Predicted transmitting ability, PY, Protein yield; SCS: Somatic cell score; SNP: Single nucleotide polymorphism.The authors declare that they have no competing interests.Conceived and designed the experiments: SDC, PJH, JBC. Performed the experiments: SDC. Analyzed the data: SDC, DJN, JBC, PJH. Wrote initial drafts of the paper: SDC, PJH. All authors read and approved the final manuscript.Table S2. Source of genes included in SNP array. Table S3. Full list of SNPs used in array.Predicted transmitting ability for selected traits of bulls used for genotyping. Click here for fileTable S5. Correlations, within DPR class, among predicted transmitting ability for traits on bulls used for genotyping. Table S6. Information for all SNPs with minor allele frequencies (MAF) > 5% and call rates > 70%.Correlations among predicted transmitting ability for traits on bulls used for genotyping. Click here for fileTable S8. Effect of tissue type in which genes were identified on the percent of genes that were significantly associated with daughter pregnancy rate (DPR). Figure S1. The ubiquitin pathway contains an overrepresentation of daughter pregnancy rate genes. Figure S2. Transcription factors which regulate daughter pregnancy rate genes.Genetic standard deviations of SNPs associated with daughter pregnancy rate. Click here for file


Microarrays are invaluable tools for genome interrogation, SNP detection, and expression analysis, among other applications. Such broad capabilities would be of value to many pathogen research communities, although the development and use of genome-scale microarrays is often a costly undertaking. Therefore, effective methods for reducing unnecessary probes while maintaining or expanding functionality would be relevant to many investigators.Toxoplasma gondii  and Plasmodium falciparum , we designed a single oligonucleotide microarray capable of supporting a wide range of applications at relatively low cost, including genome-wide expression profiling for Toxoplasma, and single-nucleotide polymorphism (SNP)-based genotyping of both T. gondii and P. falciparum. Expression profiling of the three clonotypic lineages dominating T. gondii populations in North America and Europe provides a first comprehensive view of the parasite transcriptome, revealing that ~49% of all annotated genes are expressed in parasite tachyzoites (the acutely lytic stage responsible for pathogenesis) and 26% of genes are differentially expressed among strains. A novel design utilizing few probes provided high confidence genotyping, used here to resolve recombination points in the clonal progeny of sexual crosses. Recent sequencing of additional T. gondii isolates identifies >620 K new SNPs, including ~11 K that intersect with expression profiling probes, yielding additional markers for genotyping studies, and further validating the utility of a combined expression profiling/genotyping array design. Additional applications facilitating SNP and transcript discovery, alternative statistical methods for quantifying gene expression, etc. are also pursued at pilot scale to inform future array designs.Taking advantage of available genome sequences and annotation for T. gondii transcriptome across major lineages and permitting detailed resolution of recombination points in a historical sexual cross, the multifunctional nature of this array also allowed opportunities to exploit probes for purposes beyond their intended use, enhancing analyses. This array is in widespread use by the T. gondii research community, and several aspects of the design strategy are likely to be useful for other pathogens.In addition to providing an initial global view of the  Plasmodium falciparum, using spotted oligonucleotide arrays, revealed a transcriptional program tightly coupled to the cell cycle [P. falciparum, and have been used to assess genomic variation across multiple isolates [In recent years, annotated genome sequences have become available for many important human and veterinary pathogens, facilitating the exploration of organismal biology. Genome-wide microarrays enable a variety of RNA- and DNA-based queries, contributing to our understanding of genome function and evolution ,2. For ell cycle , and furll cycle ,5. Higheisolates ,7. A newisolates . Such stisolates . SimilarToxoplasma gondii research community, we have developed a custom Affymetrix array for this protozoan parasite, a prominent source of neurological birth defects during congenital infection, and a cause of encephalitis in immunosuppressed patients. T. gondii provides an attractive organism for exploring the utility of mixed use microarrays, for several reasons. First, the parasite genome is relatively small (~65 Mb), and an annotated reference sequence is available [T. gondii replicates as a haploid, greatly reducing the probe content required for genotyping. Finally, while all of the above characteristics apply to other pathogens as well (including Plasmodium spp.), excellent experimental systems are available for T. gondii permitting cell and molecular biological studies, forward and reverse genetics, and investigation of host-parasite interactions [In order to address the diverse needs of the vailable ,11. Secovailable ,13 facilvailable , which hvailable , T. gondractions .P. falciparum genome [T. gondii and P. falciparum research communities.Taking advantage of these features, we have designed a novel multifunctional array which enables the following goals: global expression profiling of parasite genes (both nuclear and organellar), and simultaneous analysis of relevant host cell genes; genome-wide high-resolution genotyping; and pilot-scale studies for non-coding regions , alternative expression metrics (exon-level profiling), validation of gene annotation, and polymorphism and transcript discovery. This array also supports inexpensive and efficient genotyping of malaria parasites, based on ~2 K SNPs distributed throughout the m genome ,18. DespT. gondii lineages found in Europe and North America [T. gondii, enabled by complementing non-redundant genotyping probesets with individual expression profiling probes that intersect SNPs uncovered from recent sequencing of additional T. gondii isolates, validating the utility of a combined expression profiling/genotyping array design. Over 5,000 chosen SNPs are used to demonstrate high-resolution mapping of crossover points in the progeny of a historical sexual cross [Toxoplasma gene models, and SNP discovery in the T. gondii plastid (apicoplast).We have utilized these arrays to provide the first global view of tachyzoite (lytic) stage gene expression for representatives of the three dominant  America ,20, grea America  between al cross . AdditioToxoplasma gondii and Plasmodium falciparum. Table http://toxodb.org.This report describes the design of this novel multifunctional Affymetrix microarray, and its use for the aforementioned RNA- and DNA-based studies relevant to the biology of T. gondii. This allows for utilization of readily available protocols and software for labeling, hybridization, and analysis. For gene discovery and high-resolution genotyping applications, power analyses suggested that a lower degree of probe redundancy than commonly used in other systems would be sufficient for T. gondii and P. falciparum, which have relatively small genomes and replicate as haploids. Finally, pilot-scale projects were incorporated to generate preliminary data for several additional applications, including a comparison of methods for transcript profiling and analysis, examination of antisense and intron transcription, chromatin immunoprecipitation studies, expression of selected host genes, and polymorphism detection in highly variable genes.Probe design and selection required balancing space constraints on the array, a desire to employ standard well-supported experimental methods and analysis algorithms, and new opportunities afforded by custom design. Standard Affymetrix algorithms were used to select probes for traditional applications, including global parasite expression profiling, and genotyping of the several hundred well-characterized genetic markers previously reported for T. gondii research community, enabling the correlation of isolate-specific differences in gene expression with differences in virulence, drug sensitivity, differentiation, and other aspects of parasite biology [Expression profiling of the ~8,000 genes identified in the parasite genome (reference strain ME49) is of general interest to the  biology ,23. In o biology . A perfe biology ,26. The T. gondii genes were annotated in the draft 3 nuclear genome sequence, and 3'-biased PM expression profiling probes were designed for all of these genes. In order to evaluate array performance, transcript abundance for in vitro-cultivated T. gondii tachyzoites was compared with information available from three alternative sources: (i) random cDNAs from large-scale unbiased EST sequencing projects [ii) cDNA abundance inferred by SAGE  [iii) a microarray study using spotted clones corresponding to ~500 genes [As indicated in Table projects ,27, (ii)ression) , and  = type II; VEG = type III), representing the major clonal lineages that define parasite populations and pathogenesis phenotypes in the US and Europe [in vitro  -- a significant improvement over the 204 transcripts identified on glass slide arrays (41% of the genes interrogated), in SAGE tag libraries (901), or EST libraries . Proteomic studies suggest a similar level of expression [To provide a first global view of expression across the entire d Europe ,20. Exprd Europe ), which pression ,29.Biological replicates display extremely high concordance across the full range of expression, as shown in Figure -3 (adjusted for multiple testing) yields a total of 5,307 genes (68% of the genome). Further filtering to exclude genes that changed <2-fold, were unexpressed (at a 10% FDR), or were interrogated by a highly polymorphic probeset ; see genotyping methods for a description of how polymorphic probes were identified), leaves 2,078 genes displaying statistically significant between-strain differences in expression . Of these, a single outlier strain could be assigned for 1,239 genes; as indicated in Figure Extracting all genes exhibiting differential expression in any pairwise comparison at a P-value of 10T. gondii genome annotation, including 677 of the 2,078 genes differentially regulated between strains  were available in the draft 3 T. gondii populations are characterized by a largely clonal structure, with most strains isolated from North America and Europe falling into one of three dominant clonotypes referred to as types I, II, and III [In the field,  and III ,20. Thes and III ,30. GenoThree sets of probes are available for genotyping analysis at increasing resolution, as indicated in Figure T. gondii is a diploid organism that undergoes meiosis during sexual recombination, mitotic replication occurs as a haploid. As a result, clonal parasite isolates are homozygous at every locus, eliminating the need for heterozygote discrimination. The intuition that haploid genotyping should require fewer probes was confirmed by typing known SNPs using data from the hybridization of (effectively haploid) inbred mouse strains to a densely-tiled resequencing array. As shown in Figure -3). Performance falls off with distance from the SNP, dropping below 50% accuracy at ± 8 nt. Performance of adjacent SNPs on the same strand was highly correlated: as shown in Figure Plasmodium genome [T. gondii EST data, and also for probes for 2,000 coding sequence SNPs identified in the Plasmodium falciparum genome [An additional 3,490 putative polymorphisms were identified based on EST sequences from various parasite strains , as indim genome . In contm genome . This fom genome  ; Additional File T. gondii genetic markers are represented on the array, and accuracy for those SNPs carried forward is >95%. This corresponds to an average density of 1 SNP per 12 kb genome-wide, representing an ~20-fold increase in resolution over prior genotyping efforts. Plasmodium falciparum SNPs were screened using a training set comprised of four strains , yielding a total of 1,700 SNPs, confirming that the strategy of using two probes per allele is not sensitive to the extreme AT-bias of Plasmodium (>80%).These three classes of SNP analysis probes were screened to remove probesets that failed to consistently yield correct calls across a training sample spanning all three lineages , 43 type II SFPs (specific to Prugniaud strain parasites), and no type I SFPs, as indicated by diamonds in Figure i) six probes for each exon for every predicted gene, (ii) five probes for each predicted intron, and (iii) probes to assess the importance of antisense transcription (20 probes from the opposite strand for each of the 227 genes on this chromosome). In addition, probes were tiled at a 10 nt resolution across a ~750 nt span upstream of seven selected genes, enabling pilot-scale chromatin immunoprecipitation studies (not shown). Although not investigated in this paper, the availability of both standard 3'-biased probesets and probes for all exons on chromosome Ib permits a comparison of alternative methods for evaluating gene expression [Investigating the performance of standard 3'-biased probesets vs. all exon arrays, or antisense expression, would require higher density interrogation than could be justified for a low-cost array. These applications were therefore enabled for a small portion of the genome, allowing the generation of pilot-scale findings to be used in the consideration of future array designs. Chromosome Ib was selected to provide telomere-telomere analysis as it is a single, small (~1.9 Mb) chromosome, exhibiting hundreds of strain polymorphisms  . Figure pression ; 3' probT. gondii gene models are based solely on computational predictions, without support from experimental evidence such as ESTs or SAGE tags  Unannotated sequences that map to consensus sequences derived by EST clustering (ApiDoTS clusters [(clusters ) containii) Unannotated ORFs with significant BLASTX hits to the non-redundant GenBank database [ Unannotated ORFs (≥150 nt) that are significant matches  for the query set of OrthoMCL ortholog database sequences using TBLASTN, resulting in 450 ORFs.(Plasmodium yoelli (PY00596), and two adjacent unannotated EST clusters exhibiting high expression.Statistical analysis of human spike-in data suggested that a tiling density of 35 bp would be sufficient to reliably detect missed exons across a useful dynamic range of target concentrations  for 260 host genes on the array, representing a comprehensive set of cytokines, chemokines, receptors, and other genes likely to function at the parasite-host interface , and loaded into ToxoDB.org [This report describes a multifunctional microarray supporting a wide range of studies on the protozoan parasite ii Table . Expressii Table , allowinii Table . The perii Table . Exon-leii Table , generals Figure . All of xoDB.org , enablinT. gondii populations in the US and Europe [T. gondii genes identified in draft 3 annotation are expressed in tachyzoite-stage parasites , but are much more economical, improving T. gondii genotyping from the >300 kb resolution currently available using 186 RFLP markers [P. falciparum SNPs were also included on this array, providing an economical means for genotyping of the most lethal human malaria parasite [The most unusual aspect of this study is the incorporation of both expression profiling and genotyping probes on the same array, broadening the utility of this chip for biological analysis. Standard array-based genotyping strategies (40 probes) were modified in light of the discovery that same-strand probes are largely redundant Figure , particu markers  to ~37 kobesets 6% vs. 48%parasite ,18.T. gondii isolates were released subsequent to chip production, revealing >600 K biallelic SNPs , and eliminating mismatch probes . This reagent has been widely adopted by the T. gondii research community. Excess space available on the array was exploited to support high resolution, low cost genotyping, taking advantage of the discovery that 4 feature probesets are nearly as effective as 40 feature probesets. The multifunctional nature of this array has provided many unexpected advantages, including the opportunities to exploit expression profiling probes as SFP markers, and the ability to use genotyping probes for strain validation during RNA hybridization experiments. Many of the principles employed in this design are applicable to other species.The driving motivation for this array design was to support low cost whole genome expression profiling for the protozoan parasite T. gondii genome sequences (type II strain ME49) and gene models (draft III) were obtained from ToxoDB.org. The apicoplast genome sequence (type I strain RH) was from GenBank (acc# NC001799) and the mitochondrial genome sequence was inferred by alignment of sequence fragments . P. falciparum SNPs were kindly provided by X. Su [http://www.ToxoDB.org.by X. Su . A custoacc# NC00799 and t® IVT Labeling Kit . RNA used for exon-level analysis was isolated from Prugniaud strain parasites using the same procedures, but labeling was performed with the Affymetrix Whole Transcript Sense Target Labeling Kit according to manufacturer's instructions without rRNA reduction. Hybridization, washing, and scanning of arrays was performed using standard Affymetrix instrumentation and protocols for 11 micron, 169 format arrays. Biological triplicates were generated for each strain and expression values computed using the RMA implementation (default parameters) in the affy package from Bioconductor [Toxoplasma strains, which serves as the data source for Figure For expression analysis based on the 3'-biased probesets, PrugniaudΔHXGPRT, RH, and VEG strain parasites were cultured in human foreskin fibroblast (HFF) cells as previously described . Prior tonductor . DiffereT. gondii SAGE tags [rd quartile of UTR-length distribution based on UniGene cluster analysis done for this study), resulting in 1,229 genes being linked with SAGE data. The 125,741 T. gondii ESTs deposited in dbEST (NCBI) were mapped to the reference genome using Splign [38,263 unique 3-prime AGE tags  were assg Splign  and EST-For gene-level "present" calls, labeled sense-strand mRNA was used to define a null background RMA distribution for each gene using the 3'-biased probesets. P-values for presence were then assigned to each gene based on hybridizations with labeled antisense RNA. Using the Benjimini-Hochberg method these P-values were used to set a 10% FDR threshold. Exon-level presence calls were made using a similar procedure, with the null distribution of RMA values defined using antisense RNA, as exon probes are antisense in orientation to their corresponding mRNA. A 10% FDR was used for calling exon presence.DNA from each of the inbred mice DBA/2J and C57/B6 were hybridized to a 1 bp resequencing microarray designed for random regions of the C57/B6 mouse genome and 109 high confidence SNPs were identified using standard resequencing analysis algorithms . Using this data as a reference for the detection of known SNPs, P-values associated with a Kolmogorov-Smirnov test distinguishing the two strains were computed using different probe tiling strategies.Genomic assemblies of GT1 (type I) and VEG (type III) were obtained from ToxoDB.org and aligned to the reference strain ME49 (type II) using NUCmer . RegionsToxoplasma gondii genomic DNA was isolated from RH, Pru, VEG, and select recombinant progeny via scraping and pelleting cultured parasites (as above) and then using the Gentra Systems Generation DNA isolation kit  according to the manufacturer's instructions. Purified DNA was diluted in 750 ul TE pH 8.0 containing 10% glycerol with the addition of 2 ul molecular biology-grade glycogen (20 mg/ml). Approximately 800 ng of diluted DNA was added to an Invitrogen nucleic acid nebulizer on ice, and compressed nitrogen was used at 40 psi for 3 min to shear the DNA. Fragmented DNA was alcohol-precipitated, heated, and labeled for 2 hr using the Invitrogen BioPrime Array CGH Genomic Labeling module with biotin-14-dCTP according to the protocol from the manufacturer. Labeled DNA was cleaned with the Purification module and hybridized to the microarray as described above. Data analysis was conducted in Bioconductor, using custom R algorithms. Genetic markers were called using the Wilcoxon sign rank test using the 10 PM probe intensities for allele 1 versus the 10 PM probe intensities for allele 2 . EST-based SNPs were called on the basis of the mean allelic ratio of the 2 pairs of PM probes (ratio ≥ 1.5). SFP calls were made based on the distances in the background corrected (RMA) and normalized (quantile) intensity value of a polymorphic probe in a progeny hybridization to its counterparts in parental hybridizations.HXGPRT: hypoxanthine-xanthine-guanine phosphoribosyl transferase; RMA: robust multi-array average; SFP: single feature polymorphism; SNP: single nucleotide polymorphism; PM: perfect match;AB, DK, MJ, and DSR conceived and participated in the design of the platform; PHD, MB, FD, and DS carried out wet experiments. AB, PHD, MB, FC, MWW, and DSR conducted the analysis of chip data. The manuscript was drafted by AB, PHD, and DSR. The final version was read and approved by all authors.Comparison of background correction methods. The mismatch (MM) probes included for all genes on chromosome Ib, and 3,000 surrogate mismatch probes (SMM), allows comparison of PM-only , PM-MM (grey), and PM-SMM (red) methods for background correction (Prugniaud strain RNA). Background correction using the SMM probes was determined by subtracting the trimmed mean of all surrogate probes with matching GC content. Different PM-SMM trajectories observed at low PM intensity reflect increased hybridization background in high GC content probes (inset). Over most of the dynamic range, all three methods yield similar results, although PM-MM tends to attenuate signal as MM probes capture true signal in addition to background. PM-only methods may lose some sensitivity at the lower end of the dynamic range relative to either SMM or MM-corrected methods, which appear to be comparable in their performance.Click here for fileToxoplasma gene chip expression valuesValidation of . T. gondii SAGE, EST, and glass array data was mapped onto gene models, and binned abundance calls  for the lytic (tachyzoite) stage were made for an aggregate total of 3,077 genes, as described under Materials & Methods. Expression values for Prugniaud-strain tachyzoites determined using the photolithographic oligonucleotide microarray described in this report are highly concordant with results from all other platforms, with an average median difference between successive bins of ~4-fold. Horizontal black bars indicate median values; boxes show lower and upper quartiles.Click here for fileToxoplasma SNP Map. Whole genome alignments of representatives of the three main clonal lineages  were used to uncover biallelic SNPs. Each discovered SNP is classified as type I, II, or III, referring to the strain that contains the minor allele. Each stacked bar represents the SNP type counts in a 2,500 nt non-overlapping bin. The vertical distance between chromosomes corresponds to 250 SNPs.Click here for fileScreening SNPs. SNPs were screened for predictable behavior using hybridizations with RH-, Prugniaud-, and VEG-strain parasites. A, 141 genetic markers (61%) resulted in correct allele calls  in all 3 screening hybridizations. B, 1,600 EST-based SNPs (46%) were carried forward after screening . C, 90% of P. falciparum SNPs are called correctly . D, 3,554 SFPs (33%) passed filtering based on their behavior in pairwise comparisons in the three screening hybridizations. For example, type I SFPs (polymorphic probes containing a type I SNP) that were carried forward had significantly suppressed probe intensities in type I vs. type II or type III comparisons, but displayed no significant difference in a type II vs. type III comparison.Click here for fileMultiplexing experiments. The ability to reliably differentiate alleles of RFLP genetic markers that fall within coding regions using RNA hybridization data is illustrated . For example, the type I RH strain correctly exhibits high relative minor allele strength ) for most type I SNPs, but not for type II or type II. In addition, miscall rates are very low when the marker is close to the 3-prime end of the gene, but rise appreciably after ~1000 bp.Click here for fileTiling density for SNP discovery. The ability to detect known homozygous mouse SNPs decreases with increasing distance between the centers of successive probes, as illustrated by the area under the curve (AUC) of the ROC measurements derived from a custom SNP classifier applied to each gap size. A 2-bp tiling strategy, with adjacent probes on alternate strands, offers near perfect SNP detection. The inset table lists the genomic loci that were tiled.Click here for fileProbe density for exon-level analysis. HGU95 spike-in data (Affymetrix) was used to test the effects of decreasing probe number on present/absent calls using the MAS5 algorithm. Five probes offer reliable transcript detection across a dynamic range ≥8 pM; as the median exon size in T. gondii is 171 bp (inset), a tiling density of 35 bp was selected for exon discovery probes. In order to err on the side of conservatism, six probes were selected for the 'all exon' probesets on chromosome Ib.Click here for fileHuman and mouse genes included on the array. The table describes human and mouse probesets available on commercial Affymetrix arrays that were included on the T. gondii microarray.Click here for file