MMP16+ ROBO1, MMP16+ CSF1, and MMP16+ EGFR) were identified to be associated with aggressive prostate cancer in both groups. Three pairs of SNPs  were in MMP16 and ROBO1, one pair (rs2176771+ rs333970) in MMP16 and CSF1, and one pair (rs1401862+ rs6964705) in MMP16 and EGFR. The results suggest that MMP16 may play an important role in prostate cancer aggressiveness. By integrating our novel findings and available biomedical literature, a hypothetical gene interaction network was proposed. This network demonstrates that our identified SNP-SNP interactions are biologically relevant and shows that EGFR may be the hub for the interactions. The findings provide valuable information to identify genotype combinations at risk of developing aggressive prostate cancer and improve understanding on the genetic etiology of angiogenesis associated with prostate cancer aggressiveness.Angiogenesis has been shown to be associated with prostate cancer development. The majority of prostate cancer studies focused on individual single nucleotide polymorphisms (SNPs) while SNP-SNP interactions are suggested having a great impact on unveiling the underlying mechanism of complex disease. Using 1,151 prostate cancer patients in the Cancer Genetic Markers of Susceptibility (CGEMS) dataset, 2,651 SNPs in the angiogenesis genes associated with prostate cancer aggressiveness were evaluated. SNP-SNP interactions were primarily assessed using the two-stage Random Forests plus Multivariate Adaptive Regression Splines (TRM) approach in the CGEMS group, and were then re-evaluated in the Moffitt group with 1,040 patients. For the identified gene pairs, cross-evaluation was applied to evaluate SNP interactions in both study groups. Five SNP-SNP interactions in three gene pairs ( Prostate cancer accounts for 29% of cancer incidence and 9% of cancer deaths and it is the most common cancer and the second leading cause of cancer death in American men in 2012 Angiogenesis is a biological process that involves the division and migration of endothelial cells, resulting in microvasculature formation Single nucleotide polymorphisms (SNP) in angiogenesis genes may alter gene expression and influence the process of angiogenesis in prostate cancer and inhibited tumor growth in animal models VEGF -1154A and -634C alleles were associated with an increased risk of higher tumor grade et al. (2008) evaluated 58 SNPs in nine angiogenesis genes and found that three correlated SNPs  in the MMP2 were associated with overall and advanced prostate cancer IL-10 819 TT genotype tended to have a higher risk of developing a high-grade prostate cancer et al. observed that a nonsynonymous SNP (rs4072111) that changes a serine to proline in IL-16 was significantly associated with an increased risk of aggressive cancer FGFR2, an angiogenesis gene IL-18  and IL-11 (rs12709950) in their stage one scan, but none were replicated in the stage two scan Although the number of studies for evaluating the role of SNPs in angiogenesis genes is limited, several of the studies support the association between angiogenesis and prostate cancer aggressiveness. So far, results from several candidate gene and genome-wide association (GWA) studies suggest that SNPs in the angiogenesis pathway may be important in prostate cancer progression and aggressiveness. In the candidate gene studies, In order to comprehensively evaluate genetic variations in angiogenesis genes associated with prostate cancer aggressiveness, effects of both individual SNPs and SNP-SNP interactions were examined. The majority of current studies are focused on evaluating individual SNP effects; however, one-to-one associations may not be sufficient to explain the complexity of disease causality. It has recently been established that gene-gene/SNP-SNP interactions may have a higher impact on unveiling causality of complex diseases To overcome the challenges in high-dimensional data, we searched SNP-SNP interactions using the TRM approach, a two-stage Random Forests plus Multivariate Adaptive Regression Splines (MARS). Conventional studies use an additive model for SNPs, and search for pair-wise SNP interactions using logistic regression. This approach is not sufficient because an additive model assumption may not be valid. It has been shown that the genetic model selection has a great impact on the detection power of associations Two groups were used in this study. The CGEMS group was used as the primary data set to identify SNP-SNP interactions associated with prostate cancer aggressiveness. The significant results identified in the CGEMS data were then re-evaluated using the Moffitt data. All individuals in our analysis were men with European descent because data were available on men with European descent in the CGEMS dataset. Only one common demographic variable, age at enrollment, was available in the two study groups. However, due to the study design difference, the variables of age at enrollment in the two study groups are not comparable. For the prostate cancer patients, the date of enrollment was prior to prostate cancer diagnosis in the CGEMS study (a nested case-control study within a prospective cohort study), but it was after cancer diagnosis in the Moffitt cohort (a case only study). Thus, our analyses were based on unadjusted results.−4), a total of 2,651 SNPs were applied for further analyses. Linkage disequilibrium among all testing SNPs was examined based on r2 using the Haploview Tagger 2>0.8, a total of 2,177 SNPs were included for interaction analyses.There were 1,151 prostate cancer patients (659 aggressive and 492 non-aggressive patients) in the CGEMS prostate cancer genome-wide data set. The participants were selected from the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening Trial enrollment between 1993 and 2003 The Moffitt group was used in the cross-evaluation of SNPs in angiogenesis genes associated with prostate cancer aggressiveness. The Moffitt population consisted of a historical cohort of 1,040 prostatectomy cases treated at the Moffitt Cancer Center from 1986 to 2003. We identified 437 aggressive cases and 603 non-aggressive cases based upon the same prostate cancer aggressiveness criteria used in the CGEMS group. There were 49%, 42% and 8% patients in the age group of <60, 60–69 and > = 70 years-old, respectively, at enrollment of the Moffitt study. There were 681 angiogenesis SNPs genotyping using the Illumina GoldenGate™ assay . The study protocol was approved by the Institutional Review Board of the University of South Florida .In the CGEMS group, three inheritance models  were assessed using logistic regression models, and the best model was selected based on the minimum p-value for each SNP. False discovery rate (FDR) q-value For exploring SNP-SNP interactions, the TRM, two-stage Random Forests plus MARS, was applied Because Random Forests does not allow for missing values, the sporadically missing genotyped data were imputed using IMPUTE version 2.0 with the HapMap3 CEU+TSI data as the reference population. Among 2,177 SNPs for interaction search, the missing data rate was low: the median missing rate was 2.6% and maximum was 5.6%. In order to preserve all SNPs in the analyses, our interaction analyses were based on the combined dataset composed of the original genotyped data and the imputed data. The individual SNP effects were evaluated using the original data.The TRM approach does not provide variable significance using p-values, so the bootstrap method was applied for selecting factors in the final model and reducing false positive findings. The bootstrap frequencies of a null model, no association between the simulated outcome and the testing SNPs, were applied for setting the cut-point of variable selection in TRM. In this null model, we independently generated a binary outcome variable with 659 subjects in one group and 492 subjects in the other group, which was consistent with the aggressiveness status in the CGEMS data set. We obtained 500 bootstrap samples by sampling with replacement from the null model. For each identified factor , the false positive frequencies based on 500 bootstrap samples were calculated. The 95% percentile of the bootstrap frequencies in the false positive factors was 4.2%. For conservative purposes, we kept only those with a bootstrap frequency greater than 5%. For easy interpretation, the identified factors were included in the multivariable logistic regression for obtaining odds ratio and their 95% confidence intervals.MMP16+ ROBO1) in the Moffitt group. In Step 3, the identified SNP interactions from the Step 2 were re-evaluated in the CGEMS group. The best “interaction patterns” (such as dominant-dominant model) were detected separately in both groups using MARS. Additionally, we checked whether the identified interaction models were better than the models with their main effects only by using the stepwise logistic regressions. Data analyses were performed using SAS 9.3. TRM was performed using MARS 2.0 , and R package of randomForest and varSelRF.The flow chart of the cross-evaluation for detecting SNP-SNP interactions is shown in COL4A3, PDGFD and ELK3) were associated with prostate cancer aggressiveness in both the CGEMS and Moffitt groups with a p-value less than 0.05. Two SNPs (rs10498214 and rs6436661) in the COL4A3 were significantly associated with prostate cancer aggressiveness. Those with the CC genotype compared with CT and TT genotype in rs10498214 tended to have a higher risk of aggressive prostate cancer (odds ratio (OR) = 1.63 and p-value = 0.028 for CGEMS; OR = 1.53 and p-value = 0.047 for Moffitt). The CC and CT genotype of rs6436661 in the COL4A3 was negatively associated with prostate cancer aggressiveness . Men with the CC genotype in rs488753 (PDGFD) were more likely to develop aggressive prostate cancer than those with the CT and TT genotype . The CC and CT genotype of rs2268509 in the ELK3 was positively associated with prostate cancer aggressiveness .In the Cancer Genetic Markers of Susceptibility (CGEMS) group, we evaluated the main effect of 2,651 angiogenesis SNPs associated with prostate cancer aggressiveness status (yes/no) using logistic regression models. There were 279 SNPs in 75 genes with a raw p-value less than 0.05. Among these SNPs, the largest FDR q-value was 0.053. This indicates that less than 15 false positive findings were expected among the top selected SNPs. These significant SNPs were then assessed in the Moffitt group. Among these 279 SNPs, 160 SNPs were available in the Moffitt data. Four SNPs in three genes (CSF1) and rs1477908 (in MMP16) were selected, and 12 two-way SNP-SNP interactions were identified. For selecting factors in the final model and internal validation, the bootstrap method was applied. Using a bootstrap frequency of 5% as a cut-point, the top seven factors were selected: two main effects and five interacting SNP pairs. The two main effects were the core factors in these SNP pairs. Among the five interacting SNP pairs, rs1477908 was involved in two SNP pairs and rs3093040 was included in the other three SNP pairs. The five gene pairs of the identified SNP-SNP interactions were MMP16+ ROBO1, MMP16+ CSF1, CSF1+ FBLN5, CSF1+ HSPG2, and MMP16+ EGFR, which in a rank order based on the bootstrap frequencies. As shown in The SNP interactions in angiogenesis genes were evaluated using the TRM approach in the CGEMS group. A total of 14 factors were selected using the TRM approach . Two maiMMP16)+rs1387665 (ROBO1) and rs6994019 (MMP16)+rs3093040 (CSF1). Prostate cancer patients with the AA+AG/GG genotype combination of the SNP pair of rs1477908 and rs1387665 were more likely to develop aggressive prostate cancer  than those with the genotype combination of AA+ AA. Patients with the GG/GA genotype in rs3093040 were more likely to have an aggressive prostate cancer than those with the AA genotype, but this effect was significantly modified by rs6994019. Among those with the GG/GA genotype in rs3093040, men with the GG genotype of rs6994019 were more likely to develop aggressive prostate cancer  than those with the AA genotype of rs3093040; nevertheless, this positive association was not significant for those with the GT/TT genotype of rs6994019 .Although these five SNP-SNP interactions were significant in the multivariable model, we further evaluated whether these SNP pairs were independent predictors for prostate cancer aggressiveness. The five interacting SNP pairs were fitted individually in a logistic regression model. In each 2-way interaction in MMP16) and rs1387665 (ROBO1) was available in the Moffitt group. As shown in These 5 important SNP-SNP interactions were further evaluated using the Moffitt group  were searched  in the gene pair of MMP16 and ROBO1 were associated with prostate cancer aggressiveness. In the Moffitt group, prostate cancer patients with the AA+GG/GA genotype in the SNP pair of rs1467251 and rs7625555 had a lower chance of developing aggressive prostate cancer than other genotype combinations in the same SNP pair . In the CGEMS group, the low risk group was those with the GA/AA+GG/GA genotype in the same SNP pair . As for the interaction of rs1824717 and rs7625555, the high risk group of aggressive prostate cancer in the CGEMS group was the combination of AA and AA genotype of this SNP pair ,and the high risk group in the Moffitt set was the combination of AA/AG and GA/AA genotype . With the MMP16 and EGFR, men with the genotype combination of GA/AA and AA in a SNP pair of rs1401862 and rs6964705 tended to be less likely  have aggressive prostate cancer than other genotype combinations of the SNP pairs in the CGEMS group. We also observed the significant interaction pattern of this SNP pair in the Moffitt group.Three gene pairs were observed to have at least one SNP-SNP interaction with a similar interaction pattern in the two study groups. The similar interaction pattern was defined as the identified genotype combinations in the two study groups, which are overlapped and with the same direction in terms of a prostate cancer aggressiveness risk. Three  were in the gene pair of The main effects of SNPs involved in the significant interactions are shown in −5 to 6×10−4) with a medium to large effect size were successfully detected even with a relatively small sample size of approximately 1,000. The odds ratios of these SNP interactions were categorized from a medium (OR≥1.5) to large effect size (OR≥2) Our findings identified five SNP-SNP interactions in the angiogenesis genes associated with prostate cancer aggressiveness in the CGEMS group using the novel TRM approach. Five highly significant SNP-SNP interactions . The most prominent protein in the network was EGFR, which interacted with the remaining five proteins that were involved in angiogenesis.Our identified gene-gene interactions may be biologically relevant based on the network analysis. The interactions of the five gene pairs  were demonstrated using cross-evaluation in the CGEMS and Moffitt groups. Particularly, the former three gene pairs had at least one SNP-SNP interaction with a similar interaction pattern in the two study groups. Among the identified gene pairs,  network  was propet al. performed a meta-analysis of 10 prostate cancer microarray expression datasets to identify the common signatures at both the gene and pathway levels associated with prostate cancer risk, and the EGFR pathway was found in nine datasets The epidermal growth factor receptor (EGFR) is a critical protein in proliferation of epithelial cells and is involved in oncogenesis. The EGFR binds the epidermal growth factor (EGF) and has been shown to play an important role in regulating prostate cellular growth and function MMP16 and ROBO1, our top one selection, is promising. ROBO1 is cleaved by MMPs and translocates into the nucleus of cancer cells, which suggests that ROBO1 may act beyond a receptor as a signaling molecule et al. reported a down-regulation of MMP16 in malignant prostate tissues MMP16 has been shown to be associated with pancreatic cancer cell migration and invasion ROBO1 expression was significantly increased in prostate tumors and hepatocellular carcinoma as compared to normal tissue The interaction between et al. observed a potential role in pancreatic cancer. The mouse model of pancreatic cancer without Csf1 gene had a significant decrease in angiogenesis and reduction in tumor number et al. compared expression of FBLN5 between prostate tumors, benign prostatic tissues and different prostate cancer cell lines. All analyses, microarray, immunohistochemistry (IHC), and RT-PCR consistently suggested down-regulation of FBLN5 in prostate tumors FBLN5 is known as a target gene for TGF-β HSPG2 deficient mice showed impaired angiogenesis, delayed wound healing and tumor growth et al. demonstrate a positive correlation between expression of HSPG2 and high Gleason tumors Other proteins in the genetic interaction network were reported to be associated with prostate cancer. Colony stimulating factor-1 (CSF1) is a protein that increase tumor angiogenesis et al. evaluated more than 600 reported associations and found less than 4% of the results were replicable among 166 associations that had been studied more than three times COL4A3, PDGFD and ELK3) with significant main effects in our two study groups did not overlap with the SNPs with significant interactions. Thus, it is highly recommended to consider both main effects and interactions for comprehensively evaluating gene variations in genetic association studies.Our study demonstrated that frequent inconsistent results of individual SNPs may be partially due to SNP-SNP interactions. Similar SNP-SNP interaction patterns were observed in the majority of our results, but the individual SNP effects for the SNPs involved in the interactions could not be replicated in the two study groups. For the genetic association validation studies, it is well known the individual SNP results are difficult to reproduce. Hirschhorn Our study findings , generatIn summary, this study successfully detected the genotype combinations at risk of aggressive prostate cancer and explored the underlying complicated biological associations among angiogenesis genes associated aggressiveness of prostate cancer. The gene network associations based on SNP interactions were also observed in several studies for various diseases Table S1MMP16+ROBO1 and CSF1+FBLN5 associated with prostate cancer aggressiveness.SNP-SNP interactions of (DOC)Click here for additional data file.Table S2MMP16+CSF1 andCSF1+HSPG2 associated with prostate cancer aggressiveness.SNP-SNP interactions of (DOC)Click here for additional data file.Table S3MMP16+EGFR associated with prostate cancer aggressiveness.SNP-SNP interactions of (DOC)Click here for additional data file.


The success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs  and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.2 rbetween adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, 2 rranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average 2 rranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.Average 2 r> 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Our estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds. The extent of non-random association of gametes at different loci, or linkage disequilibrium (LD), has become the focus of many recent studies in both humans and animals -4. GainiPrevious studies have shown that the extent and persistence of LD in livestock -16 is muThe implementation of genomic selection using GEBV has been successful in dairy cattle ,20,21, a2 rfor all SNP on the same chromosome using equation (1). Average 2 rbetween adjacent markers within breed was estimated using equation (2). Average 2 rat various distances was computed by grouping all SNP combinations by their pairwise distance in classes of 100 kb of length starting at 0 to 10 Mb. Figure 2 rover distance in each breed. In addition, Table 2 rfor adjacent markers and at 0.5, 1 and 5 Mb. The average 2 rbetween adjacent SNP was largest in the Duroc animals (2 r= 0.46), followed by Hampshire (2 r= 0.44), whereas Yorkshire and Landrace exhibited the smallest average 2 r followed by Hampshire (2 r= 0.37), Yorkshire (2 r= 0.34) and the lowest for Landrace (2 r= 0.30). Using only every 10th marker, average 2 rdecreased to around 50% of the original 2 r(2 r= 0.20-0.25), and using every 100th marker average 2 rranged from 0.05-0.07 at an average marker distance of 6.5 Mb, which was comparable to the results found for average 2 rat 5 Mb.To estimate LD, we genotyped 351 animals in 117 sire/dam/offspring trios across four breeds of pigs  using the Illumina PorcineSNP60 BeadChip . We usedij ras the square root of ij2 rin equation (1) between all possible combinations of SNP i and j respectively, using the sign of the non-squared numerator. If 2 rbetween two markers is equal in two populations, but their corresponding r has opposite sign, the gametic phase is reversed [k, k'R) of inter-marker ij rbetween two populations k and k' (Equation 3). For all pairwise comparisons of breeds we estimated k, k' Rand the percentage of SNP with reversed sign of r. Similar to our computation of average 2r, we grouped SNP pairs in classes of inter-marker distances 100 kb long and computed persistence of phase within each class starting at 0 up to 10 Mb  in both breeds but phase is reversed between them. High positive values are a result of high 2 rand equal phase in both breeds [2 rwith increasing marker spacing. The slope of the decline was lower for the correlation between Landrace and Yorkshire when compared to other breed comparisons. Applying a z-test with Fishers' transformation [p < 0.001, n = 1,520) than all other breed combinations. Results for the correlation of phase were not significantly different  in the Duroc-Hampshire, Duroc-Landrace, Duroc-Yorkshire, Hampshire-Yorkshire, and Hampshire-Landrace pairings . Due to the similar long range 2 r(0.035) at 10 Mb we would have expected the Landrace population to have an effective population size comparable to that of Duroc and Yorkshire. However, using pedigree data Welsh et al. [D') from 0.29 to 0.41 using 15 microsatellite markers. In contrast, using 2 rinstead of D' and thereby correcting for minor allele frequency, Harmegnies et al. [2 rranging from 0.15 to 0.19 for marker distance < 1 cM and 0.10-0.12 for markers spaced between 1 cM and 5 cM, using 29 microsatellite markers on SSC15, comparable to our results of 2 rbetween 0.16-0.22 for markers spaced between 1 and 5 Mb. Du et al. [2 rfrom 4,500 SNP markers in six commercial lines of pigs and found estimates of average 2 r= 0.51 for markers less than 0.1 cM apart, and estimates of 0.21 and 0.07 at marker distances of 1 cM and 5 cM respectively. Similarly, our populations had average 2 rof 0.15 to 0.20 and 0.05 to 0.08 at marker distances of 1 Mb and 5 Mb, respectively. A recent study conducted by Uimari & Tapio [2 rand effective population size in Finnish Landrace and Yorkshire populations. Uimari & Tapio [2 rof 0.43 and 0.46 for adjacent markers in the Finnish Landrace and Yorkshire populations, respectively, which was higher than our results of 0.36 for Landrace and 0.39 for Yorkshire. In addition, Uimari & Tapio reported that the 2 rfor markers spaced at 5 Mb decreased to 0.09 and 0.12 in the Finnish Landrace and Yorkshire breeds, respectively. In the present study, 2 rdeclined further to 0.05-0.06 at 5 Mb marker spacing for Landrace and Yorkshire (Table 2 rfor distant (2 r> 0.2 for 1 Mb) markers in the Finnish populations could be explained by smaller effective population size of the Finnish populations, causing higher 2 ron average. This is partially confirmed by comparing the estimated effective size of the Finnish populations  [e N= 74, 113 for Landrace and Yorkshire, respectively), where the current effective population size for Finnish Yorkshire is approximately half that of our Yorkshire population. Compared to recent estimates from Canadian populations we found estimates of average 2 rfor markers with pairwise distance below 100 kb to be consistent in Landrace  and Yorkshire  [2 rfor markers with pairwise distance below 100 kb were considerably higher in the US population (0.42) compared to the Canadian population (0.31) [Current effective population size of the breeds used in this study was previously estimated, using pedigree information, to be between 74 (Landrace) and 113  . Consisth et al.  estimateh et al. ,27. Nsens et al.  found r2u et al.  estimatectively)  (Ne = 74r: 0.41 to 0.57 at an average pairwise marker distance of 1 Mb). This decrease however was less than the decrease de Roos et al. [Persistence of phase can be used to infer upon the history of a species and relatedness of breeds within that species as well as on reliability of across population GWA and GEVB prediction . Persists et al.  observeds et al.  at incre02(1-c)2Tr, where 02 ris a measure of LD in the common ancestral population, c is the recombination distance between markers and T is time since breed divergence in generations [c will be almost 0, so that correlation of phase at those short distances can serve as an estimation of 02 rin the common ancestral population. Since correlation of phase was comparable in the pig populations (0.87-0.92) for markers with pairwise distance below 10 kb to that reported in Australian cattle (0.80-0.97) [T is smaller in our pig breeds than it is in the cattle breeds. The expected correlation of r between two breeds can be expressed as -2cT e[c. The slope of this regression is an estimate of -2T. Consequently, the slope divided by -2 is the number of generations (T) since these two breeds have diverged [T of 40 and 02 rof 0.92. We observed a correlation of phase of 0.57 and 0.30 at 1 Mb and 5 Mb, respectively, between these two breeds, indicating that a T of 40 may overestimate the actual time since breed divergence. One possible cause of this observation is admixture between these two breeds, causing more common LD between them than what would be expected from fully diverged breeds [If two populations diverged from a common ancestral population, their correlation of phase can be expressed as erations . For mar80-0.97) , LD in tas e-2cT . To estidiverged . Resultsd breeds . We obtad breeds , Duroc, 2 rbetween markers and QTL), and the size of the training population [Our results have several important implications for future implementation of genomic selection in swine. Accuracy of prediction of genome wide marker assisted selection can be directly affected by the chosen marker density  and heritability 2 h= 1 [The currently used marker panel, containing approximately 40,000 usable markers, had average n et al.  to reachas shown  that usias shown , where has shown . Weigel as shown  used apppulation . Assuminpulation . To assepulation , used thpulation , and thepulation . As noten humans  indicaten humans , the accn humans ,39. The 2 ras a measure of LD across the genome as well as correlation of r, which measures phase agreement between breeds. We found 2 rof approximately 0.4 for markers less than 100 kb apart, which is higher than comparable estimates reported for North American Holstein cattle [2 rbetween markers with pairwise distance larger than 1 Mb, indicating a smaller past effective population size of these pig breeds. We also report a relatively slow rate of decay of LD over distance, observing 2 raround 0.2 at 1 Mb. The comparably high long range LD is an indicator that good accuracy can be expected for future implementations of GEBV in pigs using 10%  of the SNP used in the current assay or less, along with genotype imputation. We would encourage future research in genomic selection in swine to especially focus on the possible benefits of the combined use of reduced marker panels and genotype imputation. To successfully promote the use of genomic selection in swine it will be necessary to increase cost efficiency while maintaining high accuracy of prediction. Currently no low density panels for SNP genotyping are publicly available for swine, but the presented results will be available to aid in the development of efficient SNP platforms. Relatively low persistence of phase reported here implies that the use of multi-breed panels estimating SNP effects for genomic selection will likely be limited, especially when using low density genotypes, but the merit of combining reference haplotypes for genotype imputation should be further investigated.We used the PorcineSNP60 chip  to obtain cattle  as well n cattle . The samM = 62,163) Genotyping BeadChip (Illumina Inc.) [CallRate < 0.9 M= 5080). Only autosomal SNP were considered in this study, leading to the exclusion of all SNP with an uncertain map position on build 10 of the pig genome sequence, as well as SNP on the sex chromosomes . To exclude non-segregating SNP from the analysis we removed markers with minor allele frequency (MAF) below 5% within each breed separately. The number of fixed SNP varied substantially between breeds: we excluded MAF < 5% M= 13,646 SNP in Duroc, MAF < 5% M= 15,405 SNP in Hampshire, MAF < 5% M= 7,631 SNP in Landrace and, MAF < 5% M= 8,665 SNP in Yorkshire. Additionally, SNP were excluded for failure to meet Hardy Weinberg Equilibrium (p < 0.001) within breeds causing HWE < 0.001 M= 117, 85, 146, and 176 SNP to be discarded in Duroc, Hampshire, Landrace, and Yorkshire respectively. After applying the described filtering criteria, a total of 30, 26, 29, and 32 trios were included for the Duroc, Hampshire, Landrace and Yorkshire breeds, respectively. And a total of 34,129, 32,370, 40,144 and 39,110 SNP spaced at an average distance of 70, 74, 60 and 61 kb satisfied the SNP selection criteria for Duroc, Hampshire, Landrace and Yorkshire, respectively.For this study sire/dam/offspring trios of the Duroc, Hampshire, Landrace and Yorkshire breeds were selected from the National Swine Registry (NSR) pedigree. Selected parents were unrelated for at least two generations. All animals were genotyped using the Illumina PorcineSNP60 (Number of markers na Inc.)  at a com2 rcan be reliably estimated from the current sample size (results not shown). Alleles for each SNP were re-coded as 0/1, keeping the reference allele constant across all four populations, allowing for later determination of phase agreement. Haplotypes and code needed to reproduce these results are publicly available at https://www.msu.edu/~steibelj/JP_files/LD_estimate.html.Haplotypes were obtained for the founder animals using the trio option of BEAGLE , phasing2 rwas estimated, using allelic frequencies of the founding animals, according to the following equation:For all pairs of SNP i, pj pare the marginal allelic frequencies at the th iand th jSNP respectively and ij pis the frequency of the two-marker haplotype [2 rfor SNP pairs in each interval was estimated as the arithmetic mean of all th iand th jelement of the currently considered interval:where aplotype , using taplotype . Marker 18 autosomes (l), with l MSNP per chromosome. To estimate average 2 rbetween adjacent markers for different marker densities a certain percentage of markers  were removed before average 2 rwas estimated using equation (2). To select markers, an increasing proportion of SNP were sequentially removed solely considering their map position, so that for instance: to reduce a panel to 50%, every second marker was kept for analysis, for 25% every fourth was kept and so on.where 2 rat larger distances are close to zero, which would cause correlation of phase to be close to zero as well. Persistence of phase was then estimated as:To estimate persistence of phase only markers with minimum MAF of 5% in all breeds were included in the analysis, resulting in 22,340 common SNP across all breeds. Correlation of phase was estimated for intervals of 100 kb (from 0 to 10 Mb). We excluded markers with pairwise distance above10 Mb to decrease the computational load. Estimates of average k, k' Ris the correlation of phase between ij(k) rin population k and ij(k') rin population (k) k', sand (k') sare the standard deviation of ij(k) rand ij(k') rrespectively, and ij racross all SNP i and j within interval p for population k and k' respectively.where JPS, ROB, CS, and CWE designed the experiments. CS and ROB identified trios for data collection and oversaw the collection of tissue samples. CWE coordinated the genotyping. JPS and YMB completed statistical analysis and wrote the manuscript. All authors read and approved the paper.


Toll-like receptors (TLRs), a large group of proteins which recognize various pathogen-associated molecular patterns, are critical for the normal function of the innate immune system. Following their discovery many single nucleotide polymorphisms within TLRs and components of their signaling machinery have been discovered and subsequently implicated in a wide range of human diseases including atherosclerosis, sepsis, asthma, and immunodeficiency. This review discusses the effect of genetic variation on TLR function and how they may precipitate disease. Caenorhadbitis elegans to humans [Drosophila [The immune system is comprised of two parts, the innate and adaptive systems. The innate immune system is the first line of defense against invading organisms and is highly conserved across species. Pathogens are identified on the basis of their molecular structures by a limited repertoire of so-called pattern recognition receptors (PRRs). One major family of PRRs are the Toll-like receptors (TLRs), a group of type I membrane glycoproteins evolutionarily conserved from o humans , 2. The osophila . TLRs arosophila . Currentosophila , 6. The osophila . This TIosophila , 4. Withosophila -12. TLR3osophila -16. The osophila ; TRAM paOne way of classifying bacteria is on the basis of differential staining of their cell walls namely Gram-negative and Gram-positive. Several components of the bacterial cell wall are TLR ligands the most potent of which is lipopolysaccharide (LPS). The process by which Gram-negative bacterial LPS activates TLR4 is well known. The first step involves LPS binding to the lipopolysaccharide-binding protein LBP. CD14, a lipid-binding protein, attaches to LPS-LBP complexes and delivers the LPS to MD-2, which in turn activates TLR4 by promoting oligomerization of the receptor -20. OnceGenetic SNPs such as single nucleotide SNPs (SNPs) are common SNPs found within a population -28. In tSNPs can be presented in several ways and as there is currently no consensus this can lead to confusion. Two examples follow which describe how SNPs are presented in this review. An example of a coding region SNP is Asp299Gly, Asp is the wild-type amino acid, 299 is the position of the altered amino acid within the protein, and Gly is the “new” amino acid arising from the SNP. For SNPs outside the coding region, the nucleotide 5’ of the ATG-translation initiation codon is -1, the previous -2, etc. For example, -159C/T is located 159bp upstream of the ATG-translation initiation codon, the wild-type base is C, and the altered base arising from the SNP is T. In the literature SNPs are often referred to in short-hard as polymorphisms or variants. 1).In this review we focus on those SNPs that have been described in TLRs and their accessory proteins. A table is provided listing the SNPs for each TLR and the diseases these SNPs are associated with  cases carriers of the Asp299Gly SNP were more frequent than in the male control cases [The study of Kiechl ol group . This fi factors , 62. How factors  or with  factors , 65 was ol cases . Why theol cases . The lacol cases . Statins are a group of drugs that are very effective for the treatment of cardiovascular disease , 69. Whiin-vitro model fibrinogen was found to activate TLR4, curiously and in marked contrast to LPS, TLR4 receptors with the Asp299Gly and/or Thr399Ile SNPs were more responsive to fibrinogen activation [Fibrinogen is associated with coronary heart disease, potentially as a causal factor, and promotes the production of inflammatory proteins , 80. In tivation . This intivation .The TLR4 SNPs Asp299Gly and Thr399Ile have also been associated with diseases other than atherosclerosis. Crohn’s disease has received a large amount of interest with respect to the TLR4 Asp299Gly and Thr399Ile SNPs. This is in part because the disease is believed to be dependent upon defects in the innate immune response . In two TLR4 is critical for the airway inflammatory response  and agenet al. [Rare TLR4 SNPs do exist in addition to the Asp299Gly and Thr399Ile SNPs . These ret al.  describeMD-2 and CD14 are essential for the function of TLR4. Genetic variation exists in both proteins. Two coding SNPs within MD-2, Thr35Ala  and Gly5One particular CD14 SNP, -159C/T  has been investigated for its potential role in a wide variety of diseases. An association between the -159C/T SNP and inflammatory bowel diseases (including Crohn’s) has been noted. T allele and homozygous TT genotype frequencies were elevated in Crohn’s disease patients when compared to healthy individuals , 112. ThAsthma, allergic rhinitis, and atopic dermatitis are diseases arising from abnormal immunoglobulin (Ig)-E responses. TT homozygotes were found to have the lowest Ig-E levels in children who had positive skin test for aeroallergens  and in aOne early report identified a higher frequency of the T allele in myocardial infarct survivors compared to healthy individuals . HoweverA study characterizing sequence variation within the MyD88, Trif, Tram, and Tirap adaptors in healthy individuals from sub-Saharan Africa, Europe, and East Asia has shown that MyD88 and TRIF have evolved under purifying selection indicating deleterious SNPs within these two genes would not survive for long. The other adaptors showed signs of adaptive evolution restricted to specific populations . This maThe non-synonymous TLR1 SNP Ile602Ser is important for the ability of the receptor to signal. This is likely to be due to an inability of the 602Ser SNP to traffic to the cell surface . In a whMycobacterium leprae induced monocytes to synthesize IL12, and monocytes from patients with the Arg677Trp SNP produced a lower amount of this cytokine [The TLR2 Arg677Trp mutation was identified in individuals with lepromatous leprosy. Cell lysates from cytokine . This ficytokine .et al. linked the Arg753Gln mutation with an increased risk of sepsis, and linked the finding to staphylococcal infections [Staphylococcus aureus however [Arginine to glutamine substitution at residue 753 influences the risk of tuberculosis . This SNfections . However however .Stimulation of whole blood from two donors heterozygous for the Arg477stop SNP displayed lower secretion of pro-inflammatory cytokines when compared to control .Of the over 100 single nucleotide SNPs within the TLR3 gene four result in amino-acid substitutions; Asn284Ile, Tyr307Asp, Leu412Phe, and Ser737Thr. None of these four SNPs were found to be linked to each other. The Leu412Phe SNP is very prevalent and found in 20% of one sampled population with values reaching as high as 50% in patients with enteroviral myocarditis/cardiomyopathy . IntriguLegionella pneumophila [Flagellin, the TLR5 ligand, is the main pro-inflammatory stimulus in lung epithelial cells. The common TLR5 SNP, Arg392Stop, prevents flagellin from signaling and increases susceptibility to pneumonia as caused by umophila . The 392umophila . A repliumophila . In a Jeumophila  SNP.TLR6 contains a coding SNP in the extracellular domain that converts Serine-249 to Proline. This SNP potentiates IFN-γ release following injection of the Bacillus Calmette-Guérin (BCG) vaccine . The Seret al. identified three single nucleotide SNPs in TLR7 with an allele frequency of greater than 5%, these being Gln11Leu, 1-120T / G found in intron 1, and a synonymous SNP which had did not change the amino acid at codon 801[The study of Schott codon 801. The codcodon 801. The Glncodon 801. Presenccodon 801. Studiescodon 801 was founcodon 801.The TLR8 SNPs Met1Val and -129G/C render individuals more susceptible to Crimean-Congo hemorrhagic fever  and tubeOver 60 publications have linked the TLR9 mutations -1486 T/C, -1237 T/C, +1174 G/A, and/or 2848 A/G to the predisposition of a wide variety of inflammatory disorders including non-Hodgkin’s lymphoma , cervicaFor TLR10, two SNPs Pro344Pro and Iso775Val have been found to be associated with asthma in a study of 47 subjects . SeveralSince the discovery of TLR11, no association studies have been published linking the receptor to a disease. However, mice lacking the TLR11 gene are highly susceptible to kidney infection by uropathogenic bacteria . In reviewing the literature it is clear that the role of TLR mediated inflammation is important for numerous human diseases. However, the association studies described herein raises several important and interesting questions. et al. in their study of rare TLR4 SNPs [The selection pressure exerted by foreign agents, such as bacteria, has been so great and pervasive throughout human history that one would expect that mutations which prevented the immune system from activating would be so deleterious as to disappear from the population relatively rapidly, a conclusion put forward by Smirnova LR4 SNPs , 108. HoLR4 SNPs , 73. SimLR4 SNPs . et al. [Another potentially confounding issue has been highlighted by Ferwerda et al. . Many stet al. . In vitro assays investigating TLR functions have been shown to be prone to conflicting results arising from bacterial contamination of the ligands used in the experiments [Explaining how these TLR SNPs affect these various diseases described above is likely to be a complex question. Taking again TLR4 as an example, lipopolysaccharide (LPS) activates the receptor. Studies on the effect of the common TLR4 mutations Asp299Gly and Thr399Ile upon LPS activation have been mixed. Reports have shown that the SNPs inhibit LPS activation  whereas eriments . Anothereriments , 83. AGEeriments  their pr


Bos taurus) for identifying polymorphic SNPs in cervids Odocoileus hemionus (mule deer and black-tailed deer) and O. virginianus (white-tailed deer) in the Pacific Northwest. We found that 38.7% of loci could be genotyped, of which 5% (n = 1068) were polymorphic. Of these 1068 polymorphic SNPs, a mixture of putatively neutral loci (n = 878) and loci under selection (n = 190) were identified with the FST-outlier method. A range of population genetic analyses were implemented using these SNPs and a panel of 10 microsatellite loci. The three types of deer could readily be distinguished with both the SNP and microsatellite datasets. This study demonstrates that commercially developed SNP chips are a viable means of SNP discovery for non-model organisms, even when used between very distantly related species (the Bovidae and Cervidae families diverged some 25.1−30.1 million years before present).Single nucleotide polymorphisms (SNPs) are growing in popularity as a genetic marker for investigating evolutionary processes. A panel of SNPs is often developed by comparing large quantities of DNA sequence data across multiple individuals to identify polymorphic sites. For non-model species, this is particularly difficult, as performing the necessary large-scale genomic sequencing often exceeds the resources available for the project. In this study, we trial the Bovine SNP50 BeadChip developed in cattle ( Single nucleotide polymorphisms (SNPs) are increasingly becoming the marker of choice for investigating contemporary and evolutionary genetic processes e.g. , . SNPs et al.Ovis canadensis) and thinhorn sheep  using the OvineSNP50 BeadChip developed for domestic sheep (Ovis aries). Similarly, Pertoldi et al.Bos taurus) to genotype 2 209 polymorphic loci in European (Bison bonasus) and American bison (B. bison bison and B. bison athabascae). These studies confirm that cross-species application of commercial SNP chips can be a successful strategy for SNP discovery in non-model organisms. This strategy, however, has only been applied to SNP development in non-model species closely related to the focal species. Domestic sheep diverged from bighorn and thinhorn sheep approximately 3.1 million years ago (MYA) Developing a panel of SNP markers can be a challenge when working with non-model organisms. While next-generation sequencing technologies have greatly reduced the cost of DNA sequencing B. taurus is used to genotype DNA samples from a diverse species complex of deer indigenous to North America: mule deer and black-tailed deer (Odocoileus hemionus ssp.), and white-tailed deer (O. virginianus) In this study, the potential utility of commercial SNP chip technology for identification of SNPs in non-model organisms is tested between two lineages that diverged approximately 25.1−30.1 MYA, deer (family Cervidae) and cattle (family Bovidae) Of the 54 609 SNPs on the chip, 21 131 (38.7%) were scored successfully in at least 90% of individuals, and 1068 of these loci were polymorphic. Minor allele frequency (MAF) is widely used to describe the genetic variability of two-allele SNPs, and refers to frequency of the least common SNP allele. MAF for each locus overall and within each deer lineage is detailed in lositan identified 878 SNP loci as neutral, 116 as being under positive selection and 74 under balancing selection after adjustment for multiple testing (E) and observed heterozygosity (HO) were generally lower for SNPs than for microsatellites, though this difference between marker types was only significant in mule deer  (−162) and the 878 neutral SNPs (3.0×10−123), attesting to the high discriminatory power of these markers. Although P(ID) was an order of magnitude higher for microsatellites  value of at least 10-3–10-4 recommended for wildlife forensic applications The analysis in  testing . Departu testing . The sta testing , likely ule deer . FIS diftellites . The ovees ((ID)  was extrgenepop returned significant  departures from panmixia in all pairwise comparisons. The analyses in structure all returned K = 3. For the microsatellites, the highest ΔK value was at K = 3, with the mule deer, black-tailed deer and white-tailed deer each partitioned into distinct clusters  typical of microsatellites therefore lead to higher values of D relative to loci with low mutation rates and low numbers of alleles, such as SNPs mD is similarly elevated increased by high levels of heterozygosity O values detected for microsatellites in mule deer and black-tailed deer than for SNPs  and Cervidae (O. hemionus and O. virginianus) Of the 54 609 loci on the BovineSNP50 BeadChip, 21 131 (38.7%) SNPs were successfully genotyped in at least 90% of individuals, and 1068  were polymorphic in deer. In comparison, Pertoldi et al. Both neutral loci and loci potentially under selection were detected in this study, including 878 neutrally evolving, 116 under the influence of positive selection, and 74 influenced by balancing selection . A suiteST, D and mD) reported lower differentiation between mule deer and black-tailed deer than between white-tailed deer and either O. hemionus lineage ((ID) values both overall and within individual lineages suggests that these SNPs would be very useful for fine-scale population genetic analyses requiring unambiguous individual identification. In this study, we used only ‘pure’ representatives of each lineage  differed markedly between datasets . In accordance with these expectations, positive FIS values were returned for all lineages for microsatellites  and for SNPs in black-tailed deer and white-tailed deer. In contrast, statistically significant negative FIS values were returned in mule deer when all 1068 SNPs or the 878 neutral SNPs were analyzed  had a minor allele frequency (MAF) less than 0.1 . The position of each SNP on both Btau4.0 and UMD3.1 is provided in O. virginianus and O. hemionus to 2n = 60 in the bovid B. taurusB. taurus genome has been successful for next-generation sequences generated from O. virginianusOne of the most attractive incentives for using model species to identify SNPs in non-model species is the availability of annotations that link SNP variation to DNA sequences and ultimately to biological processes. Although no deer genomes have yet been fully sequenced and annotated, the genomic location of each SNP identified in this study can be mapped on various versions of the O. h. cerrosensis), Florida Key white-tailed deer  and Columbian white white-tailed deer in western Oregon (O. v. leucurus) as ‘Endangered’ Axis spp, revised to genus Hyelaphus in Elaphurus davidianus; Patagonian huemul, Hippocamelus bisulcus).The SNPs characterized in this study would likely be useful in a variety of applications for an array of cervid species, given the high cross-species amplification success we observed. Neutral SNPs can be readily applied to more traditional population genetic analyses, such as characterizing population structure, quantifying genetic diversity and inferring migration rates. Loci under natural selection could be used to investigate genetic mechanisms underpinning natural selection and adaptation, or to differentiate recently diverged populations, species and ecotypes that are otherwise difficult to distinguish using neutral loci Canis lupus familiaris) could likely identify polymorphic SNPs in a wide range of Carnivora species that would otherwise require extensive DNA sequencing. The cross-species utilization of SNP chips is therefore an exciting avenue of future research.This study demonstrates the potential utility of commercially available SNP chip technology for identifying SNP loci in non-model organisms. As polymorphic SNPs were identified between lineages that diverged up to 30.1 MYA, SNP chips developed for model organisms can likely identify SNPs in a far wider range of organisms than previously realized. The porcine, ovine, equine and bovine SNP chips, for example, could be used to collectively to develop a panel of SNPs for wide range of highly divergent ungulates; while SNP chips developed for dogs  are more widespread than O. hemionus, being found throughout northern South America, Central America, Mexico, central and eastern North America and in a number of isolated populations in western North America. White-tailed deer can be subdivided into as many as 38 subspecies Mule deer and black-tailed deer are both classified as To evaluate the feasibility of cross-species SNP chip genotyping as a means of SNP discovery, tissue samples were collected from twelve mule deer, twelve black-tailed deer and four white-tailed deer in Washington and Oregon, USA  between ST-outlier method lositanlositan simulates the expected distribution of Wright’s inbreeding coefficient FSTvs expected heterozygosity (HE) for a given set of genetic markers under the island model of migration ST/HE ratio), whereas loci under balancing selection are expected to show lower levels  of differentiation lositan was run for 10 000 000 simulations, under the “neutral” mean FST and forced mean FST settings, with a two-tailed significance level of 0.05. The mule deer, black-tailed deer, and white tailed deer were designated as different ‘populations’ in the analysis. P-values were adjusted for multiple testing using the B-Y method of false discovery rate correction multtestGenetic analyses of wild populations depend on accurately characterizing whether the genetic loci used are under selection. Theoretical models in population genetics typically assume that the markers employed are selectively neutral; including loci under selection can bias inferences about migration rates, genetic diversity, population genetic structure, and phylogenetic relationships. Loci should therefore be screened for signatures of selection prior to population genetic analyses, in order to ensure that appropriate analytical models are used and results are interpreted correctly genepop 4.1 arlequin 3.5.1.2 IS for each deer lineage was calculated in genetix(ID) was calculated for each suite of loci over all deer and within the mule deer and black-tailed deer lineages The statistical properties of the newly identified SNPs were compared with the 10 neutral microsatellite loci to verify that the SNPs were behaving in a predictable fashion. A range of common statistical analyses were implemented using all 1068 polymorphic SNPs identified here see , the 878genepop 4.1 structure 2.3.3 a priori as mule deer, black-tailed deer or white-tailed deer) to allow population structure to be detected at lower levels of divergence and with less data than earlier versions of structureK = 1−6, with 50 000 burn-in steps and 500 000 iterations for each value of K. Tests were performed three times for each value of K, and the ΔK statistic of Evanno et al. K for each data set. Factorial correspondence analysis (FCA) was implemented in genetix 4.05.2 To determine if either suite of SNPs  could be used to distinguish between mule deer, black-tailed deer and white-tailed deer, significant differences in allele frequencies were assessed using Fisher’s exact test in ST was calculated in genetixDgenodivemDpopulations 1.2.31 ST is one of the most commonly used measures of genetic differentiation and is used in lositan to detect loci under selection, despite being strongly affected by high levels of polymorphism D provides an unbiased quantification of differences in allele frequencies between populations without being affected by levels of genetic diversity and heterozygosity the way FST and its analogues are mD performs well in recently diverged lineages and when mutation rate is low Three measures of genetic distance were calculated to further confirm that the newly identified SNPs exhibit patterns of genetic variation and structure in accordance with theoretical expectations. Weir and Cockerham’s Table S1Genome location, outlier-analysis in LOSITAN and Minor Allele Frequency (MAF) data of the 1068 polymorphic SNPs identified in O. hemionus and O. virginianus. Only SNPs that were genotyped in at least 90% of individuals were included in the analysis. The chromosomal position of each SNP on the Bos taurus genome assemblies UDM3.0 and BTAU4.0 is included. N/A values in the UMD3.0 assembly indicate that SNPs that are not mapped to this genome assembly. Zero values on the BTAU4.0 assembly are indicative of SNPs that could not be mapped to this assembly.(XLS)Click here for additional data file.


The popularity of massively parallel exome and transcriptome sequencing projects demands new data mining tools with a comprehensive set of features to support a wide range of analysis tasks.SeqGene, a new data mining tool, supports mutation detection and annotation, dbSNP and 1000 Genome data integration, RNA-Seq expression quantification, mutation and coverage visualization, allele specific expression (ASE), differentially expressed genes (DEGs) identification, copy number variation (CNV) analysis, and gene expression quantitative trait loci (eQTLs) detection. We also developed novel methods for testing the association between SNP and expression and identifying genotype-controlled DEGs. We showed that the results generated from SeqGene compares favourably to other existing methods in our case studies.http://seqgene.sourceforge.net.SeqGene is designed as a general-purpose software package. It supports both paired-end reads and single reads generated on most sequencing platforms; it runs on all major types of computers; it supports arbitrary genome assemblies for arbitrary organisms; and it scales well to support both large and small scale sequencing projects. The software homepage is  Massively parallel sequencing of exome and transcriptome has been widely adopted to effectively interrogate the key protein-coding and non-coding RNA regions. Exome sequencing (exome-Seq) technology has been especially effective for identifying single-nucleotide polymorphisms (SNPs) and small insertions/deletions (indels) that may cause diseases and other phenotypes. To name a few examples, Ng et al.  have fouhttp://seqanswers.com[However, exome-Seq and RNA-Seq face several bioinformatic challenges, including the development of efficient methods to perform basecalling, assembly, alignment and post-alignment on large amounts of data. There listed more than 350 software tools on swers.com includinswers.com is a tooswers.com is mainlswers.com supportsswers.com supportsThe major components of SeqGene are illustrated in Figure pileup' function reads the alignment results in '.sam' format and reports chromosomal positions for candidate SNPs and indels. From the 'pileup' output, SeqGene's 'snp' function will filter the SNPs and indels based on a number of criteria: 1) the total coverage, i.e., the number of reads covering a candidate SNP (default 20); 2) the base quality, when the quality string is present, any base calling with low Phred quality will be removed from the coverage (default 10); 3) minor sequenced strand frequency, i.e., the proportion of reads covering both strands must reach certain threshold (default 0.1); and 4) mutated bases frequency, i.e., the proportion of mutated reads must be significant among all reads covering the position (default 0.25).Detecting genomic variants  via whole-genome sequencing, RNA-Seq and exome-Seq is an essential approach to understanding the association of genotypic difference to phenotypic consequences with the eventual goal of personalized genomics for medical purposes ,12-15. AThe multi-criteria SeqGene mutation filter is designed to be versatile to handle various exome-Seq and RNA-Seq projects. For example, in detecting somatic mutation in cancer samples, one can use a lower allele percentage threshold to account for altered ploidy of cancer samples. In high-depth targeted sequencing, one can increase the coverage threshold to improve the false discovery rate. In addition, since the SNP filter works with '.sam' file, it can work with sequencing data from many sequencing platforms and with various alignment software including Bowtie, BWA 1919, and Nsnp' function also performs mutation annotation such as gene model annotation , miRNA and other non-coding RNA annotation, consequence of the mutation , dbSNP annotation [The 'notation , hetero-notation  data cangenotyping' function generates genotyping calls on the mutation positions across one or more samples. All positions that pass the SNP filter will be called either 'heterozygous mutations' or 'homozygous mutations'; positions that fail to pass the SNP filter will be labelled 'quality control' for unknown genotypes; positions that are not mutated is called either 'homozygous reference' or 'quality control' depending on whether the coverage is above or below the SNP filter threshold. One can use the 'genotyping' function to aggregate mutations across multiple samples to identify mutations that match specific contrasts.The 'sam2wig' function efficiently converts the alignment file into the per-base-resolution coverage file in '.wig' format. For exome-Seq, the 'exon_qc' function report all the missing and defective regions with poor coverage, the quantile of the average exon coverage across exome (coverage sensitivity) and the percentage of total mapped reads aligned onto target exon regions (coverage specificity). For RNA-Seq, the 'rpkm' function output the number of reads covering the genes and estimate the expression abundance using the average coverage as well as RPKM (reads per kilobase of exon model per million mapped reads) [phenotyping' function aggregates one or more samples and generates the expression table for all transcripts and exons across all samples. The visualization of coverage and SNPs for each gene can be generated using the 'snpview' function in scalable vector graphics (SVG) format which supports user interactions such as zooming and linking to Ensembl Genome Browser [The 'd reads)  as the n Browser .eqtl' function in SeqGene was computed on expression and genotype data using the 'lm' function in the R 'stats' package. The genotype data can be provided by the users or generated from the RNA-Seq data using the 'genotyping' function. In the latter case, the genotyping are limited to those moderately or highly expressed genes on which a sufficient number of reads were mapped for reliable genotyping calls.A quantitative trait locus (QTL) is a region of DNA that is associated with a particular phenotypic trait. eQTLs are genomic loci that regulate expression levels of mRNAs or proteins. By assaying gene expression and genetic variation simultaneously on a genome-wide basis in a large number of individuals, eQTL analysis can map the genetic factors that underpin individual differences in genome-wide gene expression pattern. Detecting eQTLs through RNA-Seq has been demonstrated as a robust and statistically powerful method in recent studies -25. One diffexp' function in SeqGene can compute fold change, Student's t-test p-value, Wilcoxon test p-value and false discovery rate (FDR) for all transcripts and exons between two treatment groups. For more complex study designs, one can directly work with the expression table generated using the 'phenotyping' function and the methods borrowed from the microarray gene expression analysis, such as from Bioconductor's limma [A common application of RNA-Seq is to identify DEGs between two or more treatment groups. The ''s limma  package,tr and gene expression variable e. For each gene, the linear model is denoted as:A more general way to describe a study design for identifying DEGs is a linear regression model, which describes the linear relationship between treatment variable n is the number of samples, i eis the gene expression value of sample i, i tris the treatment group of sample i , β0 is the intercept parameter, β is slope parameter, and i εis the error term for sample i. This linear model can describe multiple group comparison as well. The test for β≠0 is equivalent to a two-group Student's t-test .where m SNPs with its region, the so-called GCDEG is illustrated in a linear regression model as below:As shown in eQTL studies -25, the m is the number of SNPs within the gene region, ij SNPis the genotype of the jth SNP for the ith sample, j βis slope parameter for the jth SNP, β' is slope parameter for treatment after controlling for genotypes. Here we consider only the SNPs in gene regions. The GCDEG strategy is to test both parameter β≠0 in equation (1) and adjusted parameter β'≠0 in equation (2) and require both tests to be significant. The genotype information can be obtained by other sources. In fact, RNA-Seq data can be used for genotyping moderately to highly expressed genes. In SeqGene, the GCDEG method is implemented in the 'diffexp' function which employs the linear mixed-effects model 'lme' in R package 'nlme'.Where DNAcopy' package in Bioconductor [cnv' function, the log2 RPKM estimation of each exon was used as normalized probe signals for chromosomal segmentation and copy number calls. Note that intergenic and intronic CNV calls might not be accurate since these regions are not generally covered by the exome-Seq data. Also note that a reference  is needed for absolute copy number calls.We implemented an interface in SeqGene to the 'onductor  for CNV G which contains N genes (nodes). Using Johnson's algorithm [d for each pair of genes, where i, j dis the shortest distance from gene i to gene j. If there is no path from gene i to gene j, i, j dis set to equal to N. The shortest distance from gSNP to gDEG is notated as gSNP, gDEGd, which is used as the test statistic for the SNP-DEG association using distance matrix d as the background. The p-value for gSNP, gDEG dis defined by:Detecting significant SNP-expression association using eQTL is effective, however, it requires a large sample size (dozens and above) to generate sufficient statistical power for the genome-wide test. We therefore devised a new pathway topology-based strategy that is especially suited for DEG studies with limited sample size. The assumption of this method is that a SNP-harbouring gene (gSNP) may alter the regulation of the expression of itself and/or a downstream gene (gDEG). The significance of the SNP-DEG association is determined by the topological distance between a gSNP and a gDEG in a regulatory pathway. Therefore a cis-acting SNP  is considered most significant. The further down the pathway, the less significant of the association. To calculate the distance between any gSNP and gDEG pair, we merge all KEGG pathways  graphs ilgorithm , we compI(x) is the indicator function where SeqGene's major functions Figure  were impbuildannot' and corresponding instructions for building additional annotation packages for other organisms from Ensembl, UCSC Genome Browser or arbitrary assemblies.SeqGene's algorithms were optimized and one can expect the analysis tasks finish within minutes to a few hours. For example, SeqGene's SNP pileup function runs at similar speed as SAMtools  which waTo test SeqGene's mutation detection algorithm, we performed exome-Seq on a trio family  with no history of inherited diseases. Genomic DNA was extracted from saliva using Oragene DNA Kit  and sonicated using bioruptor . Sonicated DNA (3 ug) was used to make a library for paired-end sequencing  and fragments with approximately 200 -250 bp insert DNA were select and amplified. After quality control, 750 ng of the library was hybridized to biotinylated cRNA oligonucleotide baits from the SureSelect Human All Exon kit , purified by streptavidin-bound magnetic beads, and amplified for 12 cycles. After purification, the library was paired-end (80 × 80 bp) sequenced using Illumina Genome Analyzer IIx . The exome probes cover 38 Mb of human genome corresponding to the exons and flanking intronic regions of 23,739 genes in the National Center for Biotechnology Information Consensus CDS database (September 2009 release) and also cover 700 miRNAs from the Sanger v13 database and 300 noncoding RNAs from Ensembl GRCh37.56.The sequencing reads were aligned to Human reference genome (Ensembl GRCh37.56) using Novoalign  with defMendelian error rates of the identified SNPs were calculated as an indirect indication of genotyping quality. As demonstrated in Figure Using the same settings in Table snp' function can provide detailed annotations to the SNPs based on the gene model categorization . The resultant annotation file can be aggregated into cross-sample format using 'genotyping' function. Other filtering and analysis with the annotation files are possible. For example, the users can obtain non-synonymous mutations using 'polyphen' function and the output can be submitted to PolyPhen server [In addition, SeqGene's 'n server  for furtIn this example, we showed the SeqGene's capability on expression quantification and eQTL by reanalyzing a public data set from the international HapMap project ,36. The sam2wig' and 'rpkm' functions to quantify gene expression of individual samples. SeqGene's 'phenotyping' function is then used to tabulate gene expression across multiple RNA-Seq samples. The genotype information was obtained from the international HapMap project [eqtl' function, which is capable to report both cis-  and trans- (at a distance) eQTLs to a gene. Figure KB-1839H6.1. The genetic marker is dbSNP entry rs1042927, which is located on chromosome 11, whereas the gene KB-1839H6.1 is located on chromosome 22. Therefore, this is a trans-eQTL which maps far from the location of its gene-of-origin gene. The Bonferroni-adjusted p-value of this eQTL is 1.39e-5. The 'snpview' function in SeqGene will further display the wiggle plot superimposed on the gene model, as shown in Figure We aligned the short reads to the UCSC Genome Browser hg19 human reference genome  using To project . The expsam2wig', 'rpkm', and 'phenotyping' functions. Then the 'diffexp' function was used to perform two-group comparison between the 'stimulated' and 'unstimulated' samples to identify DEGs and GCDEGs. DEGs were selected using Student's t-test p-value < 0.01. GCDEGs were selected using two cutoff values: Student's t-test p-value < 0.01 and genotype-controlled p-value < 0.01. We compared the variance components on the selected DEGs and GCDEGs using SeqGene by the'varcomp' function in R package 'ape'. Figure We demonstrate the novel GCDEG method in SeqGene by reanalyzing a recently published RNA-Seq dataset . The samWe reanalysed the exome sequencing data from a recent study by Yan et al. . The datWe then performed CNV analysis on the nine pairs of samples using the 'cnv' function. For each cancer sample, its control blood sample was used to normalize the signals. The 'cnv' function generated results in '.seg' format which include genomic break points estimation and mean signals for all genomic regions. The output '.seg' file was loaded in to Integrative Genomics Viewer (IGV)  for visuWe also recorded run time and memory usage when performing different tasks for this relatively large scale project Table . Note thWe reanalyzed paired RNA-Seq and exome-Seq data derived from breast cancer cell line, HCC1954 reported by Zhao et al. . Our goaWe applied Tophat  for the We developed an open-source software tool, SeqGene, to support massively parallel exome-Seq and RNA-Seq data analysis. SeqGene supports functions of base-resolution read coverage, quality control, SNP/indel identification and annotation, RNA and DNA depth quantification, ASE, CNV, eQTL, DEG, and KEGG pathway analysis. Among the many functions of SeqGene, we have also implemented novel methods for genotype-controlled differentially expressed genes (GCDEGs) identification, and SNP-DEG association test using KEGG pathways. We have demonstrated that SeqGene is a useful data mining tool to support a wide variety of analysis tasks in exome-Seq and RNA-Seq data.AML: acute myeloid leukemia; ASE: allele specific expression; CBS: circular binary segmentation; CNV: copy number variation; DEG: differentially expressed gene; eQTL: expression quantitative trait locus; FDR: false discovery rate; GCDEG: genotype-controlled differentially expressed gene; GWA: genome-wide association; IGV: integrative genomics viewer; LCL: lymphoblastoid cell lines; miRNA: MicroRNA; QTL: quantitative trait locus; RPKM: reads per kilobase of exon model per million mapped reads; SNP: single nucleotide polymorphism; SVG: scalable vector graphics; UTR: untranslated regions.The authors declare that they have no competing interests.http://seqgene.sourceforge.net. SeqGene requires Python 2.6 or 2.7 and CNV, DEG, GCDEG, eQTL and KEGG pathway functions also require R and certain Bioconductor packages. SeqGene is cross-platform software and has been tested on Linux-, Macintosh- and Windows- based workstations. SeqGene is free for academic use and require a license from the author for commercial applications.The SeqGene software, annotation packages and user's manual can be accessed at XD designed and implemented the SeqGene, performed the analysis and wrote the manuscript.


GNAS) domain on bovine chromosome 13 and genetic merit for a range of performance traits in 848 progeny-tested Holstein-Friesian sires. The mammalian GNAS domain consists of a number of reciprocally-imprinted, alternatively-spliced genes which can play a major role in growth, development and disease in mice and humans. Based on the current annotation of the bovine GNAS domain, four of the SNPs analysed  were located upstream of the GNAS gene, while one SNP (rs41694646) was located in the second intron of the GNAS gene. The final SNP (rs41694656) was located in the first exon of transcripts encoding the putative bovine neuroendocrine-specific protein NESP55, resulting in an aspartic acid-to-asparagine amino acid substitution at amino acid position 192.Genes which are epigenetically regulated via genomic imprinting can be potential targets for artificial selection during animal breeding. Indeed, imprinted loci have been shown to underlie some important quantitative traits in domestic mammals, most notably muscle mass and fat deposition. In this candidate gene study, we have identified novel associations between six validated single nucleotide polymorphisms (SNPs) spanning a 97.6 kb region within the bovine guanine nucleotide-binding protein Gs subunit alpha gene (GNAS SNP (rs41694646) is associated (P ≤ 0.05) with a range of performance traits including milk yield, milk protein yield, the content of fat and protein in milk, culled cow carcass weight and progeny carcass conformation, measures of animal body size, direct calving difficulty  and gestation length. Association (P ≤ 0.01) with direct calving difficulty  and maternal calving difficulty  was also observed at the rs43101491 SNP. Following adjustment for multiple-testing, significant association (q ≤ 0.05) remained between the rs41694646 SNP and four traits  only. Notably, the single SNP in the bovine NESP55 gene (rs41694656) was associated (P ≤ 0.01) with somatic cell count--an often-cited indicator of resistance to mastitis and overall health status of the mammary system--and previous studies have demonstrated that the chromosomal region to where the GNAS domain maps underlies an important quantitative trait locus for this trait. This association, however, was not significant after adjustment for multiple testing. The three remaining SNPs assayed were not associated with any of the performance traits analysed in this study. Analysis of all pairwise linkage disequilibrium (r2) values suggests that most allele substitution effects for the assayed SNPs observed are independent. Finally, the polymorphic coding SNP in the putative bovine NESP55 gene was used to test the imprinting status of this gene across a range of foetal bovine tissues.SNP genotype-phenotype association analyses indicate that the single intronic GNAS gene cluster contributes to several physiological and metabolic disorders, including obesity in humans and mice. Similarly, the results presented here indicate an important role for the imprinted GNAS cluster in underlying complex performance traits in cattle such as animal growth, calving, fertility and health. These findings suggest that GNAS domain-associated polymorphisms may serve as important genetic markers for future livestock breeding programs and support previous studies that candidate imprinted loci may act as molecular targets for the genetic improvement of agricultural populations. In addition, we present new evidence that the bovine NESP55 gene is epigenetically regulated as a maternally expressed imprinted gene in placental and intestinal tissues from 8-10 week old bovine foetuses.Previous studies in other mammalian species have shown that DNA sequence variation within the imprinted  While the ENSEMBL database reports one SNP (rs41255672) in the 3'UTR of GNAS, our sequencing of PCR amplicons generated from genomic DNA isolated from 10 different animals (using amplicons spanning exons 7 to 14 of the bovine GNAS locus) failed to detect a heterozygous SNPs that would allow for a test of GNAS imprinting in tissue samples derived from each animal (results not shown).To determine the imprinting status of the bovine rs41255672 SNP, a coding SNP within NESP55 (rs41694656) was polymorphic in four foetuses ranging in age from 8 to 10 weeks old. Reverse-transcription PCR (RT-PCR) analysis of RNA samples derived from a wide range of tissues for these foetuses indicated that the NESP55 transcript was expressed in brain, heart, intestine, liver, lung, muscle and placental (caruncle and cotyledon) tissues in both 8 and 10 week old foetuses  remains unknown. Using the coding SNP (rs41694656) within NESP55 and DNA sequence traces for genomic DNA and cDNA template samples, the imprinting status of NESP55 was analysed in the current study in the four A/G heterozygous foetal samples which were either 8 or 10 weeks old  from both 8 and 10 week old foetal samples . The position of each SNP relative to the bovine GNAS and NESP55 genes (i.e. upstream/intronic/exonic) was based on the currently annotated transcriptional units contained within the B. taurus GNAS domain. The position of the six assayed SNPs within the GNAS domain is depicted schematically in Figure r2  (P ≤ 0.001) and gestation length (P ≤ 0.01). Association between this SNP and direct calving difficulty remaining after adjustment for multiple testing (q ≤ 0.05). This SNP also tended to be associated (P ≤ 0.10) with maternal calving difficulty  and perinatal mortality --no other SNP analysed in this study was associated with perinatal mortality. Significant associations with direct calving difficulty due to offspring size (P ≤ 0.01) and maternal calving difficulty (P ≤ 0.01) were also observed at the rs43101491 SNP, which is located 14.2 kb upstream of the GNAS gene; however these associations were no longer significant after adjustment for multiple-testing. No significant associations were observed between any of the above-listed traits and the remaining three SNPs located upstream of the GNAS gene .In addition to associations with milk, carcass and body size traits, the NESP55 transcript (rs41694656) displayed no significant associations with any of the milk production, carcass, body conformation or calving traits analysed but it was associated (P ≤ 0.01) with somatic cell count. However, association with somatic cell count no longer remained after adjustment for multiple testing. Sequence alignment indicates that this SNP lies within the first exon of putative maternally expressed NESP55 transcript (GenBank accession U77614.1); the NESP55 exon is situated ~89.9 kb upstream of the first GSα exon. The G-to-A nucleotide substitution at this locus results in a non-synonymous aspartic acid-to-asparagine amino acid substitution at amino acid position 192 in the NESP55 protein , (2) udder health , (3) carcass traits , (4) animal size in live animals ; (5) subjectively assessed subcutaneous fat level on live animals [angularity and body condition score], and (6) calving traits . All phenotypic data were kindly supplied by the Irish Cattle Breeding Federation http://www.icbf.com and a detailed description of all traits is provided in Additional File A range of phenotypic traits were analysed in this study and were categorised into six broad categories: (1) ration],  udder hei.e. a sire). Systematic environmental effects on the progeny are adjusted for and the random non-genetic variation associated with the progeny's phenotypes is minimised, thus facilitating a more accurate measure of genetic merit. This increased study power is particularly beneficial for low heritability traits where the proportion of phenotypic variance attributable to additive genetic differences is low. The disadvantage of such a study design is that the performance traits included for analysis are limited to those routinely measured on progeny. The average number of progeny per sire analysed here was 842 daughter-parity records. When coupled with the mixed model methodology used and the de-regression of the predicted transmitting ability (PTA), this implies that the associations reported herein are independent of pedigree structure.The phenotypes used in this study are sire genetic merit based not on data on the sires themselves but on the performance of their female progeny across multiple generations. Using known relationships among animals, performance records on relatives are used to estimate the genetic merit of an animal  was the dependent variable for all traits with the exception of the milk production traits, including somatic cell count, which were daughter yield deviations  expressed on a PTA scale. Models used in genetic evaluations in Ireland, as well as variance components, have been summarised in detail previously [e somatic cell count) are estimated in Ireland using a repeatability animal model across the first five lactations. PTAs for calving interval and survival are estimated using a multi-trait animal model, including data from the first three lactations. PTAs for milk yield are used to adjust PTA for survival for differences in genetic merit of milk yield; hence, this survival trait is functional survival. PTAs for cow carcass weight, progeny carcass weight, progeny carcass fat score  and pro [et al. ), measuri.e. the phenotypic trait wished to be improved genetically) is calving ease scored by commercial Irish farmers and the predictor trait is calving ease scored prior to 2002 in progeny test and pedigree herds. A similar approach is used to estimate breeding values for gestation length and perinatal mortality with the exception that an animal model is used. Direct calving difficulty refers to the additive genetic effect of the genotype of the calf  while maternal calving difficulty refers to the additive genetic effect of the genotype of the calf's dam (e.g. pelvic weight of the dam). Perinatal mortality is a dichotomous variable scored by farmers as calf dead at birth or within 24 hours [Genetic evaluations for calving ease are undertaken using a bivariate animal-dam model so that PTAs for direct and maternal calving ease are both generated. In the bivariate model the breeding-goal trait  are standardised to the mean and standard deviation of the base population. Both angularity and BCS are subjective measures of the subcutaneous fat levels of the live animal. All PTAs were deregressed using the procedure outlined by Berry and colleagues .i.e. square root of the heritability). The use of highly accurate phenotypes in association analyses is vital to obtain accurate estimates of associations.Only sires with a reliability, less parental contribution, of > 60% were retained for inclusion in the association analysis. A total of 742 sires fulfilled these criteria for inclusion in the analysis of milk, fat and protein yield as well as milk fat and protein concentration. 501 sires were included in the association analysis with calving interval, while 477 sires were included for association analysis with calf survival. The number of sires with a reliability of > 60% for the carcass traits was 446 and the number of sires with a reliability of > 60% for angularity and body condition score varied and was 521 and 504, respectively. The main advantage of using high reliability sire PTAs generated from progeny performance is the increased accuracy of the phenotype compared to actual phenotypes of individual animals. This is particularly true for low heritability traits where the accuracy of the genetic merit of an animal based on a single measure is low (The association between each SNP and performance was quantified using weighted mixed linear models in ASREML  with indi.e. linkage disequilibrium), and between phenotypes, traditional multiple testing adjustments that assume independence among the regressors (e.g. Bonferroni permutation) were not appropriate. Spectral decomposition of the square root of the pair-wise linkage disequilibrium between the SNPs was used to determine the effective number of variables (i.e. SNPs); an effective number of 4.22 SNPs were identified. Furthermore, the phenotypes were grouped into six categories as described previously. Adjustment [i.e. 4.22 effective SNPs times six groups of traits).Regression on individual SNPs were initially undertaken to identify spatial patterns of SNPs associated with performance. Because of the covariances between SNPs  values. This Microsoft Excel file contains D' and r2 measures of LD for each GNAS domain pairwise SNP combination.Click here for fileDescriptions of the performance traits assessed in the present study. This Microsoft Word file contains detailed information for each of the phenotypic trait analysed as provided by the Irish Cattle Breeding Federation (ICBF) http://www.icbf.com.Click here for file


Whole-exome sequencing provides a cost-effective means to sequence protein coding regions within the genome, which are significantly enriched for etiological variants. We describe a panel of single nucleotide polymorphisms (SNPs) to facilitate the validation of data provenance in whole-exome sequencing studies. This is particularly significant where multiple processing steps necessitate transfer of sample custody between clinical, laboratory and bioinformatics facilities. SNPs captured by all commonly used exome enrichment kits were identified, and filtered for possible confounding properties. The optimised panel provides a simple, yet powerful, method for the assignment of intrinsic, highly discriminatory identifiers to genetic samples. Whole-exome sequencing (WES) is presently one of the most efficient means of identifying aetiological genetic mutations , minimisin vitro and in silico. Therefore, sample tracking must be contiguous throughout both data generation and analysis.The high start-up investment required for in-house WES is currently prohibitive to many groups so sample preparation and/or sequencing is commonly outsourced. This transference of sample custody, combined with the complex sample preparation workflow, makes sample mix-ups possible, and difficult to detect. In both clinical and research contexts, ensuring provenance of data is essential to allow the accurate assignment of clinical details to sequence data. It is possible that samples may be misidentified at any stage of the analytical process, both post hoc confirmation of sample identity is therefore highly desirable.Consequent to sample mix-ups in a research setting, erroneous data and sample matching may result in a loss of power for identification of causal variants . In a clGenetic sample identification methods have an advantage over alternative sample management systems in that the genetic 'label’ is intrinsic to the biological sample itself, removing the possibility of manual labelling errors. Single nucleotide polymorphisms (SNPs) are increasingly utilised for DNA-based identification of human samples, with several benefits compared to standard forensic methods -7. ExistSeveral methods for genetic tracking of human biological samples have been previously described, some of which are application specific - for example, for transcriptome microarray studies ,9,10. AlHere we describe an optimised panel of SNPs for which WES data are typically informative, the genotypic profile of which can be utilised to extract intrinsic identifiers from human genomic DNA. These SNP profiles have high discriminatory power, even in large datasets. The profile derived from this panel can be compared to an independently genotyped profile for the same individual, allowing accurate validation of data and sample pairings, at a modest cost per sample.Regions of overlap between three current commonly used whole-exome enrichment kits, , and common SNPs dbSNP 137, ) were es7,  were Primary candidate selection criteria required SNPs to: 1) represent bi-allelic substitutions, excluding substitutions of complementary bases, that is, A↔T and G↔C transversions; 2) be technically amenable to both accurate WES and orthogonal genotyping, that is, not present in large-scale genomic repeats , or homoFollowing primary candidate identification steps, SNPs were further optimised by the following requirements: 1) be located at least 10 bp from exon boundaries; 2) not be situated in regions with a high sequence similarity to non-target regions, that is, no non-target BLAT score >100 , as thisFinally, SNPs were prioritised for inclusion in the panel by proximity of the AFs to 0.5, across HapMap populations, in order to maximise discriminatory power.in silico analysis of samples was performed as previously described [A set of 91 in-house exome samples was evaluated for depth of sequence coverage for the candidate SNPs. Exome capture was performed using Agilent SureSelect Human All Exon V3 (n = 22) and V4 (n = 55), Illumina TruSeq Exome Enrichment (n = 9) and Nimblegen SeqCap EZ Human Exome Library V3.0 (n = 5). Exome enrichment, sequencing and escribed ,19.The power of sample resolution for the panel was validated using data from phase 1 of the 1000 Genomes Project   and the Simulated datasets were generated by taking individual population AFs for each SNP as input and generating random SNP profiles in accordance with Hardy-Weinberg equilibrium based upon this; the randomisation of each SNP was independent of all other SNPs. We then quantified the rate of non-unique profiles per simulated dataset. We performed 20,000 independent replicates of dataset generation in all cases.We applied the panel to a batch of 48 samples exome sequenced by an external service provider, for which orthogonal genotypes were obtained concurrently through an independent genotyping provider using KASP genotyping . Following plating of DNA samples for dispatch, a replicate plate was made directly from the primary plate, to be dispatched for the orthogonal genotyping. Genotypes derived from exome data and orthogonal genotyping assays were compared using PLINK  and custThis study was approved by the Southampton and South West Hampshire Research Ethics Committee (09/H0504/125). Informed consent was obtained for all participants.In total, 26.2 Mbp of genome sequence was found to overlap all three commonly applied whole-exome capture kits, containing 9,493 common SNPs Figure A,B. Of tThe 24 biallelic SNPs afford 48 points of allelic comparison. Testing the optimised panel in the 1000 Genomes Project data  , an avercombined = 3,780), there remained an average of 17.8 allele mismatches across the profiles. Eighteen UK10K sample pairs produced duplicate profiles. On investigation of these pairs, they were found to share >98% genotypic concordance across an extended panel of 1,662 SNPs in all cases, compared to an average of 42%, with a range of 27 to 77%, for all sample pairs with unique SNP profiles  to the 1000 Genomes Project data , 1000 Genomes Project pilot average , as wellThe effect of dataset size on the frequency of collisions was investigated for populations present in 1000 Genomes Project phase 1 data . An expoApplication of the SNP panel to our batch of 48 samples revealed a discrepancy between exome and orthogonal genotypes for two samples dispatched in adjacent wells, suggesting a reciprocal transposition Figure . The occValidation of sample identity is essential in order to ensure data integrity and validity of conclusions drawn from data. We have described a powerful tool for the identification and validation of data provenance throughout the workflow of WES data collection and analysis. The power of discrimination, that is, the precision with which samples can be uniquely identifiable, is sufficient and robust for most projects on the current scale of up to 10,000 samples, with inbuilt redundancy of SNPs to protect against technical failures. In WES, the exome enrichment process provides the limiting step for the availability of data on SNPs for use in sample identification. As such, this panel will also be of utility for whole-genome sequencing data, where there is no such limitation on SNP coverage. This will be beneficial where there are mixed datasets of both whole-genome sequence and WES data.NGS is now developing as the diagnostic methodology of choice across a range of applications, including mutation scanning in targeted gene panels and WES for congenital disorders, as well as high depth analysis for tumour profiling. Whilst the service model for delivery of these tests is not fully resolved at this stage, there will certainly be economic arguments for centralising certain tests. This will have the effect of increasing the throughput requirements as well as physically moving samples between labs. Both of these factors will increase the opportunity for sample misidentification.Even for testing within a single lab, the use of inherent sample and data identification methods, as described in this study, seems a robust approach to fulfil the regulatory requirement for providing a full audit trail and ensuring data provenance ,27. The The discriminatory power of the panel may be reduced for various reasons, such as geographically localised variation in AFs, and degradation of DNA samples, resulting in incomplete data. We have shown our panel to have a high discriminatory power across a diverse range of populations. Additionally, the discriminatory power will be marginally reduced where many relatives are sequenced. In the case of highly consanguineous families, sample tracking methods such as barcoding will afford optimal certainty in these particular cases. Should concerns over insufficient discriminatory power arise, additional SNPs may be added to the panel from the existing list of candidates ; CHB: Han Chinese in Beijing, China; CHS: Southern Han Chinese; JPT: Japanese in Tokyo, Japan; Mbp: megabase pair; NGS: next-generation sequencing; SNP: single nucleotide polymorphism; WES: whole-exome sequencing; YRI: Yoruba in Ibidan, Nigeria.The authors declare that they have no competing interests.RJP performed analysis and interpretation of data, and drafted the manuscript, JG, GA and AC contributed to analysis, CJM contributed to data interpretation and manuscript preparation and SE conceived and supervised the project, and contributed to manuscript preparation. All authors read and approved the final manuscript.List of all candidate SNPs with evaluated properties.Click here for fileDistribution of pairwise genotype concordance between samples. Pairs resulting in duplicate SNP profiles (n = 18) and pairs between samples with unique SNP profiles  within the combined dataset of 3,780 samples are shown. Concordance across the 1,662 SNPs detailed in Figure iles n = 8 and paiClick here for file