In genome-wide association studies, high-level statistical analyses rely on the validity of the called genotypes, and different genotype calling algorithms (GCAs) have been proposed. We compared the GCAs Bayesian robust linear modeling using Mahalanobis distance (BRLMM), Chiamo++, and JAPL using the autosomal single-nucleotide polymorphisms (SNPs) from the 500 k Affymetrix Array Set data of the Framingham Heart Study as provided for the Genetic Analysis Workshop 16, Problem 2, and prepared standard quality control (sQC) for each algorithm. Using JAPL, most individuals were retained for the analysis. The lowest number of SNPs that successfully passed sQC was observed for BRLMM and the highest for Chiamo++. All three GCAs fulfilled all sQC criteria for 79% of the SNPs but at least one GCA failed for 18% of the SNPs. Previously undetected errors in strand coding were identified by comparing genotype concordances between GCAs. Concordance dropped with the number of GCAs failing sQC. We conclude that JAPL and Chiamo++ are the GCAs of choice if the aim is to keep as many subjects and SNPs as possible, respectively. A crucial step in the data generation process of genome-wide association studies is genotype calling. Here, qualitative genotypes are derived from measured signal intensities of the two alleles of a single-nucleotide polymorphism (SNP). Because missing or erroneous genotypes can flaw the high-level statistical association analysis, a series of different genotype-calling algorithms (GCAs) have been proposed .The outcome of these GCAs can differ substantially . We ther® Human Mapping 500 k Array Set.Hybridization probe intensity CEL data of 6,848 participants in the Framingham Heart Study was provided as Problem 2 for the Genetic Analysis Workshop 16 (GAW16) . GenotypWe limited our analyses to the 2,466 participants of the 332 families with complete genotypes in the nuclear families.Three different GCAs were considered for comparison. Bayesian robust linear modeling using Mahalanobis distance (BRLMM) has been recommended by the manufacturer for the 500 k Array Set . Chiamo+Only those SNPs provided in the GAW16 BRLMM data set were used for further analysis. Furthermore, X-chromosomal SNPs and SNPs with different strand codings in the GCAs were excluded.For the remaining SNPs, deFinetti triangles presenting allele and genotype distributions were generated for all three GCAs  was <1%, or if the missing frequency (MiF) was <2%.Samples with a call fraction <97% were excluded, and sQC was performed separately for all three GCAs. Specifically, SNPs were excluded if the exact lack-of-fit test for Hardy-Weinberg equilibrium (HWE) revealed We defined seven different groups of SNPs after sQC according to Figure We termed an individual to be concordant for the considered GCAs if the GCAs yielded the same result (genotype or missing) for the specific SNP. We then derived concordance fractions on the SNP level. Confidence intervals were estimated as 95% exact Blyth-Still-Casella confidence intervals (95% CI).Analyses were performed in the statistical package R, version 2.7.1, with the GenABEL, version 1.4-1 library . The anaA total of 486,605 SNPs were provided for GAW16. We excluded 63,950 SNPs because of different strand coding or allele flips, leaving 422,655 SNPs for further analysis. The deFinetti triangles  more often than BRLMM and JAPL . These SNPs fell in one of two groups: The first had a MAF lower than 20% but only 2.5% heterozygous subjects. For the second, one could imagine that they form the rudiment of a second curve with maximum at the point . As in the first group of SNPs, this curve was only observed for SNPs with MAF<20%. Because this curve is usually seen for X-linked SNPs if males and females are pooled, we investigated the genotype frequencies for a series of these SNPs by sex but we detected no differences.In contrast, Chiamo++ more often led to genotype distributions at the boundary of the deFinetti triangle than BRLMM and JAPL , where the boundary was defined by a frequency of <2 × 10The call fraction was >0.97 for all individuals in JAPL. Five subjects were excluded using Chiamo++. BRLMM called these five participants and an additional four with a fraction of <0.97. This finding is in line with the conclusions drawn by the developers of JAPL, who state that their algorithm was specifically designed to deal with uncertain genotypes which are said to be missing by other GCAs .Based on the subset of individuals who passed sQC for all three GCAs, the observations from the deFinetti triangles are confirmed by results of sQC Table : for ChiIn total, the highest number of SNPs fulfilling all sQC criteria was obtained using Chiamo++ (77.36%) and the smallest number was obtained using BRLMM (74.05%). 351,207 SNPs (83.10%) passed the sQC in at least one algorithm. Of these SNPs, 78.55% fulfilled all sQC criteria for all three GCAs jointly  for SNPs that did not pass sQC in all considered GCAs. This might be due to the fact of disagreement in calling genotypes as "missing".Among the investigated GCAs, JAPL is recommended if the aim is to keep as many subjects as possible for analysis. Chiamo++ would be preferred if the number of SNPs for further analysis needs to be high. By comparing the concordances between different calling algorithms, otherwise-undetected errors in strand coding were identified. Considering SNPs that did not pass the sQC in at least one of the considered algorithms, the concordance frequency is considerably lower.BRLMM: Bayesian robust linear modeling using Mahalanobis distance; EM: Expectation maximization; GAW16: Genetic Analysis Workshop 16; GCA: Genotype-calling algorithms; HWE: Hardy-Weinberg equilibrium; MAF: Minor allele frequency; MiF: Missing frequency; SNP: Single-nucleotide polymorphism; sQC: Standard quality control.The authors declare that they have no competing interests.MV participated in the design of the study, did the calling, prepared the figures, and drafted the manuscript. AS participated in the design of the study and figures, and did the sQC. IRK participated in the design of the study and its coordination and helped to draft the manuscript. AZ conceived of the study, participated in its design, and helped to draft the manuscript. All authors read and approved the final manuscript.


Large-scale genome-wide association studies are increasingly common, due in large part to recent advances in genotyping technology. Despite a dramatic drop in genotyping costs, it is still too expensive to genotype thousands of individuals for hundreds of thousands single-nucleotide polymorphisms (SNPs) for large-scale whole-genome association studies for many researchers. A two-stage design has been a promising alternative: in the first stage, only a small fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in the second stage. In this report, I developed an approach to select and prioritize SNPs for association studies with a two-stage or multi-stage design. In the first stage, the method not only evaluates associations of SNPs with the disease of interest, it also explicitly explores correlations among SNPs. I applied the approach on the simulated Genetic Analysis Workshop 15 Problem 3 data sets, which have modeled the complex genetic architecture of rheumatoid arthritis. Results show that the method can greatly reduce the number of SNPs required in later stage(s) without sacrificing mapping precision. A two-stage design has been a promising strategy for genome-wide association studies -5, primap-values, which basically assumes all single-nucleotide polymorphisms (SNPs) are independent and in linkage equilibrium. Based on data from the HapMap project [, the assumption of linkage equilibrium is unlikely to hold when using SNP arrays with hundreds of thousands markers because many nearby SNPs are in high linkage disequilibrium. The Bonferroni correction is highly conservative and may partially explain the preliminary negative results from the CGEMS project: none of the 300 K SNPs are significantly associated with prostate cancer at a genome level of 0.05 after the Bonferroni correction. Permutations tests can be performed for the replication-based analysis, but it is not straightforward to extend permutation tests to joint analysis [Generally speaking, there are three test strategies that can be adopted in stage two, namely, replication-based analysis, joint analysis assuming homogeneity between stages, and joint analysis that allows heterogeneity between stages -4. In a  project  and someanalysis . In addiα (such as 0.05 without the Bonferroni correction) in stage one is used to ensure that no true signals will be filtered out. On average, Mα SNPs will be selected to stage two, where M is the total number of markers in stage one which is 300 K or 500 K. However, most of the Mα SNPs are false positives with respect to the disease in study. Furthermore, if a SNP shows a moderate association with the disease and has been selected in stage one, it is highly likely that its nearby SNPs that are in high LD with it will also be selected to stage two. In other words, many of the Mα SNPs may also have high LD. Therefore, I propose to apply a clustering algorithm to all SNPs that have been selected from stage one to explore the dependence relationship among the Mα SNPs. More specifically, all the Mα SNPs are first ranked according to their significance levels. Starting from the SNP with the highest rank , all of the SNPs that are highly correlated with it (with the pairwise LD D' larger than a predefined threshold) will be grouped as a cluster conditional on the requirement that they are within a certain physical distance (which is a parameter). The cluster will be represented by the SNP with the highest rank. The process will continue in the decreasing order of SNP ranking for all SNPs that have not yet been clustered, until all the SNPs have been processed. At the end, the algorithm returns a set of clusters, each represented by a SNP with the highest rank within its cluster. A SNP can only be grouped to a nearby representer (defined by the distance threshold) to eliminate false signals of LD that can occur between two SNPs by chance. SNPs in a cluster are not necessarily consecutive. Clearly, the above clustering algorithm can reduce the number of SNPs to be considered in stage two and its effectiveness depends upon correlations among SNPs, as well as the two parameters. Joint analysis assuming heterogeneity is adopted in this study because it has higher power than replication-based analysis and it requires fewer assumptions. A proper significance level has to be derived for such an analysis. In general, suppose a liberal significance level α with the critical value c1 is used in stage one. Let X1 denote the χ2 test statistic based on samples in stage one. Only markers with X1 > c1 will be further considered in stage two. For a marker to be genotyped in stage two, let X2 denote the test statistic using samples from stage two. Under the null hypothesis of no association, X1 and X2 are independent and follow χ2 distributions with 1 degree of freedom. For the joint analysis, the statistic X is equal to the summation of X1 and X2. Notice that X and X1 are not independent even under the null distribution. Let f(x) and F(x) denote the probability density function and the cumulative distribution function of χ2 distribution with 1 degree of freedom, the significance level of X with a value c can be calculated based on the following formula through numerical methods:The algorithm was developed based on the following observations. For high-density SNP markers , it is likely that nearby SNPs are in linkage disequilibrium (LD). In a two-stage design, usually a liberal significance level HLA-DRB1 locus and Locus D. The total number of SNPs is 17,820, with an average inter-marker interval of 10 kbp, which corresponds to a 300 K array. As a comparison, I also applied the algorithm on the SNP data of chromosome 18 that mimic a 10 K SNP chip set. SNP data on chromosome 1 were used to evaluate the type I errors. I first constructed data sets for a case-control study with a two-stage design. For each data set, only one affected child was randomly chosen as a case subject from each nuclear family with an affected sib pair. One child is selected as a control subject from each normal family. Therefore, all cases and controls are independent. Because some alleles around the HLA-DRB1 locus have very strong effects on the disease status, only a very small fraction of cases and controls were randomly selected for testing from all subjects (1500 cases and 2000 controls). Let n denote the total number of subjects tested in stage one and stage two together, where an equal number of cases and controls were tested. For chromosome 6, n took the values of 100, 200, and 300. Let f denote the fraction of the number of subjects in stage one, and f took the values of 0.3, 0.4, and 0.5 in this experiment. I assumed only nf subjects were genotyped for all m SNPs in stage one. The Pearson χ2 statistic was used to select a subset of k SNPs for stage two based on a significance level of 0.05 without adjustments. The clustering algorithm was then applied to the k SNPs with a LD threshold D' = 0.8 and a distance threshold of 100 kbp for chromosome 6. For each parameter combination, 100 independent replicates were randomly sampled from the original data sets. I have investigated and compared the power, costs, significance levels, and prediction errors (the distances from the predicted locations to the true gene location) of three methods, namely, the one-stage design using all data, the two-stage design without clustering, and the two-stage design with clustering. For chromosome 18 and chromosome 1, because the total number of markers on each chromosome is much smaller than the number of SNPs on chromosome 6, and the effect of Locus E on chromosome 18 is much smaller than the HLA-DRB1 locus, a different set of parameters has been used .I applied the above clustering algorithm within a two-stage design using the joint analysis on the simulated data sets of Problem 3. All analyses were carried out with knowledge of true disease gene locations. I first tested the above algorithm on the dense SNP set on chromosome 6, which contains the HLA-DRB1 locus, all three methods have returned more than one significant SNPs that are close to the locus, even with as few as 100 individuals , even after being adjusted by the number of total tests. The power of joint analysis with two-stage designs is close to that of the single stage design. The two-stage design with clustering achieves slightly better results than the original two-stage design.Because of the unusually strong effect of the HLA-DRB1 locus. Interesting, there are no significant differences between the three methods  from the location of the ds Table . AlthougBy clustering nearby SNPs that are in high LD, one can significantly reduce genotyping costs in stage two. On average, the number of SNPs for the second stage with clustering (781 ± 12) is only about one half of the number of SNPs without clustering (1846 ± 72). Those numbers are very robust with regard to sample sizes and the fractions of samples being genotyped in stage one. The costs for the two methods with a two-stage design are the same for stage one . And the cost of the two-stage design with clustering is about half of it without clustering. The overall saving depends on the cost ratio of genotyping a single SNP in stage one and in stage two.HLA-DRB1 locus that contributes to the development of rheumatoid arthritis (RA). But the disease allele has a very low frequency (0.0083) the above procedure cannot detect the signal with small sample sizes .There is another locus on chromosome 6 about 5 cM away from the The same procedure has been applied on chromosome 18 (with 303 markers) using a different set of parameters. Results show that almost no SNPs that are significant in stage one can be grouped together when the LD threshold D' = 0.8, even when the distance threshold as large as 5 Mbp. Therefore, the above approach is effective when using very dense SNP sets such as 300 K or 500 K arrays.No genes on chromosome 1 have effects on RA in the simulated data, so it was taken as a data set in evaluating type I errors for the three methods. Because this is another data set mimicking a 10 K SNP chip, the results from the two-stage designs with and without clustering are quite similar, and both methods have correct type I errors at both 0.05 and 0.01 level . The one-stage design using Bonferroni correction has correct but much lower error rates, which means a Bonferroni correction is conservative even for SNPs with low correlations.For very dense SNP arrays, it is highly likely that SNPs within a short distance are not independent from each other. In this report, I have investigated a strategy of evaluating SNP correlations within a two-stage design using case-control samples, and have applied the algorithm on the Problem 3 simulated data sets of GAW15. The strategy can reduce the genotyping costs in stage two by half with similar or better performance  on data sets based on 300 K SNP arrays. Two-stage designs are promising for genome-wide association studies. As illustrated in this paper, advanced processing in stage one can further reduce genotyping costs in later stages without sacrificing mapping precision. A potential drawback using SNPs with little redundancy is that a failed assay in stage two for a marker SNP will lose information on a whole region of the genome.The author(s) declare that they have no competing interests.


A newly developed maize Illumina GoldenGate Assay with 1536 SNPs from 582 loci was used to genotype a highly diverse global maize collection of 632 inbred lines from temperate, tropical, and subtropical public breeding programs. A total of 1229 informative SNPs and 1749 haplotypes within 327 loci was used to estimate the genetic diversity, population structure, and familial relatedness. Population structure identified tropical and temperate subgroups, and complex familial relationships were identified within the global collection. Linkage disequilibrium (LD) was measured overall and within chromosomes, allelic frequency groups, subgroups related by geographic origin, and subgroups of different sample sizes. The LD decay distance differed among chromosomes and ranged between 1 to 10 kb. The LD distance increased with the increase of minor allelic frequency (MAF), and with smaller sample sizes, encouraging caution when using too few lines in a study. The LD decay distance was much higher in temperate than in tropical and subtropical lines, because tropical and subtropical lines are more diverse and contain more rare alleles than temperate lines. A core set of inbreds was defined based on haplotypes, and 60 lines capture 90% of the haplotype diversity of the entire panel. The defined core sets and the entire collection can be used widely for different research targets. Globally, maize is one of the most important food, feed, and industrial crops. Continued improvement and cultivar release with new target traits will require the most precise manipulation possible of the estimated 59,000 genes in the maize genome http://www.generationcp.org/subprogramme1.php].Inbred lines selected from hybrids, populations or landraces are the fundamental resources for maize breeding and genetic research. Molecular markers such as restriction fragment length polymorphisms and simple sequence repeats (SSRs) or microsatellites were widely used to estimate the relationships among diverse lines. Marker-based relationships have been used in breeding programs to estimate the coefficient of pedigree and to establish heterotic groups and patterns for hybrid breeding lycopene epsilon cyclase, related with provitamin A content in the maize kernel. The diversity of alleles of this gene was investigated using an allele mining strategy and has demonstrated that there is a much higher frequency for alleles favorable to human health in tropical lines than in temperate lines A number of studies have been presented for marker based diversity investigation focusing on specific germplasm with limited sample sizes , including U.S. Corn Belt lines 2r, the squared value of the correlation coefficient of the allelic states of two given polymorphic loci, is the most commonly used. Levels of LD in maize have been reported in multiple studies. Tenaillon et al., Association mapping using diverse genotypes in plants is a new and powerful tool that has begun to yield promising results in identifying the functional variation in both known and unknown genes associated with important agronomic and economic traits . The breakdown of linkage disequilibrium (LD) across the genome of an organism is a key factor affecting the precision and accuracy provided by association mapping, and is in turn affected by many genetic and non-genetic factors, including recombination, drift, selection, mating pattern, and admixture www.panzea.org]. Several high throughput genotyping platforms have been developed for commercial use Another class of marker, single nucleotide polymorphisms (SNPs) is present in all plant and animal genomes in huge numbers. Nearly one million maize SNPs are currently available in public databases [With the continuing efforts of over 40 years, CIMMYT has selected and released more than 500 inbred maize lines . These were selected from pools and populations of highly variable germplasm, including landraces from all over Latin America, and some germplasm from temperate populations mixed in as well. Studies have already been performed to investigate the diversity of some of the selected inbred lines Of the 1536 maize SNPs present in our oligo pool assay (OPA), 1311 SNPs (85%) were successfully called in the 632 lines with less than 20% missing data. Eighty two of the 1311 successful assays were monomorphic in all 632 lines, which may have been caused due to errors in sequencing and/or SNP development. A final total of 1229 SNPs were used for further data analysis. Heterozygosity ranged from 0 to 9.9%, with an average of 2.5%, well within expected ranges for residual heterozygosity found in inbred maize lines.One sample (CML312) was repeated in four different plates as a control. Over 98% of the data points were identically scored in the four repeated samples. Only one type of genotyping error was found, where a SNP was called as homozygous in one plate but heterozygous in the repeated sample in another plate. For any given pair of repeated samples, the genotyping errors ranged from 0.08% to 1.7% with an average 0.8%; this is comparable with other maize SNP assays in silico onto the maize genome and the detailed map and information is available at http://cmap.cimmyt.org/cgi-bin/cmap/viewer?data_source=CMAPsaved_link_id=5. All SNPs were well distributed across the 10 chromosomes, and coverage ranged from 65 SNPs on chromosome 6 to 211 SNPs on chromosome 1. This represented 538 loci with an average 2.3 SNPs per locus , providing an opportunity to investigate LD at the whole genome level, as well as to study the effect of the following factors on LD: chromosomes, MAF, sample size, and subgroups of different geographic origins.in silico to the maize physical map were used for this analysis . Mean 2r increased significantly with MAF. For example, from 0–0.5 kb, mean 2r for MAF≥0.05 was 0.22, but increased to 0.27 and 0.38 using data with MAF≥0.1 and 0.2, respectively.Three different minimum allelic frequency thresholds  were used to study the effects of MAF on the extent of LD. As can be seen in Five subsets of different sample sizes  were randomly selected from the entire set with ten repetitions each using SNPs with MAF≥0.05 to study the effect of sample size on the extent of LD. As shown in 2r estimates between the randomly selected subsets and the entire set increase with increasing subsample size. Correlations between ten selected samples of n = 25 and the entire sample ranged from 0.51 to 0.57 with an average of 0.55. When sample size was increased to 50, the average correlation value increased to 0.75. Correlations reach 0.91 when the selected sample size is increased to 400 , have been released and used extensively to develop new hybrid maize varieties. In this study, we compared the genetic identity of 21 CML lines with same name but maintained in different labs for more than 30 years. Although 17 of the lines were still genetically similar, we found 4 lines with a mismatch ratio more than 10%, two of which reached nearly 20% mismatch. Genetically, this is a very low level of similarity for supposedly identical lines, suggesting that many heterozygous loci existed in the maize inbreds developed by CIMMYT which might have become fixed differently in different institutes; or that outcrossing has occurred at one or both institutions during seed increase and regeneration. This serves as a reminder for much care to be taken for future germplasm exchange and conservation, especially for genetic research.2r increases with the increase of MAF, and a similar phenomenon was also observed in other species 2r. In this study, removing SNPs with very low MAFs also lead to lower numbers of SNPs available for study, which can also lead to bias of LD estimates. A small sample size (e.g. n = 25) can also lead to the biased estimation for LD. However, there are no significant differences for the mean 2r when sample sizes are over 50, especially when the given extent interval of LD is less than 2 kb , mean han 2 kb . Howeverhan 2 kb , becausehan 2 kb .Genome-wide association is a powerful tool that is widely used in human genetic studies 2r can be used predict the power for genome-wide association studies that given numbers of markers will have. The average decline of LD distance for single marker association mapping in this panel is 5–10 kb (2r is only 0.077 (median 2r = 0.033), implying that statistical power may be even lower for detecting the nucleotide changes encoding quantitative traits based on weak correlations between adjacent SNPs. Increasing marker density ten times to 4,800,000 may not increase power significantly since the mean 2r only increases to 0.207 (median 2r = 0.096). In addition, considerable variation exists for 2r within a given LD distance. For example, the 50th percentile for 2r is very small , and leA previous simulation study showed that more power was achieved by increasing the number of individuals in the population than by increasing the SNP density within a candidate gene In this study, we used 632 diverse lines and 1229 SNPs derived from 538 loci to estimate the LD of maize in the whole genome level. The LD decay distance differed among chromosomes and ranged between 1 to 10 kb, increased with the increase of MAF and with smaller sample sizes, was much higher in temperate than in tropical and subtropical lines. These results provide useful information for understanding the maize genome structure and further genome-wide association studies.Two independent panels of diverse/commercial inbred lines were included: one from Cornell University containing 281 temperate  maize inbreds http://www.panzea.org/db/gateway?file_id=2007_candidate_snp).All SNPs were developed from the coding regions of 582 candidate genes, about half of which comprised putative drought-related loci. To identify SNPs from the genomic sequences, primers were designed based on the sequences of selected loci and used to amplify products of 600–1000 bp in four test lines. Primers that amplified successfully were used to amplify the same genes in the 27 diverse inbred lines used as parents of the Nested Association Mapping (NAM) population The SNP genotyping was performed on an Illumina BeadStation 500 G  at the Cornell University Life Sciences Core Laboratory according to the manufacture's protocol http://www2.genome.arizona.edu/genomes/maize. Only the top blast-hits against the reference sequences were considered using an e-value threshold of e−18. Blast matches to multiple loci, with the same top e-value were all selected for further interrogation. Of the 732 reference sequences, 13 had multiple hits to the genome and 3 matched contigs with unknown locations. A total of 1483 unique SNPs have therefore been mapped in silico onto the maize genome. A set of unique loci was constructed from the 1229 successfully called SNPs. The relative distance for each SNP was summed and SNPs within a total of 10 kb distance were assigned to the same locus. This was done because some genes were very long and had been sequenced in two or three amplicons; however, none of the genes were longer than 10 Kb and this distance would keep SNPs from separate amplicons from the same gene  together in the same locus. A total of 538 loci were identified. A total of 211 SNPs were unlinked to other SNPs, and the rest formed linked groups of 2 or more SNPs from contiguous DNA sequences , of which there were 327 , and which group the line was assigned to .(0.75 MB DOC)Click here for additional data file.


Genome-wide association studies (GWAS) have helped to reveal genetic mechanisms of complex diseases. Although commonly used genotyping technology enables us to determine up to a million single-nucleotide polymorphisms (SNPs), causative variants are typically not genotyped directly. A favored approach to increase the power of genome-wide association studies is to impute the untyped SNPs using more complete genotype data of a reference population.Random forests (RF) provides an internal method for replacing missing genotypes. A forest of classification trees is used to determine similarities of probands regarding their genotypes. These proximities are then used to impute genotypes of untyped SNPs.We evaluated this approach using genotype data of the Framingham Heart Study provided as Problem 2 for Genetic Analysis Workshop 16 and the Caucasian HapMap samples as reference population. Our results indicate that RFs are faster but less accurate than alternative approaches for imputing untyped SNPs. Thus, the majority of all known SNPs in the genome are evaluated only indirectly with commonly used genotyping platforms. Consequently, today's GWAS are usually not able to genotype causal variants but will detect association with a nearby SNP in high linkage disequilibrium (LD). Although this approach has proved successful in many cases, it is still likely that a great number of causal variations are yet undetected and that the power of GWAS could be increased by performing statistical tests with disease influencing SNPs directly [Recently, genome-wide association studies (GWAS) have expanded our knowledge about genomic variants that influence susceptibility to complex diseases such as myocardial infarction ,2. One iOne preferred approach to increase the power of GWAS is to combine data from several studies , thus inIn our work, we applied the random forests (RF) imputation approach to untyped SNPs . We evalRF is a data-mining method that is able to produce accurate classifiers even when many variables are observed in relatively few individuals. Furthermore, it provides estimates of variable importance, generates an unbiased generalization error estimate, and includes a technique for estimating missing data. Using its classifying power, we have recently shown that a screening of SNPs by RF is suitable to detect promising candidate SNPs in GWAS for complex diseases .A specific feature of RF is the ability to replace missing values through an iterative process . To use Thus, the original method was modified accordingly. The procedure for imputing missing SNPs comprises several steps as shown in Figure The data is enriched in two successive steps as follows:a) The original data is merged with a subset of HapMap  genotypeb) The data is subsequently modified as follows: to begin, the original data set that contains only genotype information is considered as Class 1. A new synthetic data set with the same number of probands and SNPs is created and labelled as Class 2. This synthetic data is created by sampling at random without replacement from the univariate distributions of the original data. The sampling is separately performed for each SNP. Thus, each SNP has the same univariate distribution as the corresponding SNP in the original data. The random SNPs of Class 2 are independently distributed and contain no dependency structure . The oriMISSING.Missing and undefined genotypes are internally marked with the label Missing values of each SNP are roughly imputed by its median value. This initial crude imputation is essential because RF cannot handle missing data .A classification forest is grown. The trees are built on the new data set created in Step 1.An important part of the RF imputation method is the proximity matrix, which contains the pair-wise similarities between all pairs of probands. Specifically, the proximities are determined as follows: first, they are set to zero. Then, each proband is classified by all trees in the forest. For each tree, if two probands are evaluated by exactly the same series of decision rules, their proximity is increased by one. A detailed description of decision tree rules and classification is given in Breiman et al. .MISSING are re-estimated. The updating is separately performed for each SNP. The new value is calculated as follows: at a specific SNP, each proband holds exactly one genotype value of the set 0, 1, or 2. This also applies to former missing genotypes that were roughly imputed or updated during a previous iteration. For each proband with a genotype marked as MISSING, the new genotype is calculated using a weighted average of the genotypes of remaining probands. Each weight is calculated based on the proximity between the two samples as determined in Step 5.Genotypes internally marked as It is recommended to perform Steps 4-6 at most five times .The resulting data set of this iterative procedure consists of HapMap data, imputed original data, and synthetic data. The imputed original data set is extracted and the imputation is finished.Algorithm 1 was implemented in C++ language.The assessment of the imputation quality was performed as follows:1) A subset of the Framingham Heart Study  genotype2) From all available SNPs in the original data set, 10% were drawn without replacement. All genotypes of these SNPs were deleted in this data set to mimic a situation with untyped SNPs.3) Imputation of the deleted SNPs was performed as described in Algorithm 1.4) For each SNP, the imputed genotypes were compared with the corresponding original genotypes. The imputation accuracy of a SNP equals the number of correctly imputed genotypes divided by the number of all imputed genotypes. The result reflects the quality of the SNP's imputation.Quality and computing time of the imputation depend primarily on two parameters, namely, the number of trees in each forest and the number of iterations in Algorithm 1. To obtain the optimal trade-off between computing time and imputation quality, several RF imputation runs were performed using different parameter settings. In addition, a potential correlation of SNP imputation quality with minor allele frequency (MAF) was subsequently investigated.As a standard approach, the untyped SNPs were also imputed using the computer program IMPUTE  using de® Human Mapping 500k Array Set  as recommended [p-value < 0.0001 for deviation from Hardy-Weinberg equilibrium in control group were excluded, resulting in 336,206 SNPs. Finally, only SNPs of chromosome 22 were selected. The resulting data contained the genotypes of 3,775 SNPs. The mean distance between two adjacent SNPs amounted to 3488 bp. About 10% (n = 376) of the SNPs were deleted to represent untyped SNPs in a real-world data set as previously described. The mean MAF of SNPs was 0.2286, with a standard deviation of 0.1348. The minimal MAF was 0.0134 and MAF maximum was 0.4993. The mean distance between an untyped SNP and the nearest genotyped SNP amounted to 3916 bp. For reference HapMap data [Data from 6752 participants in the Framingham Heart Study  were pro Set 488,46 SNPs aommended . SNPs wiThe optimal trade-off between computing time and imputation quality was obtained by using 300 trees and five iterations. In this setting, RF imputation required 5 minutes on a quad-core computer with a 2.33 GHz processor. The mean accuracy amounted to 62.70% with a standard deviation of 17.88%. The minimal and maximal accuracy was 34.78% and 97.32%, respectively. Imputation accuracy and MAF of a SNP were found to be strongly correlated as shown in Figure IMPUTE required 20 minutes computing time on a computer with a 2.33 GHz processor. The mean accuracy was 92.62%, with a standard deviation of 10.61%. The minimal and maximal accuracy was 52.49% and 100.00%, respectively. MAF and accuracy were not found to be correlated (Figure The RF imputation procedure consumes an acceptable amount of computing time and imputes considerably faster than the alternative standard approach. An imputation of a full GWAS SNP data set might be feasible even on slow computers.However, this advantage is accompanied by a lower quality of imputation compared to IMPUTE. Obviously, the imputation quality of a SNP strongly depends on its MAF. Further theoretical study is needed to investigate whether the expected imputation accuracy of a SNP can be roughly estimated by calculating the maximum of its genotype frequencies.To conclude, we presented an approach of imputing untyped SNPs using RF. The procedure is computationally feasible. However, for a highly accurate imputation of untyped SNPs, alternative methods may be more appropriate.GAW: Genetic Analysis Workshop; GWAS: Genome-wide association study; LD: Linkage disequilibrium; MAF: Minor allele frequency; RF: Random forests; SNP: Single-nucleotide polymorphisms.The authors declare that they have no competing interests.DFS carried out the analysis, the programming of all C++/C/R program code, and drafted the manuscript. SS merged HapMap data and Framingham Heart Study data and drafted the manuscript. IRK participated in the design and coordination of the study. AZ conceived of the study and finalized the manuscript.


Many common and chronic diseases are influenced at some level by genetic variation. Research done in population genetics, specifically in the area of single nucleotide polymorphisms (SNPs) is critical to understanding human genetic variation. A key element in assessing role of a given SNP is determining if the variation is likely to result in change in function. The SNP Integration Tool (SNPit) is a comprehensive tool that integrates diverse, existing predictors of SNP functionality, providing the user with information for improved association study analysis. To evaluate the SNPit system, we developed an alternative gold standard to measure accuracy using sensitivity and specificity. The results of our evaluation demonstrated that our alternative gold standard produced encouraging results. The mission of population health studies is to promote the public's health and prevent diseases. Nine of the top ten leading causes of mortality in the US have significant genetic components . To illuSingle nucleotide polymorphisms (SNPs) are a powerful tool used in association studies looking at genetic variation. Differences in the genome occur most frequently through SNPs; it has been estimated that between 5 and 15 million common SNPs exist, depending on the population studied . Due to Creating a resource to integrate knowledge of polymorphic positions in the genome with functional predictions will facilitate the identification of causal polymorphisms. Current databases and prediction algorithms can provide annotated information ranging from protein function to transcription factor binding to evolutionary conservation. However, none of these resources attempts to consolidate information from diverse data sources, or attempts to use inference rules to synthesize new knowledge from the existing information. Our system, SNP Integration Tool (SNPit), provides such a tool .There are a limited number of resources out there that integrate SNP resources, an even smaller number of which have had evaluations of their performance. The UCSC Genome Browser  is one oregion for SNPs with strong functional predictions based on data from multiple tools. Using a generalized federated database system approach, SNPit addresses these limitations making it easier to add new sources and adapt to evolving sources. SNPit can thus easily integrate diverse structural, evolutionary, and expression data for SNPs, and can be extended to other data types as they become available. Using SNPit, population geneticists will be able to more efficiently identify candidate causative genomic variations for human disease after an initial statistical association has been found.In all these systems, only subsets of all possible SNP predictors are examined and none of the systems evaluate their performance regarding the functional annotation of SNPs. In addition, all of these annotation systems use a local data warehouse approach to storing data, limiting both its extensibility and accuracy in terms of up-to-date information. Finally, though there are numerous databases available that catalogue specific parameters of genetic variation data, there is no central way to query a SNPit is built on the BioMediator foundation . BioMediCurrently, the SNPit integration system has been implemented across several biological domains, linking data related to functional SNPs from sources accessible through BioMediator. BioMediator has interfaces to over 15 public databases  as well as many private databases of experimental results  .The central element to BioMediator's generalizability is the source knowledge base (SKB). The SKB includes descriptions of the data sources, mappings from the source to the mediated schema, and the mediated schema itself. The mediated schema is a general outline that incorporates all the common objects and mappings for the data sources. The SKB can be customized for the various end users. The other three components to the system include: 1) generalized wrappers that translate the data sources syntactically; 2) a metawrapper that goes between the data source and the user query and translates semantically; and 3) the query processor which allows users to query against the mediated schema  synonymous/non-synonymous, b) splicing, and c) regulatory SNPs. Synonymous/nonsynonymous SNPs occur in the coding region of the genome and affect whether or not the amino acid is changed (non-synonymous being a change in amino acid). Splicing SNPs occur at the intron/exon boundary when proteins are being made, a SNP at this location can affect how the intron is spliced out. Regulatory SNPs occur in the promoter of the sequence and are thought to affect gene expression.For example, to evaluate the missense/nonsense category of a particular gene, we retrieved the mutations from HGMD, performed some manipulations so that were properly formatted for SIFT analysis, and then found the corresponding conditional probabilities that predict whether the change in amino acid detrimentally affects the protein. For the probabilities that are low, that would suggest that a change in amino acid would be detrimental. We then chose a threshold (0.2) to determine which SNPs would be scored as a true positive. For the true positives category, we found the unique sequences of those SNPs that are in the same candidate gene as before, and found the corresponding sequences for those rs numbers and submitted them to SNPit's protein function prediction section. The same threshold was selected for determining which SNPs would be deleterious.Using this alternative ("pseudo") gold standard, we measured accuracy initially using sensitivity and specificity of the SNPit system. We submitted the same annotation category to both SNPit and HGMD and measuring the difference between the two. We repeated the process for five different genes . Each gene contained around 700–1,500 SNPs. Each SNP was submitted to both systems.The alternative gold standard uses HGMD as its main source of positive evidence for a functional SNP in one of the three categories and dbSNP as its source for non-evidence (thus a SNP in dbSNP with no corresponding HGMD entry is presumed to be lack of evidence for function). We defined sensitivity as the ability to detect the positive evidence for SNP functionality, and specificity as the ability to detect non-evidence for SNP functionality. A true positive (TP) result would be when the test is correctly classified as functionally relevant, a true negative (TN) result would be when the SNP is correctly classified as being non-functional; false positive (FP) would be when the test is incorrectly classified as being functional, and false negative (FN) would be when the test incorrectly classifies the test as being nonfunctional . HGMD prIn addition, we calculated the sensitivity and specificity of the entire federated system. Rather than separating the SNPs being tested by category as done during the first phase of the evaluation, we tested how the entire federated system returns annotation information on tested SNPs. Thus, in the second phase of the evaluation, we examined whether or not any of the SNPs are flagged by any of the categories as functionally promising. We tested HGMD against all three categories: non-synonymous, regulatory, and splicing, with true positives defined as those that are flagged as interesting by at least one of the three categories. Likewise, we ran those SNPs that are in dbSNP but not in HGMD against all three categories to establish the false negatives.We evaluated five candidate genes: CFTR, BRCA1, tp53, NF1, BRCA2 using both evaluation techniques as described in the Methods section. A portion of the SNPs evaluated in cycle 1 for the splicing category is presented in Figure For the second cycle, in which we combined all the categories together, the results improved Figure . For theThe graph of sensitivity versus specificity for both cycles demonstrated that when you group the categories together, an increase in sensitivity is observed Figure . This inInformatics has opened up the field of genomic research, providing new computational approaches to gathering and analyzing genetic data, both of which can provide more powerful tools to determining genetic influences of diseases. The number of SNP annotation systems that are currently available is limited, and noneof them to our knowledge have had evaluated the functional annotation accuracy of their system. We have built and tested a system (SNPit) that addresses gaps in the existing systems with regards to SNP functional annotation. In addition, in order to evaluate the SNPit system we created an alternative gold standard to measured accuracy using sensitivity and specificity. The initial results are encouraging, though improvements are certainly still possible.Our team continues to improve and expand upon SNPit through the elicitation of user feedback. The SNPit system is currently accessible over the Internet to both population geneticists and molecular biologists. Addition of a logical inference engine (Jess) on top of the BioMediator data integration platform has been shown to successfully predict the functional annotation of anonymous sequences .Furthermore, recent work had demonstrated that support for probabilistic inference on top of the BioMediator data integration system can improve protein functional annotation . Thus, wThe future of both public health and translational informatics lies in the concept of preventive, predictive, and personalized medicine. The discoveries from genetic variation studies can contribute to preventive, predictive, and personalized medical discoveries through a variety of methods including the discovery of new drug targets andearlier identification of subsets of patients for surveillance purposes.Once a genome wide association study is completed, researchers need to understand the biological mechanism between the observed phenotype and the genetic variation, identifying the annotation of the SNPs being studied is an important component. Genetic researchers faced with a long list of SNPs associated statistically with a phenotype need to be able to narrow this list focusing on those SNPs most likely to be causally related to the phenotype. Our system, SNPit, provides such an annotation tool. We define the users of our proposed tool, SNPit, to be genetic epidemiologists, public health geneticists, biological scientists, molecular biologists, and any other scientists that work with genome wide association studies and SNPs.Project name: SNPitSNPit homepage: Recommended internet browsers: Firefox or Internet ExplorerProgramming language: JavaThe authors declare that they have no competing interests.TS designed, implemented, and evaluated the SNPit system and wrote the primary draftsof this paper. CC provided expertise on SNP annotation and guided the development of the project. PTH provided biomedical informatics expertise and guided the development of the project. All authors participated in the drafting of this paper and approved the final draft for submission.


T2 tests, i.e., TH based on allele coding and TG based on genotype coding. The objective was to identify associations between single-nucleotide polymorphisms (SNPs) or markers and rheumatoid arthritis. In specific candidate gene regions, we evaluated the performance of Hotelling's T2 tests. Then Hotelling's T2 tests were used as a tool to identify new regions that contain SNPs showing strong associations with disease. As expected, the strongest association evidence was found in the region of the HLA-DRB1 locus on chromosome 6. In the region of the TRAF1-C5 genes, we identified two SNPs, rs2900180 and rs3761847, with the largest and the second largest TH and TG scores among all SNPs on chromosome 9. We also identified one SNP, rs2476601, in the region of the PTPN22 gene that had the largest TH score and the second largest TG score among all SNPs on chromosome 1. In addition, SNPs with the largest TH score on each chromosome were identified. These SNPs may be located in the regions of genes that have modest effects on rheumatoid arthritis. These regions deserve further investigation.We performed a genome-wide association scan on the North American Rheumatoid Arthritis Consortium (NARAC) data using Hotelling's  T2 tests on known associations. Then, we used the Hotelling's T2 tests to identify additional SNPs that showed strong association with RA. These SNPs are located in regions that are very likely related to the disease and deserve further investigation.Rheumatoid arthritis (RA) is the most common inflammatory joint disease and has an autoimmune etiology. The exact cause of RA is still unknown, but it is well known that RA has a strong genetic component . The HLAT2 test developed by Fan and Knapp [N cases from an affected population and M controls from an unaffected population. When analyzing SNPs, we study bi-allelic markers with two alleles, which we denoted by 1 and 2 that can form three genotypes 1/1, 1/2 and 2/2. Then a coding vector can be defined for each case/control by either i) genotype coding or ii) allele coding. Let Xi and Yj denote the coding vector for the ith case and the jth control, respectively. In our study, Xi = τ for genotype 1/1, Xi = τ for genotype 1/2, and Xi = τ for genotype 2/2 were used in the genotype coding, whereas the allele coding simply counts the number of allele 1 of a genotype. If multiple markers are available, the coding vectors of each case/control can be combined together. For instance, the allele coding vector of a case/control of n SNPs is an n-dimensional vector; and the genotype coding vector of a case/control of n SNPs is 2n-dimensional. For multi-allelic markers, the coding method is described by Fan and Knapp [We used the Hotelling's nd Knapp  and Xionnd Knapp  to analynd Knapp . Let us T2 test statistic [where tatistic  is definT2 for allele coding as TH and the Hotelling's T2 for genotype coding as TG. Assume the sample sizes N and M are large enough so that the large sample theory applies. Under the null hypothesis of no association, the statistic TH (or TG) is asymptotically distributed as a central chi-square χ2 statistic with n (or 2n) degree(s) of freedom if n SNPs are used in the analysis. Under the alternative hypothesis of association, TH (or TG) is asymptotically distributed as a non-central chi-square χ2 statistic [In the following, we will denote the Hotelling's tatistic ,8,10.T2 test statistics, we have developed a SAS Macro (hotel_cc.sas) to implement the method, which is available online [Based on the Hotelling's e online .p-value < 10-5) and dropped all SNPs which are not on the autosomes. After this filtering, 490,613 SNPs on 22 autosomes were used in our analysis. The strongest signal was found in the region of the HLA-DRB1 gene on chromosome 6 at location 32,654,524-32,686,031 bp. In Figure TH and TG scores reached the highest value around the location of 32.5 Mb in the region of HLA-DRB1. Graphs III and IV showed the results in the region of HLA-DRB1 gene (the legend indicates location of the HLA-DRB1 gene). Most of the test scores in the region were very significant.First, we applied the Hotelling's test statistics and performed a genome-wide scan on the NARAC data by analyzing one SNP at a time. The NARAC data contained a total of 2062 individuals (868 cases and 1194 controls). Our analysis used data from 22 autosomes. The RA data of Genetic Analysis Workshop (GAW) 16 included 545,080 SNP-genotype fields from an Illumina 550 k chip . We dropped all SNPs with low call rates (less than 95%) or not in Hardy-Weinberg equilibrium in the controls . These SNPs are all located around the HLA-DRB1 gene. It is interesting that both TH and TG reached the highest scores at the same four SNPs . Interstingly, TH reached the 5th highest score at SNP rs3763309 and the 6th highest at SNP rs3763312; conversely, TG reached the 5th highest score at SNP rs3763312 and the 6th highest at SNP rs3763309. Actually, the order of two SNPs for TH and TG that reached the 7th and 8th highest scores switches too; in addition, TH and TG reached the 9th to 13th highest scores at the same SNPs (data not shown). Thus, the region of the HLA-DRB1 gene contains multiple SNPs that are highly associated with RA. In addition, the p-values of the test TG were generally smaller than those of TH, i.e., the genotype coding test TG leads to more significant results than the allele coding test TH. This observation is consistent with the evidence for non-additivity of DRB1 effects [We present the six SNPs on chromosome 6 with the highest test scores in the left-hand part of Table T2 tests [TH = 650.81 with 7 degrees of freedom , and test score for genotype coding was TG = 694.82 with 35 degrees of freedom . The results were consistent with those using individual SNPs above. On the basis of individual SNP analysis, we performed a forward analysis of multiple SNPs. Using the most significant SNP rs2395175 as baseline, we added one SNP a time for an analysis of two SNPs. We identified that each of three SNPs, rs660895, rs6910071, and rs3763312, contributed significant association in addition to the contribution of the base SNP rs2395175 . Moreover, the most significant result was from the two SNPs rs2395175 and rs660895. Then, we added one SNP at a time to the two most significant SNPs; we found each of the two SNPs, rs6910071 and rs3763312, contributed significant association . Finally, four SNPs together were found to be significantly associated with RA .It is well known that the HLA-DRB1 alleles are associated with RA . We perfT2 tests . The tesTH = 34.21 of SNP rs2900180 was the largest , and TH = 32.17 of SNP rs3761847 was the second largest among all SNPs on chromosome 9 . Other SNPs on chromosome 9 that showed highest scores were also reported on the right-hand side of Table TH were the same as the ones identified via TG . Thus, all association is from SNP rs2900180 in the region of the TRAF1-C5 genes.Graphs V-VIII of Figure TH = 48.88 of rs2476601 was the largest TH score among all SNPs on chromosome 1 , and the TG = 49.99 of rs2476601 was the second largest . In this region, only SNP rs2476601 stood out; other SNPs of top 20 test scores are not located in the region. Hence, we did not analyze multiple SNPs.In the region of the PTPN22 gene on chromosome 1, we identified one SNP (rs2476601) that was reported to be associated with RA by Begovich et al. . The SNPTH and TG were from SNPs located very close to the candidate genes HLA-DRB1, TRAF1-C5, and PTPN22, respectively. Therefore, the SNPs with high test scores are of interest for further investigation to identify genes that have modest effect on RA. In Table TH scores among all SNPs of each chromosome. We chose to present the results based on the test statistic TH, since it is more robust than TG in terms of more stable type I error rates [TH are the same as those identified by PLINK, except rank switches on chromosomes 11 and 16. It is possible that other SNPs that have high test scores are worthy of further study. Due to the limited length of this article, we could not present detailed genome-wide test data here but we will provide detailed information on request.From the results in the candidate regions on chromosomes 6, 9, and 1, we noticed that the highest test scores of or rates . To makeT2 tests do not adjust for population substructures. Thus, some of the strong signals could be due to false positives. Further study is necessary to clarify these issues.The results of our genome-wide scan provided a large number of SNPs that have high test scores. One reason for this is the large sample size of NARAC data. For further study, one may start with the regions that contain the SNPs that have highest test scores, i.e., the regions with strongest signals. The Hotelling's T2 test does not include a multiplicity adjustment. However, we can perform a very conservative (assuming independence of the tests) Bonferroni analysis as follows. In the RA study, we analyzed 490,163 SNPs in total across the whole human genome. Therefore, there are 490,163 TH (or TG) tests. For the most significant SNP (rs2900180) with the highest test scores on chromosome 9 on the right hand-side of Table p-value of TH = 34.21 is 4.95 × 10-9. After adjusting for the multiple tests, the probability to get such a result by chance is 4.95 × 10-9 * 490,163 = 0.0024. Hence, the result is still very significant. For the least significant SNP (rs10985073), the p-value of TH = 28.16 is 1.12 × 10-7. After adjusting for the multiple tests, the probability to get such a result by chance is 1.12 × 10-7 * 490,163 = 0.055, which is close to the 0.05 significance level. The rest of the results in Tables The Hotelling's We compared our results with those in literature -6 and foT2 tests. In the candidate regions of the HLA-DRB1, TRAF1-C5, and PTPN22 genes, we identified SNPs that have the highest test scores across chromosomes 6, 9, and 1, respectively. Given the encouraging results in the candidate gene regions, the regions containing SNPs with high test scores are of interest for further investigation to map genes which have modest effects on RA. We provided the SNPs and their positions that had the largest scores for each chromosome. The regions of these SNPs deserve more investigation to map RA genes.We performed a genome-wide association scan for RA data by applying Hotelling's GAW: Genetic Analysis Workshop; NARAC: North American Rheumatoid Arthritis Consortium; RA: Rheumatoid arthritis; SNP: Single-nucleotide polymorphismThe authors declare that they have no competing interests.CIA and RF conceived the main idea of the study. LC, MZ, and WVC performed statistical analysis under the direction of CIA and RF. LC, CIA, and RF wrote the manuscript. MZ and WVC provided comments to improve the writings of the manuscript. All authors read and approved the final manuscript.


TNNT1], retinol dehydrogenase 13 [RDH13], and TCF3 fusion partner [TFPT]), which are located within the previously identified BCSE-linked region on the telomeric end of bovine chromosome 18 (BTA18). In addition, we developed single nucleotide polymorphisms (SNPs) within these three candidate genes and nine other genes that are contained in this genomic BCSE-region to perform association analyses with BCSE in German Brown cattle.Bilateral convergent strabismus with exophthalmos (BCSE) is a widespread inherited eye defect in several cattle populations. Its progressive condition often leads to blindness in affected cattle and shortens their length of productive life. Furthermore, breeding with BCSE-affected animals is forbidden by the German animal welfare laws. We performed a mutation and association analysis for three candidate genes  products for 17 more genes in the BCSE region and searched them for polymorphisms. All markers detected were genotyped in 48 BCSE-affected German Brown cows and 48 breed and sex matched controls and tested for association with BCSE.We performed cDNA analyses of all three candidate genes using eye tissues of three affected German Brown cows and three unaffected controls. Furthermore, we screened the exonic and the adjacent genomic sequences of TNNT1. Four SNPs were associated with the BCSE phenotype in single marker-trait analyses. These SNPs were located within DHDH (dihydrodiol dehydrogenase dimeric), CPT1C , TNNT1, and NALP7. The marker-trait association for haplotypes including five SNPs of CPT1C, SYT5 (synaptotagmin V), RDH13, and NALP7  revealed a significant association with BCSE. We identified three individual haplotypes that were significantly associated with BCSE. These haplotypes spanned the region from 56.05 Mb to 62.87 Mb on BTA18.In total, we detected 29 SNPs in 12 genes. In the coding sequence of the three candidate genes, we identified 10 exonic SNPs and a new splice variant of The haplotype association analysis corroborated the results of the linkage study that the telomeric end of BTA18 harbors a gene responsible for BCSE and further refines the BCSE region to a 6.82 Mb interval ranging from 56.05 Mb to 62.87 Mb on BTA18. Nervus abducens and Nervus oculomotorius) might be involved. Histopathological examination of the nuclei of abducens nerves showed significant differences between BCSE-affected and unaffected cows in the number of nerve cells. BCSE-affected animals had a decreased number of nerve cells in both nuclear regions (Nuclei n. abducentis dexter and sinister), and this may be related with paresis of the M. rectus lateralis and the lateral parts of M. retractor bulbi, which is also involved in lateral eye movement [Bilateral convergent strabismus with exophthalmos (BCSE) is a heritable eye defect that ccurs in many cattle breeds, e.g., Jersey, German Fleckvieh, German Holstein, and German Brown -4. The imovement . The hismovement .The defect sometimes causes changes in the behavior of the affected animals such as aggressiveness, shying, and panic in everyday situations. The first signs of BCSE can appear as early as the age of six months, but most of the affected animals are not noticed before first breeding. This eye anomaly is incurable .MARC-USDA linkage map) on the telomeric end of BTA18 between the microsatellites, BMS2785 (72.01 cM) and BM6507 (78.84 cM), using linkage and haplotype analysis. The Zmean and LOD score peaked at marker DIK5109 (77.60 cM) [BMS2785) and 63.0 Mb (BM6507). These marker positions were determined using BLAST analysis for Btau_4.0 (Bos taurus genome assembly 4.0).In a previously performed whole genome scan using multipoint non-parametric linkage and haplotype analysis in a total of 159 German Brown cattle, we identified a genomic region harboring a locus responsible for BCSE on bovine chromosome 18 (BTA18) . We mappPOLG [ANT1 [C10orf2 [KIF21A (CFEOM1) on centromeric HSA12q12 [ARIX (CFEOM2) on HSA11q.13.3-q13.4 [CFEOM3 on HSA16q24.2-q24.3 [CFEOM3A on HSA12p11.2-q12 [KIF21A), on BTA15 at 51.34 Mb (ARIX), and on BTA18 from 11.5 to 14.0 Mb (CFEOM3). The loci for DRS were mapped to HSA8q13 (DURS1) [DURS2) [DURS1) and BTA2 between 14.7 and 21.3 Mb (DURS2). Therefore, none of these loci or genes identified for CCDD in humans are mapping within the QTL for BCSE.We could identify misinnervation syndromes in humans with similarities in pathology and clinical features to BCSE in cattle. Progressive external ophthalmoplegia (PEO), Duane retraction syndrome (DRS), and congenital fibrosis of the extraocular muscles (CFEOM) belong to this group of diseases in humans. PEOs are characterized by slowly progressive bilateral immobility of the eyes accompanied by ptosis. The three candidate genes, POLG , ANT1 [9[C10orf2 . CFEOM [[C10orf2  and DRS [C10orf2 ,14 belon[C10orf2 . The var[C10orf2  result fHSA12q12 ,17, ARIX.3-q13.4 , CFEOM3 .2-q24.3 , and CFE11.2-q12 . The bov (DURS1)  and HSA2 [DURS2) . The ortLOC540740 to PRKCG is consistent with the human gene order. The second block between EPN1 (epsin 1) and TFPT  is inverse  compared to the gene order of the human genome assembly 36.2. In our analysis, we considered the interval from LOC540740 (54.98 Mb) to TFPT (63.54 Mb), which included the linked BCSE region and its flanking regions on BTA18.Comparison of the gene order on the telomeric end of BTA18 (Btau_4.0) with the corresponding region on HSA19 (NCBI Build 36.2) showed two blocks of synteny . The genDIK5109.The aim of this study was to identify single nucleotide polymorphisms (SNPs) associated with BCSE within the previously determined BCSE region and within the coding sequence of possible candidate genes contained in this region. Candidate genes were chosen due to their expression profile and their proximity to the microsatellite, TNNT1), is located about 200 kb proximal of DIK5109 at 62.50 Mb. The protein product of TNNT1 is a component of the thin filament of the sarcomere and has the function to prevent actin-myosin interaction in resting muscle. TNNT1 is highly expressed in skeletal muscles [RDH13), is located in close vicinity to TNNT1 at 62.70 Mb. RDH13 belongs to the short-chain dehydrogenases/reductases (SDR) family and is mostly expressed in cranial nerve tissue and in the retina where it was detected in the inner segment of the photoreceptor cells [RDH5 and RDH12 were shown to cause fundus albipunctatus and retinal dystrophy in human, which can be accompanied by strabismus [TFPT), is ubiquitously expressed mainly in the brain, hematopoietic cell lines, and eye tissue.The first candidate gene, troponin T type 1  of three unaffected and three severely affected cows (BCSE stage 3) [For cDNA analysis, we took biopsies from the retina, stage 3) . These sTissue samples were conserved using RNAlater solution (Qiagen). RNA was extracted from the ocular tissues using the Nucleospin RNA II-Kit  and transcribed into cDNA using SuperScript III Reverse Transcriptase .BLAST searches with the corresponding human reference mRNA sequences for TNNT1 (NM_003283), RDH13 (NM_138412) and TFPT (NM_013342). Bos taurus. In addition, we verified the sequence homology between the proteins of the three candidate genes in cattle, mouse, and human using the ClustalW alignment program  archive for ESTs and the bovine genome for annotated genes by cross-species  program .EE371552), isolated from muscle tissue with 89% identity to the human TNNT1 mRNA sequence, and the bovine mRNA of TNNT1 (NM_174474) with an identity of 90% to human TNNT1 mRNA (NM_003283).We found a bovine EST , which cover 77% of the human mRNA sequence with an identity of 88% and the bovine mRNA of RDH13 (NM_001075345). The first EST (DV925005) was isolated from the skin of an embryo and the second (DV828503) from fetal pons.For DV851209, CO881320, and CO873631) that were isolated from brain tissue covering the whole human TFPT mRNA sequence with an identity of 86%. Furthermore, we identified the bovine TFPT employing a genomic BLAST analysis with the bovine mRNA sequence (NM_001075274).We found three overlapping bovine ESTs (Primer3 software (We amplified the cDNA sequence corresponding to the open reading frames (ORF) of the three candidate genes. We used the ESTs and the annotated gene information for primer design with software .RDH13 and the exons of TNNT1 and TFPT, which harbored SNPs detected by cDNA analyses  genomic DNA, 3 µl 10X PCR buffer, 6 µl 10X PCR Enhancer , 0.6 µl (10 µM) of each primer, 0.6 µl dNTPs (10 mM each), and 0.2 µl (5 U/µl) Taq polymerase . The reactions were performed in TProfessional thermocyclers  and started with 5 min initial denaturation at 95 °C followed by 36 cycles at 95 °C for 30 s, optimum annealing temperature (Ta) around 58–60 °C for 1 min, and extension at 72 °C for 45 s. The PCR was completed with a final cooling at 4 °C for 10 min. After purification of the PCR products with MinElute 96 UF Plate (Qiagen), the amplicons were directly sequenced with the DYEnamic ET Terminator Cycle Sequencing kit  on a MegaBACE 1000 capillary sequencer . Sequence data was analyzed using the Sequencher 4.7 program .We analyzed a total of 41 PCR products within 20 genes . We geno2-tests for genotypes, alleles, and trend of the alleles was performed using the CASECONTROL procedure of SAS/Genetics . The ALLELE procedure of SAS was used for estimation of allele frequencies and tests for Hardy–Weinberg equilibrium (HWE) of genotype frequencies. Statistical calculation of pairwise linkage disequilibrium (LD) was performed and pictured using HAPLOVIEW 4.0 [2≥0.8 [A case-control analysis based on χVIEW 4.0 . We used0 [2≥0.8  to detecTNNT1, RDH13, and TFPT. The other nine SNPs were discovered in nine different genes located in the 8.56 Mb interval between LOC540740 (similar to inward rectifier potassium channel) and TFPT. The genotypic distributions of the 27 genotyped SNPs were in Hardy–Weinberg equilibrium. The SNPs within LOC540740 and exon 11 of TNNT1 were not in Hardy–Weinberg equilibrium. Thus, these SNPs were not considered in the subsequent association analyses. The results of the tests for HWE, the observed heterozygosity (HET), polymorphism information content (PIC), and minor allele frequencies for the developed SNPs are shown in In total, we developed 29 SNPs within 12 genes. Of these 29 SNPs, 20 were located within the three candidate genes, TNNT1  is located in the 5′UTR 14 bases upstream of the start codon. A C>G SNP  is located at position 151 of bovine mRNA (NM_001075345) in exon 1. This SNP changes a CGG triplet to a GGG triplet and thus causes an amino acid exchange from arginine to glycine (p.Arg11Gly). This means there is a change from a charged alkaline amino acid to a nonpolar amino acid. The second SNP , which results in an amino acid exchange from glutamine to glutamate (p.Gln233Glu), was found at position 33 of bovine exon 7. This G>C transversion changes a GAG triplet to a CAG triplet, which has the effect that a polar and uncharged amino acid is replaced by an acidic, nonpolar, and charged amino acid in the primary structure of the protein. In addition, we detected two synonymous SNPs in the coding sequence of exon 2 and 5. Within the introns of RDH13, we detected one SNP in introns 1, 2, and 3 and two SNPs each in intron 5 and 6  is an A>T transversion, which causes an amino acid exchange from threonine to serine (p.Thr9Ser). However, both amino acids are polar, uncharged, and differ in only one ─CH3 side chain. The second SNP  in exon 2 alters the protein structure due to a G>T transversion, which changes a GGC triplet to a TGC triplet (p.Gly23Cys). This means that the nonpolar amino acid, glycine, is exchanged with the polar, sulfur-containing amino acid, cysteine. The third exonic SNP found in the ORF of TFPT is a synonymous mutation. This G>A SNP  at position 72 in exon 5 changes a CTG to a CTA triplet, which has no effect on the amino acid sequence of TFPT. In addition, we identified one SNP in introns 2 and 3 of TFPT.In the coding sequence of DHDH, CPT1C, TNNT1, and NALP7  within TNNT1 reached significant results in allele and trend test statistics  in exon 5 with an allelic χ2 value of 3.20 was close to the threshold of 0.05  for the SNPs between intron 1 and intron 2 of RDH13. By tagging with threshold r2≥0.8, we detected five SNPs in RDH13, which were representative for the total of 12 RDH13 SNPs. Therefore, only these five SNPs of RDH13 were used in the haplotype association analysis. The SNPs within the other genes were not in LD , SYT5 , RDH13 , and NALP7 , was significant . In total, there were eight different haplotypes of these markers that had a frequency of at least 1% (CPT1C (56.05 Mb) to NALP7 (62.87 Mb) on the telomeric end of BTA18. Further, two haplotypes adjacent to the proximal and distal region of this aforementioned associated region were tested for association with BCSE. The first haplotype proximally to CPT1C consisted of the SNPs , and the second haplotype distally to NALP7 included the SNPs . Both adjacent haplotypes did not show significant results in marker-trait association tests with BCSE .We tested the association of haplotypes with BCSE including three to eight SNPs and permutated the number of SNPs that were in Hardy–Weinberg equilibrium. The marker-trait association including five SNPs located in the genes, least 1% . Three ileast 1% . The sigLOC540740 to TFPT. In a previously performed whole genome scan, this region was found to be linked with BCSE in German Brown dairy cattle [CPT1C and NALP7, two SNPs were significantly associated with BCSE in association tests for single markers. Within DHDH, TTYH1, and SYT5, three more SNPs reached values close to the significance threshold of p=0.05.We developed a total of 29 intragenic SNPs within an 8.56 Mb region on BTA18 extending from RDH13, TFPT, and TNNT1. We identified four missense mutations within the coding sequence of these three genes and also detected a new splice variant of TNNT1. Since none of the SNPs within the genes  were significantly associated with BCSE in association tests for single markers, these genes are unlikely to be causal for this eye defect. However, the detected polymorphisms may be of importance in studies for other bovine diseases especially the SNPs within RDH13. These SNPs could be involved in the genetic pathology of retinal dystrophy or related diseases like the defects reported to be associated to other members of the short-chain dehydrogenases/reductases (SDR) family [Most of the 29 SNPs were detected in the sequences of the potential candidate genes, ) family ,28.PEO, CFEOM, and DRS) in humans [In cattle, genes influencing the development of strabismus are not yet known. Therefore, neuromuscular eye-disorders in humans with already identified causal genes and even developmental and biological characteristics may be used as candidates for BCSE. However, all potential candidate genes characterized as causal for these syndromes  in neurons of the brain [CPT1C is believed to modulate the palmitoyl-CoA pool associated with the ER and therefore to regulate the synthesis of ceramide and sphingolipids. Ceramide and sphingolipids are important for signal transduction, modification of neuronal membranes, and brain plasticity [CPT1C at 56.05 Mb on BTA18, this gene may be a candidate for BCSE.Particularly, SNPs within potential candidate genes like he brain . Expresshe brain . The funasticity -37. Sinc


We have validated thedeveloped pipeline by mining chickpea ESTs for interspeciesSNPs, development of CAPS assays for SNP genotyping, andconfirmation of restriction digestion pattern at the sequencelevel.The large amounts of EST sequence data available from a single species of an organism as well as for several species within a genus provide an easy source of identification ofintra- and interspecies single nucleotide polymorphisms(SNPs). In the case of model organisms, the data available arenumerous, given the degree of redundancy in the deposited ESTdata. There are several available bioinformatics tools thatcan be used to mine this data; however, using them requires acertain level of expertise: the tools have to be usedsequentially with accompanying format conversion and stepslike clustering and assembly of sequences becometime-intensive jobs even for moderately sized datasets. Wereport here a pipeline of open source software extended to runon multiple CPU architectures that can be used to mine largeEST datasets for SNPs and identify restriction sites forassaying the SNPs so that cost-effective CAPS assays can bedeveloped for SNP genotyping in genetics and breedingapplications. At the International Crops Research Institute forthe Semi-Arid Tropics (ICRISAT), the pipeline has beenimplemented to run on a Paracel high-performance systemconsisting of four dual AMD Opteron processors running Linuxwith MPICH. The pipeline can be accessed through user-friendlyweb interfaces at  The bioinformatics analysis oflarge biological datasets demand solutions involving serialization of severalsteps into a pipeline as well as parallelization of certain steps within thepipeline. One such example is the analysis of nucleotide datasets such asexpressed sequence tags (ESTs) for identification of sequence variations orsingle nucleotide polymorphisms (SNPs), which can be used as molecular markersin genetics and breeding applications. A number of sequential steps areinvolved, each of which need to be performed with a different bioinformaticstool. The tools can be implemented within a pipeline, with code to take care ofintermediate tasks like data format conversions, parsing of output files, andsummarizing output files. For large datasets, these pipelines may need to beconstructed so as to permit parallel processing of some or all of the stepswithin a pipeline, thereby reducing the time taken for job completion as wellas to handle common memory problems that occur when using the pipeline on asingle processor. Mining of SNPs inlarge datasets of ESTs is one such problem that may require parallel processingof some steps of the pipeline.Single nucleotide polymorphisms (SNPs) are highly stable genetic markers that can beused in the study of complex genetic traits and genome evolution . MiningIdentifying SNPs from EST data involves the steps of sequence clustering and assembly followed by detection. Several software pipelines have been published, many of which are available open source for use by the community. Most pipelines have been designed specifically for resequencing projects; using data quality scores like those derived from Phred and thusrequiring trace data. One popular tool is Polybayes  that impWe report here another pipeline of public domaintools but with a difference. It is a web-based application wherein all thetools in the pipeline have been implemented within a parallel framework and canwork on beowulf clusters and SMP's. The pipeline outputs and their use wouldvary depending upon the combination of pipeline component software used.Clustering and assembly of very large EST datasets can be accomplished, whichcould be used in gene expression studies, differential gene expression studies,and in the identification of unigenes. The pipeline can be used to identifySNPs and also convert identified SNPs into cleaved amplified polymorphicsequences (CAPS) markers. The pipeline can be accessed either throughuser-friendly interfaces or the command line option. The validation of the SNPspredicted through this pipeline has been shown using a set of chickpea ESTs;and the optimized CAPS assays have been confirmed by sequencing the restrictiondigested fragments.The pipeline uses the open source tools MegaBlast forclustering , PCAP fohttp://www.openpbs.orguse the Apacheserver, with a PostgreSQL database backend. The installation instructions areprovided in the pipeline package.The use of MPICH for writing wrappers, using thepython programming language, makes the whole application portable. The pipelineincludes PERL code for format conversion. The pipeline prerequisites are themodified version of MegaBlast, PCAP, Polybayes, and SNP2CAPS software programs.The web interfaces for the pipeline developed using OpenPBS Cicer species. This dataset was pipelined through the software for detection of interspeciesSNPs. All the ESTs are available in the public domain.While several datasets of different sizes were tested on the pipeline, the dataset that hasbeen experimentally validated is a small dataset consisting of 1499 ESTsgenerated from twenty-six different Cicer species: C. pungens (ICC 17138), C. bijugam (ICC 17122), C. microphyllum (ICC 17248), C. judaicum (ICC 17148), C. cuncetaum (ICC 17162), C. yamashitae (ICC 17116), C. pinnatifidum (ICC 17152), C. reticulatum (ICC 17123 and PI 489777), and C. arietinum  were used for validation of the SNP pipeline. The DNA was extracted from two-week old seedlings using the protocol of Mace et al. [A total of 12 genotypes representing 8 ens ICC 138, C. bμL of reaction mixture containing 10 ng ofgenomic DNA, 1X PCR buffer, 1.5 mM MgCl2, 0.1 mM of dNTP mix, 0.2 mM of each primer, and 0.2 U of TaqDNA Polymerase (Bioline). Amplifications were performed in an AppliedBiosystems thermal cycler using a touchdown amplification profile. Theamplification cycles were: initial denaturation of 3 minutes at 95°C followedby 5 cycles of denaturation for 20 seconds at 94°C, touchdown from 60°C to 55°Cwith 1°C decrease in each cycle for 20 seconds followed by extension at 72°Cfor 30 seconds. The next 30 cycles were denaturation for 20 seconds at 94°C,annealing at 56°C for 20 seconds and extension at 72°C for 30 seconds followedby final extension of 20 minutes at 72°C and stored at 4°C until further use.The PCR products were run on 1.2% agarose to check for amplification.Amplifications were carried out in 20 μL of the amplified products of final concentration of 100 ng were digested using 7.5 units of the restriction enzyme.The digested products were separated on 1.2% agarose gel electrophoresis.10 μL of PCRproduct. The Exo/SAP added PCR product was subjected to 37°C for 45 minutes and 80°Cfor 15 minutes in the thermal cycler. The Exo/SAP treated ampliconswere mixed with 1 μL of BigDyeTerminator V3.1, 2 μL of 5 Xdilution buffer and 3.2 picomoles of primer (forward and reverse) and thevolume was made to 10 μL. Thesequencing PCR profile was as follows: initial denaturation of 96°C for 30 seconds,followed by 60 cycles of 96°C for 10 seconds, 50°C for 5 seconds, and 60°C for 4 minutes. Allreactions were stored at 4°C until further use.The amplicons were purified using 1 unit of Exonuclease I and 1unit of shrimp alkalinephosphatase (SAP) per 5 μL of 125 mMEDTA and 25 μL ofabsolute ethanol and incubated for 15 minutes at room temperature. The platewas spun at 4000 rpm for 30 minutes at 4°C and inverting the plate on tissue poured offthe Ethanol/EDTA mix. To each well, 60 μL of 70% ethanol was added and again spun at4000 rpm for 20 minutes at 4°C. The ethanol was poured off as earlier. The plate was air-driedand 10 μL of HiDiformamide added and the products denatured  andsequenced using an ABI3700 automated sequencer.The products wereprecipitated using 2.5 The sequenceddata along with the sequences of ESTs  werealigned and analyzed using BioEdit.The pipeline can be usedto cluster and assemble very large EST datasets, return unigenes, identify SNPpolymorphisms, and generate a table of SNPs, indels, number of reads, PICvalue, haplotype, and so forth. Identified SNP polymorphisms can be alsoconverted to CAPS markers if desired see . The advThe steps of the pipeline with input and output at the end of each step are outlined.MegaBlast: input file consists of EST sequences in Fasta format. Files containing clusters of homologs are returned.Filtering: the output of the previous step can be filtered for clusters that have more than one genotype andcan thus become the basis for identification of interspecies polymorphisms.PCAP: MegaBlast output can be submitted for assembly. Output consists of assembly results in .ace file formatthat can be visualized using freely available alignment editors (Gendoc/Consed). These files may be converted to the .aln format to serve asinput to the SNP2CAPS program.Polybayes: output of PCAP serves as an input to this program. The program uses a python script to generatequality values if quality/trace data is not submitted. The output of this program in .aln file format can be visualized (Gendoc/Consed) or read by custom scriptsthat returns number of indels, haplotypes, polymorphism information content, and genetic variability  potential SNPs are identified based on a redundancy criterionthat every allele is represented by >1 sequence in a contig; (ii) all the sequencesin a haplotype have the same nucleotide in every polymorphic site; (iii) thenumber of false positives predicted are reduced by the script that considersthe alignment quality of the neighboring bases, searching a specified windowsize of 10 bases around each candidate single base-pair mismatch. The user's decision on thereliability of the identified polymorphism is further aided by the PIC value,number of haplotypes reported, and  SNiPpER .Cicer species was pipelined through the software for detection ofinterspecies SNPs. All the ESTs are available in the public domain. TheMegaBlast output resulted in 118 clusters, of which 11 clusters containedsequences from multiple species. These clusters could be assembled into 19contigs, interspecific SNPs could be identified from the alignment in 15contigs. Of 184 putative SNPs identified, the SNP2CAPS program predicted 73CAPS markers. The SNP2CAPS output returned restriction sites to the SNPs andprimers were designed using Primer3.In order to demonstrate the utility of this SNP pipeline, a small dataset consisting of1499 ESTs generated from twenty-six different XmnI, NlaIII, AccI, AciI, BanI, HpaII, XbaI, TaqI, EcoRV, RsaI, SalI, TaqI, BstNI, and HaeIII) weretested on the 12 chickpea genotypes. Out of 17 primer-enzyme combinations,restriction patterns were observed in the case of five primer-enzymecombinations. For example, AciI and HaeIII could restrict theamplicons obtained with CL3e primer. Similarly, the amplicons obtained withCL4a, CL20, CL99 showed restriction with EcoRV, BstNI, and XbaI,respectively.To assay the identified SNPs in chickpea germplasm, the pipeline helped to identify therestriction enzyme sites for 73 of the 184 identified SNPs in the contigs. A total of eight primer pairs  were chosen for amplification basedon predicted fragment length. The primer pairs were used to amplify each of the12 genotypes. Since sometimes there was more than one restriction enzyme forassaying the SNP per primer, a total of 17 primer-restriction enzymecombinations (using the common restriction enzymes: HaeIII (GGCC) in case of two genotypes  out of 11genotypes examined at position 1173 with respect to the original EST sequences.Indeed, these two genotypes showed restriction digestion in the CAPS assay. Inaddition, one more genotype  showedrestriction, however, at this particular position , this genotypedoes not have an SNP. Nevertheless, we expect that the SNP should be present inthe other direction to that which has been currently sequenced; we anticipatethis after analyzing the sequence of three ESTs from which the SNP was derived.In order to verify the SNPs at sequence level, the PCR amplicons for all 12 genotypes withCL3e primers were sequenced using the corresponding CL3e forward and reverseprimers. Reasonably good-quality sequence data were obtained for 11 genotypes.These sequences, along with the sequences of three ESTs , which revealed the SNPs in the contig CL3e, were aligned andvisualized for SNPs using BioEdit see . The seqVarious tools are available that allow analysis ofsequence datasets for polymorphism detection. A task like this, however,involves a number of sequential steps and substeps each of which needs to beperformed with a different bioinformatic tool. Using these tools efficientlyinvolves intermediate tasks like data format conversions, the need to writeadditional code for filters or to retrieve particular kinds of data, besidesbeing time consuming. These are often problems when the dataset is large andnot easily manageable. Putting large datasets through such pipelines mayrequire processing of some tools in a parallel manner to allow faster runtimes. For this to be feasible either the individual subunits within softwareneed to be parallelized if possible or MPI wrappers have to be implemented forthe individual software components of the pipeline to permit them to functionon clusters. The software pipeline reported in this paper does exactly this byallowing the user to run some or all steps of the process in parallel. Themodified MegaBlast component of the pipeline speeds up the clustering processowing to a greedy algorithm and batch processing, in this pipeline it isimplemented with MPI. The program PCAP can process several millions of readsand use multiple processors for sequence assembly. In this pipeline, this programhas been implemented with an improved MPI wrapper. While the results in Most software avoid the problem of large clusters byusing a maximum cluster size of 20–50 for SNP discovery. The pipeline is notlimited by cluster size as can be seen from Cicer ESTsderived from 26 different species. The program SNP2CAPS involves the screening ofmultiply aligned sequences for restriction sites followed by a selectionpipeline that allows the deduction of CAPs candidates by the identification ofputative alternative restriction patterns. Any primer pair flanking the SNPsite then becomes suitable for CAPS marker analysis. In order to utilize theidentified SNPs in chickpea genetics and breeding applications, 14 putative(and commonly used) restriction enzymes for assaying the SNPs were tested on 12 Cicer species using 17 primer-enzymecombinations. An unequivocal restrictionpattern was observed in 5 (30%) primer enzyme combinations. The reason for notgetting restriction patterns in 12 primer-enzyme combinations (70%) isattributed to not having been able to genotype all the species from which theSNPs were derived. In this study, we have tried to include all the Cicer species from the ICRISAT Genebank.Nevertheless, we anticipate that access to all species from which the publicEST dataset was derived, will allow the entire restriction pattern to bevisualized. For further validation, analysis of sequences from 11 genotypesderived with one primer pair clearly allows visualization of the restrictionsite for the enzyme HaeIII. Indeed, these two genotypes showedrestriction when the amplicons generated in these genotypes were digested withthe enzyme. A study of the sequences of the three ESTs  from which this SNP was derived shows the existence of anotherrestriction site for HaeIII in the region; however, the sequencedata available for the 11 amplicons does not cover this region. In summary,this study clearly confirms the identification and genotyping of true SNPs inthe experiments.In silico analysis revealed 184 interspecificSNPs in 15 contigs of The pipeline described in this manuscript differsfrom publicly available pipelines/workflows and workflow management systems (WMSs)like the BioWMS , KDEBio