The standard expression quantitative trait loci (eQTL) detects polymorphisms associated with gene expression without revealing causality. We introduce a coupled Bayesian regression approach—eQTeL, which leverages epigenetic data to estimate regulatory and gene interaction potential, and identifies combination of regulatory single-nucleotide polymorphisms (SNPs) that explain the gene expression variance. On human heart data, eQTeL not only explains a significantly greater proportion of expression variance but also predicts gene expression more accurately than other methods. Based on realistic simulated data, we demonstrate that eQTeL accurately detects causal regulatory SNPs, including those with small effect sizes. Using various functional data, we show that SNPs detected by eQTeL are enriched for allele-specific protein binding and histone modifications, which potentially disrupt binding of core cardiac transcription factors and are spatially proximal to their target. eQTeL SNPs capture a substantial proportion of genetic determinants of expression variance and we estimate that 58% of these SNPs are putatively causal. Das et al. present a novel Bayesian approach called expression Quantitative Trait enhancer Loci (eQTeL), which effectively integrates genetic and epigenetic information to identify combination of regulatory genomic variants underlying expression variance. Using various functional data, the authors show the variants identified by eQTeL are likely to be causal. Numerous expression quantitative trait loci (eQTL) studies have been performed to determine the cell-type-specific regulatory architecture of the human genome23455678Recently, a few approaches have incorporated regulation specific epigenetic data into association studies5678989eQTeL is a fully Bayesian approach , which i10We statistically validated our method using human heart data as well as realistic simulated data and demonstrated that it can predict an individual's expression from the genotype more accurately compared to other methods. SNPs identified by our method include regulatory SNPs with small effect sizes. Further assessment of functional relevance of identified SNPs suggest that they tend to (i) overlap a high resolution DNAse footprint, (ii) have an allele-specific DNAse footprint, (iii) preferentially disrupt putative binding of core cardiac regulators and (iv) be spatially proximal to their putative target gene. We also estimate that 58% of SNPs identified by eQTeL  of regulatory-interaction potential θ (Methods). The potential θ of a SNP is modelled as a combination of (i) features for regulatory potential and (ii) features for SNP-gene interaction P, via a logistic function. Vector α represents feature weights that are shared across all genes, thus we learn a single genome-wide model of regulators. This choice of modelling α obviates the need to explicitly scale genetic and epigenetic factors.In our approach (see equations below and Methods for details), expression θ=1) depends not only on correlation between corresponding SNP and gene but also on the regulatory and interaction features, as well as the current estimates of feature weights. This leads to a semi-supervised22θ that improves the mixing rate ) and compared with the performance of other eQTL methods  cross-data set generalizability  depends on various factors such as sample size, noise to signal ratio, number of hypothesis tested (number of SNPs) and effect size of SNP. The effect size, in turn, depends on the allele frequency of SNP, thus low allele frequency limits statistical power to detect regulatory SNPs1eQTeL leverages LD information to judiciously choose combinations of SNPs (per gene) which explains a much greater proportion of expression variance  integration of epigenetic data, (ii) allowing multiple causal variants per genein vivo protein binding and are at least 12-fold more likely to be functional than the next closest method.Putative causal regulatory SNPs are expected to be bound by regulatory proteins. Earlier studies have shown enrichment of regulatory elements near causal SNPs789M=3.005 and P value <3.83 × 10−17) allele-specificity relative to SNPs identified by eqtnminer, consistent with eeSNP having a regulatory impact . For each cell line, we selected heterozygous SNPs by inspecting genotyped data or pooled reads from different histone modifications, DNAse-seq and CTCF. We first assessed allelic differences in footprint reads for human cardiac myocyte (HCM) (see Methods). As shown in The spatial proximity of eeSNP with its target promoter is a pre-requisite for cis-regulation. Spatial proximity has been experimentally determined using chromatin interaction analysis with paired-end tags (ChIA-PET) assaysA likely mechanism by which a regulatory SNP may affect gene expression is by disrupting binding of specific transcription factors63536In the absence of extensive experimental data, it is difficult to estimate the proportion of eeSNPs that are causal. However, similar to a previous approachHere we have introduced a novel Bayesian approach, eQTeL, that integrates genetic and epigenetic data in a statistically consistent manner to identify putatively causal genetic variants underlying the expression variance. We have shown that (i) eQTeL identifies combinations of SNPs (eeSNPs) that, compared with other methods, explain substantially greater portion of expression variability, (ii) eQTeL is especially effective in identifying SNPs with small effect sizes, (iii) 58% of the identified eeSNPs are likely to be causal, (iv) eeSNPs can predict sample specific expression much more accurately, (v) eeSNPs are much more likely to be bound by a regulatory factor in an allele-specific manner, (vi) eeSNPs preferentially disrupt core cardiac transcription factor binding and (vii) eeSNPs tend to be spatially proximal to their target genes. Taken together, our results strongly suggest that eQTeL captures a substantial proportion of putative causal regulatory genetic determinants underlying transcriptomic variance.It is important to note limitations of eQTeL. First, eQTeL can only detect cis-eQTL and not trans-eQTL. Second, like other model-based association methods, eQTeL's computational speed is a bottleneck; however, using parallel cores and certain reasonable compromises in parameter estimation procedure, the computational burden can be substantially reduced. Third, eQTeL assumes normality of expression data, therefore the expression data needs to be pre-processed accordingly,which can be particularly problematic for certain kinds of high throughput data. Fourth, eQTeL can only detect SNPs with small effect size if they have high regulatory potential. Finally, eQTeL statistically infers the potentially causal SNPs and further experimental validations are required to establish causality.eQTeL can effectively resolve LD and discriminate putative regulatory SNPs from myriad associated SNPs. This lays a foundation for future experimental studies to characterize genetic variants underlying disease risk. Finally, eQTeL can be extended by integrating additional layer of molecular data—easily achieved in Bayesian framework—to directly infer SNP that causes disease.1 epigenetic features Ei that were used to predict if a SNP i lies in a regulatory region. In addition, we also have R2 interaction features Pij that are predictive of the interaction between SNP i and gene j. We refer to a SNP that has high regulatory potential and high interaction potential as interacting-regulator, regardless of whether it associates with gene expression. Further, if the SNP is associated with gene expression, we refer to that SNP as expression-regulator. In our eQTeL approach, we model the regulatory-interaction potential θij between SNP i and gene j as a combined function of epigenetic features Ei and interaction features Pij. Specifically, we use a Bayesian logistic regression model:There are RFij is a concatenated set of features consisting of both Ei and Pij, and Bern is the Bernoulli distribution. The coefficients α are shared across all genes.where j depends not only on the allele status of candidate SNPs, but also on the estimated regulatory-interaction potential of the SNP i and gene j pair. Specifically, given gene expression in n samples Yj=, we model the vector of expression Yj for gene j as a linear function of the allele status for all candidate SNPs, X={X1,⋯, Xp} where Xi is allele status of SNP i over the n samples:In our model, the expression of gene ij of SNP i on the expression of gene j is non-zero only when indicator variable γij=1. In other words, γij=1 signifies whether SNP i is associated with the expression of gene j. Xγ,j  refers to a subset of SNPs for which γij=1.where the effect βj enters our gene expression model through the prior distribution on the indicator variables γij,If a SNP lies within a genomic region that is deemed to be (i) a regulator, and (ii) interacting with the target gene, then the SNP is likely to affect the gene's expression. Thus, the regulatory-interaction potential for each pair of SNP i and gene ϕ(θ) is defined so that π being our prior probability for each SNP to be expression-regulator and let π0=π/ρ be the prior probability when the SNP does not reside in such a region, where ρ is an amplification factor. An uniform prior for π∈ is defined, where m and M are respectively the minimum and the maximum number of expected expression-regulators. However, no substantial difference in results was observed when we just fixed ρ=100 was used because performance of model was insensitive to choice of ρ∈.where the function E(θ) and (b) the fraction of interacting-regulators which are expression-regulators, that is, π. This is because the overall sparsity is a product of the two factors, that is, logE(ϕ(θ))≈E(θ)logπ assuming ρ>>>1. Thus, the effective sparsity constraints are less conservative on SNPs that lie within an interacting-regulator in our eQTeL model, which allows us to capture potential causal expression-regulator SNPs with small (but non-zero) effects on expression variance  the fraction of SNPs which are interacting-regulators, that is, variance .We also employ a standard prior distribution, Zeller's g-priorand we also define the following prior distributions for the rest of the parameters asα, α0 is the bias term, and b is the prior for α, and is set to 0, except for b0 (the prior for α0), which can be used to control the sparsity on the number of interacting-regulators. We expect 1% of all SNPs to be regulators. To achieve this level of sparsity in number of regulators, b0 was set to log(e/(p−e)), where e is expected number of interacting-regulators, and was set to p/100. That is, b0=log(1/99).The first element of Refer to n=313) were acquired from patients from the Myocardial Applied Genomics Network . Left ventricular free-wall tissue was harvested at the time of cardiac surgery from subjects with heart failure undergoing transplantation and from unused donor hearts. Genomic DNA was extracted using the Gentra Puregene Tissue Kit  according to manufacturer's instructions. Total RNA was extracted using the miRNeasy Kit (Qiagen) including DNAse treatment. RNA concentration and quality was determined using the NanoVue Plus spectrophotometer  and the Agilent 2,100 RNA Nano Chip (Agilent). To assess gene expression, RNA was hybridized with Affymetrix Genechip ST1.1 arrays using manufacturer instructions. CEL files were normalized with the robust multi-array analysis using the oligo package in BioconductorSamples of cardiac tissue . We have no reason to believe that this selection is favourable to eQTeL.The genes were selected such that they had at least one significantly associated SNPs based on univariate-eQTL (Matrix eQTL). 1,880 genes were thus selected using FDR threshold of 1E-6 using Matrix-eQTL  that have at least one SNPs significantly associated with expression . We chose K=10 because number of eGenes plateaued at K=10. Factors from PEER were regressed out from the expression and residual expression was used for further analyses.To determine number of factors (K) to be removed using PEER, we used approach similar to Lappalaninen Linear regression assumes normality of the expression data. We converted the residual data from PEER to standard normal distribution before performing the association analysis.P<10−6). A total of 360,046 SNPs passed QC and were available for analysis. To improve cross study comparisons, genotype imputation was performed using the Minimac (v 2012.11.16)  filters to exclude unreliable samples, samples with cryptic relatedness and samples that were not genetically inferred Caucasian. After QC filtering, 313 individuals remained. All analyses were conducted using software package PLINK16) ref.  program.PLINKα) estimation of eQTeL. Epigenetic data were obtained from ENCODE, Roadmap epigenome project and GEO database for following heart tissues: AoAF, HCM, HCF, fetal-hearts, adult-hearts, left ventricle, right ventricle, arota, and right atrium. Because DNAse I footprints were used to validate eeSNPs, they were excluded from the feature importance  and (ii) the difference in association score  between the best and the second best SNPs was greater than a threshold .Using an approach similar to Lappalanien ccausal,y which is the ratio of means of TF motif disruption score between the causal and a set of presumed non-causal SNPs. For motif y, we similarly calculated the enrichment score for eeSNPs ceeSNP,y. Following Lappalainen et al., we then estimated the fraction of eeSNPs likely to be causal as For each TF motif, we obtained the disruption at each SNP  thus obtaining two distributions, one for causal SNPs and another for the presumed non-functional background. Using distribution of motif disruption score for causal SNP, we identified TF motifs that are preferentially disrupted by causal SNPs. For each of such motif y, we calculated an enrichment score Functional explained variance and expression predictability was defined as explained variance by subset of expression-regulators that mapped to a DNAse I footprint.Simulation was done on 200 genes. We used 174,800 SNPs (874 SNPs per each gene) for 313 samples from MAGNet genotype data. 1% of total SNPs were declared as enhancers. We estimated, number of causal regulatory SNPs and distribution of explained expression variance by genotype by running eQTeL in MAGNet data. Using estimated number of causal regulators from MAGNet, expression-regulators were selected among enhancer per gene. Effect-size of each expression regulator was generated from P value<0.0002) were further considered. For each such case, the difference in the binding score for the two alleles was computed, as the difference in log. For each motif, the binding differential score for eeSNPs and the control SNPs were compared using Wilcoxon test and the motifs which had at least 1.5-fold greater differential among eSNPs and a P value<0.05 were identified.For each of the 981 vertebrate TF motif from TRANSFAC databasein vitro protein binding event were detected using DNAse-footprint. For each tissue, we calculated fraction of number of SNP that have a footprint in the 50 bps flanking it.FromDNAse hypersensitivity (DHS-seq) reads for heart cells (HCM sample) were obtained and mapped to eeSNPs (and control SNPs). Heterozygousity at each SNP locus was ascertained by the presence of multiple alleles among the reads mapping to the SNP location. For each such locus, the allelic imbalance was calculated as the difference in the number of reads mapped to each allele. The allelic imbalance was plotted against the overall signal intensity rank.K562, Hela, Nb4 and MCF7. For each 50 bps flanking an eeSNP (or control SNP) and the target promoter pair, number of ChIA-pet reads supporting the spatial proximity of the two loci were recorded. The ChiA-pet support for each SNP-gene pair was then compared for different methods after controlling for the genomic distance between the SNP and its target gene.ChIA-pet assay identified spatially proximal genomic regions where at least one of the region is bound by PolII. Because ChiA-pet data is unavailable for heart-related cell types, we pooled multiple ChiA-pet data from In www.cbcb.umd.edu/software/goal) as a R-package under MIT license.The implementation of eQTeL with its source code is freely available at .Supplementary Figures 1-15, Supplementary Notes 1-7 and Supplementary ReferencesRegulatory SNP in MAGNet detected by eQTeLReplicated eQTL between MAGNet and GTEx


Fragaria × ananassa. Short-read sequences from one diploid and 19 octoploid accessions were aligned to the diploid Fragaria vesca ‘Hawaii 4’ reference genome to identify single nucleotide polymorphisms (SNPs) and indels for incorporation into a 90 K Affymetrix® Axiom® array. We report the development and preliminary evaluation of this array.A high-throughput genotyping platform is needed to enable marker-assisted breeding in the allo-octoploid cultivated strawberry F. iinumae (3.9%), and speculative “codon-based” SNPs (5.9%). In genotyping 306 octoploid accessions, SNPs were assigned to six classes with Affymetrix’s “SNPolisher” R package. The highest quality classes, PolyHigh Resolution (PHR), No Minor Homozygote (NMH), and Off-Target Variant (OTV) comprised 25%, 38%, and 1% of array markers, respectively. These markers were suitable for genetic studies as demonstrated in the full-sib family ‘Holiday’ × ‘Korona’ with the generation of a genetic linkage map consisting of 6,594 PHR SNPs evenly distributed across 28 chromosomes with an average density of approximately one marker per 0.5 cM, thus exceeding our goal of one marker per cM.About 36 million sequence variants were identified in a 19 member, octoploid germplasm panel. Strategies and filtering pipelines were developed to identify and incorporate markers of several types: di-allelic SNPs (66.6%), multi-allelic SNPs (1.8%), indels (10.1%), and ploidy-reducing “haploSNPs” (11.7%). The remaining SNPs included those discovered in the diploid progenitor The Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for cultivated strawberry and is commercially available to the worldwide scientific community. The array’s high success rate is likely driven by the presence of naturally occurring variation in ploidy level within the nominally octoploid genome, and by effectiveness of the employed array design and ploidy-reducing strategies. This array enables genetic analyses including generation of high-density linkage maps, identification of quantitative trait loci for economically important traits, and genome-wide association studies, thus providing a basis for marker-assisted breeding in this high value crop.The online version of this article (doi:10.1186/s12864-015-1310-1) contains supplementary material, which is available to authorized users. A central goal of several international consortia led by the RosBREED project  has beenFragaria, Malus, Prunus, and Rubus. Polyploidy is prevalent in many plant families, and it is estimated that 50 to 70% of flowering plants are polyploids )F. chiloensis and F. virginiana are octoploids, as defined by chromosome number (2n = 8x = 56), and so the default expectation was that any given SNP locus would be represented eight times in the genome . Thus, in principle, genotyping of a target SNP segregating in only one subgenome would have to be successfully achievable in an octoploid context wherein an identified marker allele would be present in two, one, or zero copies in combination, respectively, with six, seven, or eight background alleles. Thus, the marker genotypes to be differentiated by genotyping would be: AABBBBBB, ABBBBBBB, and BBBBBBBB, where A is the “marker allele”. The parallel diploid case would be AA, AB, and BB, without the many additional B alleles. Thus, the preponderance of “background” alleles in an octoploid as compared with the diploid setting is the source of excessive signal from the array probe(s), resulting in cluster compression  of the octoploid genomes is almost 25% less than the expected four times the size of the average diploid strawberry genome , of whic by FISH . Also, se Figure . In addie Figure , with muh Figure , the staPHR category was low , targeting mSNPs is not recommended from an efficiency standpoint.Technical ploidy reduction was sought by means of four SNP discovery strategies. First, SNPs classified as multi-allelic (mSNPs) were thought to hold potential for technical ploidy reduction on the basis that one subgenome might contain alleles that were not represented in the other homeologs Figure B and C aow Table . ConversPHR class, offers useful insight. By far, the SNP-SNP strategy was the most effective haploSNP strategy, based on two criteria. First, the greatest numbers of haploSNP candidates were in the SNP-SNP class and are therefore more abundant to target  was obtained in the SNP-SNP category  that could be tested on the Affymetrix Axiom array. Here, the rationale was to test a strategy for developing polymorphic SNPs based on physical location without the need for previously obtained data on sequence variation. However, the conversion rate to useful polymorphism of 5,648 codon-based SNPs on the array was very low (<1%), with zero and seven codon-based markers being incorporated into the HK and F2D linkage maps respectively. Further analysis of these data is in progress, and may reveal opportunities for modification and improvement of the codon-based strategy.For array design in allo-polyploids of high ploidy level, a combination of targeting both standard, di-allelic SNPs and ploidy-reducing haploSNPs may be most effective, as these two SNP categories may be complementary with respect to their patterns of genomic distribution. The former may well represent regions of reduced effective ploidy due to true local ploidy reduction or to sequence diversification. The latter may better represent regions that remain at high effective ploidy levels. Furthermore, given the reported trend of biased patterns of gene loss/retention post polyploidization , combiniThe Affymetrix IStraw90 Axiom array is the first high-throughput genotyping platform for allo-octoploid strawberry. In the design of the array, strategies were successfully developed and applied that enhanced cluster resolution by achieving technical ploidy reduction. The most effective strategy was “SNP-SNP”, in which a subgenome-specific SNP located within 6 bp of a marker SNP was exploited as a probe destabilization site. Presence of diploid-like clusters even in the standard di-allelic SNP category indicated that effective ploidy levels have already been reduced in the octoploid strawberry at multiple genomic regions due to subgenome sequence diversification and subgenomic deletions. Genotyping procedures for polyploids were improved by the addition of new functionalities to the Axiom Best Practices Genotyping Workflow, which streamlined automatic genotyping for compressed clusters and for complex clustering patterns. Validation of the array indicated that combining standard and ploidy-reducing haploSNPs is a useful approach for high-density genome scans and linkage mapping of allo-polyploids of high ploidy levels.


Campylobacter is the leading cause of foodborne diarrhoeal illness in humans and is mostly acquired from consumption or handling of contaminated poultry meat. In the absence of effective licensed vaccines and inhibitors, selection for chickens with increased resistance to Campylobacter could potentially reduce its subsequent entry into the food chain. Campylobacter intestinal colonisation levels are influenced by the host genetics of the chicken. In the present study, two chicken populations were used to investigate the genetic architecture of avian resistance to colonisation: (i) a back-cross of two White Leghorn derived inbred lines [(61 x N) x N] known to differ in resistance to Campylobacter colonisation and (ii) a 9th generation advanced intercross (61 x N) line.Campylobacter jejuni following experimental infection was found to be a quantitative trait. A back-cross experiment using 1,243 fully informative single nucleotide polymorphism (SNP) markers revealed quantitative trait loci (QTL) on chromosomes 7, 11 and 14. In the advanced intercross line study, the location of the QTL on chromosome 14 was confirmed and refined and two new QTLs were identified located on chromosomes 4 and 16. Pathway and re-sequencing data analysis of the genes located in the QTL candidate regions identified potential pathways, networks and candidate resistance genes. Finally, gene expression analyses were performed for some of the candidate resistance genes to support the results.The level of colonisation with Campylobacter resistance in chickens is a complex trait, possibly involving the Major Histocompatibility Complex, innate and adaptive immune responses, cadherins and other factors. Two of the QTLs for Campylobacter resistance are co-located with Salmonella resistance loci, indicating that it may be possible to breed simultaneously for enhanced resistance to both zoonoses.The online version of this article (doi:10.1186/s12864-016-2612-7) contains supplementary material, which is available to authorized users. Campylobacter is the leading cause of foodborne acute enteritis in humans in the developed world. The condition is usually self-limiting and symptoms last for 5–7 days, but in some cases the infection may be complicated by severe sequelae  back-cross and the 9th generation AIL (61 x N) birds are listed in Additional file C. jejuni colonies were detected by direct plating of homogenates in any bird of resistant line 61. In contrast, significant levels of C. jejuni colonisation were identified in susceptible line N birds, with the number of birds showing colonisation rising over time after infection. The results are consistent with the original report  line for IBDV have been conducted to investigate the host response to these infections [Campylobacter intestinal colonisation resistance  Act 1986, with the approval of the Ethical Review Committee of The Pirbright Institute (under project licence PPL 30/2462) and the Animal Welfare and Ethical Review Body of The Roslin Institute (under PPL 60/4420).1 (resistant to Campylobacter colonisation) and N (susceptible to Campylobacter colonisation) were originally derived from White Leghorn flocks at the USDA-ARS Avian Disease and Oncology Laboratory in East Lansing, MI, USA. The lines have been maintained by random mating within the specified-pathogen-free (SPF) flocks at the Pirbright Institute in the UK since 1972 (line 61) and 1982 (line N). To generate the back-cross (n = 288 for the present study), F1 progeny (61 x N) were crossed with the susceptible line N.Chicken inbred lines 61 and N were given to INRA Tours by the Pirbright Institute over a decade ago. A 9th generation AIL between lines 61 and N was generated there. At each generation, animals from distinct families were crossed to minimise inbreeding and increase the number of recombinations as suggested by Darvasi and Soller [Colonies of lines 6d Soller . In totaCampylobacter-free gut flora originally taken from the caecal contents of an adult SPF chicken. Birds were orally challenged at three weeks of age with 108 CFU C. jejuni strain 11168H, a hypermotile variant of the sequenced strain NCTC11168 that readily colonises chickens [Campylobacter colonisation, control birds (n = 4–5 per line) and infected birds (n = 8–10 per line) were killed at 2, 3, 4 and 5 dpi and a sample of caecal contents was obtained. In addition, caecal tonsil samples of the birds killed 5 dpi were removed to RNAlater for RNA extraction, and gene expression analysis, as described below. Three separate challenge experiments were performed for logistical reasons on the back-cross and AIL populations to assess the level of Campylobacter colonisation in which birds were killed at 5 dpi and caecal contents were removed to measure the Campylobacter load and blood samples were taken for DNA extraction and genotyping, as described below.In the parent lines, back-cross and AIL, day-old birds were orally inoculated with 0.1 ml of an overnight Mueller-Hinton broth culture of chickens  or with Campylobacter load ≤ 100 CFU/g of content)/presence of colonisation.The level of bacterial colonisation in the caecal contents of infected birds was determined in all birds from the back-cross and AIL populations as well as the respective parents as described previously . BacteriThis part of the study was conducted as two separate experiments described below.1 and N) to identify fully informative markers for the back-cross mapping study. All SNPs were available through Ensembl (www.ensembl.org) and were analysed using existing panels of chicken SNPs. Markers were selected in the parent lines on the basis of their fixation for the alternate allele at each position, providing maximum information content in the back-cross population. A total of 1,385 SNPs were selected from the 18,000 SNPs panel for analysis in the present study. Genotyping and quality control were performed as described before [www.ensembl.org).Over 18,000 genome-wide SNPs were screened in the parent lines (6d before . A full http://gridqt1.cap.ed.ac.uk) as described previously [QTL analysis was performed by regression interval mapping  using theviously .P ≤ 4.12 × 10−5 and P ≤ 8.24 × 10−4 for genome-wide (P ≤ 0.05) and suggestive  levels, corresponding to -log10(P) of 4.38 and 3.08, respectively. Searches for significant SNPs were performed also at chromosome-wide level (P ≤ 0.05).The back-cross genotypes were also analysed in a GWAS in order to compare with and validate interval mapping results. The following thresholds were used for quality control: minor allele frequency (MAF) < 0.05 and call rate > 95 %. Deviation from Hardy-Weinberg equilibrium was not considered as a criterion for excluding SNPs since this was a back-cross population. After the quality control, 1,212 SNP markers remained for further analysis. The software GEMMA  was usedIndividual markers found to be significant in GWAS were further verified in an association analysis where each SNP was fitted as a fixed effect simultaneously with other fixed effects previously fitted  in a mixed model. The analysis was carried out with the software ASREML . This anCampylobacter colonisation resistance, LD among SNPs was calculated as an r-square value using the software Plink [Finally, to evaluate the extent of LD and identify potential regions of causal mutations for re Plink . Furtherre Plink .® Axiom® HD) [www.ensembl.org).AIL birds were genotyped using a 580 K SNP high density whole genome SNP array (Affymetrixiom® HD) . The SNPThe phenotypes were treated both as continuous and as binary data (based on a case-control model). In the case of the binary analysis, 133 birds with no or minimal colonisation where considered as controls and the remaining 85 birds were considered as cases.P ≤ 1.75 × 10−7 and P ≤ 3.50 × 10−6 for genome-wide (P ≤ 0.05) and suggestive levels, corresponding to -log10(P) of 6.75 and 5.45, respectively. In addition, a search for SNPs significant at the chromosome-wide level was performed.The AIL data was analysed using GWAS. To investigate any population stratification present in the population, a genomic relationship matrix was generated from all individuals. This genomic relationship matrix was converted to a distance matrix that was used to carry out classical MSA. All analyses of population stratification were performed using GenABEL software . The samIndividual SNP markers that were significant at the genome- and chromosome-wide level in GWAS were further verified in an association analysis as described for the back-cross. This model also yielded estimates of the magnitude of the significant SNPs effects as described previously by .Finally, the extent of LD among the significant SNP markers was calculated and LD blocks were built as described for the back-cross.http://www.ensembl.org/biomart/martview/) and the Gal-gal4 assembly were used to locate genes in the candidate regions for Campylobacter colonisation resistance identified by the QTL mapping analysis and GWAS for the back-cross and AIL populations. In the case of GWAS, the limits of genomic candidate regions were determined by the LD block structure and the LD present among the significant SNP markers and the neighbouring markers.The Biomart data mining tool within the Ensembl database (http://www.ensembl.org/Tools/VEP) and the Gal-gal4 assembly were used to determine the location of the significant SNPs identified in the GWAS analyses on the reference genome. The effect of SNPs located in exonic regions on the protein sequence was determined with a SIFT prediction [The Variant Effect Predictor tool within the Ensembl database (ediction  of the p1 and N) were fully re-sequenced to 15–20 fold coverage, using pools of 10 individuals per line. Sequencing was performed on Illumina GAIIx platform using a paired end protocol [Campylobacter colonisation resistance, derived from both the back-cross and AIL analysis results, was extracted. More specifically, the extracted sequence included the gene in question and 1 kb upstream and 1 kb downstream of the gene. The SNVs between the two parental lines and the reference genome in these regions were detected using the Mpileup tool for SNP calling (SAMtools v0.1.7). Detected SNVs were annotated using the VEP tool within the Ensembl database (http://www.ensembl.org/Tools/VEP). Information for all SNVs present in the regions of interest was collated. SNV that were in common in the two parental lines but different from the reference genome were filtered out. We separated SNVs into categories based on their relationship to genes. These categories were exonic, intronic, and 1000 bp upstream and downstream. Within the exonic category, there were additional subcategories of UTR and CDS. For SNV’s occurring in CDS regions we also predicted SNV effect as being synonymous, missense, nonsense (stop gain), or splicing. SIFT predictions of the effects of CDS SNVs (tolerated or deleterious) were also acquired through VEP. While mutations in transcribed regions can lead to altered transcriptional products, mutations in regulatory regions can affect transcription rates which ultimately alters phenotype. However, due to the lack of regulatory element annotation in chickens, we developed our own pipeline for predicting TATA boxes and CpG islands. Tata boxes are short DNA regions which act as Transcription Factor Binding sites. CGIs are interspersed DNA sequences that deviate significantly from the average genomic pattern by being GC-rich, and predominantly non-methylated. TATA boxes were predicted by looking for TATA box motifs identified in humans  within the region 20–40 bp upstream of each gene. CpG islands were predicted by using 100 bp windows and scanning across the 1000 bp upstream region with 1 bp intervals to find >200 bp regions with >50 % GC content and an Observed/Expected ratio greater than 0.6 . By intersecting our SNV data with our predicted TATA boxes and CpG islands we were able to analyse putative regulatory region mutations.The two parental lines , rate of non-synonymous to synonymous SNV (dN/dS), rate of exonic SNV, rate of intronic SNV.Campylobacter colonisation resistance were analysed using the IPA programme (www.ingenuity.com) in order to identify canonical pathways and gene networks constructed by the products of the genes located there. IPA constructs many possible upstream regulators, pathways and networks serving as hypotheses for the biological mechanism underlying the data based on a large-scale causal network derived from the Ingenuity Knowledge Base. Then, IPA infers the most suitable pathways and networks based on their statistical significance, thus a threshold above which the pathways are significant is derived.The gene lists in the QTL candidate regions for CXCLi1 and CXCLi2. Total RNA from a caecal tonsil was isolated from four replicate control birds and eight replicate infected birds per line (61 and N) using the RNeasy Mini Kit  following the manufacturer’s instructions. For each gene, mRNA levels were quantified using TaqMan quantitative RT-PCR. Primers and probes for the 28S rRNA, CXCLi1 and CXCLi2 genes were as described previously [Genes whose expression was studied included eviously , 79. Taqeviously . Data waAll animal experiments were conducted in accordance with the Animals (Scientific Procedures) Act 1986, with the approval of the Ethical Review Committee of The Pirbright Institute (under project licence PPL 30/2462) and the Animal Welfare and Ethical Review Body of The Roslin Institute (under PPL 60/4420).http://www.ncbi.nlm.nih.gov/SNP/snp_viewBatch.cgi?sbid=1062063. The same data is also available at the National Avian Research Facility website through the link: http://www.narf.ac.uk/VariationDatabase.The SNP data used in this study is available in NCBI dbSNP at the following web page:


There is a large amount of functional genetic data available, which can be used to inform fine‐mapping association studies (in diseases with well‐characterised disease pathways). Single nucleotide polymorphism (SNP) prioritization via Bayes factors is attractive because prior information can inform the effect size or the prior probability of causal association. This approach requires the specification of the effect size. If the information needed to estimate a priori the probability density for the effect sizes for causal SNPs in a genomic region isn't consistent or isn't available, then specifying a prior variance for the effect sizes is challenging. We propose both an empirical method to estimate this prior variance, and a coherent approach to using SNP‐level functional data, to inform the prior probability of causal association. Through simulation we show that when ranking SNPs by our empirical Bayes factor in a fine‐mapping study, the causal SNP rank is generally as high or higher than the rank using Bayes factors with other plausible values of the prior variance. Importantly, we also show that assigning SNP‐specific prior probabilities of association based on expert prior functional knowledge of the disease mechanism can lead to improved causal SNPs ranks compared to ranking with identical prior probabilities of association. We demonstrate the use of our methods by applying the methods to the fine mapping of the CASP8 region of chromosome 2 using genotype data from the Collaborative Oncological Gene‐Environment Study (COGS) Consortium. The data we analysed included approximately 46,000 breast cancer case and 43,000 healthy control samples. P‐values from purely likelihood‐based hypothesis tests such as Wald or Cochran‐Armitage tests. These can be used to rank variants in order of association in both genome‐wide association studies, and fine‐mapping studies focussing on a much smaller genetic region, in which causal association with the disease has already been convincingly established. Fine‐mapping studies that fail to take into account known functional information are arguably not making full use of all the available data.Association between genetic variants and the occurrence of a disease is typically analysed using snptest2  be the prior probability of a group j SNP being causal.Step 4: Elicit from the expert the prior probability (p0) that none of the variants analysed is causal.N to be the total number of SNPs to partition into the J groups and nj to be the number of SNPs in group j. We assume that the event that SNP i is causal, is independent of the event that SNP k is causal (i≠k). If we further assume that δ[1],...,δ[J] are small then we haveδ[1],...,δ[J] we have to solve 1−p0=∑j=1Jnjδ[j]. If we make the further assumption that δ[j+1]=Rδ[j] for j=1,...,J−1 then we can getδ[j] values are then calculated using δ[j+1]=Rδ[j]. The accuracy of Equation nj and δ[j]. In the binomial expansion (1−δ[j])nj the ratio of the (u+1) th  to the uth term is δ[j](nj−u)/(u+1) so the smaller the δ[j]nj=Rj−1δ[1]nj is, the better Equation j for which Rj−1/nj is largest also provides an indication of where extra terms may be needed in the binomial expansions. The resulting values derived from Equation p0≈∏j=1J(1−δ[j])nj. If the approximation appears to be poor, then quadratic terms can be considered in the binomial expansion yielding a quadratic equation aδ[1]2+bδ[1]+c=0, where a=∑j=1Jnj2R2j−2+∑j=1J−1∑j*>jJnjnj*Rj+j*−2, b=−∑j=1JRj−1nj and c=1−p0. A poor second‐order approximation could yield a quadratic without real roots. In this case a numerical method such as the uniroot function in R should be used to solve p0=∏j=1J(1−δ[j])nj, where δ[j+1]=Rδ[j].Define i is causal and SNP k is causal are independent takes no account of the likely number of causal SNPs in the region. If it is desirable to incorporate prior knowledge about the number of causal SNPs, then this can be achieved with some modification to Equation pm represent the prior probability that there are m causal SNPs in the region. Then it follows thatδ[j+1]=Rδ[j] and instead allow group‐specific multiplicative increases by letting δ[j+1]=Rjδ[j]. It then follows thatAssuming that the events that SNP δ[1]2. We used Equation CASP8 region variants because we didn't have reliable information about the number of causal SNPs in the region. In addition the expert had no reason to believe that the multiplicative increase in prior probabilities was unrealistic.This yields a quadratic in J=4 groups, we selected one SNP from each group with similar MAF (between 0.037 and 0.049). The selected SNPs from each of the four groups were chosen to be in very high LD with each other. All pairwise D′ values for these four causal SNPs were 1 except for one pair with D′=0.916 (r2=0.839). We simulated sets of 1,000 datasets with the selected SNP in each group as the causal SNP with a per‐allele OR of 1.1 and then analysed the data using WBF, with a prior on the logOR of N. After calculating Δ values for all SNPs, we ranked and filtered them using these values. By choosing SNPs in very high LD, we limit the effect of the underlying LD structure when using different causal variants so that the differences seen are the result of the different δ values assigned. This was also checked by carrying out BF filtering on the four sets of simulations.After carrying out the elicitation and assigning the SNPs to W) is equal for all SNPs in a dataset. The solid grey ROC curves show the results for W=0.01,0.02 and 0.04. The dashed line shows the results of using WEB=β1^c2−Vc, where β1^c and Vc relates to the unknown values for the causal SNP. The solid black line shows the results of using WEB=β1^p2−Vm, where Vm is the median of V across all SNPs and β1^p is the median of the |β1^| values of the top p% of SNPs ranked by likelihood (here we use p=30). WEB=β1^c2−Vc represents the theoretical upper bound corresponding to the case where β1^c and Vc are known. In Figure WEB values across the 1,000 simulated datasets for each of the six MAF/OR scenarios. The minimum value of WEB is zero in each of the six scenarios.Figure WEB=β1^302−Vm generally performs well when the causal variant has an MAF of 0.08 , the performance was competitive in two scenarios (lower effect sizes) but poor when the causal SNP OR was 1.14. The reasons for the relatively poor performance in this one case are not clear. We investigated different values of the percentile p in WEB and found values around p=30 generally gave the best performance in all six scenarios considered (results not shown).Using WEB and then again in the calculation of the BF. This has the potential to lead to overfitting. To assess whether our use of empirical Bayes leads to overfitting we estimated WEB on a training dataset and then used this value of WEB in a test dataset of the same size. We randomly selected 500 pairs of training and test data from the 1,000 simulated datasets (for each MAF). Within each pair we estimated WEB in the training data and used this value in the test dataset. We repeated this random sampling of 500 pairs 20 times and show the ROC curves for these 20 random samples in Figure WEB=β1^p2−Vm represents the case where WEB is calculated and implemented on the same data and is reproduced from Figure WEB=β1^p2−Vm (test train) represents our test‐train approach.A potential criticism of our empirical Bayes approaches is that the same data get used twice, once to inform the prior via calculation of If our empirical Bayes approach leads to overfitting, we would observe a meaningful reduction in performance. There is no evidence of overfitting in the plots on the right of Figure CASP8 region in the 1,000 genomes data. The encode variables that were chosen for step 1 and the summary variables assigned in step 2 are given in Table encode variables, with ‘gene’ being the only one we used that didn't have any values missing. The percentages missing for each variable are shown in Table Using the steps outlined in the Methods section, we were able to assign prior probabilities of causal association to simulated SNPs in the J=4 groups (step 3), depending on the SNP‐specific outcomes and the expert's decision rules relating to what thresholds to apply to each ENCODE variable within the summary variable  by updating with the BF using δ[j] are assigned using this method, the δ[j] values depend upon the relative numbers of SNPs in each group as can be seen in Equation If the causal SNP is in group 3 or 4, the TPR is greater than 0.95 at FPRs as small as 0.11. When the values of R value of 5 is high, reflecting the expert's understanding of the disease mechanism. If less is known about the disease, investigators may specify lower values. To assess the effect of a lower value of R we did the same analysis with R=2. The results are presented in Figure An R=5 to those using R=2 and BF filtering by examining the numbers of SNPs  retained when the TPR is fixed at 90% when the causal SNP is each of the four groups. These results are given in Table We also compared the results of posterior probability filtering with CASP8 data from which we were able to calculate WEB=β1^p=302−Vm≈0.0018. We used this value of W to calculate an approximate BF for each SNP. Due to the set of CASP8 SNPs in the study being different to those in the 1,000 genomes data used for simulations, the number of SNPs assigned to each prior probability group was different. The numbers are provided at the bottom of Figure p0=0.4 and both R=5 and R=2 gave the prior probabilities of association in Table We fitted univariate logistic regression models to the iCOGS CASP8 data ranked by posterior probability of causal association (Δ) using R=5. For notational purposes we use Δ[R] to indicate the value of R used to calculate Δ. Table [2], BFs and the more standard P‐values for comparison. These sets of results demonstrate the difference that including prior information can make. Although BF ranking is based purely on the data from the study and sets equal prior probabilities of causal association for each SNP, using Δ[5] specifies the priors to be markedly different across the SNP prior groups. When using Δ[2], there is less prior difference across the SNP groups, thus each SNP is ranked somewhere between the rank using Δ[5] and the rank using the BF. Because of the large sample size, the likelihood is highly informative even when using Δ[5]. This is evidenced by the fact that the top 16 SNPs ranked by BF alone are all still in top 20 ranked by Δ[5], so that the prior probability does not dominate the likelihood. However, we also see that in the top 20 ranked by Δ[5], there are no group 1 SNPs . In fact the highest ranked group 1 SNP is ranked 87th using Δ[5], but has a BF of 101 that is higher than some of the group 3 and four SNPs in the table (it is ranked 28th by BF and 39th by Δ[2]). A previous study  not only changes the ranks for these 12 SNPs within the LD block, increasing the weight given to those SNPs with higher prior probabilities , but also includes in the higher ranks SNPs from outside the LD block. For example, SNP number 893 ) and MAF of 0.047) is ranked 45th by BF but is ranked seventh when ranked by Δ[5].Including prior information can help to pinpoint the causal signal in blocks of high LD that ranking by BF, or any statistic calculated from the genotype data alone, cannot. Many of the SNPs in Table [5] contain only 93 in the top 10% ranked by BF. Of these 173 SNPs, 15 were assigned the highest prior probability, 92 the next highest and 62 and 4 the two lowest probabilities, respectively. These are equivalent to 100%, 41%, 12% and 0.4% of the total SNPs assigned to groups 4, 3, 2 and 1, respectively. The SNP in the top 10% based on Δ[5] that is ranked lowest by BF is SNP number 889, which only has the 1,731st highest BF value , but due to having been assigned the highest prior probability, it is ranked 169th by posterior probability.The 173 top ranking SNPs (top 10%) using Δ1) to use with the Wakefield BF approximation and also propose a general framework for assigning prior probabilities of association (δ) to genetic variants, combining functional SNP data with expert elicitation. Through simulation, we showed that when using the WBF approximation with a prior of the form β1∼N to filter SNPs in the CASP8 region, using W=WEB=β1^p=302−Vm generally gave higher TPRs for a given FPR across a range of values of W likely to be used in fine‐mapping studies looking for variants with modest effect sizes. We found that using the median of the absolute value of the effect size estimates in the top 30% (p=30) ranked by likelihood performed well in our simulated data but our investigations were limited to the CASP8 region and a different value may be needed in a different region with substantially different LD structure.BFs provide a coherent framework for combining information from a genetic association analysis with information from other sources. We have developed an empirical Bayesian prior distribution for the logOR . Indeed even two causal SNPs with similar effect sizes could also have disparate effect size estimates in small sample sizes. In regions harbouring a single causal variant this is obviously not an issue. So the effectiveness does not necessarily depend on variant MAF, but on the presence of a single causal SNP. The method may work well in regions with multiple causal SNPs, if the causal SNPs have similar effect sizes and the sample size is large, because then the estimates are likely to be similar for SNPs in high LD with either causal SNP. Care should be taken in smaller fine‐mapping studies because rare variants may have relatively high effect estimates. Inclusion of these rarer variants in the top This work was motivated by the large quantities of SNP‐level functional data now freely available online. Incorporating external data such as these could be a way of disentangling the signals coming from high LD regions harbouring a causal SNP, a problem invariably encountered in fine‐mapping studies. A common methodology to differentiate between the large number of SNPs in a region is to use the results of genome‐wide association study (GWAS) and then systematically examine functionality databases to justify the top hits. We have formalised the incorporation of functional information by using it to inform the prior probability of causal association.encode SNP‐level data that we used to assign specific prior probabilities for each SNP group currently have the disadvantage of not being complete for all the SNPs across the genome and in fact is quite sparse for some functional indicators. This means that in addition to potential uncertainty around the prior probabilities, there is likely to also be uncertainty about how to handle SNPs for which there is some functional information missing. We dealt with missing values in the encode data by replacing them with zeros under the assumption that they are missing because they didn't exceed some measurement threshold, but it is unclear how appropriate this is. It may be more robust to impute these missing values, using the recorded values for other SNPs showing reasonable correlation in some set of ENCODE variables. This approach would be particularly challenging because the functional effect of an SNP depends on the sequence around it and so this would have to be taken into account in the imputation in some way. Understanding the reasons for the data being missing provides the key to knowing how best to handle them.The functional P‐value weighting [Saccone et al., Although data external to the association study are not often used in the initial analysis, there are several structured methods other than through BFs in which it can be in incorporated. These include encode [Encode Project Consortium, There are also different methods by which δ values could be assigned to variants in a study. An alternative method of grouping is to obtain SNP scores from the RegulomeDB database [Boyle et al., An alternative way to integrate functional information into this kind of analysis is to use it to form a BF rather than a prior probability [Knight et al., Pickrell  jointly encode are regularly updated. We anticipate that methods such as those described here will become increasing important as these data become more complete.Functionally informative data are expected to become more complete in the near future and large databases such as


Rosa multiflora using 454 sequencing. Separate de novo assemblies were performed in order to identify single nucleotide polymorphisms (SNPs) within and between rose varieties. SNPs among tetraploid roses were selected for constructing a genotyping array that can be employed for genetic mapping and marker-trait association discovery in breeding programs based on tetraploid germplasm, both from cut roses and from garden roses. In total 68,893 SNPs were included on the WagRhSNP Axiom array. Next, an orthology-guided assembly was performed for the construction of a non-redundant rose transcriptome database. A total of 21,740 transcripts had significant hits with orthologous genes in the strawberry (Fragaria vesca L.) genome. Of these 13,390 appeared to contain the full-length coding regions. This newly established transcriptome resource adds considerably to the currently available sequence resources for the Rosaceae family in general and the genus Rosa in particular.In order to develop a versatile and large SNP array for rose, we set out to mine ESTs from diverse sets of rose germplasm. For this RNA-Seq libraries containing about 700 million reads were generated from tetraploid cut and garden roses using Illumina paired-end sequencing, and from diploid  Whereas, cut rose is economically the most important ornamental crop worldwide (761 million euros in The Netherlands in 2011), the rose genome sequence has not been completed yet. In fact, a long history of interspecific hybridization and selection  genome  the development of a 68K genotyping SNP marker array on the Axiom platform, and the (iii) construction and (iv) annotation of a non-redundant rose transcriptome for tetraploid roses using the genome sequence of diploid strawberry. This study generates valuable genomic resources  and resulted in the construction of a large SNP array that can serve as a genotyping platform for future studies in diploid and tetraploid roses.Here, we present the results of (i) three transcriptome Rosa hybrida, Yan et al., Rosa multiflora hybrid 88/124-46  according to the manufacturer's instructions. The K5 RNA samples were prepared by pooling equal amounts of total RNA isolated from petals of the three flower stages as mentioned above. For GR total RNA was isolated from the 12 garden roses according to Chang et al.  with modRNA samples of the K5 parents were sequenced by ServiceXS  using Illumina's standard operation protocols (2 × 75 bp paired end) on a Genome Analyser II. The RNA samples from the 12 garden rose cultivars (GR) were sent to GATC Biotech  where 12 cDNA libraries were sequenced using 2 × 100 bp paired end sequencing on a HiSeq 2000. For the pools of stressed and unstressed Rh88 leaves two random primed normalized cDNA libraries were constructed and sequenced in two 454 FLX Titanium runs at the Roche 454 sequencing center in Branford (USA). The first pool consisted of eight independent RNA isolations of untreated leaves and the second pool consisted of equal amounts of RNA from four independent extractions of all stress-treated leaves.Illumina reads were pre-processed using Prinseq-lite (vs. 0.20.3) which included the trimming of nucleotides having a phred score lower than 25, the trimming of 10 nucleotides from the 5′ end to remove the bias of the nucleotide content of the reads due to the random hexamer priming  with the same trimming and filtering as the Illumina reads except that reads smaller than 100 bp were discarded. Duplicate reads were removed using USEARCH v5.2.32 Edgar, . The reaDe novo transcriptomes were assembled per sample set using Trinity  between K5 parents P540 and P867, (2a) among 12 garden rose cultivars, (2b) in a subset of those, namely between the two garden roses MC and RND , and (3) within Rh88 Figure . For eace-value 1-30). Transcripts with two or more hits were discarded since this indicates that they may be present several times in the genome.The resulting SAM file was used for SNP calling using QualitySNPng . The genome sequence of Fragaria vesca was used to identify and remove sequences with potential splice junction sites. For this, markers were searched against the Fragaria sequences  and discarded using custom perl scripts if their sequences matched with fewer than 68 bp (95%). All A/T and C/G polymorphisms were also excluded since genotyping these SNPs requires twice the number of probes using the Axiom platform. As a last step the mined SNP markers of the three sets were compared to remove redundancy across sets.To prevent interference with chloroplast DNA during the array hybridization all sequences around SNP markers were screened against the chloroplast genome of Fragaria vesca protein code, and the annotation in Fragaria whenever available.The selected SNPs were submitted to Affymetrix  for a final analysis to determine whether the probes could be synthesized. Affymetrix discarded SNPs that shared similar sequences, as this could interfere with hybridization. We decided to include two probes for each SNP on the array, each probe targeting one of the strands (coded as AX_set_ID), as this allows an additional quality check during genotype calling with dosage scoring  was used to search for protein hits  with all retained rose transcripts of this study. The transcripts with a significant tBLASTn hit with a F. vesca protein were grouped and assembled using CAP3 with default settings. The assembled transcripts were compared to the F. vesca proteins using BLASTx, and if the highest-scoring protein returned the Fragaria gene originally used as the highest scorer, then the two genes were considered as putative orthologs, and the transcript was selected and tentatively named after the most likely orthologous gene in F. vesca (Rh-Fv transcripts). Next, the longest ORF of each selected rose sequence was determined  were mined for our rose Rh-Fv transcripts. They were also scanned for protein signatures from superfamilies reported in various databases such as Smart, Tigr, Panther, Pfam, FPrint, Profilescan, ProDom, and Gene3D  and GR (12 garden rose cultivars) were obtained by using Illumina paired-end sequencing Figure , Table 1Sequencing of cDNAs from petals of the tetraploid cut rose K5 parents gave 44 million 75 bp paired-end reads. After quality trimming and merging, 81% of the reads remained either as paired-end (12 million) or as single end reads (23 million). In total ~78,000 transcripts were obtained, but ~18,000 (24%) of these were subsequently discarded since they were less abundant and possibly splice variants. For SNP calling it is better to avoid these. On one hand they could represent paralogous genes, which would lead to nucleotide differences between paralogs rather than between alleles of the same locus. On the other hand, if they are from splice variants they may map in multiple contigs, which could mean that all of these would unnecessarily be excluded from the SNP calling. Ultimately, approximately 30,000 transcripts per genotype , 26,364 SNPs among the 12 garden rose cultivars (named: G_transcript number_SNP number), 14,293 SNPs between MC and RND (named: M_transcript number_SNP number) and 1882 SNPs between alleles of Rh88 (named D_transcript number_SNP number). Probes for both strands are on a genotyping array which we named the WagRhSNP rose array totaling 137,786 probes  were investigated for functions and involvement in processes such as disease resistance and defense mechanisms, flower development and flower color  of our dataset were compared to the ROSAseq database . This database contains 80,714 rose EST clusters (based on 454 sequencing) longer than 100 nucleotides (average length of 444 ± 209.4 bp), annotated with the Fragaria vesca genome. Of these, 56,899 EST clusters had a BLASTn hit to 14,302 Rh-Fv transcripts of our study with a mean nucleotide identity of 96.2%. In general multiple ROSAseq EST clusters mapped to a single Rh-Fv transcript: 95% of these Rh-Fv transcripts matched with up to 10 EST clusters from the ROSAseq database, while ca. 85% of the ROSAseq EST clusters matched a single Rh-Fv transcript . Advantages of the Axiom array system for SNP detection include the large number of probes that fit on the array, the small size of conserved probe sequences , and that the array can be produced for 480 samples (5 microtiter plates) onwards using photolithographic templates . Axiom arrays are being developed for other rosaceous crops as well, notably a 90K array for octoploid strawberry within the RosBREED project  species and are probably segmental allopolyploids, many of the SNPs on the array that have been identified as polymorphism within and between tetraploid cultivars, may be polymorphic in diploid germplasm as well. Zhang et al.  but transcript depth was limited (max. 400 reads/transcript). Finseth and Harrison (The individual assemblies were performed without a reference genome and are therefore difficult to validate but they highlighted the diversity among the samples and they were used to identify reliable SNPs. Ruttink et al.  indicatell Table . We procll Table . The thrHarrison  concludeHarrison  came to Fragaria genome (Supplementary Table ESM Fragaria identified by Dubois et al. (The orthology-guided assembly based on the s et al.  in theirRosa. SNPs in many of these genes are present on the 68k WagRhSNP Axiom array, which will support candidate gene identification. The dense SNP array with 68,893 SNPs will enable producing dense genetic maps that are useful in genetic research and marker-assisted breeding.Our data provides the most comprehensive transcriptome resource currently available for rose with 13,390 expressed full-length genes identified. This resource adds significantly to the currently available genomics and bioinformatics resources for the genus Conception of the study: CK-B, VG, FK, WW, TD, CM, PA, MS; Collection of material: CK-B, MV, WW, VG, DS, PA; Production of the data: CK-B, MV, WW, DS, DT, MS; Assembly: DE; SNP selection and array design: GE, RV, WW, CM, PA, MS; Annotation of the rose transcripts: CK-B, GE; Writing of the manuscript: CK-B, PA and MS. All authors read and approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodiophora brassicae, is an important disease on Brassica species worldwide. A clubroot resistance gene, Rcr1, with efficacy against pathotype 3 of P. brassicae, was previously mapped to chromosome A03 of B. rapa in pak choy cultivar “Flower Nabana”. In the current study, resistance to pathotypes 2, 5 and 6 was shown to be associated with Rcr1 region on chromosome A03. Bulked segregant RNA sequencing was performed and short read sequences were assembled into 10 chromosomes of the B. rapa reference genome v1.5. For the resistant (R) bulks, a total of 351.8 million (M) sequences, 30,836.5 million bases (Mb) in length, produced 120-fold coverage of the reference genome. For the susceptible (S) bulks, 322.9 M sequences, 28,216.6 Mb in length, produced 109-fold coverage. In total, 776.2 K single nucleotide polymorphisms (SNPs) and 122.2 K insertion / deletion (InDels) in R bulks and 762.8 K SNPs and 118.7 K InDels in S bulks were identified; each chromosome had about 87% SNPs and 13% InDels, with 78% monomorphic and 22% polymorphic variants between the R and S bulks. Polymorphic variants on each chromosome were usually below 23%, but made up 34% of the variants on chromosome A03. There were 35 genes annotated in the Rcr1 target region and variants were identified in 21 genes. The numbers of poly variants differed significantly among the genes. Four out of them encode Toll-Interleukin-1 receptor / nucleotide-binding site / leucine-rich-repeat proteins; Bra019409 and Bra019410 harbored the higher numbers of polymorphic variants, which indicates that they are more likely candidates of Rcr1. Fourteen SNP markers in the target region were genotyped using the Kompetitive Allele Specific PCR method and were confirmed to associate with Rcr1. Selected SNP markers were analyzed with 26 recombinants obtained from a segregating population consisting of 1587 plants, indicating that they were completely linked to Rcr1. Nine SNP markers were used for marker-assisted introgression of Rcr1 into B. napus canola from B. rapa, with 100% accuracy in this study.Clubroot, caused by  Plasmodiophora brassicae Woronin, can result in a 10–15% reduction in seed yield globally on Brassica species and related crops [P. brassicae consists of a primary phase in root hairs and a secondary phase in the cortex and stele [Clubroot disease, caused by ed crops . The chaed crops . The lifnd stele .B. napus L.) in 2003 and has spread rapidly. Genetic resistance is considered to be the most effective means for clubroot management [B. rapa [B. oleracea [B. napus [Arabidopsis thaliana [Raphanus sativus [CRa and Crr1, have been isolated from Chinese cabbage lines of B. rapa. They encode Toll-Interleukin-1 receptor / nucleotide-binding site / leucine-rich-repeat  proteins [On the Canadian prairies, clubroot was first identified on canola  using single nucleotide polymorphisms (SNP) and insertion / deletion (InDel) markers because of their high efficiency [Lines of rassicae  have beerassicae , 43 and possible –48, so rficiency , 50. Alsficiency .Brassica species [Next generation sequencing (NGS) has ignited a revolution in life sciences. NGS has had a major impact on crop improvement, allowing the rapid development and application of genomics tools in plant breeding . It enab species , 54. Ano species . BSA and species –59. To m species –62.Brassica species and close relatives. However, no robust SNP markers for MAS are available. Recently, Rcr1 was mapped to chromosome A03 of B. rapa based on resistance to pathotype 3 of P. brassicae, and differentially expressed genes associated with Rcr1 were identified via RNA-seq [Rcr1 against other pathotypes of interest to canola producers in Canada, and to use bulked segregant RNA-seq analysis to i) characterize genome-wide DNA variation; ii) examine DNA variation in the Rcr1 target region and identify the most probable candidates of Rcr1; iii) develop SNP markers associated with Rcr1; and iv) validate SNP markers to select for Rcr1 via MAS in canola (B. napus) breeding.More than 10 clubroot resistance genes and more than 20 QTLs have been identified in  RNA-seq . The objB. rapa subsp. chinensis) cv. “Flower Nabana” (FN), a hybrid developed by Evergreen Y.H. Enterprises, Anaheim, CA, USA, is highly resistant to pathotype 3 of P. brassicae. Rcr1 was identified and genetically mapped in 1587 plants from a F1 population that segregated for resistance (R) and susceptibility (S) [Rcr1 locus, and canola line ACDC, which is a clubroot-susceptible B. rapa doubled haploid developed at the Saskatoon Research Centre of Agriculture and Agri-Food Canada. An additional 200 plants in the F1 population were self-pollinated, but most of the plants did not produce seed due to self-incompatibility in B. rapa. Seed was obtained from 38 plants, resulting in 38 F2 lines that could be evaluated for resistance to multiple pathotypes of P. brassicae. These lines were tested for resistance to pathotypes 2, 3, 5 and 6 under controlled conditions at the University of Guelph, ON, Canada. Plant seedlings were grown in soil-less mix in tall narrow plastic pots  and inoculated with 5 ml of resting spore suspension (1×106 spores ml−1) at 10 days after seeding. Roots of 7-14 plants per line were assessed for clubroot severity at 6 weeks after inoculation using a standard 0 to 3 scale where: 0 = no clubbing; 1 = small clubs only; 2 = moderate clubs; and 3 = severe clubbing. A disease severity index (DSI) [Pak choy (lity (S) . This poex (DSI) , 64 was 2 lines and controls for resistance to the pathotypes of P. brassicae was replicated two times.The highly susceptible Shanghai pak choy cv. “Mei Qing Choi”  was included as a susceptible control for each group to ensure that the inoculation was highly effective. Evaluation of the FB. rapa reference genome v1.2 (B. rapa subsp. pekinensis cv. “Chiifu”) [B. rapa reference genome v1.5, which was downloaded at http://brassicadb.org/brad/downloadOverview.php. The reference genome consists of 10 chromosomes and 40,357 scaffolds. The total lengths of chromosomes and scaffolds are about 258 million bases (Mb) and 27 Mb, equivalent to about 90% and 10% of the reference genome, respectively. To simplify downstream data analysis, only the 258 Mb chromosome sequences were used in the current study, so the short reads from the RNA-seq project were assembled into 10 chromosomes A01 to A10 of B. rapa. The program SeqMan NGen 11  was used for short read assembly. Two methods were used: 1) short reads from three biological replicates each of the R and S bulks were assembled into the reference genome, resulting in six assembly files—hereafter, this method is called the single sample assembly (SSA); 2) short reads from a pool of the three R bulks and a pool of the three S bulks were assembled into the reference genome, resulting in two assembly files—hereafter called the pooled sample assembly (PSA). Standard assembling and filtering parameters were used.At 15-days post-inoculation, root tissue from 9 R plants was combined to form the R bulk and from 9 S plants to form the S bulk; together, the two bulks comprised one biological replicate as described by Chu et al. 2014 . In totaChiifu”) . In the B. rapa “Chiifu” [Discovery of variants (SNPs and InDels) in comparison with the DNA sequences in the “Chiifu”  were per“Chiifu” .http://www.lgcgroup.com/) following the manufacture’s instruction. PCR reactions were performed using a StepOne Plus Real Time PCR System . FN8, a line derived from the B. rapa cv. FN and homozygous at the Rcr1 locus, was included for this study.Selected SNPs identified in the target region were confirmed using the Kompetitive Allele Specific PCR (KASP) method  [P. brassica, the most prevalent pathotype identified in western Canada [P. brassica [Rcr1, we tested 38 F2 lines derived from the F1 segregating population for resistance to pathotypes 2, 3, 5 and 6. As expected, FN conferred resistance to pathotype 3 (0 DSI), but ACDC (95 DSI) and “Mei Qing Choi” (100 DSI) were highly susceptible  were highly susceptible to pathotype 3 (severity > 80 DSI) so their parental F1 plants likely did not carry Rcr1. Severity was low to moderate in the other 30 lines (0-52 DSI), which indicates that the parental F1 plants for these lines likely carried the Rcr1 gene. To further confirm the F1 phenotypes determined by F2 lines, DNA samples from the original 38 F1 plants were assessed using two microsatellite markers, MS7-9 and sN8591, that flanked Rcr1 [1 parental plants of the F2 lines with low to moderate DSI carried the allele associated with resistance; the 8 F1 plants with high DSIs in their F2 lines carried the allele associated with susceptibility   of P. brn Canada . The resbrassica . To deteceptible . Eight oked Rcr1 . The 30 tibility , confirm2 lines to pathotypes 2, 5 and 6 was also assessed. The parental cv. FN was highly resistance to pathotypes 2, 5 and 6, but ACDC and “Mei Qing Choi” were highly susceptible . In contrast, clubroot severity was low to moderate (0-55 DSI) in the other 30 lines  and 11,093.8 Mb per R bulk, and 114.0 M and 10,005.8 Mb per S bulk using the SSA method . This prB. rapa chromosomes from the pool of three R bulks, and 322.9 M sequences, 28,216.9 Mb in length, with 109 fold coverage were assembled from the pool of three S bulks . The numbers of SNPs and InDels varied among the chromosomes , indicatThe numbers of SNPs and InDels identified using the PSA method were higher than with the SSA method, with 776.2 K SNPs and 122.2 K InDels in the R bulk, and 762.8 K SNPs and 118.7 K InDels in the S bulk. As with the SSA method, there was a strong positive correlation between the numbers of SNPs and InDels with the correlation coefficient (r = 0.99) in either R or S bulks. The proportion of SNPs and InDels on each chromosome was 87% and 13% in both the R and S bulks , which iB. rapa genome using the SSA method, and poly variants 27% (range 24.4%-36.3%). There were no differences among chromosomes in the proportion of mono and poly variants except for chromosome A03, where the clubroot resistance gene Rcr1 is located, which carried more ploy variants and fewer mono variants  than the other chromosomes  or different  . Mono vaomosomes .With the PSA method, the proportion of mono variants was higher than for the SSA method for each chromosome . OverallRcr1 was mapped in an interval of 0.28 centi-Morgan on B. rapa chromosome A03 and located in the 24.26-24.54 Mb of A03 [Bra038750, Bra019396 and Bra019394) did not show any expression and no short reads were assembled into the reference genome, so no variants could be identified. Short reads with sequence counts < 20 and number of variants < 1 was associated with 11 genes . More than 20 sequence counts with > 1 variants were associated with 21 genes in both R and S bulks  encode TNL-class disease resistance proteins.A previous study reported that b of A03 . In the  S bulks . Informandex.php . Four geBra019409, Bra019410, and Bra019413) had the most poly variants, with means of 81.7, 75.0 and 34.7 poly variants per gene, respectively, followed by four genes  with means of 33.3, 31.3, 29.0 and 24.0. These seven genes exhibited more poly variants than the other 14 genes (P ≤ 0.05). The fourth TNL-class gene (Bra019412), with mean of 4.0 poly variants, was one of the 14 genes that showed few polymorphic variants  consistently carried the highest number of poly variants among the 21 genes.The PSA method identified more poly variants among the 21 genes in the target region than the SSA method . For botBra019409 and Bra019410 carried many more non-synonymous variants than the other two TNL genes  that reside in the genes Bra019413, Bra019412, Bra019410, Bra019409 and Bra019406 respectively  occur in Canada. Pathotype 3 is the most prevalent on canola [More than 10 clubroot resistance genes including  B. rapa –23. Thesal hosts . At leasn canola , 68.Rcr1 was previously mapped on chromosome A03 based on resistance to pathotype 3 [Rcr1 confers resistance against the range of Canadian pathotypes, a F2 population consisting of 38 lines was developed. The F2 lines were equivalent to self-pollinated BC1 populations (BC1S1). There were two genotypes “Rr”, heterozygous at Rcr1 locus, which should be resistant and “rr”, homozygous susceptible alleles, which should be susceptible phenotype in F1. Theoretically, segregation for resistance and susceptibility from “Rr” plants in the F2 lines is 3:1 and that from “rr” should be 0:1. Ideally, X2 analysis would have been performed. Unfortunately, the small number of plants available for evaluation of plant resistance in this study made X2 analysis impractical. We speculated that the eight F2 lines with high severity were from “rr” F1 plants and the 30 F2 lines with low to moderate severity were from “Rr” F1 plants. Similar results were obtained by repeating the experiments. In addition, the results were confirmed by analysis of the molecular markers  for those “Rr” derived F2 lines against each pathotype were not exactly same. It also could be caused by some escapes from “rr” plants especially for those “rr” derived F2 lines. The original intent was to assess 200 F1 plants in this study, but only these 38 plants produced sufficient seed in the F2 generation for assessment. Resistance to pathotype 8 was also associated with resistance to pathotype 3 in one iteration of the assessment (data not show), but there was not sufficient seed to repeat that portion of the study. Resistance to this range of pathotypes indicates that Rcr1 could confer broad spectrum resistance to many of the pathotypes of P. brassicae that are present in Canada. Another possibility is that the resistance to this range of pathotypes could be controlled by linked genes. Clearly, this still needs to be determined.hotype 3 , but thehotype 3 . To dete markers , Table 4Brassicas such as B. napus [B. rapa [B. rapa population that carried Rcr1, and identified the most probable candidates for Rcr1 based on DNA variants and development of SNP markers tightly linked to the gene for MAS. Also, SNP variants were found to be the most common DNA sequence variation, accounting for over 80% of the B. rapa transcriptome in the current study. Similar results were reported in rice, with an average of 99,955 putative SNPs and 14,617 putative InDels [RNA-seq generates a wealth of short DNA sequence reads from random places in the transcriptome. Transcriptome analysis and identification of SNPs based on RNA-seq have been carried out recent years in many organisms, including B. napus , 69, 70 [B. rapa , 54. Thee InDels  and the e InDels . Recentle InDels .B. rapa genome using both the SSA and PSA approaches, which generally produced similar results. The SSA method, in which three biological replicates of R and S bulks were assessed, provided estimates of variance that could be used to assess the statistical significance of variation of SNPs and InDels. This approach is very useful for identifying differentially expressed genes through RNA-seq. In contrast, the PSA approach, which merged the data across biological replicates, is much less amenable to statistical analysis. However, it produced a greater depth of sequencing data, providing more reliable results in discovery of variants. Therefore, this approach would be preferred in future SNP discovery through bulked segregant RNA sequencing.A wealth of SNPs and InDels were identified in the B. rapa Chinese cabbage “Chiifu” v1.5 [Rcr1 and its surrounding region than in other parts of genome.In both R and S samples, variants from the reference genome DNA sequences in fu” v1.5  were verRcr1 was fine mapped in an interval of 0.28 centi-Morgan, flanking by microsatellite markers MS7-9 and sN8591 on B. rapa chromosome A03 [Bra019406, Bra019409, Bra019410, Bra019412 and Bra019413) in the target region were genotyped using KASP method. Each of the markers co-segregated with Rcr1, indicating that they were completely linked to Rcr1 and closer to Rcr1 than the markers previously described by Chu et al 2014 [Rcr1 location further with the recombinants in the mapping population, because no recombination events could be identified in the set of recombinants. Therefore, it was essential to obtain accurate SNP and Indel profile on the genes of interest through analysis of RNA-seq data. Four of the five genes identified encode proteins related to disease resistance that belong to TNL families. Non-synonymous, nonsense, frameshift and synonymous variants were identified in the TNL genes. It is unlikely that the genes Bra019412 and Bra019413 are the candidates for Rcr1; although they showed differential expression between R and S bulks [Bra019409 and Bra019410 carried higher number of variants that could cause changes in amino acid sequence and higher number of synonymous variants, previously considered as 'silent', but which can cause changes in protein expression, conformation and function [Rcr1. It is also possible that a combination of the TNL genes is required for the expression of Rcr1 resistance. Rcr1 was mapped in the same interval as one of the cloned clubroot resistance gene CRa [CRa was related to the TNL genes in the target region [CRa coding sequence is 4223 bp (GenBank: AB751517.1). We searched the B. rapa genome using the 4223 bp sequence at http://brassicadb.org/brad/blastPage.php and found that CRa was homologous to the four TNL genes Bra019410, Bra019412, Bra019409 and Bra019413 with score and E value at 1905, 0.0; 823, 0.0; 739, 0.0 and 504, e-141 respectively. This indicates that CRa is a gene homologous to the TNL genes especially Bra019410, but obviously different from any of the TNLs. Identifying the TNL gene that corresponds with Rcr1 and relationship of Rcr1 with CRa will be addressed after Rcr1 has been cloned.some A03 . In this al 2014 . However S bulks , few polfunction . Therefogene CRa . Howevert region . The comRcr1, based on testing the recombinants identified from 1587 plants. Most of the SNPs proved to be very reliable markers in use for introgression of Rcr1 into elite canola breeding lines. Furthermore, MAS with the polymorphic SNP markers was confirmed to have detected Rcr1 in the BC1 population described by Chu et al [Rcr1 into canola using MAS.Breeding for clubroot resistance in canola at many locations in Canada is severely constrained because the pathogen, or the pathotype of interest, does not occur at or near the site of established breeding institutions. Use of highly specific markers in MAS could be to accurately assess the reaction of lines under controlled (i.e. indoor) conditions. A number of techniques are available for genotyping SNPs. NGS is an emerging method of SNP genotyping that is being increasingly adopted for discovery applications. However, this method can be expensive and time-consuming in terms of informatics needs, and currently generates datasets with a large proportion of missing data . KASP ofhu et al  with 100S1 FigA, R bulks by SSA; B, S bulks by SSA; C. R bulks by PSA; D. S bulks by PSA.(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


To perform a blind study to assess the capability of the Ion Personal Genome Machine™ (PGM) system to sequence forensically relevant genetic marker panels and to characterize unknown individuals for ancestry and possible relatedness.Twelve genomic samples were provided by a third party for blinded genetic analysis. For these 12 samples, the mitochondrial genome and three PGM™ panels containing human identity single nucleotide polymorphisms (SNPs), ancestry informative SNPs, and short tandem repeats (STRs) were sequenced on the PGM™ system and analyzed.All four genetic systems were run and analyzed on the PGM™ system in a reasonably quick time frame. Completeness of genetic profiles, depth of coverage, strand balance, and allele balance were informative metrics that illustrated the quality and reliability of the data produced. SNP genotypes allowed for identification of sex, paternal lineage, and population ancestry. STR genotypes were shown to be in complete concordance with genotypes generated by standard capillary electrophoresis-based technologies. Variants in the mitochondrial genome data provided information on population background and maternal relationships.All results from analysis of the 12 genomic samples were consistent with sample information provided by the sample providers at the end of the blinded study. The relatively easy identification of intra-STR allele SNPs offered the potential for increased discrimination power. The promising nature of these results warrants full validation studies of this massively parallel sequencing technology and its further development for forensic data analysis. The advent of massively parallel sequencing (MPS) technologies offers an alternative to current DNA typing methods. Comprehensive coverage of multiple forensically relevant genetic markers made possible by MPS technologies can provide a wealth of data for use in criminal investigations -6. WhileThe Ion Torrent Personal Genome Machine® (PGM™)  is one available MPS benchtop platform. The PGM™ is a high-throughput sequencer that employs semiconductor-sequencing technology . This seTwelve genomic samples were provided by a sample exchange group through the Green Mountain DNA Conference for a blinded genetic study. These samples were used to evaluate the potential of MPS on the PGM™ system to reliably analyze unknown samples, based on the self-declared information of the donors of these samples. The mitochondrial genome, identity SNPs, ancestry informative SNPs, and STRs (the latter three enabled by using PGM™ panels) were sequenced on the PGM™ system and resultant data were analyzed for these 12 samples.The study was conducted in June of 2014. Twelve blinded genomic DNA samples were kindly provided by George Duncan, Ron Fourney, and Bruce McCord for this study. The samples were obtained under informed consent from volunteers at the Broward Sheriff’s Office. Additionally, the policies and procedures approved by the Institutional Review Board for the University of North Texas Health Science Center in Fort Worth, TX were followed. These single-source samples arrived with arbitrarily assigned Sample Identification Numbers that were used to delineate the samples throughout the study. Extraction and quantification were completed by the sample providers using a Qiagen EZ1Advanced robot with a Qiagen DNA Investigator kit  and a Promega Plexor HY real-time kit  on a 7500 Real Time instrument (Thermo Fisher Scientific), respectively. The concentrations of these samples ranged from 6.1 nanograms (ng)/microliter (μL) to 40 ng/μL, and a total volume of 7 μL per sample was provided.Conventional STR typing was performed on the 12 genomic samples using the AmpFlSTR® Identifiler® Plus PCR Amplification Kit (Thermo Fisher Scientific) and the AmpFlSTR® Yfiler® PCR Amplification Kit (Thermo Fisher Scientific) and one nanogram of DNA for each reaction per the recommended manufacturer’s protocols ,14. The HID-Ion AmpliSeq™ Identity Panel and HID-Ion AmpliSeq™ Ancestry Panel. The HID-Ion AmpliSeq™ Identity Panel (Thermo Fisher Scientific) provides primers for amplification of 90 autosomal SNPs and 34 upper Y-clade SNPs . The HID-Ion AmpliSeq™ Ancestry Panel (Thermo Fisher Scientific) contains primers for amplification of 165 autosomal SNPs with an average amplicon size of 130 bp .ade SNPs . The aveade SNPs . Additiof 130 bp . AdditioLibrary preparation. Libraries for the two SNP panels were prepared and sequenced separately. One ng of input genomic DNA from each sample was used for library preparation. PCRs were completed using the Ion AmpliSeq™ Library Kit 2.0 (Thermo Fisher Scientific), the HID-Ion AmpliSeq™ Identity Panel or HID-Ion AmpliSeq™ Ancestry Panel (Thermo Fisher Scientific), a GeneAmp® PCR System 9700 thermal cycler, and the manufacturer’s recommended protocol  in an emulsion PCR. The emulsion PCR was completed using the Ion PGM™ Template OneTouch™ 2 200 Kit (Thermo Fisher Scientific) and the Ion OneTouch™ 2 (Thermo Fisher Scientific) following the manufacturer’s recommended protocols  following manufacturer’s recommended protocols. The enrichment of the template-positive ISPs was assessed using the Ion Sphere™ Quality Control Kit (Thermo Fisher Scientific) on the Qubit® 2.0 Fluorometer.rotocols . EmulsioSequencing and data analysis. Samples were sequenced on an Ion 318™ v2 Chip (Thermo Fisher Scientific) with the PGM™ system using the Ion PGM Sequencing 200 Kit™ v2 (Thermo Fisher Scientific) and the manufacturer’s recommended protocols (rotocols . Sequencrotocols .HID-Ion STR 10-plex Panel. The HID-Ion STR 10-plex Panel (prototype from Thermo Fisher Scientific) contains primers for amplification of the amelogenin marker and nine STR markers . Amplicon size for this panel ranges from 75-170 bp , a GeneAmp® PCR System 9700 thermal cycler, and the manufacturer’s recommended protocol (™ Template OneTouch™ 2 400 Kit (Thermo Fisher Scientific) instead of a 200 bp kit  instead of a 200 bp kit and the HID_STR_Genotyper (v2.0) plugin vs the SNP specific plugin . Allele coverage ratios (ACRs) were calculated by dividing the coverage of one allele by the total coverage at that locus . Sequence coverage ratios used the bp sequence of the STR alleles vs the repeat structure of the alleles. Sequence coverage ratios were calculated by dividing the number of reads used to make nominal repeat length allele calls by the total number of reads at that marker. Genotypes from the Y-SNPs were used to identify manually each male’s Y-clade according to Y-DNA Haplogroup Tree 2013  and provThe sequencing runs for the Identity SNPs, Ancestry SNPs, STRs, and mitochondrial genome generated 330 Mbps, 349 Mbps, 247 Mbps, and 958 Mbps of sequence data, respectively. Taking into account a predicted error rate of one percent (Q20), the sequencing runs for the Identity SNPs, Ancestry SNPs, STRs, and mitochondrial genome produced 243 Mbps, 255 Mbps, 128 Mbps, and 798 Mbps of sequence data, respectively. The mean read length of the Identity SNPs, Ancestry SNPs, STRs, and mitochondrial genome libraries was 93 bps, 84 bps, 105 bps, and 201 bps, respectively.™ Identity Panel enables typing of 90 autosomal SNPs and 34 upper Y-clade SNPs. Genotypes were generated for all 124 Identity SNPs on all male samples and for all 90 autosomal Identity SNPs on all female samples. The performance of the autosomal SNPs and Y-SNPs in the Identity Panel was analyzed separately. The average depth of coverage across the 90 autosomal SNPs in the Identity panel was 2233 × . The coverage for each marker fell within two standard deviations of the mean (569 × -3898 × ), indicated by the horizontal bars in The HID-Ion AmpliSeqIn addition to evaluating the overall performance of the Identity panel, genotypes from the Identity SNPs were used to provide information about each of the 12 samples analyzed. The Y-SNPs, in particular, are useful for identifying sex, familial relationships, and paternal lineage. Out of the 12 samples analyzed, 5 donors were identified as male and 7 donors as female . Differe™ Ancestry Panel enables typing of 165 autosomal SNPs. Genotypes were generated for all 165 SNPs on all 12 samples. The average depth of coverage across the 165 SNPs in the Ancestry Panel was 1511 × . The coverage for each marker fell within two standard deviations of the mean (240 × -2783 × ), indicated by the horizontal bars in The HID-Ion AmpliSeqThe HID-Ion STR 10-plex Panel contains primers for amplification of Amelogenin and 9 STR markers. Genotypes were generated for all 10 markers on all 12 samples . GenotypThe mitochondrial genome was sequenced for the 12 individuals included in this study, and depth of coverage across the entire mitochondrial genome ranged from 489-7029 × . CoveragPreviously discussed genotype data from the Identity SNPs and STRs allowed for expansion and refinement of relationships indicated by mitochondrial DNA sequence data. Sample 14 was found to be the mother of sample 15 and the daughter of samples 16 and 17. Likelihood ratios were generated for this pedigree  with in-house Thermo Fisher Scientific software. STR profiles produced a combined likelihood ratio of 300 million. SNP profiles produced a combined likelihood ratio of 3.34 E46, assuming independence among the SNPs. These likelihood ratios strongly supported this pedigree structure. Population background information generated by the Ancestry SNP panel also supported this pedigree structure.After completing the genetic analyses, the sample providers supplied all sample source data that had been collected on these 12 individuals, which then were compared with the genetic data generated in this blind study . While pThis blinded genetic study evaluated the utility of the PGM™ system in sequencing and analyzing large, forensically relevant genetic marker panels. Four genetic marker systems were run manually on the PGM™ in about 1 week’s time by 1 individual. Information obtained from typing these markers is useful in delineating the identity, sex, population ancestry, and familial relationships of unknown persons. Completeness of genetic profiles, depth of coverage, strand balance, and allele balance were evaluated as informative metrics for the quality and reliability of the data produced, and each metric indicated that thorough and accurate data had been generated. Additionally, all results from analysis of the 12 genomic samples were consistent with sample source information provided by sample providers at the end of the blinded study. While limited to only 12 individuals, this study demonstrated the feasibility of typing a large multiplex of forensically relevant genetic markers on the PGM™ system.Full profiles were obtained for all 12 samples with SNP, STR, and mitochondrial DNA marker systems. Only 1 ng of DNA was used to prepare each library, and results showed there was consistently high coverage across all markers analyzed. Successful typing at 1 ng of input DNA and relatively high coverage suggest that this MPS technology has the potential sensitivity of detection that could reach the same level of current DNA typing technologies. While there was little variation in coverage among samples, there was some variation in coverage among the different markers. These differences in coverage from marker to marker are likely due primarily to differences in PCR amplification efficiency. However, the dynamic range for varied coverage is accommodated better with MPS systems than with CE-based systems. With the latter methodology, a multiplex marker system with some markers having very high signal output can impact substantially the signal of low signal markers. High and low signal markers with MPS data can be tolerated without distortion of signal output. The differences in coverage seen in autosomal SNPs vs Y-SNPs in the Identity Panel likely can be attributed to the additional coverage the autosomal SNPs receive in females due to the lack of a Y-chromosome. This trend also can be seen in the differences in autosomal SNP coverage between males and females. For example, rs1490413, an autosomal SNP in the Identity Panel, had an average coverage of 2385 × ± (613) in males vs an average coverage of 2703 × (±675) in females. Strand balance calculations showed that coverage was obtained from both strands of the DNA for each marker set, reinforcing the accuracy of the genotype calls. ACRs displayed well-balanced data for all the markers analyzed indicating that the risk of allele drop-out was low for the amount of input DNA used in the study. The complete profiles, coverage, strand balance, and allele coverage ratios seen in this study support robust and reliable data.In this study, population ancestry assignments were limited to major populations. This process is marker dependent, reference population dependent, and software dependent. Limiting the assignments to major populations allowed for higher confidence in assignments. Despite these efforts, slight discrepancies were found in the population background assignments supported by genetic data and the population background reported by the donors of the samples . As prevMPS offers an avenue for expanding forensic typing capabilities. When analyzing STRs, some loci displayed within repeat sequence variants that cannot be detected by CE methods. This additional variation allows for greater discrimination power for such loci, which is helpful when trying to differentiate between individuals with similar profiles at those loci. In addition, sequence variants within STRs may help resolve mixtures better than can be accomplished with CE data. For example, several individuals in this study had the same size allele 13 at the D8S1179 locus . ConsideThe results of this blinded study indicate that the PGM™ is a promising platform for forensic genetics analyses. The results also support that the MPS platform, chemistries, and software used are sufficiently reliable to continue and perform full validation studies, which already are under way. Validation efforts will focus on sensitivity of detection, expanding population data sets, determining stochastic effect levels, evaluating reproducibility, and analyzing mixtures and mock case samples.