These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, −1tt'V is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power  of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of  We describe novel methods for finding significant associations between a genome wide panel of SNPs and multiple complex traits, and further for distinguishing between genes with effects on multiple traits and multiple linked genes affecting different traits. The method uses a meta-analysis based on estimates of SNP effects from independent single trait genome wide association studies (GWAS). The method could therefore be widely used to combine already published GWAS results. The method was applied to 32 traits that describe growth, body composition, feed intake and reproduction in 10,191 beef cattle genotyped for approximately 700,000 SNP. The genes found to be associated with these traits can be arranged into 4 groups that differ in their pattern of effects and hence presumably in their physiological mechanism of action. For instance, one group of genes affects weight and fatness in the opposite direction and can be described as a group of genes affecting mature size, while another group affects weight and fatness in the same direction. Polymorphisms that affect complex traits (quantitative trait loci or QTL) may affect multiple traits. This pleiotropy is the main cause of the genetic correlations between traits, although another possible cause of genetic correlation is linkage disequilibrium (LD) between the QTL for different traits. A positive genetic correlation that is less than 1.0 between two traits, such as weight and fatness, implies that some QTL affect both traits in the same direction, but other QTL may affect only one trait and a small number may even affect the traits in the opposite direction. Identifying QTL with different patterns of pleiotropy should help us to understand the physiological control of multiple traits. Although genome wide association studies (GWAS) are usually performed one trait at a time, it is not uncommon to find that two traits are associated with SNPs in the same region of a chromosome. This has been described as cross phenotype association In practice, the apparent effect of a SNP on a trait is estimated with some experimental or sampling error. Consequently, even if there is a single QTL in a region of the chromosome, the SNP with the strongest association may vary from one trait to another causing the estimated position of the QTL to vary between traits. If one QTL can explain the findings for the multiple traits then a multi-trait analysis might result in higher power to detect QTL and greater precision in mapping them. Multiple-trait analysis of linkage experiments has been reported to increase the power to detect QTL n individual trait GWAS.These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by a n×n correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, −1tt'V is approximately distributed as a chi-squared with n degrees of freedom. The meta-analysis used here, although approximate, appropriately models the variances and covariance among the t-values regardless of the overlap in individuals measured for the different traits. The different amount of information for the different traits  is accounted for in the analysis.In principle, provided the computing power exists, a multi-trait GWAS is statistically straightforward. However, typically not all subjects have been measured for all traits, and when different traits have been investigated in different experiments, the individual subject data may not even be available. Therefore we present an approximate, multi-trait meta-analysis that uses as data the estimated effects of the SNPs from To distinguish between pleiotropy and multiple, linked QTL, we use two different analyses. Firstly, we consider whether all SNPs in a region do or do not show the same pattern of effects across traits. Secondly, we fit the most significant SNP from the multi-trait analysis in the model to test whether this does or does not eliminate the evidence for a second QTL.An aim of GWAS is to identify the genes and polymorphic sites in the genome that cause variation in complex traits. Choosing the most likely candidate genes from the region surrounding a SNP is usually based on the relationship between the function of the gene and the trait. Assuming that some QTL show pleiotropy, the pattern of pleiotropic effects would be an important clue to the nature of the causative mutation and the function of the gene in which it occurred. Genes that belong to the same pathway might have a similar pattern of pleiotropic effects. Therefore we investigate whether QTL can be clustered into groups with a similar pattern of pleiotropic effects and hence into physiologically similar groups.The objectives of this study were to test the power of a multi-trait, meta-analysis to detect and map pleiotropic QTL affecting growth, feed conversion efficiency, carcass composition, meat quality and reproduction in beef cattle. We also investigate whether these pleiotropic QTLs can be placed in groups with a similar pattern of effects and hence similar underlying physiological mechanisms.In this study, the 10,191 cattle had real or imputed genotypes for 729,068 SNP, although not all cattle were measured for all traits. The cattle were sourced from 9 different populations . Single trait genome wide association studies were performed for the 32 traits listed in In the multi-trait analysis we measured the effect of a SNP on each trait by a signed t-value (effect/standard error of effect) and approximated the (co)variance matrix among the traits using the correlation matrix of these SNP effects. Then the effects of a SNP across the 32 traits were combined with this correlation matrix to perform a multi-trait chi-squared test with 32 degrees of freedom of the null hypothesis that a SNP has no effect on any trait.P<5×10−7)  . This cohan 3.5% . TherefoTo validate the associations found in the multi-trait analysis, we used individual level data. The data were split into a discovery sample and a validation sample. The whole data were split into five sets by allocating all of the offspring of randomly selected sires to one of the five datasets. Then one of the 5 divisions was randomly selected as a validation population and the other 4 divisions as the reference population. In this way no animal used for validation had paternal half sibs in the reference population.P<10−5) were retained and then to avoid identifying a large number of closely linked SNPs whose association with traits is due to the same QTL, only the most significant SNP in a 1 Mb interval was selected for validation.From the multi-trait analysis of the discovery dataset, the most significant SNPs (P<0.05) and 71 of the 72 had an effect in the same direction as in the discovery sample.For each SNP we calculated the linear index of 22 traits. This linear index had maximum correlation with the corresponding SNP. Then the association between a SNP and its corresponding linear index was tested in the validation sample. To do this we needed individual animals that had been measured for nearly all traits. However, the bulls and cows, which had been measured for 10 reproductive traits, were not measured for the other 22 traits. Therefore we based the validation on animals measured for the 22 non-reproductive traits and calculated the linear index for each SNP based on these 22 traits . Out of P<10−5 and one SNP per Mb) and of these 60 (76%) had an effect in the same direction in the validation sample as in the discovery sample, but only 13 of the 79 were significant at P<0.05 in the validation sample. This shows that multi-trait analysis detected more associations and validated a higher percentage of them than the best single trait analysis. For other single traits the proportion of significant SNPs from the reference that were validated was even lower than for PW_lwt.To compare the power of detecting QTL in the multi-trait analysis to that in the single-trait analysis, we performed the same validation analysis for the single trait post weaning live weight (PW_lwt), which is one of the traits with the highest number of significant associations . For PW_P = 1.32×10−27) was located at 47,728 kb. The multi-trait analysis represents a good compromise between the positions from the 4 single trait GWAS and may be the best guide to a single QTL position explaining all the associated traits.Many of the significant SNPs in both single trait and multi-trait analyses were linked and might be associated with the same QTL. As an example of the multi-trait approach to improve precision, P<10−5) in the multi-trait analysis was calculated. SNPs were classified according to their distance from the nearest gene and the proportion of SNPs at each distance from a gene that were significant (PLAG1 that affects many traits CAST) There are many regions of the genome, similar to that illustrated in i  along with the 28 lead SNPs were simultaneously fitted in the model, and then the multi-trait statistic was re-calculated for SNPi to test the effects of the SNPi across traits after fitting the 28 lead SNPs. Many highly significant SNPs from the multi-trait analyses were found within narrow regions on chromosomes (BTA) 3, 5, 6, 7, 14, 20 and 29 . If therAll 28 lead SNPs remained significant in this conditional analysis, even after fitting the other 27, showing that each tags a different QTL. For instance, on BTA 7 the 2 lead SNPs at 93 and 98 Mb remain significant as does a SNP at 95 Mb . This coP<5×10−7) scattered throughout the genome  along with 28 lead SNPs were simultaneously fitted in the GWAS model. The results from this conditional analysis show that the lead SNP is significant (P<10−5) for all 4 traits, but once this SNP is included in the model, no other nearby SNPs reach this level of significance for any of the 4 traits.In For each pair of SNPs among the 28 lead SNPs, the correlation of their effects across the 32 traits was calculated . There aGroup 1 consists of 4 lead SNPs located on BTA 5 (BTA5_47.7 Mb), 6 (BTA6_40.1 Mb), 14 (BTA14_25.0 Mb) and 20 (BTA20_4.9 Mb). This group clustered as an outer branch separate from the other 24 lead SNPs , indicatGroup 2 consists of SNPs on BTA 7 (BTA7_98 Mb), 10 (BTA10_92 Mb), 25 (BTA25_3.7 Mb), 26 (BTA26_28.0 Mb), and 29 (BTA29_44.8 Mb) with high correlations between 2 SNP on BTA 7 and 29. These SNPs have an allele that increases meat tenderness  and fatness  5. The SGroup 3 consists of 7 SNPs that are located on BTA 2 (BTA2_25.2 Mb), 3 (BTA3_80.1 Mb), 6 (BTA6_12.7 Mb), 13 (BTA13_34.9 Mb), 17 (BTA17_24.9 Mb), 19 (BTA19_25.1 Mb), and 25 (BTA25_14.5 Mb). There was weaker clustering and lower correlations between these SNP compared to groups 1 and 2. The SNPs of Group 3 have an allele that increases both fatness and weight but has little effect on height or IGF1 . This diGroup 4, the biggest group, consists of 12 SNPs in a loose cluster. Moderate correlations appeared between some SNPs on BTA 7 (BTA7_93.2 Mb), 9 (BTA9_100.5 Mb), 21 (BTA21_0.9 Mb), 21 (BTA21_19.0 Mb), 23 (BTA23_43.9 Mb), 4 (BTA4_77.6 Mb) and 8 (BTA8_59.2 Mb) . This grPLAG1 has a more marked effect on age at puberty (AGECL) than others in the group; the SNP on BTA5 changes the distribution of fat between the P8 site on the rump and rib site and the intramuscular depot. Thus it is possible for the each SNP to have a unique pattern of associations with phenotypic traits.Although the SNPs within a group share some features they also differ in some of their associations. For instance, in Group 1 the SNP on BTA 14 near P<10−5) SNPs for the 28 linear indexes corresponding to the 28 lead SNPs. Out of 28 linear indexes, 19 had more than 70 significant SNPs and hence a FDR of less than 10%. Linear index BTA5_47.7 Mb, BTA6_40.1 Mb, BTA14_25.0 Mb, and BTA20_4.9 Mb (where the name corresponds to the location of the QTL defining the pattern of effects) have associations with over 1,000 significant SNPs across the genome. For the index based on the lead SNP BTA5_47.7 Mb, the significant SNPs included 615 SNPs on BTA 5, 64 on BTA 6, 24 on BTA 11, 907 on BTA 14, 19 on BTA 17, 18 on BTA 20. This reiterates the result obtained in the cluster analysis because SNPs on BTA 5, 6, 14 and 20 are the lead SNPs in Group 1 and the additional SNPs on these chromosomes may be tagging the same QTL as the lead SNPs. However, there are also significant SNPs associated with this linear index on BTA 11, 17, 19, 21 and 25.The pattern of pleiotropic effects might be an important clue to the nature of the causative mutation and the function of the gene in which it occurred. Genes that operate in the same pathway might be expected to show the same pattern of pleiotropic effects. For each of the 28 lead SNPs, we searched for additional SNPs with a similar pattern of effects. To do this we used the linear index of 22 traits that showed the highest association with a lead SNP, as previously defined for validation of the multi-trait analysis, and performed a new GWAS using the linear index as a new trait. P<5×10−7) was found. The SNP was then assigned to the same group as the lead SNP defining that linear index. The results are shown in P<5×10−7) from each set. The number of significant SNPs assigned to each of the 4 Groups were as follows: 1) 2,076; 2) 398; 3) 169 and 4) 176. The positions or regions of the most significant SNPs in the expanded groups are listed in The additional significant SNPs were assigned to the 4 groups as follows. For each SNP, the linear index with which it showed the most significant association  and that these SNPs are more likely to be validated in a separate sample of animals. The most significant SNP in the multi-trait analysis provides a consensus position across the traits affected and a consistent set of estimates of the QTL for the various traits. This is in contrast to single trait analyses that often report the effect of different SNPs on each trait while neglecting the pattern of effects of the QTL across traits. For instance, the multi-trait analysis makes it clear that the QTL in Group 1 increase weight and decrease fatness whereas QTL in Group 3 increase both.We demonstrated that our multi-trait analysis has a lower FDR than any one single trait analysis  would contain the covariances among the errors in the estimates of SNP effects. The error variance of a t-value with 1000's of degrees of freedom is very close to 1.0. Our approximation to V also has diagonal elements of 1.0 because it is a correlation matrix. The covariance between the errors in t-values for two different traits depends on the overlap in individuals measured for the two traits. If the two traits are recorded on different individuals, there is no covariance among the errors; whereas if the two traits are measured on the same individuals, the error covariance will be mainly determined by the phenotypic correlation between the traits because single SNPs explain little of the phenotypic variance. We approximate these error covariances by the correlation between t-values across 729,068 SNPs. Since most SNPs have little association with a given trait, these correlations represent phenotypic correlations in the case where both traits are measured on the same individuals. If the two traits are measured on different individuals, then the correlation of t-values is close to zero as it should be. And if there is a partial overlap between the individuals measured for the two traits, then the correlation of t-values will represent this. Thus the meta-analysis used here, although approximate, appropriately models the variances and covariances among the t-values regardless of the overlap in individuals measured for the different traits. Therefore we hope it will be widely useful including in the analysis of published GWAS results where only the effect of each SNP and its standard error are available.Bolormaa et al. We distinguished between two linked QTL and one QTL with pleiotropic effects using two types of evidence. When one QTL explains the results, the SNPs in the region are highly correlated in their effects across traits and when the best SNP is fitted in the model the significance of the effects of the other SNPs drops markedly as illustrated by the results for BTA 14 in Pleiotropy of individual QTL contributes to the genetic correlation between traits. If two traits have a high and positive genetic correlation it implies that most QTL affect them both in the same direction. For instance, most SNPs with significant effects affect height and weight in the same direction and thus help to explain the known high genetic correlation between these two traits PLAG1. In other cases there are candidates that are homologous to known genes affecting growth and composition in other species . However, there are QTL in Some significant SNPs map near to already known genes with effects on the traits studied, such as calpain 1, calpastatin and CAPN1) and calpastatin (CAST) that affect tenderness via muscle fibre degradation We defined 4 groups of SNPs by a cluster analysis of the 28 lead SNPs such that SNPs within a group have a somewhat similar pattern of effects across traits. These groups were expanded by including SNPs whose effects were correlated with those of one of the lead SNPs in the group. If the 4 groups of QTL represent different physiological pathways, one might expect the genes that map near the QTL of a group to show some similarity of function. To an extent this is so. Group 2 SNPs, which are associated with tenderness, include SNPs near calpain 1 . PLIN is required for maximal liposis and utilization of adipose tissue Of the SNPs in Group 1, one on BTA 14 probably tags LEPR), the SNP on BTA 13 is near LPIN3 (which regulates fatty acid metabolism), the SNP on BTA 21 is again near PLIN indicating that this QTL has similarities to both groups 1 and 3 . We do not know which, if any, of these genes contain causal mutations but it seems likely that the QTL within each group are somewhat heterogeneous. This would not be surprising given the complexity of feedback mechanisms of growth of mammals. It may be that changes to either muscle or fat growth indirectly affect growth of the other tissue.Group 3 SNPs affect fatness and the SNP on BTA 3 are in the leptin receptor gene  increased power to detect and map QTL and 2) use of summary data on SNP effects when individual level data are not available. We have also presented two methods (one new) for distinguishing between linked and pleiotropic QTL , and found pleiotropic QTL which appear to cluster into 4 functional groups based their trait effects. We used linear indices of 22 traits 1) to validate the effects of SNPs on multiple traits and 2) to find additional QTL belonging to the 4 functional groups. We identified candidate genes in those groups that have known biological functions consistent with the biology of the traits.Animal Care and Use Committee approval was not obtained for this study because no new animals were handled in this experiment. The experiment was performed on trait records and DNA samples that had been collected previously.http://res.illumina.com/documents/products/datasheets/datasheet_bovinehd.pdf) comprising 777,963 SNP markers; the BovineSNP50K version 1 and version 2 BeadChip  comprising 54,001 and 54,609 SNP, respectively; the IlluminaSNP7K panel comprising 6,909 SNP; and the ParalleleSNP10K chip  comprising 11,932 SNP. All SNP were mapped to the UMD 3.1 build of the bovine genome sequence assembled by the Centre for Bioinformatics and Computational Biology at University of Maryland (CBCB) . The approaches used for performing quality control and imputation were described in In total, 729,068 SNP data were used in this study. Those SNP were obtained from 5 different SNP panels: the Illumina HD Bovine SNP chip  score above 0.6) was less than 90% or they had duplicate map positions (two SNP with the same position but with different names) or an extreme departure from Hardy-Weinberg equilibrium . Furthermore, if the call rate per individual was less than 90%, those animals were removed from the SNP data. The SNP data were edited within breed group and within each platform and were subsequently combined.B. taurus and 520 B. indicus) were used as a reference set to impute from the 50 K genotypes of each pure breed within the corresponding breed type. For the four composite breeds, all the HD genotypes  were used as a reference set to impute the 50 K genotypes of each composite breed up to 800 K. The number of genotypes for each platform used as reference animals for imputation and number of animals used in this study is given in 2 values, for the accuracy of imputation provided by BEAGLE, are in After all the quality control tests were applied, 729,068 SNP of the HD SNP chip were retained on 1,698 animals and the missing genotypes were filled using the BEAGLE program Bos taurus (Bt) breeds , 1 Bos indicus (Bi) breed (Brahman cattle), 3 composite (Bt×Bi) breeds , and 1 recent Brahman cross population . Details on population structure of those animals have previously been described by Bolormaa et al. The cattle were sourced from 9 different populations of 3 breed types. They include 4 different Phenotypes for 32 different traits including growth, feed intake, carcass, meat quality, and reproduction traits were collated from 5 different sources: The data sources included the Beef Co-operative Research Centre Phase I (CRCI), Phase II (CRCII), Phase III (CRCIII), the Trangie selection lines, the Durham Shorthorn group  was additionally fitted as a covariate one at time . The model used to analyse the traits consistently included dataset, breed, cohort and sex as fixed effects. Other fixed effects varied by trait. The fixed effects were fitted as nested within a dataset. Further details of the models used in the analysis are reported by Johnston et al. Mixed models fitting fixed and random effects simultaneously were used for estimating heritabilities and associations with SNP. Variances of random effects were estimated in each case by REML. The estimates of heritability were calculated based on all animals with phenotype and genotype data and their 5-generation-ancestors using the following mixed model: trait ∼ mean + fixed effects + animal + error; with animal and error fitted as random effects. The individual animal data for the 32 traits were used to perform genome wide association studies (GWAS), in which each SNP was tested for an association with the trait. The association between each SNP and each of the traits was assessed by a regression analysis using the ASReml software i  across all (32) traits studied. Our multi-trait test statistic is approximately distributed as a chi-squared with 32 degrees of freedom. It tests a null hypothesis stating that the SNP does not affect any of the traits. For each SNP, the multi-trait statistic was calculated by the formula:it is a 32×1 vector of the signed t-values of SNPi for the 32 traits i′t is a transpose of vector it (1×32) −1V is an inverse of the 32×32 correlation matrix where the correlation between two traits is the correlation over the 729,068 estimated SNP effects  of the two traits.We applied a new statistic to find the significance level of SNPs in a multi-trait analysis. This statistic determines the importance of the effects of SNP2 is distributed as a χ2(1) under the null hypothesis. Therefore, if the SNP effects on n different traits were estimated independently with no error covariance, the sum of the t2  would be distributed as a chi-squared with n degrees of freedom. Our approximate analysis would generate exactly this test statistic if the t values for different traits had no error covariance. If the t values for different traits had an error (co)variance matrix D, then the correct test statistic would be D by the correlation between the estimated SNP effects across the 729,068 SNPs. We assume that most SNPs have little or no effect on most traits, so most of the (co)variance between effects is error covariance. However, the SNPs that do have a real effect on a trait will inflate the variance of SNP effects above 1.0. Therefore we convert the covariance matrix of SNP effects (D) to a correlation matrix (V) because this returns the diagonal elements to 1.0 which we know is the correct error variance for t statistics. Although it is not proof of the method, perhaps we offer the following intuitive analysis. If the SNP effects on different traits were estimated in independent GWAS then the correlation of SNP effects would be low and V≈I and the test statistic would be the sum of independent chi-squares, as expected. On the other hand, if the SNP effects on different traits were estimated from the same individuals, then the correlation of error variances would be driven mainly by the phenotypic correlations between the traits. In this case the correlation of SNP effects would also reflect these phenotypic correlations and the test statistic we use would be a good approximation of the correct test statistic.This approximation method is justified as follows: t-values based on many degrees of freedom have an error variance close to 1.0 and tP is the P-value tested , A is the number of SNP that were significant at the P -value tested and T is the total number of SNP tested.The increase of power of QTL detection was investigated by comparing FDR calculated in multi-trait test with FDR calculated in single-trait GWAS. Following Bolormaa et al. To validate SNP effects in the multi-trait test, we developed a new approach that uses a linear index of traits that had maximum correlation with the SNP. This new approach was carried out as follows: 1) Splitting data as reference and validation populations; 2) Predicting missing phenotypes using multiple regression approach; 3) Performing single-trait GWAS in the reference population to get the SNP effects based on only the reference population; 4) Calculating a linear index of 22 traits for each SNP, which had maximum association with the SNP in reference population; and 5) Validating SNP effects using GWAS to discover if there is any association between the corresponding linear index and SNP.Splitting data. For validation purposes, a 5-fold cross validation schema was carried out. The whole data were split into five sets by allocating all of the offspring of randomly selected sires to one of the five datasets. Then one of the 5 divisions was used as a validation population and the other 4 divisions as the reference population. In this way no animal used for validation had paternal half sibs in the reference population.Predicting missing phenotypes. The linear index on individual animals could only be calculated for animals with all traits measured. This required individual animal level data. Therefore this process was restricted to the 22 non-reproduction traits since the cows and bulls, on which the reproductive traits were measured, were not recorded for carcass traits. Even among these 22 traits, not all animals were measured for all traits. Before the missing phenotypes were predicted, the raw phenotypes for each trait were corrected for fixed effects using the following model: corrected phenotype = phenotype – fixed effects. So missing values were filled in by a prediction using multiple regression on the traits that were recorded on that animal. This multiple regression procedure uses the actual effects  of 729,068 SNPs for 22 traits that were estimated based on all animals  in order to have the same units with phenotype values. For each animal, SNP effects for the 22 traits were reordered so that those traits with a phenotypic value preceded those traits with missing values. Then the (co)variance matrix of SNP effects among the 22 traits were calculated and inverted: ooUis the inverse of (co)variance matrix of SNP effects between the traits with a phenotype value, nnU is the inverse of (co)variance matrix SNP effects between traits with a missing record, and onUvs noUis the inverse of (co)variance matrix of the traits with and without a missing record. The missing phenotypes (ny) were then predicted using the following formula: oy is a vector of the traits measured on a particular animal.GWAS in reference population. The individual trait GWAS and the multi-trait significance test on signed t-values described in the previous sections were performed using only the reference population. Only the most significant SNP from a sliding window of every 1-Mb-interval was retained to avoid identifying a large number of closely linked SNPs whose association with traits is due to the same QTL or due to LD to an index SNP. If a SNP in each 1-Mb-interval was significant at P<10−5 then it was selected to be validated in the validation population using the linear index of 22 traits.Calculating linear index. A linear index (Iy) of 22 traits that has maximum correlation in the reference population with each selected SNP was derived. This linear index was calculated for each animal. The phenotype values and the effects of the SNP are used to calculate the linear index, so the actual effects of the SNP  were in the same units as the trait values. The following formula was used to calculate a linear index: b′ is the transpose of a vector of the estimated effects of the SNP on the 22 traits (1×22) that was estimated from only the reference population, −1C is an inverse of the 22×22 (co)variance matrix among the 22 traits calculated from the estimated SNP effects of 729,068 SNPs only in the reference population, and y is a 22×1 vector of the phenotype values for 22 traits for each animal in the validation sample.Validating SNP effects using GWAS. The association between each linear index (Iy) and each SNP was then tested in the validation population. The Iy was treated as a new trait (dependent variable). The association was assessed by a regression analysis (GWAS) using the following model: Iy∼ mean + SNPi + animal + error, where animal and error were fitted as random effects and SNPi were fitted as a covariate one at a time (other fixed effects were removed from the trait measurements before forming the linear index).In order to see whether the SNPs validated in the validation population have the same direction of effects (positive or negative) as SNPs in the reference population, we repeated the steps 2, 4, and 5 by using the phenotypes of the reference population instead of the phenotypes of the validation population. Then the directions of SNP effects for the linear index in both reference and validation populations were checked and the proportion of SNPs whose effects were in the same direction in the reference population was calculated.www.ensembl.org/biomart/) and SNPs were classified according to their distance from the nearest gene. The SNPs were placed in bins 1) <100 kb upstream of the start site or downstream of the stop site, 2) 100–200 kb upstream or downstream, etc., in 100 kb bins. SNPs between the start and stop sites were placed in a separate bin . For each bin the proportion of SNPs that were significant (P<10−5) in the multi-trait analysis was divided by the total number of SNPs in that bin.The gene start and stop positions were identified using Ensembl , based on the multi-trait analysis, were selected. Two SNPs on the same chromosome were only selected if they clearly represented two different QTL based on the test for pleiotropy vs linkage. In no case were the SNPs less than 2 Mb apart.The 28 lead SNPs were selected as follows: On each chromosome the one or two most significant SNPs  and 28 lead SNPs were fitted simultaneously as covariate effects. Then a multi-trait chi-squared statistic was calculated for each SNP to test the effects of the SNP across traits after fitting the 28 lead SNPs.The regression analyses (GWAS) were performed again but additionally the 28 lead SNPs were fitted simultaneously in the model. The statistical model used was trait ∼ mean + fixed effects + SNPFor each pair of SNPs among the 28 lead SNPs, the correlation of their effects across the 32 traits was calculated. Then this correlation matrix was used to do the hierarchical clustering of the 28 lead SNPs leading to 4 groups or clusters as shown in the dendrogram drawn using the heatmap function of the R program Iy) treating it as a new trait (dependent variable). The following model was used: Iy ∼ mean + fixed effects + SNPi + animal + error, where animal and error were fitted as random effects and the ith SNP  was fitted as a covariate effect.For each of the 28 lead SNPs, we searched for additional SNPs with a similar pattern of effects. To do this we used the linear index that showed the highest association with a lead SNP, as previously defined for validation of the multi-trait analysis. A new GWAS was performed for each of 28 linear indexes (P<5×10−7) with at least one of the indexes based on lead SNPs were selected for assigning into 4 groups. These additional significant SNPs were assigned to the same group as the lead SNP whose linear index with which they had the most significant association.The SNPs that have significant associations (www.ensembl.org/biomart/) and, in some cases, a plausible candidate for the phenotypic effect was identified.The genes that occur within 1 Mb of the SNPs in this expanded list were identified using Ensembl (


Deviation from multiplicativity of genetic risk factors is biologically plausible and might explain why Genome-wide association studies (GWAS) so far could unravel only a portion of disease heritability. Still, evidence for SNP-SNP epistasis has rarely been reported, suggesting that 2-SNP models are overly simplistic. In this context, it was recently proposed that the genetic architecture of complex diseases could follow limiting pathway models. These models are defined by a critical risk allele load and imply multiple high-dimensional interactions. Here, we present a computationally efficient one-degree-of-freedom “supra-multiplicativity-test” (SMT) for SNP sets of size 2 to 500 that is designed to detect risk alleles whose joint effect is fortified when they occur together in the same individual. Via a simulation study we show that the SMT is powerful in the presence of threshold models, even when only about 30–45% of the model SNPs are available. In addition, we demonstrate that the SMT outperforms standard interaction analysis under recessive models involving just a few SNPs. We apply our test to 10 consensus Alzheimer’s disease (AD) susceptibility SNPs that were previously identified by GWAS and obtain evidence for supra-multiplicativity ( Despite of thousands of confirmed disease susceptibility variants Although pointed out previously The risk allele threshold models proposed by Zuk et al. given set of SNPs displays “supra-multiplicativity” of allelic risks. In this paper, we present a powerful one degree of freedom (d.f.) regression test for deviation from multiplicativity which simultaneously addresses interactions of all orders and which is particularly powerful in the presence of threshold models.The search for deviation from multiplicativity in all medium-sized SNP sub sets of a GWAS panel is clearly unfeasible and not a realistic strategy in the coming years. However, it is an important research question how to decide whether a In sappears . In othePower for pairwise interaction is typically absent , with th . As expeWhen d to 0.5 , the porWhen d to 0.3 , power dlly, at  , power vIn At low significance levels, particularly at http://www.kompetenznetz-demenzen.de) and at the interdisciplinary memory clinic of the Department of Psychiatry and Department of Neurology at the University Hospital in Bonn, Germany. Diagnosis of AD dementia was established according to NINCDA-ADRDA criteria We applied the SMT to known Alzheimer’s disease (AD) susceptibility loci. According to the GWAS catalogue As expected, our data shows overwhelming evidence for association of APOE with AD , . For thePrior to application of the SMT, we screened the data for SNP-dominance effects, and two- and three-way interactions. None of the dominance or pairwise interaction terms was nominally significant. Analysis of three-way interaction of 120 SNP triples was not significant after adjustment for multiple testing  lead to strong supra-multiplicativity which can be detected by the SMT, as shown in our simulation study. Moreover, the SMT remained powerful when we altered the LPLMs such that marginal SNP effects were allowed to vary substantially. In addition, we exemplified the usefulness of the SMT for small SNP sets by showing that it is much more powerful than standard interaction tests under 3-SNP-recessive models.We presented the SMT as a test for deviation from multiplicativity. It can be applied to sets of a size of up to Zuk et al. Our analysis of known Alzheimer’s disease (AD) susceptibility SNPs in previously unpublished data revealed supra-multiplicativity .Our work got its stimulus from the manuscript of Zuk et al. We consider a set of In other words, we use the marginal effects of the SNPs as covariates and investigate if the disease risk increases sharply when By design, the proposed test can detects various types of deviation from multiplicativity. Therefore, we call it a supra-multiplicativity test (SMT). The SMT is constructed to optimize power in the presence of LPLMs. More generally, one can expect the SMT to be powerful when a substantial portion of the sub sets of the SNP set under investigation deviate from multiplicativity. In addition, effect directions should not be reverted in combination with other risk alleles since the test is build up on risk allele counts per individual.n, and, for each replicate, randomly selected allele frequencies from a uniform distribution and randomly assigned relative risk values Let In order to assess power of the SMT, we investigated limiting pathway liability models (LPLMs) as suggested by Zuk et al. We also investigated the potential impact of incomplete SNP coverage on the power of the SMT. To this purpose, we assumed that the true causal SNPs of the 30-SNP LPLM (The LPLMs suggested in We investigated “completely recessive” models defined by three SNPs. A baseline penetrance of 0.03 was assumed. Only individuals which were homozygous for the risk allele at all three SNPs were assigned an increased penetrance. We simulated data under different scenarios, defined by varying population allele frequencies and risk genotype penetrance values. In detail, we simulated the model REZ-A with allele frequencies of the three SNPs of 0.2, 0.5 and 0.8, respectively, and a penetrance of 0.20 for the high risk 3-SNP-genotype. Model REZ-B was defined by allele frequencies 0.2, 0.3 and 0.4 and a high risk genotype penetrance of 0.70, model REZ-C was defined by allele frequencies 0.4, 0.5 and 0.6 and a high risk genotype penetrance of 0.10, and, model REZ-D was defined by allele frequencies 0.6, 0.7 and 0.8 and a high risk genotype penetrance of 0.05. The resulting allelic relative risks ranged from 1.05 to 1.19 under model REZ-A, from 1.03 to 1.06 under REZ-B, from 1.05 to 1.08 under REZ-C, and, from 1.10 to 1.13 under REZ-D.


High-throughput SNP genotyping has become an essential requirement for molecular breeding and population genomics studies in plant species. Large scale SNP developments have been reported for several mainstream crops. A growing interest now exists to expand the speed and resolution of genetic analysis to outbred species with highly heterozygous genomes. When nucleotide diversity is high, a refined diagnosis of the target SNP sequence context is needed to convert queried SNPs into high-quality genotypes using the Golden Gate Genotyping Technology (GGGT). This issue becomes exacerbated when attempting to transfer SNPs across species, a scarcely explored topic in plants, and likely to become significant for population genomics and inter specific breeding applications in less domesticated and less funded plant genera.Eucalyptus from a mixed Sanger/454 database with 1,164,695 ESTs and the preliminary 4.5X draft genome sequence for E. grandis. A systematic assessment of in silico SNP filtering requirements showed that stringent constraints on the SNP surrounding sequences have a significant impact on SNP genotyping performance and polymorphism. SNP assay success was high for the 288 SNPs selected with more rigorous in silico constraints; 93% of them provided high quality genotype calls and 71% of them were polymorphic in a diverse panel of 96 individuals of five different species.We have successfully developed the first set of 768 SNPs assayed by the GGGT for the highly heterozygous genome of Eucalyptus species belonging to three sections within subgenus Symphomyrtus and still satisfactory across species of two additional subgenera, although polymorphism declined as phylogenetic distance increased.SNP reliability was high across nine Eucalyptus notwithstanding its nucleotide diversity ≥2%. The development of a much larger array of informative SNPs across multiple Eucalyptus species is feasible, although strongly dependent on having a representative and sufficiently deep collection of sequences from many individuals of each target species. A higher density SNP platform will be instrumental to undertake genome-wide phylogenetic and population genomics studies and to implement molecular breeding by Genomic Selection in Eucalyptus.This study indicates that the GGGT performs well both within and across species of  High-throughput, high density SNP genotyping has become an essential tool for QTL mapping, association genetics, gene discovery, germplasm characterization, molecular breeding and population genomics studies in several crops and model plants -7. The ain silico SNP identification now provide a very effective alternative to amplicon resequencing for SNP development in plants  × [E. urophylla × E. globulus]) (DG × UGL) 1,596 transmission inconsistencies were seen, i.e. a genotyping miscall rate of 4.3%, only 678 SNPs (88.3%) had 100% heritability and 80% of the inheritance miscalls were concentrated in 71 SNPs. Only 17 SNPs displayed miscalls in both families concurrently, revealing potentially more problematic SNPs. Upon inspection of the SNPs clustering graphs most inheritance miscalls in both families were due to the two parents being homozygous AA and BB and offspring not having the expected genotype AB but rather one of the two homozygous ones.SNP assay quality was further assessed by estimating heritability of allelic transmission in parent-parent-offspring trios involving different SNP validation was possible for 50 SNPs for which five or more genomic reads overlapping at the SNP position with sequence quality Q ≥ 20 were obtained. Given the limited sample size available (number of observed reads at the SNP site) a less conservative alpha level α = 0.1 was used to increase the power of the binomial test used to declare sequence-based genotypes. In other words, by increasing the chance of obtaining a statistically significant result, the probability of correctly declaring a sequence-based homozygous genotype in spite of the small number of observed reads was increased although at the expense of an increase in Type I error, i.e. erroneously declaring the genotype as homozygous when in fact it is heterozygous. Sequence-based genotypes at 43 of the 50 SNPs (86%) matched the Golden Gate assay called genotypes  developed from in silico was estimated at 61.5% while for SNPs developed by targeted amplicon resequencing it was slightly higher, at 73% but also no specific selection for more conserved SNP flanking sequences was carried out  generated from 21 individual trees of E. grandis using the 454 pyrosequencing technology [Three EST datasets were used in this work. The first one is a Sanger 5' sequenced set of 101,240 ESTs from  project . The secchnology . These achnology . Poly  were grouped separately into clusters, each one expected to correspond to a single DNA segment and then aligned with the help of the user-supplied and internally computed base quality information using Phrap for Sanger reads or Newbler for 454 reads. Clustering was carried out using an algorithm based on the dissimilarity measures criteria with subword comparisons. Evaluation of the suitability of string dissimilarity measures for EST clustering was reported earlier [Eucalyptus species of origin.ESTs derived from each  earlier . The wcd earlier  used in  earlier . Clusterin silico evaluation for assessing the probability that a given site was polymorphic. SNP prediction was carried out using PolyBayes version 3.0 [SNP≥ 0.99.A general pipeline was put in place based on the software MIRA 2 - Whole Genome Shotgun and EST Sequence Assembler V2.9.25 with the enhanced 454 and Illumina/Solexa support. MIRA is an EST sequence assembler that specializes in reconstruction of pristine mRNA transcripts, while at the same time detecting and classifying single nucleotide polymorphisms (SNPs) occurring in different variations thereof . MIRA cosion 3.0  with a pin silico estimated minor allele frequency (MAF) at the SNP site; (2) the number of EST reads by target species aligned at each SNP site; and (3) the presence of additional SNPs along the SNP flanking sequences. The impact of these parameters on SNP reliability and polymorphism validated by the GGGT assay was evaluated by sequentially implementing increasingly stringent in silico SNP filtering levels as follows:Following the Polybayes probability and SNP vicinity scores provided by the alignment, a set of parameters were defined to construct filters to select SNPs for the GGGT assay design. Initially all SNPs had to have at least 60 bases available on each SNP side to allow designing allele and locus specific oligos for the GGGT assay. Additionally the following parameters were used: (1) the bi-allelic SNPs with ≥ 5 reads on SNP position, and a minimum of one read with the alternative base;bi-allelic SNPs with ≥ 5 reads on SNP position and a Minor Allele Frequency (MAF) ≥ 0.2;E. globulus or E. gunnii; these species belong to the section Maidenaria, phylogenetically separate from E. grandis, E. urophylla and E. pellita that belong to section Latoangulatae;same as F1 plus a minimum of one read derived from same as F2 plus a minimum of 100 bases on each SNP side without repetitive elements and a minimum of 20 bases flanking the SNP on each side without any additional SNPs in the contig;same as F3 but increasing to a minimum of 60 bases flanking the SNP on each side without any additional SNPs in the contig.Eucalyptus grandis used as reference was downloaded from the EUCAGEN  site at http://Eucalyptusdb.bi.up.ac.za.As the SNPs were derived from cDNA sequences, the occurrence of intron/exon border in the flanking sequences upon which the oligos are later designed, may cause significant genotyping failure . To mitiFrom each one of the five filtered subsets of SNPs a random sample between ~300 and 600 SNPs was derived to be subsequently submitted to the Assay Design Tool (ADT) made available by Illumina with a target of having between 96 and 288 SNPs to be effectively designed and validated. Each SNP receives a SNP score that is a predictor of the likelihood of genotyping success using the GGGT assay. ADT generates scores for each SNP that could vary from 0 to 1. According to the Illumina standard recommendations, a SNP with score ≥ 0.6 has a high probability to be converted into a successful genotyping assay. Based on the ADT scores returned by Illumina, a subset of SNPs to be included in the SNP bead array was randomly selected from the final set of SNPs obtained with a more stringent ADT ≥ 0.8 for each one of the five filter treatments.Eucalyptus was compiled. These were genes involved in lignin biosynthesis [A list of 42 genes described in the literature as being putatively associated with relevant wood phenotypes in ynthesis ,70, geneynthesis ,72, exprynthesis ,74 and aynthesis . GenBankSymphomyrtus but to different sections. Sample sizes were N = 24 for E. grandis (section Latoangulatae), N = 24 for E. globulus (section Maidenaria), N = 16 for E. urophylla (section Latoangulatae), N = 16 for E. camaldulensis (section Exsertaria) and N = 16 for E. nitens (section Maidenaria), totaling 96 individuals. For E. grandis, twelve trees from each of two different provenances were sampled, Atherton (17°15'S 145°28'E) and Coffs Harbor (30°18'S 153°07'E); for E. globulus twelve trees were from Jeeralang (38°24'S 146°28'E) and twelve from Flinders Island (40°00'S 148°07'E); for E. urophylla all trees were from Flores Island (8°39'S 122°15'E), for E. camaldulensis all trees were from Walsh River (17°17'S 144°88'E) and for E. nitens trees were from Eastern Ebor (30°24'S 152°29'E). Small samples of N = 8 individuals were also genotyped for other six species of more limited world relevance to provide a preliminary assessment of SNP transferability across a wider inter-specific range and across two additional subgenera. These were: E. dunnii, E. saligna, E. tereticornis, these four also belonging to the subgenus Symphomyrtus, and E. cloeziana and E. pilularis belonging to two different subgenera, Idiogenes and Eucalyptus, respectively. DNA extractions from fresh expanded leaves were carried out as described earlier using a modified CTAB procedure [Population samples of unrelated trees of each one of the five most widely planted and bred species worldwide were used for SNP reliability, inter-specific transferability and polymorphism assessment. All these five species belong to the same subgenus rocedure  and quanSelected SNPs were used to construct an Illumina bead array of 768 SNPs based on the GoldenGate assay . Out of these 768 SNPs, 696 were distributed across the five SNP selection filters while 72 SNPs were derived from specific candidate genes. Genotyping was performed using an Illumina BeadStation 500 GX  at the Genome facility of the Interdisciplinary Center for Biotechnology Research (ICBR) of the University of Florida using the protocol described earlier [SNP reliability was evaluated by the GeneTrain and the GeneCall scores. The GenTrain score, a statistics with a value between 0 and 1, was estimated for each SNP to assess the quality of the shapes of the genotype clusters (homozygous and heterozygous) and their relative distance to each other. A GenCall score, estimated for each datapoint , is designed to rank particular DNA samples or SNP loci and is obtained by the product of the GenTrain Score and a data-to-Bayesian-model fit score as implemented by the Genome Studio software. Genotypes with lower GenCall scores are located further from the center of the genotype cluster and have a lower reliability. To assess the reliability of each individual SNP the GenCall scores for all typed samples for each SNP were used to estimate a GeneCall50 (GC50) score that corresponds to the 50th percentile (median) of the distribution of the GenCall scores for that SNP. A GeneCall50 score threshold ≥ 0.40 was used to declare a SNP reliable. SNPs genotyping performance was additionally assessed by the call rate for each SNP using a GeneCall score cutoff ≥ 0.25 for each datapoint  following the Illumina recommended threshold for GGGT . ReliabiPolymorphism was evaluated by the conventional MAF (Minimum Allele Frequency) parameter. A SNP was considered polymorphic if MAF ≥ 0.05. Polymorphism was evaluated consolidating data for all samples together, irrespective of species, and also for each species separately but only for those species that had at least 16 samples (32 chromosomes) analyzed. Species for which only eight individuals were analyzed could not provide acceptable estimates of polymorphism level.Goodness-of-fit for normality tests were carried out on GeneTrain and GeneCall50 scores estimated for each SNP. Mann-Whitney non-parametric tests were carried out to assess the impact of the SNP filtering levels (F0 through F4) on GeneTrain and GeneCall50 scores. Furthermore, using a GeneTrain score ≥ 0.4, a GC50 score ≥ 0.4, a call rate ≥ 95% as thresholds for declaring a reliable SNP and a MAF ≥ 0.05 as a threshold to declare a polymorphic SNP, pair-wise Chi-square Pearson contingency tests were used to assess the impact of the five filtering levels on the final proportions of SNPs declared as being reliable and polymorphic. The final SNP conversion rate estimated by the proportion of polymorphic SNPs (MAF ≥ 0.05) within the ones deemed reliable  was estimated for the whole data set and for each one of the main five species separately.Eucalyptus species as parents. Both parents and 24 offspring individuals were randomly selected from each one of two segregating populations, one derived from a E. grandis × E. urophylla interspecific cross and the second one from a four-species cross involving a E. dunni × E. grandis male parent and a E. urophylla × E. globulus female parent. Heritability was estimated using Genome Studio internal heritability report tool by counting the proportion of correct allelic transmissions for each set of 24 trios.Heritability of SNP allelic transmission was calculated for each SNP using data from a total of 48 parent-parent-offspring trios involving different E. camaldulensis by comparing the GoldenGate called genotypes with NGS based genotypes derived from shotgun genomic reads (Illumina 2 × 76 bases paired-end reads) providing an estimated 2X coverage of the 630 Mbp Eucalyptus genome. Sequence based genotypes were called only for SNP positions that had at least five clustered reads on the reference genome and sequence quality Q ≥ 20. A sample size of 5 reads provides an expected probability of 0.9375 of detecting at least one read with the alternative allele if the genotype is heterozygous. Sequence based genotypes were declared based on a simple binomial test where a null hypothesis of an expected 1:1 ratio of the read counts was set. When the null hypothesis was not rejected a heterozygous genotype was declared. Rejection of the null hypothesis, on the other hand, led to the inference of a homozygous genotype at the SNP.Genotypes for queried SNPs were validated for a sample of DG was responsible for project conception, statistical analyses, manuscript writing, overall supervision of project execution and funding; OSJr was involved in the conception and development of the SNP discovery and design pipeline, and contributed to statistical analyses and manuscript preparation; MK contributed to SNP genotyping; BML contributed to DNA preparation; DAF was responsible for candidate gene selection and helped with data analysis; GJPJr conceived the bioinformatics pipeline development and was responsible for overall bioinformatics supervision. All authors have read and approved the final manuscript.Eucalyptus SNPs developedInformation on the 768 . SNPs are organized in two separate lists, one with the 696 genome-wide SNPs selected with the five in silico sequential filtering levels (locus names start with F0 through F4 - see methods) and a second one with the 72 candidate gene SNPs. Information on the SNP type, SNP flanking sequences used for GGGT assay design, ADT score, Call rate, Minor allele frequency, GeneTrain score and GeneCall50 score is provided, estimated from the 96 DNA samples typed of the five main Eucalyptus species . Contig sequence used for SNP discovery and BLAST annotation are also provided.Click here for fileSupplementary material S2. Counts and percentages of polymorphic SNPs (MAF ≥ 0.05) from a total of 711 reliable SNPs in all 26 combinations of the five main Eucalyptus species surveyed.Click here for fileSupplementary material S3. Results of the NGS-based validation of SNP genotypes. Golden Gate Genotype calls (GGGT genotype) for a Eucalyptus camaldulensis individual were compared to sequence-based genotypes inferred from Illumina short read sequencing based on a binomial test where a null hypothesis of an expected 1:1 ratio of the read counts was set. When the null hypothesis was not rejected a heterozygous genotype was declared. Rejection of the null hypothesis, on the other hand, led to the inference of a homozygous genotype at the SNP. Shaded in grey are the seven SNPs genotypes that showed divergent results between GGGT and NGS genotype calls.Click here for fileSupplementary material S4. Results of logistic regression of the in silico variables used in the SNP discovery and filtering pipeline in E. grandis on SNP Reliability and SNP Polymorphism treated as binary characters .Click here for file


Novel software utilizing cloud computing technology to cost-effectively align and map SNPs from a human genome in three. http://bowtie-bio.sourceforge.net/crossbow/.As DNA sequencing outpaces improvements in computer speed, there is a critical need to accelerate tasks like alignment and SNP calling. Crossbow is a cloud-computing software tool that combines the aligner Bowtie and the SNP caller SOAPsnp. Executing in parallel using Hadoop, Crossbow analyzes data comprising 38-fold coverage of the human genome in three hours using a 320-CPU cluster rented from a cloud computing service for about $85. Crossbow is available from  Improvements in DNA sequencing have made sequencing an increasingly valuable tool for the study of human variation and disease. Technologies from Illumina , Applied Biosystems  and 454 Life Sciences  have been used to detect genomic variations among humans -5, to pret al. [Drosophila melanogaster genome from simulated short reads on a 512-node BlueGene/L supercomputer in less than 4 hours of total elapsed time. Though these efforts demonstrate the promise of parallelization, they are not widely applicable because they require access to a specific type of hardware resource. No two clusters are exactly alike, so scripts and software designed to run well on one cluster may run poorly or fail entirely on another cluster. Software written for large supercomputers like BlueGene/L is less reusable still, since only select researchers have access to such machines. Lack of reusability also makes it difficult for peers to recreate scientific results obtained using such systems.Two recent projects have leveraged parallelism for whole-genome assembly with short reads. Simpson et al.  use ABySet al.  using a et al.  assembleAn increasingly popular alternative for large-scale computations is cloud computing. Instead of owning and maintaining dedicated hardware, cloud computing offers a 'utility computing' model, that is, the ability to rent and perform computation on standard, commodity computer hardware over the Internet. These rented computers run in a virtualized environment where the user is free to customize the operating system and software installed. Cloud computing also offers a parallel computing framework called MapReduce , which wThe CloudBurst project  exploredWe present Crossbow, a Hadoop-based software tool that combines the speed of the short read aligner Bowtie  with theCrossbow is open source software available from the Bowtie website . CrossboCrossbow harnesses cloud computing to efficiently and accurately align billions of reads and call SNPs in hours, including for high-coverage whole-human datasets. Within Crossbow, alignment and SNP calling are performed by Bowtie and SOAPsnp, respectively, in a seamless, automatic pipeline. Crossbow can be run on any computer cluster with the prerequisite software installed. The Crossbow package includes scripts that allow the user to run an entire Crossbow session remotely on an Amazon EC2 cluster of any size.et al. dataset [To measure Crossbow's accuracy where true SNPs are known, we conducted two experiments using simulated paired-end read data from human chromosomes 22 and X. Results are shown in Tables  dataset .SOAPsnp can exploit user-supplied information about known SNP loci and allele frequencies to refine its prior probabilities and improve accuracy. Therefore, the read simulator was designed to simulate both known HapMap  SNPs andFor these simulated data, Crossbow agrees substantially with the true calls, with greater than 99% precision and sensitivity overall for chromosome 22. Performance for HapMap SNPs is noticeably better than for novel SNPs, owing to SOAPsnp's ability to adjust SNP-calling priors according to known allele frequencies. Performance is similar for homozygous and heterozygous SNPs overall, but novel heterozygous SNPs yielded the worst performance of any other subset studied, with 96.6% sensitivity and 94.6% specificity on chromosome 22. This is as expected, since novel SNPs do not benefit from prior knowledge, and heterozygous SNPs are more difficult than homozygous SNPs to distinguish from the background of sequencing errors.et al [To demonstrate performance on real-world data, we used Crossbow to align and call SNPs from the set of 2.7 billion reads and paired-end reads sequenced from a Han Chinese male by Wang et al . Previouet al . Since CWe downloaded 2.66 billion reads from a mirror of the YanHuang site . These rTable Both Crossbow and SOAP+SOAPsnp exhibit a very high level of agreement with the BeadChip genotype calls. The small differences in number of covered sites (<2% higher for Crossbow) and in percentage agreement (<0.1% lower for Crossbow) are likely due to the SOAPsnp study's use of additional filters to remove some SNPs prior to the agreement calculation, and to differences in alignment policies between SOAP and Bowtie. After filtering, Crossbow reports a total of 3,738,786 SNPs across all autosomal chromosomes and chromosome X, whereas the SNP GFF file available from the YanHaung site  reports The above results were computed on a Hadoop 0.20 cluster with 10 worker nodes located in our laboratory, where it required about 1 day of wall clock time to run. Each node is a four-core 3.2 GHz Intel Xeon  running 64-bit Redhat Enterprise Linux Server 5.3 with 4 GB of physical memory and 366 GB of local storage available for the Hadoop Distributed Filesystem (HDFS) and connected via gigabit ethernet. We also performed this computation using Amazon's EC2 service on clusters of 10, 20 and 40 nodes  running Hadoop 0.20. In each case, the Crossbow pipeline was executed end-to-end using scripts distributed with the Crossbow package. In the 10-, 20- and 40-node experiments, each individual node was an EC2 Extra Large High CPU Instance, that is, a virtualized 64-bit computer with 7 GB of memory and the equivalent of 8 processor cores clocked at approximately 2.5 to 2.8 Ghz. At the time of this writing, the cost of such nodes was $0.68 ($0.76 in Europe) per node per hour.Before running Crossbow, the short read data must be stored on a filesystem the Hadoop cluster can access. When the Hadoop cluster is rented from Amazon's EC2 service, users will typically upload input data to Amazon's Simple Storage Service (S3) , a serviTransfer time depends heavily on both the size of the data and the speed of the Internet uplink at the source. Public archives like NCBI and the European Bioinformatics Institute (EBI) have very high-bandwidth uplinks to the >10 Gb/s JANET and Internet2 network backbones, as do many academic institutions. However, even at these institutions, the bandwidth available for a given server or workstation can be considerably less (commonly 100 Mb/s or less). Delays due to slow uplinks can be mitigated by transferring large datasets in stages as reads are generated by the sequencer, rather than all at once.To measure how the whole-genome Crossbow computation scales, separate experiments were performed using 10, 20 and 40 EC2 Extra Large High CPU nodes. Table Figure Hadoop is an implementation of the MapReduce parallel programming model. Under Hadoop, programs are expressed as a series of map and reduce phases operating on tuples of data. Though not all programs are easily expressed this way, Hadoop programs stand to benefit from services provided by Hadoop. For instance, Hadoop programs need not deal with particulars of how work and data are distributed across the cluster; these details are handled by Hadoop, which automatically partitions, sorts and routes data among computers and processes. Hadoop also provides fault tolerance by partitioning files into chunks and storing them redundantly on the HDFS. When a subtask fails due to hardware or software errors, Hadoop restarts the task automatically, using a cached copy of its input data.A mapper is a short program that runs during the map phase. A mapper receives a tuple of input data, performs a computation, and outputs zero or more tuples of data. A tuple consists of a key and a value. For example, within Crossbow a read is represented as a tuple where the key is the read's name and the value equals the read's sequence and quality strings. The mapper is generally constrained to be stateless - that is, the content of an output tuple may depend only on the content of the corresponding input tuple, and not on previously observed tuples. This enables MapReduce to safely execute many instances of the mapper in parallel. Similar to a mapper, a reducer is a short program that runs during the reduce phase, but with the added condition that a single instance of the reducer will receive all tuples from the map phase with the same key. In this way, the mappers typically compute partial results, and the reducer finalizes the computation using all the tuples with the same key, and outputs zero or more output tuples. The reducer is also constrained to be stateless - that is, the content of an output tuple may depend only the content of the tuples in the incoming batch, not on any other previously observed input tuples. Between the map and reduce phases, Hadoop automatically executes a sort/shuffle phase that bins and sorts tuples according to primary and secondary keys before passing batches on to reducers. Because mappers and reducers are stateless, and because Hadoop itself handles the sort/shuffle phase, Hadoop has significant freedom in how it distributes parallel chunks of work across the cluster.The chief insight behind Crossbow is that alignment and SNP calling can be framed as a series of map, sort/shuffle and reduce phases. The map phase is short read alignment where input tuples represent reads and output tuples represent alignments. The sort/shuffle phase bins alignments according to the genomic region ('partition') aligned to. The sort/shuffle phase also sorts alignments along the forward strand of the reference in preparation for consensus calling. The reduce phase calls SNPs for a given partition, where input tuples represent the sorted list of alignments occurring in the partition and output tuples represent SNP calls.A typical Hadoop program consists of Java classes implementing the mapper and reducer running in parallel on many compute nodes. However, Hadoop also supports a 'streaming' mode of operation whereby the map and reduce functions are delegated to command-line scripts or compiled programs written in any language. In streaming mode, Hadoop executes the streaming programs in parallel on different compute nodes, and passes tuples into and out of the program as tab-delimited lines of text written to the 'standard in' and 'standard out' file handles. This allows Crossbow to reuse existing software for aligning reads and calling SNPs while automatically gaining the scaling benefits of Hadoop. For alignment, Crossbow uses Bowtie , which eTo report SNPs, Crossbow uses SOAPsnp , which cCrossbow's efficiency requires that the three MapReduce phases, map, sort/shuffle and reduce, each be efficient. The map and reduce phases are handled by Bowtie and SOAPsnp, respectively, which have been shown to perform efficiently in the context of human resequencing. But another advantage of Hadoop is that its implementation of the sort/shuffle phase is extremely efficient, even for human resequencing where mappers typically output billions of alignments and hundreds of gigabytes of data to be sorted. Hadoop's file system (HDFS) and intelligent work scheduling make it especially well suited for huge sort tasks, as evidenced by the fact that a 1,460-node Hadoop cluster currently holds the speed record for sorting 1 TB of data on commodity hardware (62 seconds) .Several new features were added to Bowtie to allow it to operate within Hadoop. A new input format (option --12) was added, allowing Bowtie to recognize the one-read-per-line format produced by the Crossbow preprocessor. New command-line options --mm and --shmem instruct Bowtie to use memory-mapped files or shared memory, respectively, for loading and storing the reference index. These features allow many Bowtie processes, each acting as an independent mapper, to run in parallel on a multi-core computer while sharing a single in-memory image of the reference index. This maximizes alignment throughput when cluster computers contain many CPUs but limited memory. Finally, a Crossbow-specific output format was implemented that encodes an alignment as a tuple where the tuple's key identifies a reference partition and the value describes the alignment. Bowtie detects instances where a reported alignment spans a boundary between two reference partitions, in which case Bowtie outputs a pair of alignment tuples with identical values but different keys, each identifying one of the spanned partitions. These features are enabled via the --partition option, which also sets the reference partition size.The version of SOAPsnp used in Crossbow was modified to accept alignment records output by modified Bowtie. Speed improvements were also made to SOAPsnp, including an improvement for the case where the input alignments cover only a small interval of a chromosome, as is the case when Crossbow invokes SOAPsnp on a single partition. None of the modifications made to SOAPsnp fundamentally affect how consensus bases or SNPs are called.The input to Crossbow is a set of preprocessed read files, where each read is encoded as a tab-delimited tuple. For paired-end reads, both ends are stored on a single line. Conversion takes place as part of a bulk-copy procedure, implemented as a Hadoop program driven by automatic scripts included with Crossbow. Once preprocessed reads are situated on a filesystem accessible to the Hadoop cluster, the Crossbow MapReduce job is invoked Figure . CrossboThe reduce phase performs SNP calling using SOAPsnp. A wrapper script performs a separate invocation of the SOAPsnp program per partition. The wrapper also ensures that SOAPsnp is invoked with appropriate options given the ploidy of the reference partition. Files containing known SNP locations and allele frequencies derived from dbSNP  are distCrossbow comes with scripts that automate the Crossbow pipeline on a local cluster or on the EC2  utility et al. [We generated 40-fold coverage of chromosomes 22 and X  using Bowtie with options '-X 10000 -v 2 --strata --best -m 1'. The observed mean mate-pair distance and standard deviation for this subset were 422 bp and 68.8 bp, respectively.Bowtie version 0.10.2 was run with the '-v 2 --best --strata -m 1' to obtain unique alignments with up to two mismatches. We define an alignment as unique if all other alignments for that read have strictly more mismatches. SOAPsnp was run with the rank-sum and binomial tests enabled  and with known-SNP refinement enabled (-2 and -s options). Positions and allele frequencies for known SNPs were calculated according to the same HapMap SNP data used to simulate SNPs. SOAPsnp's prior probabilities for novel homozygous and heterozygous SNPs were set to the rates used by the simulator (-r 0.0001 -e 0.0002 for chromosome 22 and -r 0.0002 for chromosome X).An instance where Crossbow reports a SNP on a diploid portion of the genome was discarded  if it was covered by fewer than four uniquely aligned reads. For a haploid portion, a SNP was discarded if covered by fewer than two uniquely aligned reads. For either diploid or haploid portions, a SNP was discarded if the call quality as reported by SOAPsnp was less than 20.et al. [Bowtie version 0.10.2 and a modified version of SOAPsnp 1.02 were used. Both were compiled for 64-bit Linux. Bowtie was run with the '-v 2 --best --strata -m 1' options, mimicking the alignment and reporting modes used in the SOAPsnp study. A modified version of SOAPsnp 1.02 was run with the rank-sum and binomial tests enabled  and with known-SNP refinement enabled (-2 and -s options). Positions for known SNPs were calculated according to data in dbSNP  versionset al.  to dbSNPTo account for base-calling errors and inaccurate quality values reported by the Illumina software pipeline ,30, SOAPAn instance where Crossbow reports a SNP on a diploid portion of the genome is discarded  if it is covered by fewer than four unique alignments. For a haploid portion, a SNP is discarded if covered by fewer than two unique alignments. For either diploid or haploid portions, a SNP is discarded if the call quality as reported by SOAPsnp is less than 20. Note that the SOAPsnp study applies additional filters to discard SNPs at positions that, for example, are not covered by any paired-end reads or appear to have a high copy number. Adding such filters to Crossbow is future work.In this paper we have demonstrated that cloud computing realized by MapReduce and Hadoop can be leveraged to efficiently parallelize existing serial implementations of sequence alignment and genotyping algorithms. This combination allows large datasets of DNA sequences to be analyzed rapidly without sacrificing accuracy or requiring extensive software engineering efforts to parallelize the computation.We describe the implementation of an efficient whole-genome genotyping tool, Crossbow, that combines two previously published software tools: the sequence aligner Bowtie and the SNP caller SOAPsnp. Crossbow achieves at least 98.9% accuracy on simulated datasets of individual chromosomes, and better than 99.8% concordance with the Illumina 1 M BeadChip assay of a sequenced individual. These accuracies are comparable to those achieved in the prior SOAPsnp study once filtering stringencies are taken into account.When run on conventional computers, a deep-coverage human resequencing project requires weeks of time to analyze on a single computer by contrast, Crossbow aligns and calls SNPs from the same dataset in less than 3 hours on a 320-core cluster. By taking advantage of commodity processors available via cloud computing services, Crossbow condenses over 1,000 hours of computation into a few hours without requiring the user to own or operate a computer cluster. In addition, running on standard software (Hadoop) and hardware (EC2 instances) makes it easier for other researchers to reproduce our results or execute their own analysis with Crossbow.Crossbow scales well to large clusters by leveraging Hadoop and the established, fast Bowtie and SOAPsnp algorithms with limited modifications. The ultrafast Bowtie alignment algorithm, utilizing a quality-directed best-first-search of the FM index, is especially important to the overall performance of Crossbow relative to CloudBurst. Crossbow's alignment stage vastly outperforms the fixed-seed seed-and-extend search algorithm of CloudBurst on clusters of the same size. We expect that the Crossbow infrastructure will serve as a foundation for bringing massive scalability to other high-volume sequencing experiments, such as RNA-seq and ChIP-seq. In our experiments, we demonstrated that Crossbow works equally well either on a local cluster or a remote cluster, but in the future we expect that utility computing services will make cloud computing applications widely available to any researcher.EC2: Elastic Compute Cloud; FM: full-text minute-space; HDFS: Hadoop Distributed Filesystem; NCBI: National Center for Biotechnology Information; S3: Simple Storage Service; SNP: single nucleotide polymorphism.BL and MCS developed the algorithms, collected results, and wrote the software. JL contributed to discussions on algorithms. BL, MCS, JL, MP, and SLS wrote the manuscript.The following additional data are included with the online version of this article: version 0.1.3 of the Crossbow software (Additional data file Version 0.1.3 of the Crossbow software.Click here for file