The goal of genome wide analyses of polymorphisms is to achieve a better understanding of the link between genotype and phenotype. Part of that goal is to understand the selective forces that have operated on a population.ST, was highly correlated in the two data sets. Nevertheless, 40% of the variance in FST between the two studies was attributed to the differences in breed composition. Seventy six percent of the variance in FST was attributed to differences in SNP composition and density when the same breeds were compared. The difference between FST of adjacent loci increased rapidly with the increase in distance between SNP, reaching an asymptote after 20 kb. Using 129 SNP that have highly divergent FST values in both data sets, we identified 12 regions that had additive effects on the traits residual feed intake, beef yield or intramuscular fatness measured in the Australian sample. Four of these regions had effects on more than one trait. One of these regions includes the R3HDM1 gene, which is under selection in European humans.In this study we compared the signals of selection, identified through population divergence in the Bovine HapMap project, to those found in an independent sample of cattle from Australia. Evidence for population differentiation across the genome, as measured by FST difference between adjacent loci suggests that 150,000 evenly spaced SNP will be required to study selective signatures in all parts of the bovine genome.Firstly, many different populations will be necessary for a full description of selective signatures across the genome, not just a small set of highly divergent populations. Secondly, it is necessary to use the same SNP when comparing the signatures of selection from one study to another. Thirdly, useful signatures of selection can be obtained where many of the groups have only minor genetic differences and may not be clearly separated in a principal component analysis. Fourthly, combining analyses of genome wide selection signatures and genome wide associations to traits helps to define the trait under selection or the population group in which the QTL is likely to be segregating. Finally, the F The goal of genome wide analyses of polymorphisms is to achieve a better understanding of the link between genotype and phenotype. The study of a large number of polymorphisms spread across the genome will reveal aspects of the genetic structure of the population, including, in some cases, evidence of adaptive selection across the genome ,2. Furth6 single nucleotide polymorphisms (SNP) across the genome [Genome wide studies of genetic selection (GWGS) are generally performed separately to GWA despite the potential advantages of combining the information. The reasons for this separation are mainly operational. GWA studies are sampled to study a set of traits, and population stratification is avoided or tightly controlled . Often te genome , studied4 SNP and a variety of analyses were performed, including a genome wide assessment of positive selection. Several methods were used to infer evidence of positive selection with many locations identified by more than one method. These methods were 1) the analysis of population divergence using FST [The Bovine HapMap project was started with the goal of understanding the genetic structure and history of an important livestock species. The expectation was that this study would provide information on the processes of domestication, insights that are not currently available from the study of humans or other animal species. To do this, animals from 19 breeds and two outgroup species were sampled from five continents and all three major branches of cattle . This lasing FST ,15, 2) tsing FST  of derivsing FST , and 3) sing FST . HoweverST to study selection partly because 1) it is a robust easily calculated measure with a long history [This study had two main aims. The first was to compare genome wide signatures of selection found in the Bovine HapMap project with genotypes collected from a similar set of animals for which phenotypes are known . The sec history ,15,20,21 history , resultiBos taurus L.) have three major geographic groupings, the European and North Asian taurine, South Asian and East African zebu, and West and South African sanga [Cattle  and the Angus (15%) animal, with 7.5% cSNP (coding SNP) obtained from the Interactive Bovine in silico SNP database [The genotyping of the Australian sample was performed using the MegAllele™ Genotyping Bovine 10 K SNP Panel , a fullydatabase , and thedatabase . In summST analysis although we do not expect that the exclusion would make much difference to the analysis because of the small numbers involved and the amount of variation explained by each principal component. The clustering was performed for two reasons. Firstly, lumping of breeds was necessary in the dairy animals because some of the sample sizes were small (N < 10). It has been shown that it is best to avoid small samples and to use samples of equal size when comparing estimates of FST [ST, while lumping groups that have divergent genotypic frequencies should act to reduce FST. Nevertheless, it is the distribution of FST across the genome not the absolute value that is important in identifying evidence of positive selection [ST [To determine the clustering of individuals in this study, a principal component analysis (PCA) was performed on the individual genotypes of the 378 animals using the 1604 SNP that had no missing data. The data were analysed using a covariance matrix in SPLUS -31. Fulls of FST . Having election . Secondltion [ST . The anaST at each locus was calculated [ST values in this study are per locus values. Means and standard deviations were calculated not to find an unbiased average across all the data but to measure the dispersion of per locus FST in the sample. SNPs that were genotyped in all breeds were used. FST, the average heterozygosity and average allele frequency for each SNP were plotted  of pairs of immediately adjacent SNP (SNP pairs) was compared to the distance in base pairs between them. Each interval between SNP pairs was used once. SNP pairs were binned into those below 1 kb, then into 10 kb bins up to 100 kb, and then into 100 kb bins up to 1.2 Mb. The mean δFST and standard error were plotted against mean distance between the SNP pairs of each bin.To determine a genome wide pattern of positive selection, and to compare this to the Bovine HapMap sample, the Flculated  in the shigh FST , due to high FST , implyinn breeds ,21. Sevee limits ,35. To da) is half the distance between the two homozygotes, the dominance effect (d) is the difference between the heterozygotes and the average of the two homozygotes, and the allele substitution effect (α) = a + d(q-p) where p+q = 1 and p, q are the allele frequencies [ST values.The additive and allele substitution effects for the traits residual feed intake (RFI), percent meat yield (yield), and percent intramuscular fat (IMF) in beef cattle were calculated as previously described and results published elsewhere ,36,37. Iquencies . Standarquencies . In thisST versus low extreme FST was compared to the goodness of fit expectations for the same sample size using a chi-square test. The expected frequency was taken as the ratio of the observed number of SNP in the low versus the high extreme.The number of association tests with high extreme FST, we used a PCA to determine how the animals should be allocated to groups and the degree of divergence between samples. Using the individual genotypes as the data in the PCA  were used. The difference in mean per locus FST value between the Australian sample and the Bovine HapMap sample was less when only the three divergent breeds were compared. Although these mean differences are small, they are all significant because of the large samples of loci used. The dispersion of FST values was also greater in the three breed comparisons than when all breeds where used. To determine if removing some breeds would make a difference to the estimates, we removed the two African breeds, N'Dama and Sheko, and the two zebu breeds, Gir and Nelore, from the Bovine HapMap data. Removing the N'Dama and Sheko removed animals that were highly divergent on PC2 of the Bovine HapMap data, leaving only the partly overlapping European breeds. Removing the Gir and Nelore reduced the number of zebu breeds to one. This made the breed composition more similar in the two studies. It also still included the Angus, Brahman and Holstein. For all loci in the Bovine HapMap data the average FST = 0.126, S.D. = 0.0722 (N = 32470). This showed a reduction in the per locus FST, of nearly half the difference between the mean per-locus FST value of the full Bovine HapMap and Australian samples. This reduced set of breeds still showed the lower dispersion in per locus FST values found in the full sample of breeds compared to the three breed estimates.To characterise the differences in per locus Fci Table . There wST across the genome was the same in both this and the Bovine HapMap study, the windowed FST was plotted against location in the genome . In particular, locations on chromosomes 7, 10, 12, 14 and the X were obviously different. The windowed FST values between the two data sets at each locus were correlated with r = 0.346, N = 7298, P = 0. This indicated that genome location explained 12.0% of the variance in that comparison.To determine whether the global pattern of Fe Figure . There wST was explored in more detail by using subsets of breeds and subsets of loci to determine which had the more important influence on whether a signal appeared in a particular location. Using only the loci in common with all breeds in both data sets, the un-windowed FST values showed a correlation of r = 0.615 . Comparing the all breed windowed to un-windowed FST values in the same way shows a reduction in the variance explained of 68% (r = 0.346 vs r = 0.615). This reduction was smaller than the three breed comparison but the full breed comparison includes not only differences in SNP between studies but also differences in the number and composition of breeds.To determine the effect of the specific loci on the distribution of FST values for BTA 6, 14 and 25 were compared to the other chromosomes. These three chromosomes have 2–3 times higher density than the other chromosomes in the Bovine HapMap data, which in their turn have a 2–3 times higher density in the Bovine HapMap data than in the Australian data. For all breeds, comparing the Australian to the Bovine HapMap data, the correlations were r = 0.382 for comparisons of BTA6, 14 and 25 combined and r = 0.344 for the other chromosomes combined. If the loci that are windowed are only the common ones between the two studies, that is, no difference in density, then the correlation between windowed FST values is r = 0.640, essentially the same as the un-windowed values (r = 0.615).To determine the importance of differences in SNP density between the two studies, windowed FST to be considered significant using bootstrap sampling. The 99.9% confidence interval for the per locus FST in our data was 0.094 ± 0.062 while the confidence interval in the Bovine HapMap data for the same loci was 0.141 ± 0.121. In the Australian sample, the top 2.5% corresponded to a threshold FST = 0.224 and the bottom 2.5% corresponded to a threshold FST = 0.015, both outside the confidence interval for that dataset. In the Bovine HapMap data, the top 2.5% corresponded to a threshold FST = 0.284, which is outside the 99.9% confidence interval and the bottom 2.5% corresponded to a threshold FST = 0.039, which is outside a 99% confidence interval for that dataset. There were 94 loci that had an FST above the upper thresholds in both data sets, or 1.28% of 7298 SNP. There were 35 loci that had an FST below the lower thresholds in both data sets. For the SNP above the threshold in both data sets, the 94 SNP were located in 71 genomic regions of 1 Mb containing one or more SNP with high FST , six of which had an effect on more than one trait (Table 21 = 3.78 n.s.). All of these SNP had FST values greater than the upper threshold, representing divergence between the breeds. The observed frequency of 0 low FST versus 14 high FST, compared to the expected frequency of 35:94, was significant . These loci all had much larger FST values in the three breed sub-sample than in the full breed sample.Fourteen of the 129 SNP with extreme Fit Table . The samST value was within the confidence interval for the data set. Nine of the 13 SNP showed an association and had average allele frequencies of between 0.1 and 0.9 across all breeds. Only the three SNP with the highest FST values had effects on both traits.The region on BTA2:64.7 Mb that showed effects on two traits was examined in greater detail. There are eight SNP in this region separated by a total of 83.3 kb set in a region containing 31 SNP separated by a total of 9.86 Mb. Thirteen of the loci , showed essentially the same relationship to the Australian data as the rest of the data set. This suggests that SNP identity was more important than density in explaining the differences between the two studies. The similarity between the FST values between the two datasets declined significantly when all breeds and SNP were used, amounting to 68% of the variance in that comparison. This is less than the 76% when only the Angus, Holstein and Brahman breeds of the two data sets are used. This difference suggests an interaction between breeds and SNP. The composition of SNP in the two panels is neither of uniform ascertainment nor of exactly the same ascertainment as each other. It is not uniform because SNP were primarily generated by comparing a small number of animals, each of a different breed, to the Hereford sequence, and it was different, because there were slightly more breeds used to define SNP in the Bovine HapMap data and a much larger percentage of zebu SNP [As there were only 7298 loci in common to both studies, a large proportion of the difference in Fta BTA6,  and 25, The first two axes of the PCA separated breeds in a similar way in the Australian and the Bovine HapMap data with one major exception. The similarities are that the first axis clearly separated zebu from taurine and the second axis partially separated meat and dairy within European breeds in both data sets. The first axis also clearly separated zebu from purebred sanga in the Bovine HapMap data, which were at the same co-ordinates on PC1 as the European taurine group. The second axis in the Bovine HapMap study clearly separated the sanga (African taurine) from the European taurine. The differences between European cattle were minor compared to the differences between European and African cattle. Based on the Bovine HapMap data, for diversity studies, a minimum three breed panel would probably have to consist of one taurine, one sanga and one zebu breed. Sanga breeds are not common in the developed world. For breeds that are commonly available one could identify the Angus, Brahman and Holstein as a minimum group for studies of breed or locus divergence based on the similarity between the two studies. However, the greater difference between Angus and Holstein compared to other European breeds may be a consequence of the SNP ascertainment, and further study might be needed before a minimum panel of common breeds could be specified. The major difference between the two studies was that we found that the reference breed for SNP discovery, the Hereford, was located exactly where it was expected, at the intersection of PC1 and PC2, because all breeds would in effect be compared to it (cf. above). This location of the Hereford cluster differs substantially from the analysis reported in the Bovine HapMap study. There the Hereford formed a flattened outlier group well separated from the other breeds. In that report, when only the loci originating from the Jersey breed were analysed in the PCA, the Jersey animals were clustered outside the main group of taurine animals. This may indicate that the shifting clusters could be a generic factor associated with the analysis. While we have not explicitly modelled causes for the difference by performing a PCA on the Bovine HapMap data ourselves, if that explanation is correct then it may suggest that the use of a very large number of loci may lead to the unusual clustering of animals that acted as the source of the polymorphisms.ST values that are more similar at a close distance are likely to be similar due to LD. Our results show that SNP separated by more than 20 kb have δFST the same as those separated by more than 1 Mb. This is consistent with the decay in LD as measured by r2 for these [r2 reaches a lower asymptote at around 20–30 kb. These results suggest that for cattle, FST values that are consistently high or low over distances much greater than 20 kb are likely to be due to factors other than baseline LD, and presumably some of these will be due to positive selection [ST and distance between SNP can be quantified. It will require at least 150,000 evenly spaced SNP to quantify genome wide signals in cattle. As SNP are not evenly spaced in current SNP chips, it would require several fold more SNP than that to explore all regions at that minimum density.Higher densities of SNP will be required to study genome wide signatures of selection in cattle. LD causes correlation between genotypes , so FST or these  as well or these , where relection . The denST values is a resource for the further investigation of the effects of positive selection on the bovine genome. Comparing ends of the distribution in two studies does not necessarily mean that those values are more divergent than expected [ST values can be expected unless there are differences in the populations or errors in the way the genotypes are collected. Instead, the loci in this list have FST outside the bootstrap confidence limits [ST around these SNP. That should help define groups of divergent or convergent FST, which in turn would help to define the size and shape of these prospective signatures of positive selection.The list of the SNP with the most divergent Fexpected . Due to e limits  for bothST that are associated with RFI, yield and IMF than those with very low FST. IMF is strongly correlated to overall fatness of the animal and is negatively correlated with yield, larger RFI values have a positive relationship to IMF and a negative relationship to measures of muscle area in cattle, while the mean values for these traits show large differences between breeds [ST. It is interesting that of the SNP with divergent FST five out of six of those with effects on more than one of these traits have effects that are consistent with the known correlations between RFI, yield and IMF.There appears to be significantly more loci with very high Fn breeds -44. Yieln breeds . Much ofn breeds . These bST. Associations are found for SNP that do not have highly skewed allele frequencies, and associations are not just limited to a particular subsection of the sample. This suggests that these results are not merely a spurious intersection of high FST and trait association. Functional evaluation of these and other SNP will be needed to show where the causative mutations lie in this region. Full genetic evaluation of these regions will require more SNP, larger samples and more breeds. Nevertheless, our data show that signatures of selection are a useful adjunct to trait mapping.Examination of a region with enough density of SNP shows that association to the trait is not just found for markers with high FST on bovine chromosome 2 contains several genes that have been associated with selective sweeps in humans. The genes R3HDM1 (R3H domain containing 1) and ZRANB3  are associated with these cattle SNP. The functions of these genes are inferred based on their sequence structures, and references to them point to the bioinformatics literature  and human adaptation to drinking milk in adulthood [R3HDM1 [LCT. These results in cattle provide a starting point for the kinds of trait that might be associated with the signal of selection in humans.The region associated with the peak in FMay 2008 ). For thdulthood ,10. It i [R3HDM1  was alsoST across the genome is due to differences in SNP composition between studies, which suggests that evidence for selection in a region depends on the SNP that are included in a study. It also suggests that when the same genetic region is compared in different studies the same SNP should be used otherwise a large difference between selection signatures may be reported. These results also suggest that genome wide studies of selection are a useful adjunct to genome wide association studies. We found that useful signatures of selection can be obtained even when many of the sub-samples are not particularly divergent or cannot be clearly separated using a principal component analysis. If a sample used in a genome wide association study was genetically stratified, this stratification could be used to analyse gene frequency or FST distributions in sections of the genome in different breeds or populations. Such an analysis may provide evidence for a selective signature or may even help to identify which populations are likely to contain the QTL even before functional nucleotide polymorphisms were identified. While many real QTL will not be accompanied by a signal of selection, such a signal will indicate a clear relationship between selection on some aspect of the phenotype and the effects of variation at a locus. The identification of the trait under selection will require animals of the appropriate breed or group measured for a wide range of traits. The use of evidence of presumptive selection on a genome wide scale means that panels of SNP will need to be several fold larger in cattle than currently available. Although several panels are now available that include 3.4–5.4 × 104 SNP in cattle , givWB planned and performed the analyses, and drafted the manuscript, BH bled cattle, extracted DNA, RB bled cattle, extracted DNA, MT extracted DNA, LT, obtained cattle, led the bleeding of cattle. All authors reviewed the manuscript.STPlot of experiment-wide statistics for allele frequency, average heterozygosity and F. Two plots one above the other, the upper plot shows the average allele frequency plotted against the average heterozygosity for each SNP, the lower plot shows average heterozygosity plotted against FST for each SNP.Click here for fileST values in both the Bovine HapMap and Australian data setsSNP with extreme F. A table consisting of six columns listing the dbSNP identifier, the chromosome, the position in bp in the Btau 4.0 assembly, the per locus FST in the Australian and then the Bovine HapMap data, and the number of 1 Mb blocks per chromosome with loci with high or low FST.Click here for file


The availability of a large amount of SNP markers throughout the genome of different livestock species offers the opportunity to estimate genomic breeding values (GEBVs). However, the estimation of many effects in a data set of limited size represent a severe statistical problem. A pre-selection of SNPS based on single regression may provide a reasonable compromise between accuracy of results, number of independent variables to be considered and computing requirements.2a/nsnps resulted in a remarkable accuracy of GEBV estimation (0.77) in the PREDICTION generations.A total of 595 and 618 SNPS were pre-selected using a simple linear regression for each SNP, based on phenotypes or polygenic EBVs, respectively, with an average distance of 9–10 cM between them. Chromosome four had the largest frequency of selected SNPS. Average correlations between GEBVs and TBVs were about 0.82 and 0.73 for the TRAINING generations when phenotypes or polygenic EBVs were considered as dependent variable, whereas they tend to decrease to 0.66 and 0.54 for the PREDICTION generations. The pre-selection of SNPs using the phenotypes as dependent variable together with a BLUP estimation of marker genotype effects using a variance contribution of each marker equal to σ The availability of a large amount of SNP markers throughout the genome for several livestock species allows the prediction of genomic breeding values (GEBVs) as the sum of the effect of the different haplotype intervals that cover the whole genome -3. AlthoA simulated data set of 5,865 individuals generated for the XII QTL-MAS workshop was used. The genome consisted of six chromosomes, with a total of 6,000 SNP marker loci . Individuals of the first four generations (TRAINING data set) had pedigree, phenotype, and marker information available whereas those of the last three generations (PREDICTION data set) had only pedigree and marker information.Variance components and polygenic breeding values were estimated by analyzing the whole data set with the following single trait BLUP animal model using the MTDFREML package:k is the random genetic additive effect of the k-th animal ), eijk is the random residual ). The relationship matrix included 5,939 animals.where y is the trait value, SEX is the fixed effect of sex , GEN is the fixed effect of generation (j = 0–6), aData of the 4,665 animals of the TRAINING data set were analysed by a simple linear regression for each SNP.-6 for the P values of the F test was fixed to retain markers for further multiple-SNP analysis.where y is the phenotype or the polygenic EBV, SNP is the genotype at the i-th SNP . An empirical threshold of 1.6EThe effect of marker genotypes was estimated on the TRAINING data set with the following mixed linear modelb is a vector of three genotype effects for SNP k, and H is the corresponding design matrix for the SNP k, and e is the random residual. No interaction between different SNPs loci and a constant variance for each SNP locus were assumed. In studies where no variance components are estimated, the variance ratio λ = σ2e/σ2a to be used in the solution of mixed model equations is usually fixed a priori at a value (for example 1) able to remove possible dependencies among intervals while treating these factors closer to fixed effects [2e/(σ2a/nsnps). In this paper, both the two options  were tested.where y is the phenotype,  effects . On the For the estimation of marker effects using EBVs as dependent variable, the model for the estimation of the marker effect did not include the SEX and GEN factors.The genomic breeding value for each of the i animals of the whole data set was then calculated as1(GEBV1) or λ2 (GEBV2), or using polygenic EBVs . Accuracy of genomic selection was calculated as the correlation between GEBVs and true breeding values (TBVs).Thus, four genomic breeding values were calculated for each animal: using phenotypes as dependent variable with variance ratio λ2a) and residual (σ2e) variance, respectively, with a resulting heritability of the trait of 0.30. Average accuracies of EBV estimation were 0.71 and 0.33 for the TRAINING and PREDICTION generations, respectively.Variance components estimated with the polygenic animal model were 1.324 and 3.142 for additive genetic  in order to retain a comparable number of markers. The largest number of significant SNPs obtained when EBVs were used could be partially explained by less noise in EBVs which already erase the fixed effects and large proportion of residuals. The number of common SNPs selected in both analyses was 411 . The average marker distance was 0.99 cM for phenotype-selected  and 0.95 for EBVs-selected  SNPs. The distribution of selected markers across the six chromosomes is reported in Figure The number of markers retained was 595 and 618 for the screening carried out on phenotypes and polygenic EBVs, respectively. In the latter case, a more stringent threshold was adopted (1.6E1) for GEBV1 and GEBV3, 1,412 (λ2) for GEBV2 and 1,466 (λ2) for GEBV4.Most of selected SNPs are located on chromosomes four (more than 200) and 1 (about 140). Distribution of markers selected on phenotypes (white bars) or polygenic EBVs (black bars) follow approximately the same pattern. The percentage of SNPs selected by both analyses ranged between 80% (chromosome 4) and 33% (chromosome 5). Based on variance component estimates and on the number of markers retained, variance ratios used for estimation of marker genotype effects were 2.4 (λCorrelations between GEBVs and TBV in the TRAINING generations are around 0.87 and 0.72 when phenotypes or EBVs are used as dependent variable Table . These vAccuracies of GEBV estimation for the PREDICTION generations Table  are loweThe pattern of correlations between TBVs and the different GEBVs across the seven generations considered Figure  confirmsThe strategy here presented for genomic breeding value estimation, that combines a pre-selection of SNPs based on least squares regression and a BLUP estimation of marker effects, gave in general poorer results in terms of accuracy of GEBV in comparison with those reported in the literature for more sophisticated methods based on Bayesian inference and on the estimation of IBD matrix. However, the GEBVs calculated using phenotypes as dependent variable and assuming an equal contribution of each marker locus to the variance of the trait showed an accuracy that is closer to the one of the best methods.The authors declare that they have no competing interests.


Analysis of single nucleotide polymorphism (SNP) is becoming a key research in genomics fields. Many functional analyses of SNPs have been carried out for coding regions and splicing sites that can alter proteins and mRNA splicing. However, SNPs in non-coding regulatory regions can also influence important biological regulation. Presently, there are few databases for SNPs in non-coding regulatory regions.We identified 488,452 human SNPs in the putative promoter regions that extended from the +5000 bp to -500 bp region of the transcription start sites. Some SNPs occurring in transcription factor (TF) binding sites were also predicted . The result is stored in a database: SNP@promoter. Users can search the SNP@Promoter database using three entries: 1) by SNP identifier (rs number from dbSNP), 2) by gene , and 3) by disease term. The SNP@Promoter database provides extensive genetic information and graphical views of queried terms.We present the SNP@Promoter database. It was created in order to predict functional SNPs in putative promoter regions and predicted transcription factor binding sites. SNP@Promoter will help researchers to identify functional SNPs in non-coding regions. After finishing the Human Genome Project, biologists' interest has shifted to non-repetitive sequence variants in genome, by far the most common of which are single nucleotide polymorphisms (SNPs). For a variation to be considered an SNP, it must occur in at least 1% of the population. SNPs, which make up about 90% of all human genetic variation, occur every 100 to 300 bases along the 3-billion-base human genome . It is gNew bioinformatics tools and public SNP resources for SNP studies, specifically for linkage disequilibrium and disease association studies, will form part of the new scientific landscape -9. TheseAdditionally, there are many SNPs in non-coding regulatory regions. The exact functions of the non-coding regulatory region SNPs are not clear yet. However, some SNPs are predicted to be related to genes by influencing the binding affinity of transcription factors. For example, the G/C polymorphism in the promoter region of the FCGR2B promoter regulates gene expression . -783A/GSNP@Promoter is a large database that contains various types of information on the location and function for putative promoter regions in the human genome for gene regulation study. In particular, SNP@Promoter provides a platform for biologists including disease associated genes, transcription factor binding sites, and a graphic viewer.We developed an integrated computational system for identifying SNPs in non-coding regulation regions Fig . In thisWe identified TF binding sites in the putative promoter regions in the human genome. The promoter region is defined as the sequence of 5 kb upstream to 500 downstream bases of a transcription start site. The annotation information of genes, which is mapped to the genome, was obtained from the NCBI Gene database. To find TF binding sites in the putative promoter regions, we used the MATCH (Matrix Search For Transcription Factor Binding Site) program from the Transfac database (ver. 8.4) ,19. As aThe SNP annotation information was derived from a public SNP database (dbSNP ver. 126). We identified SNPs in putative promoter regions and selected SNPs that are predicted to be within TF binding sites. As a result, we mapped 488,452 SNPs and filtered out 47,832 SNPs within the putative TF binding sites.Using computational methods for predicting TFBS (TF binding sites) is not optimal due to a high false positive rate. However, recent algorithms have been improved in their reliability in TFBS prediction. Popular algorithms examine well-conserved regulatory sequences by comparing upstream sequences of orthologous genes across species -28. TherThe SNP@Promoter database adopted various gene annotations including pathways (KEGG), gene ontology (GOA), and disease information such as GAD, HGMD, and OMIM. The raw data files were integrated into the SNP@Promoter database based on a gene synonym table from HGNC (HUGO). These annotations provide insight into the effects of SNPs within TF binding sites and help users to characterize target genes regulated by SNPs.As shown in Fig.  or directly at .SNP@Promoter is a database for functional SNPs within putative promoter regions and predicted TF binding sites. The database provides genetic information and graphical views of queried terms. SNP@Promoter will help researchers to identify functional SNPs in non-coding regions. Users can access the SNP@Promoter at The authors declare that they have no competing interests.BK constructed the database. WYK developed the website and assisted to construction of database. WH and KS helped to develop the website. BK initiated this project and wrote the manuscript. DP assisted the manuscript writing. JB directed the study and helped to draft the manuscript.


Picea glauca [Moench] Voss) and black spruce (Picea mariana [Mill.] B.S.P.).To explore the potential value of high-throughput genotyping assays in the analysis of large and complex genomes, we designed two highly multiplexed Illumina bead arrays using the GoldenGate SNP assay for gene mapping in white spruce  were shared between the two species. Considering lineage divergence at least 10 Myr ago between the two spruces, interspecific comparison of homoeologous linkage groups revealed remarkable synteny and marker colinearity.The design of customized highly multiplexed Illumina SNP arrays appears as an efficient procedure to enhance the mapping of expressed genes and make linkage maps more informative and powerful in such species with poorly known genomes. This genotyping approach will open new avenues for co-localizing candidate genes and QTLs, partial genome sequencing, and comparative mapping across conifers. Single nucleotide polymorphims (SNPs) have become a genomic commodity as they are becoming indispensable in various genome scans aimed at mapping genomes -6, findia priori knowledge of DNA polymorphisms, SNPs are usually discovered through various strategies of individual or pool DNA sequencing  Voss) with 292 progeny derived from two parents selected for their high level of heterozygosity for ESTP anchor markers and for their intermediate performance for a number of traits such as somatic embryogenic capacity [Picea mariana [Mill.] B.S.P.) was representative of the hybridizing species complex Picea mariana × Picea rubens (hereafter designated as black spruce) and consisted of 283 progeny derived from the backcross-like cross BC1 (#9920002: ♀11307-03 [♀83 × ♂425] × ♂425), as previously described [Plant material consisted of one outbred Fcapacity . Plant m3 ♀83 × ♂5 × ♂425)escribed ,44. The escribed .For SNP discovery, we considered a collection of 16,500 unigenes derived from a white spruce EST database . For eac2, 250 μM of each dNTP, 400 μM of both 5' and 3' primers and 1.0 unit of Platinum Taq DNA Polymerase (Invitrogen). Peltier Thermal Cycler  was used with a initial denaturation of 4 minutes at 94°, followed by 35 cycles of 30 seconds at 94°, 30 seconds at annealing temperature optimized between 55 and 60° for each pair of primers, and 3 minutes at 72°, completed with an additional 10 minutes at 72°.For each of white spruce and black spruce, DNA was isolated from the two parents of the mapping population and their progeny, as well as from haploid megagametophyte tissue using a DNeasy Plant Mini Kit . About 5–20 ng of template DNA were used for PCR amplification. Reactions were done in 30 μl containing 20 mM Tris-HCl (pH 8.4), 50 mM KCl, 1.5–2.5 mM MgCl® 3700 Genetic Sequencer (Applied Biosystems). Sequences were analysed and assembled with Seqmerge .Each PCR fragment was sequenced with the amplification primers using BigDye Terminator v3.0 cycle sequencing ready reaction kit  and an automated ABI PrismSNPs were detected for each of the two parents for each species from heterozygous positions indicated by double-peak signatures in sequence chromatograms. For each SNP detected, haploid DNA sequences from individual megagametophyte tissue were used as a control for paralogy. Because of their haploid nature, any double peak signature in the sequence chromatogram from a megagametophyte would indicate a SNP resulting from polymorphism between gene loci, hence paralogous variation. Because these SNPs would result in fixed polymorphisms in the progeny, they were not considered for genotyping and mapping.a priori measure of SNP adequation to the GoldenGate assay and takes into account a number of parameters, including sequence conformation around the SNP, lack of repetitive elements in the surrounding sequence (200 bp upstream and downstream) and sequence redundancy against the available sequence database of the recipient species [in silico identification of SNPs in contigs resulting from the assembly of EST sequences [in silico SNPs involved EST sequences from at least two different cDNA clones and had a probability of occurrence of 0.95 or more, according to the statistical assessment conducted with a bayesian method [Gene regions were amplified by PCR and resequenced in order to identify in excess of 1,000 candidate SNPs for each of white spruce and black spruce. Out of them, 1,534 SNPs were used to construct two species-specific Illumina bead arrays of 768 SNPs using the GoldenGate assay . For each species, when more than one SNP was available for a gene, SNPs were chosen so each SNP was specific to one parental genotype. This scheme allowed to validate the SNP mapping approach, as SNPs of a same gene are expected to map at the same position on composite maps. In optimizing the choice of SNPs for a given gene in a given species, those with maximum GoldenGate assay functionality score were chosen. The functionality score is an  species . For whiequences . All chon method .The Illumina bead array technology was used to carry out all genotyping reactions in accordance with the manufacturer's protocol for the SNP GoldenGate assay . Highly θ) of 0.30 were used. The expected genome map length Ge was estimated under the assumption of random marker distribution according to the formula of [Ce was obtained according to the formula of [AFLP, RAPD, SSR, and ESTP marker data previously used for linkage mapping in the two species ,41,42 wermula of . An estirmula of  for the For each species, randomness of gene locus distribution within and among linkage groups, heterogeneity of marker distribution (G-tests) among linkage groups, and marker dispersion were analysed from the composite map as previously described . AnalyseThe homoeology of linkage groups between white spruce and black spruce was determined according to the same criterion as described by Pelgas et al. . The recA total of 487 expressed sequences were amplified and resequenced in white spruce parents, of which 394 were found with at least one orthologous SNP Table . PrimersThe reproducibility of the assay was evaluated using five replicates for each of the mapping parents also used to identify SNPs from resequencing. When estimated over all valid SNPs , on average, 99.4% of the SNP calls were concordant with expectations when using the recommended amount of DNA template (50 ng/μl in 5 μl). The rate of concordant SNP calls was lower when testing with less template DNA .GenTrain scores correspond to the reliability of SNP detection based on the distribution of genotypic classes. Thus, it is a measure of reliability based on the total array of calls for a given SNP. According to Illumina, for a SNP to be retained, a minimum GenTrain score of 0.25 is advisable ,47,57. Iin silico SNPs identified from redundancy in EST contigs [in silico SNPs was much more frequent than that for resequenced SNPs. Individuals previously used for EST sequencing and in silico identification of SNPs did not include the parents of the present white spruce mapping population. Hence, it is likely that white spruce parents were homozygous for many of these in silico SNPs, which resulted in a much higher rate of monomorphism than for resequenced SNPs. Indeed, when scoring these in silico SNPs for individuals from natural populations, many of these SNPs exhibited the expected polymorphism and the overall success rate obtained was 60% (data not shown).For white spruce, a number of  contigs  were alsA missing data is generated when the GenCall score for a particular individual and SNP is below 0.25 . GenCall2 = 51.0 with the white spruce SNP data and χ2 = 34.9 with the black spruce SNP data, d.f. = 1, P ≤ 0.01)  could be mapped over the 2,304.2 cM, including nine accessory marker loci . Therefore, coefficients of dispersion could be estimated for each composite map see . WhetherPtxmyb413 = 90004/MYB4) and three gene SNPs  were involved in synteny discrepancies. The differential positioning of the ESTP locus was already pointed out and validated by sequencing [6047 and 3656 respectively positioned onto LGs II/VII and LGs XII/III of white spruce/black spruce revealed polymorphisms. Such evidence indicates that these two anchor markers differentially positioned in white spruce and black spruce correspond to two paralogous gene loci. Another discrepancy involved SNP 2309 positioned onto white spruce LG V and on black spruce LG VI. However, linkage mapping analyses independently conducted in a second white spruce mapping population (data not shown) led to localize this gene marker on LG VI in both species. Therefore, exceptions to synteny were rejected regarding the differential positioning of these three SNP gene loci between the two species. Along with synteny, macrocolinearity was also well conserved among homoeologous linkage groups between the two spruce taxa. On average, 82% of syntenic anchor markers were positioned in the same order . Over the 12 homoeologous linkage groups, 98.1% of homologous markers were in synteny Figures . One ESTThe Illumina GoldenGate SNP assay, together with the bead array technology, has been extensively used in studies of human polymorphisms . Its useThe call rate is the fraction of genotypes called over the possible SNP calls after having excluded unsuccessful assays . In our The rate of SNPs successfully genotyped using the GoldenGate assay, or SNP conversion rate, is calculated by counting only valid SNPs displaying GenTrain score above a given cutoff. The GenTrain score of a SNP reflects the degree of separation between homozygote and heterozygote clusters and the ease of placement of individual calls within a cluster, which are key measures of signal-to-noise in the assay data . As recoBoechera stricta, a species from the Cruciferae family, a conversion rate of 96.8% was obtained over 96 resequenced SNPs by avoiding highly similar sequences such as for members of complex gene families [Arabidopsis [Arabidopsis, which genome is completely sequenced.In a study encompassing 1,536 resequenced SNPs derived from the human genome, 93.3% of SNPs were called after removal of low frequency SNPs . Two othfamilies . For canbidopsis . Under tadh genes [knox-I [myb [Two main factors may explain the lower SNP conversion rate obtained with spruces as compared to other species analysed to date with the GoldenGate SNP assay. First, we have adopted the severe criterion of GenTrain scores < 0.25 to reject SNPs, while this factor remains unknown for most of the non human studies submentioned. In the present study, such a conservative criterion translated in SNPs with a high call rate, which was necessary for accurate gene mapping. Second, the complexity of conifer genomes e.g.  may obstdh genes , knox-I  [knox-I  and myb x-I [myb  regulatox-I [myb . If the For SNPs exhibiting monomorphism (about 8% of resequenced SNPs in white spruce and 4% in black spruce), it is likely that one of the two allele-specific primers in the GoldenGate assay defaulted for these SNPs, given that resequencing was performed to discover SNPs and that we are confident that they were not sequencing artifacts. These failures might be related to the same factors as above. Thus, considering the fragmentary knowledge we have of conifer genomes and the emphasis put on sampling SNPs from regulatory genes from large gene families, the overall success rate obtained was decent. The success rate was also repeatable between the two species-specific SNP-arrays that we have independently tested. As the genome of conifers is becoming better known at the sequence level, due to several large-scale EST and BAC sequencing projects, the rates of genotyping success for new SNPs are expected to be even higher in the near future.Arabidopsis and three times larger than the human genome. For the largely unsequenced conifer genomes, sequencing their coding regions through EST sequencing and gene resequencing currently represents the most efficient approach to comparative and structural genomics using gene linkage maps [Picea, which represent a large increase over any previous gene mapping effort in gymnosperms e.g. [With around 10,000 to 40,000 Mb , spruce age maps . The SNPrms e.g. ,41,68-71Picea and most conifer chromosomes are difficult to differentiate based on standard cytological techniques [A total of 12 major linkage groups were delineated in each species composite map, which is in agreement with cytogenetic studies indicating similar karyotypes and same numbers of chromosomes (2n = 2x = 24) for both white spruce and black spruce and most other Pinaceae . Becausechniques . With thchniques ,42. Withchniques ,42. As cchniques .More than 200 gene loci were shared between the two composite maps developed herein, most of which being derived from the new gene SNP developing effort. While previous efforts to increase the number of gene or SSR anchor markers relied on using several crosses per species e.g. ,42,73,74Comparisons between the composite maps of white spruce and black spruce revealed high synteny and colinearity between their 12 homoeologous linkage groups, in spite of the divergence of their lineages more than 10 Myr ago . While pSynteny between genomic regions can only be established if markers are true orthologs ,42,75. TWhile synteny was well conserved, exceptions to colinearity between the two genomes were observed. Small inversions between the two species composite maps involving a few closely spaced anchor loci were noted for all linkage groups, but no translocation within linkage groups was observed. Most likely, these inversions resulted from analytical artifacts since the same discrepancies were observed between individual linkage maps within species. Thus, it is safe to assume that these inversions resulted from the integration of both parental datasets for each species rather than from true chromosomal inversions. Such a trend has also been observed when integrating individual linkage maps in other species ,42,73,76knox I multigene family, underlining a major chromosomal duplication in the spruce genome [The present report illustrates how new highly multiplexed SNP genotyping approaches can be used to accelerate the structural analyses of complex and largely unknown genomes. The present maps are the most advanced genetic maps for spruce with regard to gene density and will open up several opportunities. These maps are currently used for QTL and eQTL detection and their next more densified versions will help target specific regions of the genome for future BAC sequencing . The pree genome .de novo in each species. This trend is best exemplified by the small number of gene SNPs shared between mapping populations belonging to different congeneric taxa: only 45 SNPs were shared between white spruce and black spruce mapping populations out of 2,000 candidate gene SNPs discovered in both species. Using more crosses per species could contribute towards increasing the number of shared SNPs between species, but it is unlikely to change the order of magnitude of this number. On a more optimistic note, the primer transfer rate between white spruce and black spruce was high, which might facilitate resequencing efforts towards gene SNP discovery in other spruce species. However, as species from different conifer genera are targeted, the primer transfer rate is expected to diminish [Maps with increasing gene densities are also required across the conifers to extend comparative genome studies and decipher the evolution of genome structure. Such comparisons have already been undertaken based on a limited number of anchor markers e.g. ,41. Howediminish ,19.Identifying sets of homologous transcripts in other Pinaceae has improved in the recent years through efficient mining of sequence databases e.g. ,67 and bNP, JB: genotyping technology evaluation; BP, IG, NI: genetic mapping analyses; SBe, SBl, FG: resequencing, SNP discovery, design of the SNP arrays, and SNP data analyses; NP, ML, candidate gene identification; NP, BP, JB: manuscript preparation; JB, NI: project design, funding, and overall supervision. All authors have read and approved the final manuscript.Data about the SNPs. This table provide references about the genes analysed: unigeneID in ForestTreeDB, annotations, marker names, gene names, links to annotation page in ForestTreeDB, primer sequences used to amplify sequences encompassing the unigenes.Click here for file


Recent development of high-resolution single nucleotide polymorphism (SNP) arrays allows detailed assessment of genome-wide human genome variations. There is increasing recognition of the importance of SNPs for medicine and developmental biology. However, SNP data set typically has a large number of SNPs  and a few hundred of samples. Conventional classification methods may not be effective when applied to such genome-wide SNP data. P < 0.01 are chosen to identify SNPs networks based on their P values. Here SNPs networks are constructed based on Gene Ontology knowledge, and therefore each SNP network plays a role in the biological process. An analysis shows that such networks have relationships directly or indirectly to Parkinson disease.In this paper, we use shrunken dissimilarity measure to analyze and select relevant SNPs for classification problems. Examples of HapMap data and Parkinson disease (PD) data are given to demonstrate the effectiveness of the proposed method, and illustrate it has a potential to become a useful analysis tool for SNP data sets. We use Parkinson disease data as an example, and perform a whole genome analysis. For the 367440 SNPs with less than 1% missing percentage from all 22 chromosomes, we can select 357 SNPs from this data set. For the unique genes that those SNPs are located in, a gene-gene similarity value is computed using GOSemSim and gene pairs that has a similarity value being greater than a threshold are selected to construct several groups of genes. For the SNPs that involved in these groups of genes, a statistical software PLINK is employed to compute the pair-wise SNP-SNP interactions, and SNPs with significance of Experimental results show that our approach is suitable to handle genetic variations, and provide useful knowledge in a genome-wide SNP study. Single Nucleotide Polymorphism (SNP) is a DNA sequence variation occurring when a single nucleotide - A, C, G, or T - differs at the same position between individuals . SNPs arIn a SNP data set, the association between a disease and a set of relevant SNPs are investigated. Patients and normals are often categorized in groups according to their SNP genotypes . Thousands of SNPs in different regions of chromosomes are used to describe characteristics of patient/normal samples. There are two key properties of data sets for such classification task: high-dimensional and categorical.When many SNPs are used to detect the association between a disease and multiple marker genotypes, it is common to find only several numbers of SNPs having genotype patterns that are highly specific to each group of individuals. The SNPs are called the relevant SNPs, as opposed to the irrelevant SNPs that do not help much in identifying the group . Due to the large number of SNPs being irrelevant to each group, two individuals in the same group could have low similarity when measured by a simple similarity function that consider the genotypes of all SNPs. The groups may thus be undetectable by classification algorithms.P < 10−5) of five SNPs suggesting an influence of genes involved in endocytosis and lysosomal sorting in PD pathogenesis. Gao et al. [Many researchers gave efforts to find such a cohort of SNPs that having genotype patterns and highly specific to each group of individuals. Dai et al.  proposedo et al.  conducteo et al.  considerχ2 statistic is used to handle rejection hypothesis. Shrunken χ2 statistics are further constructed to analyze relevant SNPs. In [P < 0.01, which can be considered as an extension of existing Gene Ontology [The classification problem is defined for such a scenario, see for instance . Each grSNPs. In , Park etSNPs. In ,22 to seSNPs. In  method bSNPs. In  with a sSNPs. In  with staOntology  knowledgThe HapMap SNPs data  are downhttp://www.ncbi.nlm.nih.gov/sites/entrez?Db=gap). The genotyping was performed using the Illumina Infinium I and Infinium II assays. The Illumina Infinium I assay asseses 109,365 unique gene-centric SNPs while the Infinium II assay assesses 317,511 haplotype taggings SNPs based upon Phase I of the International HapMap Project. The Illumina Infinium I and II assays share 18,073 SNPs in common. Therefore, the combination of the two assays represents 408,803 unique SNPs. In the following experiment, SNPs with a > 1% missing percentage in all samples are not considered. After missing values are filtered out, the number of SNPs was decreased to 367440.The Parkinson disease SNPs data is based on a genome-wide genotyping of 270 individuals with idiopathic Parkinson Disease cases (case) and 271 neurologically normal controls (control) downloaded from the Coriell Institute for Medical Research ,we need to consider the distance from a class centroid to the overall centroid for the i th SNP. In our proposal, genotype distributions are used for measuring categorical SNPs data.In the next step, the soft thresholding In (3),we can see that if the difference between a class centroid and the overall centroid is small, it demonstrates that the difference is insignificant or is just some noise in the classification process. Let t be a test sample, the class label of t is determined by:andkπ is the prior probability of class k. It is the proportion of class k in the population. If it is unknown, it can be set to where A 10-fold cross validation is adopted in our classification procedure to evaluate the performance of the proposed nearest shrunken centroid method. In each trial, all the samples are randomly divided into 10 equal partitions. For each of the 10 partition groups, we select one of them as testing set and the remaining nine of them are considered as training sets. Ten trials are considered and the results are collected and based on this 10-fold cross validation procedure.All the SNPs that selected by the shrunken metholodgy belong to 122 unique genes. We compute all the pair-wise functional similarities of these gene products using GOSemSim, a package of Bioconductor , which iχ2 statistic and asymptotic P-value will be provided in the output file. By constructing SNPs networks with SNP pairs that have P < 0.01 significance, we can figure out some potential SNP-SNP interactions that are still unknown.For the SNPs that involved in these groups of genes, we did a statistical analysis between these SNPs and all the other SNPs selected by our method using PLINK , which iIn the first test, we take any two out of four populations in HapMap data set to set up two-class classification problems. Cross-validation is used to employ independent data sets. The results are shown in Figures In the second test, we consider a four-class classification problem, i.e., to classify the four populations: CEU, CHB, JPT and YRI. The setting is the same as that in the first experiment. Figure By shrinkage (Δ is set to 1.5), the number of SNPs used for classification is decreased from 200 to 143, 143, 142 and 142 for CEU, YRI, CHB, and JPT respectively. In Figure As shown in Table Next we consider to use Parkinson disease data set to perform experiments to show the effectiveness of the shrunken methodology and construct SNPs networks. Table We also choose Chromosome 14 as an example to demonstrate the SNPs selected by the proposed method. Figure We randomly select one trial of this 10-fold cross validation as an example to further analyze. In this trial, for all the 367440 SNPs from 22 chromosomes of Parkinson disease data set, there are totally 357 selected and 171 of them are located in gene coding area. Next we make use of the knowledge of these genes to construct SNPs networks. For the 122 genes that those 171 SNPs located in, we cluster the genes based on their similarity values using GOSemSim. The closely related biological process and molecular function roles of each gene were checked with GOSemSim with a threshold. When a similarity value between two genes is less than the threshold, their relationship is not considered. Therefore several groups of genes can be formed. As we are interested at gene-gene interactions, and we only consider the groups where the number of genes in these groups are more than one. In Table We see in Table P-value of PLINK epistasis test, we construct SNPs networks. Because there are more groups of genes when the threshold value in GOSemSim is in between 0.22-0.28, we are interested in their corresponding SNPs networks. In particular, we show in Figures P < 0.01 significance interactions in Figure For each group of genes constructed, we check all the pairwise SNP-SNP interactions using PLINK between SNPs involved in the group of genes and all the other SNPs selected by the shrunken method. Based on the We find some interesting relationships from these two SNPs networks. For example, for SNPs rs11626809 and rs2836392, which are highly interacted, their corresponding genes are RAD51L1 and ERG respectively, but located in different clusters in gene network, which means that maybe we can merge these two clusters in gene network together. Another example, rs4968656 is interacted with rs4658673, which is located in intergenic area and do not have a record in Gene Ontology until now, maybe we can make use of rs4968656's gene information, KCNH6, to further analyze the inner functions of rs4658673 and extend GO afterward.Indeed some of the SNPs selected by the shrunken method are directly or indirectly related to PD. For example, ERG and anatomical abnormalities are reported to cause retinopathy in dementia with Lewy bodies , which sIn this paper, we review the method of nearest shrunken centroid for gene expression data, and extend it to tackle SNP data classification. The main contribution of this paper is to develop a shrunken dissimilarity measure to handle SNP data classification problems. The method can be implemented on a PC very efficiently. The relevant SNPs are selected for HapMap data and Parkinson disease data. Experimental results are also reported to show the effectiveness of the method. In particular, we find some SNPs that contain in some genes which is relevant to Parkinson disease. Based on the SNPs network, we can have some unknown relationships between their corresponding genes, which can be considered as an extension of existing GO knowledge. In the future, detailed biological analysis of SNPs of other genome-wide SNP data sets will be studied. The genomic variation of data sets can take account of functional as well as linkage disequilibrium information. More importance is attached to some SNPs than others, based on their positions within the coding or regulatory regions or splice sites.The authors declare that they have no competing interests. MN designed this study and developed the new algorithm. YL designed this study, coded the program, and ran the experiments. Two authors wrote the manuscript.


The genetic structure of sheep reflects their domestication and subsequent formation into discrete breeds. Understanding genetic structure is essential for achieving genetic improvement through genome-wide association studies, genomic selection and the dissection of quantitative traits. After identifying the first genome-wide set of SNP for sheep, we report on levels of genetic variability both within and between a diverse sample of ovine populations. Then, using cluster analysis and the partitioning of genetic variation, we demonstrate sheep are characterised by weak phylogeographic structure, overlapping genetic similarity and generally low differentiation which is consistent with their short evolutionary history. The degree of population substructure was, however, sufficient to cluster individuals based on geographic origin and known breed history. Specifically, African and Asian populations clustered separately from breeds of European origin sampled from Australia, New Zealand, Europe and North America. Furthermore, we demonstrate the presence of stratification within some, but not all, ovine breeds. The results emphasize that careful documentation of genetic structure will be an essential prerequisite when mapping the genetic basis of complex traits. Furthermore, the identification of a subset of SNP able to assign individuals into broad groupings demonstrates even a small panel of markers may be suitable for applications such as traceability. Archaeozoological evidence suggests sheep were first recruited from the wild and domesticated in the Near East approximately 8000–9000 years ago The genetic history of sheep has been investigated using three major sources of genomic variation. The ovine mitochondrial genome has proven highly informative for investigations into the process of domestication, with maternal haplogroups documenting the occurrence of multiple domestication events The genomic abundance and amenability to cost effective high throughput genotyping has meant that single nucleotide polymorphisms (SNP) are now the most widely used class of genetic marker in genetics. A total of 4.4 million human SNP were genotyped during phase II of the HapMap project The aim of this study was to develop the first set of SNP distributed across the sheep genome. This relied on re-sequencing over 2600 genomic targets which have known location within the virtual sheep genome −1−1 for set 3a in −1) compared with the transcribed component of the genome  yielding fragments suitable for sequence analysis. This enabled re-sequencing of 1.226 Mb of ovine DNA in the search for polymorphisms. 6021 SNP were identified with an average density of 4.9 SNP per kb . This isAnalysis of the SNP set revealed 39 tri-allelic polymorphisms, 4350 transitions (α) and 1632 transversions (β), giving a mutational ratio (α/β) of 2.67. This ratio is consistent with SNP collections identified from cattle  and human  of the BAC-based comparative genomic contig (BAC CGCs) used to build the virtual sheep genome PN, AR = 1.839), private allelic richness (pAR = 1.874) and gene diversity  and thinhorn sheep . A total of 1394 loci revealed an allele from both wild species, resulting in an assay conversion rate of 99% (1394/1406). This suggests the SNP and associated genotyping platform may be suitable for performing a phylogenetic analysis of more closely related candidate wild progenitors of domestic sheep such as the asiatic mouflon , argali (O. ammon) and urial sheep (O. vignei). Of the SNP assays which worked across species, a total of 1355 (97%) were fixed for an allele common to both wild sheep species, while 20 markers (1.4%) displayed both alleles in at least one of the wild populations tested.In order to assess the rate of assay conversion between species, genotyping was performed using samples from both bighorn (D) between pairs of animals. The average distance between individuals within the same breed was 0.254 . As expected, the average distance between individuals drawn from different breeds was higher . Also as expected, the highest average distance was observed between domestic sheep and wild sheep . The distribution of D appears normal and smooth between domestic breeds and between sheep species  required to explain the total sum of genetic variation observed. K = 3–6. At K = 3, individuals from the African and Asian breeds appeared distinct from those drawn from either western breeds or wild sheep. At K = 4 the Soay is separated out as a distinct sub-population, at K = 5 the three Asian breeds  can be seen as distinct from other breeds and at K = 6 a subset of African and Western breeds cluster (FST is presented in FST>0.25) were observed between breed pairs sampled from different continents while the lowest values were observed between breeds of European origin such as the Merino and Italian Sarda (FST = 0.053), Suffolk and Composite (FST = 0.059) and the Merino and Rambouillet (FST = 0.060). The proportion of variation explained by geographic origin was investigated following assignment of breeds as either African , Asian  or western . The vast majority of SNP variation occurred within breeds (82.2%), with only 5.8% being diagnostic of differences between the three geographic groupings. The remaining component of variation was present between breeds of the same geographic region (12.0%).Multidimensional scaling (MDS), Bayesian model-based clustering and calculation of  cluster . The degFST = 0.082). African and American Dorpers also clustered separately , although New Zealand and American Texel were indistinguishable (FST = 0.025).Separate subpopulations of the same breed were collected from different continents for the Dorper, Dorset, Suffolk and Texel . This ofInIn, four marker panels were constructed which contained either the most informative or least informative SNP. Each panel was used in MDS analysis to evaluate their ability to cluster individuals into the four distinct groups observed in AR = 1.82±0.22) and lower average private allelic richness (pAR = 0.024±0.049) compared with those in the poorly informative set . Together, this demonstrated a subset of markers characterised by high AR and low pAR can be used for population assignment.The relative contribution of SNP to population assignment was estimated using the informativeness metric −1) similar to datasets reported in other animal genomes. The use of a small discovery panel (n = 9) likely biased the SNP discovery process towards identification of loci with common alleles. This is supported by the finding that two thirds of SNP displayed MAF ≥0.2 when genotyped across the full set of domestic animals  and genomic frequency  and distance (D) revealed that African and Asian populations each tended to display lower variability  and depressed genetic distance between individuals (D<0.25) when compared with breeds of European origin sampled from Australia, New Zealand, Europe and North America  and 2294 targets drawn from a library of BAC end sequences (BES) after screening for repeat sequences and location in the genome. BES targets were selected to be approximately one third and two thirds of the way along each BAC comparative genomic contig in the virtual sheep genome http://www.livestockgenomics.csiro.au/perl/gbrowse.cgi/vsheep1.2/. Information for all SNP is accessible through the virtual sheep genome browser at oY1http://snpcenter.grcf.jhmi.edu/). Population samples  was used to calculate design scores for the golden gate assay (performed by Illumina). A total of 1535 SNP were selected for inclusion on the array to satisfy both assay design score (≥0.6) and genomic location by including markers on each contig of the virtual genome. A single SNP located within the male specific region of the ovine Y chromosome was also included oY1. Genotyp samples  were colPN), allelic richness (AR) and private allelic richness (pAR) were determined using HP-RARE v1.0 HE) were obtained using Genetic Data Analysis v1.0 http://www2.unil.ch/popgen/softwares/fstat.htm) was used to evaluate population relatedness using pair-wise estimates of FST. The partitioning of SNP variation was conducted using an analysis of molecular variance (AMOVA) as implemented in Arlequin v3.01 Estimates within each population of the proportion of polymorphic markers (D) was calculated as one minus the average proportion of alleles shared, as described by http://pngu.mgh.harvard.edu/purcell/plink/), where the average proportion is reported as Dst. A total of 1315 SNP were used, following pruning of SNP which had MAF <0.01 and/or greater than 10% of missing genotypes. The distribution of D was plotted separately where the pairs of individuals were drawn a) from within the same breed b) from different breeds or c) from O. aries and one of the two species of wild sheep .Genetic distance between all pair-wise combinations of individuals (D) was constructed containing each pair-wise combination of all 392 individuals. Both SNP pruning and calculation of D was performed using PLINK (http://pngu.mgh.harvard.edu/purcell/plink/). Classical (metric) MDS analysis was then applied to explore the similarities in the matrix. The – cluster and – mds-plot functionality implemented in PLINK was used without the addition of any constraint. It should be noted that when MDS is based on D it is numerically identical to principal components analysis structure v 2.2 K [ln Pr(X | K) or Ln(Kn)] and its variance between replicates was used to search for the most likely number of subpopulations. The likelihood approached an asymptote and the variance between runs increased approaching K = 10 (Ln(K2) = −6.847×105±24.2; Ln(K3) = −6.74×105±317.3; Ln(K4) = −6.674×105±285; Ln(K5) = −6.602×105±35.4; Ln(K6) = −6.558×105±161.2; Ln(K7) = −6.519×105±659.8; Ln(K8) = −6.478×105±136.5; Ln(K9) = −6.464×105±440.2; Ln(K10) = −6.587×105±1254.7). At values of K>15, the likelihood dropped dramatically (Ln(K20) = −6.940×105; Ln(K25) = −6.934×105 Ln(K30) = −7.140×105) suggesting an optimal value of K<10. The solutions for K = 3–6 were visualised using distruct ver 1.1 In) was estimated using Infocalc ver1.1 In for all markers (range 0–1). The highest ranking SNP defined panels 1 (96 SNP) and 2 (384 SNP) while the lowest ranked SNP with non-zero In defined panels 3 (96 SNP) and 4 (384 SNP). MDS using each marker panel was as described previously.Multidimensional scaling (MDS) analysis used a total of 1317 SNP following removal of loci with missing genotype rate of >0.1 or MAF <0.01. An IBS matrix of distance  School of Veterinary Studies, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK; David Coltman, Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada; Allan Crawford, AgResearch, Invermay Agricultural Centre, Mosgiel, Private Bag 50034, New Zealand; André Eggen, INRA, UR339 Laboratoire de Génétique Biochimique et Cytogénétique, F-78350 Jouy-en-Josas, France; Georg Erhardt, Institut für Tierzucht und Haustiergenetik Justus-Liebig-Universität Gießen, Ludwigstraße 21 B, 35390 Gießen, Germany; Robert Forage, SheepGenomics 165 Walker St North Sydney NSW 2060 Australia; Olivier Hanotte, International Livestock Research Institute (ILRI) PO Box 30709 Nairobi, Kenya; Peter Hunt, CSIRO Livestock Industries, Armidale, NSW 2351, Australia, Han Jianlin, CAAS-ILRI Joint Laboratory on Livestock and Forage Genetic Resources, Institute of Animal Science, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100193, China; Kui Li, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 10094 PR China; Paolo Ajmone Marsan, Via Emila Parmense, 84 Universita Cattolica del Sacro Cuore, 29100 Piacenza Italy; James E. Miller, Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, LA 70803, USA; Josephine Pemberton, Institute for Evolutionary Biology, University of Edinburgh, Edinburgh EH9 3JT United Kingdom and Laurent Schibler, Laboratoire de Génétique biochimique et de Cytogénétique, INRA-CRJ, Jouy-en-Josas, France as part of the International Sheep Genomics Consortium.Table S1Genetic distance within each sheep population.(0.02 MB DOC)Click here for additional data file.Table S2Genetic differentiation between population pairs measured using FST.(0.07 MB DOC)Click here for additional data file.Figure S1Minor allele frequency (MAF) distribution for SNP identified from either expressed sequence tags (n = 375) or BAC end sequence (n = 5646). The proportion of total SNP in each MAF category is shown.(0.12 MB TIF)Click here for additional data file.Figure S2Distribution of allelic richness (top) and private allelic richness (bottom) for SNP panels 2 and 4 refer to  which ha(0.21 MB TIF)Click here for additional data file.Figure S3Minor allele frequency (MAF) distribution between breeds was used to test for the presence of strong ascertainment bias. The breeds shown were either present  or absent  from the SNP discovery panel and displayed either a low  medium (STT) or high  proportion of polymorphic loci Pn, . Breed a(0.17 MB TIF)Click here for additional data file.Figure S4http://www.livestockgenomics.csiro.au/perl/gbrowse.cgi/vsheep1.2/.SNP data available at the virtual sheep genome browser. The top half of the figure illustrates the genomic location of targets used for re-sequencing to identify SNP. The bottom half of the figure illustrates the information available for one target (DU324092). This includes SNP location, flanking sequencing and type of SNP. The virtual sheep genome browser is available at (0.31 MB TIF)Click here for additional data file.


Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.We have engaged in an international program designated the n = 70) versus long term progression-free survivors  in two phase III clinical trials.We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS. The draft sequence of the human genome published in 2001 , have shMultiple myeloma (MM) is a universally fatal disease characterized by the accumulation of malignant plasma cells in the bone marrow . It accoThe growth of MM plasma cells is dependent on a complex interplay among various growth factors, adhesion molecules and other factors in the tumor microenvironment. Thus it might be expected that genetic variations in this interplay could have a profound influence on disease initiation, progression, associated bone complications, and response. Moreover, genetic variation in immunity and inflammation is an important consideration, as are variations in genes coding for drug metabolism and transport. Indeed, death from MM commonly results from infections associated with a severely compromised immune system resulting, in part, from therapeutic toxicities that may be related to variable rates of drug metabolism .Bank On A Cure (BOAC). A cooperative program was established to bank DNA from multiple cooperative groups and institutional trials, and to develop a platform for examining the association of genetic variation with disease risk and outcomes. BOAC receives samples through Material Transfer Agreements, and clinical outcomes are provided through agreements with the Cancer Research and Biostatistics Group (Seattle) and the University of Minnesota . Currently, the bank has over 2100 samples from the USA, representing six different clinical trials, patient-provided BOAC buccal cell kit samples, and unaffected controls accumulated since 1987. In this report we describe the development of a novel custom SNP panel based on the Affymetrix/Gene Chip Targeted Genotyping Platform, which contains 3404 SNPs representing variations in a variety of cellular functions and networks, and its initial application to myeloma DNA samples collected in the BOAC bank. We examined population frequencies in affected and unaffected individuals among different ethnic groups, and we developed some novel early approaches in using the SNP panel to determine whether genomic variations in the patient population influence survival.In order to address these issues we have engaged in an international program designated as the n = 52 [n = 91 [n = 70) or greater than 3 years (n = 73).DNA was prepared from 102 Coriell cell lines [n = 52  and S932 [n = 91 , with in [n = 91 . Patient [n = 91 . S9321 pA directed, custom BOAC SNP chip design was developed with specific criteria from public and commercial databases. Rather than a total genome wide screen, a plan was undertaken to develop a custom SNP chip, focusing on functionally relevant polymorphisms playing a role in normal and abnormal cellular functions, inflammation and immunity, as well as drug responses. Candidate gene lists were created and each gene in the candidate list was systematically investigated with a selection of SNP databases to harvest SNPs that may have a functional effect on gene action. Figure The Human Gene Mutation Database  containsA systematic search of the promoter regions in all the candidate genes for SNPs present in homologous regions between human and mouse with a minor allele frequency greater than 2% were identified using the PromoLign Database . Many of® GeneChip® Scanner 3000 Targeted Genotyping System (GCS 3000 TG System), which utilizes molecular inversion probes to simultaneously identify the 3404 pre-selected SNPs. The protocol has previously been described [Genotyping was performed using the Affymetrixescribed . All genPatients from the Eastern Cooperative Oncology Group (ECOG) and Southwest Oncology Group (SWOG) trials were selected using the following criteria: they were all Caucasian and between 55 and 70 years of age at diagnosis; patients with IgA subtype were excluded . Patients with the longest progression-free survival (PFS > 3 years) and patients with the shortest progression-free survival (PFS < 1 year) were selected.Two approaches were used to determine whether there was true discrimination of SNP genotypes in the PFS analysis, when analyzed as a conglomerate data set.Leave-one-out cross-validation [1) lidation . In thislidation , and speRandomization of class labels [2) s labels . For thes labels , that isp-value were selected and used in a recursive partitioning analysis. For this recursive partitioning analysis we used RPART from the R software package, a language and environment for statistical computing. The tree-based library RPART was developed as described [We also analyzed each clinical trial data set separately and used the other clinical trial data set as a validation set. Fisher's exact test was used as a univariate screening tool to rank the SNPs by how strongly they are associated with PFS. The top 50 SNPs of each trial with the smallest escribed . The regFinally, we attempted univariate ranking and recursive partitioning of the conglomerate data set  using random forests . ValidatA final custom SNP chip panel of 3404 SNPs from 983 genes meeting the above criteria was produced for the Affymetrix/Gene Chip Targeted Genotyping Platform. A full documented list of SNPs is found in  and inclIt is noteworthy to compare the content of the BOAC SNP chip to the SNPs represented on the Affymetrix 500K Array genome wide scan. The 500K Array panel is primarily derived from two restriction enzyme cleavage fragmentations, with SNP representation for each fragment, providing a comprehensive, global SNP panel. Well over 95% of the panel is intragenic, non-coding; and thus, its primarily use is to identify copy number, chromosomal regions, and linkage. Indeed, of the 3404 SNPs on the BOAC SNP chip, only 401 are present on the 500K Array panel. Thus, while the BOAC SNP chip does not have gene wide coverage, it does have a higher density of coding and regulatory content.n = 52) and the chemotherapy arm of the ECOG-SWOG intergroup trial S9321 (n = 91). Treatment protocols are given in the Methods section. This study was in compliance with the Helsinki Declaration, and approved by the IRB at the University of Minnesota , with patient consents collected by the clinical cooperative groups' trial offices. Among all samples profiled, we had an average SNP call rate of 96%. The profiles of the Coriell panel allowed us to determine allelic frequencies in racial groups and unaffected populations. Of the 3404 SNPs on the BOAC panel, 786 were contained in the SNP500 cancer database, allowing us to determine concordance between the two Coriell data sets. We found very good agreement between our data set and the national database, with an average of > 97% concordance. We also duplicated the profile of a number of samples (n = 10), and found better than 99.7% reproducibility between duplicate samples. This concordance and duplication rate was also equivalent when comparing the BOAC SNP panel run in the USA and UK facilities, providing a cross validation between BOAC laboratory sites. Finally, for every batch run of 24 samples, the Affymetrix platform includes a control DNA sample, and this provided continuous monitoring and quality assurance across the study.For this study, a total of 279 DNA samples were profiled by the BOAC SNP chip. One hundred and thirty-six unaffected controls from the Coriell panel and spouses of myeloma patients were profiled. The Coriell panel included 31 Caucasian, 24 Asian, 23 Hispanic, and 24 African American samples of unaffected individuals. One hundred and forty-three myeloma samples were profiled, from the phase III clinical trials, ECOG E9486 (n = 92) versus the African American (n = 27) myeloma populations. Equivalent frequencies would be expected to cluster on the 45 degree angle; and it is readily apparent that frequencies of many of the SNPs vary widely between races. Indeed, the racial disparities in allelic frequencies were far more significant than could be assessed in case-control or outcome studies, so that subsequent initial survival analyses were done only on a single racial group. Moreover, given the inclusion criteria of SNPs , it is noteworthy that 401 SNPs show allelic variation only in the African Americans . In contrast, in a comparison of unaffected samples with affected samples, restricted to Caucasians, there is high concordance across the total panel of SNPs  for obtaining a table with the same or better accuracy of prediction by random chance. Specifically, the p-value is 7.7 × 10-5, which strongly indicates that the result is not due to random chance. The calculated odds ratio (OR) for survival is 3.9 CI . We subsequently focused on SNP subsets that might provide more directed functional associations and found that the best predictor of survival was achieved when just the non-synonymous SNPs and the promolign SNP subset in introns was used. The accuracy of prediction increased to 75.5% OR = 9.6 CI .The classification accuracy of the leave-one-out approach is (50+45)/143 = 0.66. If there was no true discriminating signal in the data, then the classifiers built by the leave-one-out procedure should produce a table with a relatively evenly distributed number of entries among the four cells, since the classes are of roughly the same size and the predictions should be random. However, the observed table is far from that random distribution. By using Fisher's exact test it is possible to compute the probability  with the distribution of accuracy shown in Figure p-value for survival classification of less than 0.0001. This further indicates that, as a group, the SNPs are providing a measure of true discrimination of survival.Another approach that is commonly used for classification is the generation of random subsets for training and validation. A classification model is built on the training subset, and is evaluated on a separate test set. The process is repeated and yields a distribution of accuracy on the data set labels . This is then repeated with random shuffling of the survival data sets to determine whether there is true signal accuracy. For this analysis, we again used a support vector machine classifier, as implemented by the Weka package , using aEach of the above approaches demonstrated that the SNP panel provided discrimination; however, we attempted to explore possible subsets of SNPs that may drive the association. We used Fisher's exact test as a univariate screening tool to determine the association of SNPs with each trial separately Tables  and 4, tp-value significance, but would not alter the rank order comparison. This approach does examine the association of each individual SNP; it is more likely that complex interactions may drive association of groups of SNPs not revealed by univariate ranking. Nevertheless, among the top ranked individual SNP variations in both trials were those associated with drug metabolism/detoxification/transport, including: cyp genes, multiple variants of GSTA4, SLCO, UGT1, NAT2, ABCB genes; as well as genes impacting cellular response, including: BMP2 (inducing myeloma apoptosis) [XRCC5 (DNA repair); and genes associated with proliferative responses . The association of multiple alleles of GSTA4 is particularly compelling, suggesting consistency in its impact across several variant alleles. In addition, several alleles are in linkage disequilibrium, appearing as a cluster in the list – providing quality controls (as linked genes would be expected to show the same association).In the univariate ranking, we did not correct for multiple comparisons; that would certainly reduce the optosis) , cathepsoptosis) ,32, XRCCCOMT) and one is in Ghrelin precursor (GHRL). The potential significance of these SNPs on outcomes is discussed below. The correct classification rate  was 71%, which dropped to 58% on validation testing with the E9486 trial. The specificity and sensitivities are also presented. The converse analysis was done ; and the rank order of SNPs was determined (Table FDFT) and ABCC1 (in the family of ATP transporters). The potential significance is also provided in the Discussion.The first survival separation was analyzed for clinical trial S9321, and the top 20 rank ordered SNPs are presented in Table p < 0.02) as a set for short versus long PFS classification prediction using a random forest multiple sampling approach [As an exploratory approach, we combined the data sets from both trials, then used Fisher's exact test as a univariate screening tool to determine the association of each SNP with survival. When we treated the top 165 SNPs  as well as very high concordance in replicate samples, even those run at different facilities. The concordance extended to 786 SNPs on the panel that were documented for the Coriell cell lines we have included . All of Within the Coriell cell line panel is a distribution of racial groups. It is striking how much allelic frequencies differ in the African American vs. Caucasian racial groups. It is likely there is more refinement of allelic variations associated with more geographical based lineages , as raciGSTP1 show alterations in melphalan metabolism, and have been associated with different outcomes in patients receiving high dose melphalan therapies [Disease progression, response and survival vary widely among patients. There are a number of studies that have examined variations in tumor cell chromosomal abnormalities  and geneherapies . NumerouOur approach was to provide a more global germline analysis that was driven by bioinformatic searches for potentially relevant variations in multiple genes and gene functions. This is still an exploratory approach to identify potential variations of functions that impact upon therapeutic responses and disease progression that may result in differences in survival outcomes. Rather than a linear progression of survival, we chose to examine two extreme ends of the PFS spectrum, to maximize the first steps in identifying potential functional variations. Patients were stratified by short (< 1 year) versus longer (> 3 years) PFS groups. Nevertheless, it is likely that survival is a complex endpoint resulting from both tumor progression and therapeutic failure that may impact upon multiple organ systems. Moreover, we recognized that a) tumor variation among patients may have dominant effects that are associated with survival; b) the trials we examined used multi-drug regimens, and each drug response may be impacted upon by complex genetic variations in transport, metabolism, and export; and c) sample number is still limiting statistical power. Thus, our initial approaches in this study were to determine whether germline variations had any measurable influence on survival.We felt it was important to determine, first, if there were any true discrimination of the SNP panel in the two PFS groups, when the complete SNP profile was considered. Using a variety of methods that were tested against randomly mixed sample analysis, we found the SNP panel had true signal to discriminate the short and long progression-free survivors, although the accuracy did not reach the level of prediction that would allow clinical application. Notably, a smaller subset increased the predictive power. Significantly, no individual genetic variation provided a strong, independent prediction of survival. This likely reflects the fact that individual germline variations may impact upon response, but are not solely responsible; and it is likely that such variations are the result of complex interactions. Indeed, genetic variations in the tumor cell may play a dominant role in response and survival. Thus, patient responses are likely to involve interactions affecting multiple functions within the tumor cell as well as external factors affecting tumor progression and drug response. Nevertheless, our analysis of the SNP panel as a group suggests it is likely that germline variations impact upon patient survival and deserve further attention.p-value significance but would not alter the rank order comparison. This approach also assumes association for the individual SNP. It is more likely that complex multi-SNP groupings influence response. Nevertheless, among the top SNP variations in both trials were those associated with drug metabolism/detoxification/transport, including: cyp genes, multiple variants of GSTA4, SLCO, UGT1, NAT2, ABCB genes; as well as genes impacting cellular response, including: BMP2 (inducing myeloma apoptosis), cathepsin B (inducing IL-8 dependent cellular migration and angiogenesis [XRCC5 (DNA repair); and genes associated with proliferative responses . The association of multiple alleles of GSTA4 is particularly compelling, suggesting consistency in its impact across several variant alleles. In addition, several alleles are in linkage disequilibrium, appearing as a cluster in the list – providing quality controls (as linked genes would be expected to show the same association). Surprisingly absent from the SNP association lists are cytokines, growth factors and receptors that might be expected to cause variations in disease progression and resistance, with the exception of IL-10, which has been reported in previous studies [Recognizing the limited statistical power to detect single SNPs associated with PFS, we did perform a univariate analysis to rank order the SNPs that individually best discriminated the groups in the two similar phase III clinical trials. We did not correct for multiple comparisons, which would certainly reduce the ogenesis ,32, XRCC studies .While still an exploratory analysis, the paired SNPs identified by recursive partitioning in each trial have some intriguing possible connections to PFS. COMT (catechol-O-methyltransferase) metabolizes catechol drugs, and has been linked to breast cancer risk and survival ; GHRL haThe exploratory analyses provide some of the first attempts to use larger, targeted SNP panels to develop models of genomic variations that may influence treatment outcomes, and that may deserve further analysis of functional significance. Not surprisingly, among the most significant variations correlating with survival were genes that could be functionally categorized as pharmacologic. However, the group analysis suggests various functions may interplay in disease progression and response. It is important to consider the fact that we could not identify a small driver set of SNPs that strongly associated with survival, particularly with the limited sample size. However, we note that, as a group, germline genomic variations do have impact on event-free survival. As the Bank On A Cure data set is expanding, SNP associations are being analyzed for more specific phenotypes in response, disease complications , and adverse or toxic drug effects .Heterogeneity in tumor gene deregulation certainly contributes to variation in disease outcome. It would seem appropriate to consider combining an understanding of tumor heterogeneity  with germline variations  that can lead to development of more individualized therapies that take into account both tumor and population variations.BOAC: Bank On A Cure; ECOG: Eastern Cooperative Oncology Group; IRB: Institutional Review Board; MM: multiple myeloma; OR: odds ratio; PFS: progression-free survival; SNP: single nucleotide polymorphism; SVM: support vector machine; SWOG: Southwest Oncology Group; VAD: Vincristine, Adriamycin, Dexamethasone; VBMCP: Vincristine, Busulfan, Melphalan, Cyclophossphamide, Prednisone.The authors declare that they have no competing interests.BVN is the principal investigator of study, involved in study design and writing the manuscript. CR and MH developed genotyping assays and generated genotype data. AH, JH, and JC provided input on study design and statistical analysis. SJ provided clinical outcome data from the office of ECOG Statistical Center. MO was the clinical chair of the ECOG phase III trial. VR and PG have shared chairmanship of the ECOG Myeloma Committee and participated in clinical trial study design and clinical evaluations. BB chairs the SWOG myeloma clinical design and evaluations. BD is the Clinical Director of the Bank On A Cure project described. MK supervizes integration of laboratory efforts between the USA and the UK. VR, MS, GA, GF, RG, and RM developed statistical approaches in vector machine applications. DJ participated in SNP panel design and genotyping assembly. GM co-directs Bank On A Cure with BVN, and has been involved in study design and data evaluation.The pre-publication history for this paper can be accessed here:Chromosomal distribution of BOAC SNP panel. Each SNP on the BOAC Panel is indicated in color, and indicates a broad distribution across each chromosome.Click here for file