Decision curve analysis has been introduced as a method to evaluate prediction models in terms of their clinical consequences if used for a binary classification of subjects into a group who should and into a group who should not be treated. The key concept for this type of evaluation is the "net benefit", a concept borrowed from utility theory.We recall the foundations of decision curve analysis and discuss some new aspects. First, we stress the formal distinction between the net benefit for the treated and for the untreated and define the concept of the "overall net benefit". Next, we revisit the important distinction between the concept of accuracy, as typically assessed using the Youden index and a receiver operating characteristic (ROC) analysis, and the concept of utility of a prediction model, as assessed using decision curve analysis. Finally, we provide an explicit implementation of decision curve analysis to be applied in the context of case-control studies.We show that the overall net benefit, which combines the net benefit for the treated and the untreated, is a natural alternative to the benefit achieved by a model, being invariant with respect to the coding of the outcome, and conveying a more comprehensive picture of the situation. Further, within the framework of decision curve analysis, we illustrate the important difference between the accuracy and the utility of a model, demonstrating how poor an accurate model may be in terms of its net benefit. Eventually, we expose that the application of decision curve analysis to case-control studies, where an accurate estimate of the true prevalence of a disease cannot be obtained from the data, is achieved with a few modifications to the original calculation procedure.We present several interrelated extensions to decision curve analysis that will both facilitate its interpretation and broaden its potential area of application. A treatment is then administered if i pis high enough, whereas no treatment is administered if i pis too low. To judge whether i pis high enough, one should weigh the profit P obtained by treating an individual with the disease and the loss L caused by treating an individual without the disease. The rationale is to treat individual i if and only if iP p> (1 - ip)L, i.e. if the expected profit is higher than the expected loss. Equivalently, the treatment is administered if and only if i p>tp, where t pis some threshold probability defined by tp/(1 - tp) = L/P, that is, t p= L/(L + P). The threshold probability tp, and hence the decision to opt or not to opt for the treatment, is thus a one-to-one function of the ratio L/P which is informative of how a clinician or a patient weighs the relative harms of false positive and false negative results. This quantity is typically subjective and will vary from clinician to clinician and from patient to patient. In what follows, we just assume that 0 <t p< 1. Decision curve analysis consists of showing graphically the so-called "net benefit" obtained by applying the strategy of treating an individual if and only if i p>t pin function of the threshold probability t p[tp/(1 - tp)]. Steyerberg et al. [t p= π implies t p= c p. For our example, the solution for two selected values, t p= 0.2 and t p= 0.4, is given in Figure and set it equal to zero, as explained in Baker et al.  . This underlines that the concepts of accuracy and utility of a prediction model may differ drastically in practice.Whereas an ROC curve allows to demonstrate the sensitivity and the specificity achieved at each personal π will no longer be a consistent estimate of the true prevalence, which we shall denote by π0, since the cases are often overrepresented. In this context, it is well known that one can still use logistic regression to consistently estimate odds ratios. However, a i pcalculated from a logistic regression model in a case-control study will no longer be a consistent estimate of the probability that the individual i has the disease. If one knew π0, this probability could be estimated by So far, we have implicitly assumed that our random sample was representative for the population of interest. If we now consider data from a case-control study, the empirical prevalence that isπ0 using another source than the data themselves, typically from the literature. In this context, one may calculate a' be the proportion of individuals with b' be the proportion of individuals with c' be the proportion of individuals with d' be the proportion of individuals with a' + b' + c' + d' = 1). In line with the fact that the empirical prevalence π = a' + c' is not a consistent estimate of the true prevalence π0, the quantities a', b', c' and d' are no consistent estimates of the true proportions of true positive, false positive, false negative and true negative results, respectively. Note however that a'/(a' + c') and d'/(b' + d') are still consistent estimates of the true sensitivity and the true specificity . To estimate the net benefit for the treated in a case-control study, one may thus use the mathematical expression given in the previous section, that isTo perform a decision curve analysis in a case-control study, one should hence know (or estimate) the true prevalence Similarly, the net benefit for the untreated can be estimated asand the overall net benefit ast pand the true prevalence π0. One can then perform several decision curve analyses over a plausible range of values of π0. Alternatively, for a given threshold tp, one could show the net benefit achievable by the prediction models under consideration in function of the unknown π0. Note that Pencina et al. [If the true prevalence is unknown, the net benefit is a function of both the personal threshold a et al.  used a sπ thus does not reflect the true prevalence π0. We have drawn three decision curves for which the true prevalence π0 is taken to be smaller, higher or equal to the empirical prevalence π  will not only be made in function of the threshold probability t pbut also of the assumed true prevalence π0 of the population of interest.For illustrative purposes, let us pretend that the data of our example above originate from a case-control study and that the empirical prevalence π Figure . One can0 Figure , b and cWe have recalled the foundations of decision curve analysis and discussed some new aspects. In the original proposal, prediction models are compared with respect to the net benefit for the treated where one weighs the profits and losses for those receiving the treatment. Besides the net benefit for the treated, one could also be interested in the net benefit for the untreated which weighs the profits and losses for those not receiving the treatment, as developed above. In fact, the net benefit for the untreated would correspond to the net benefit for the treated if one would reverse the coding of the outcome. However, whereas a model maximising the net benefit for the treated will also maximise the net benefit for the untreated, the difference between two models will  not be the same with respect to the net benefit for the treated or for the untreated. This actually implies that the original definition of the net benefit by Vickers et al.  is sensit p. Fortunately, the choice of the threshold only depends on the relative expression L/P, not on the absolute terms P and L, which should already greatly simplify the practice. This was one motivation of Vickers at al. [treatedu) rather than an "absolute benefit" . Interpreting the value of a net benefit is however not quite straightforward and interpreting a difference between the net benefits achieved by two different models may still be more problematic. Recently, Tsalatsanis et al. [treatedU, compared to the strategy of treating everybody, as provided by untreatedU, or compared to the strategy of doing the contrary of what is predicted by the model, as provided by the sum of treated Uand untreatedU).The crucial aspect of decision curve analysis is the choice of the threshold s at al.  to consis et al.  addresseL/P, and which is easily interpretable. The overall net benefit considered in the present paper does satisfy the first two properties, while it may still be difficult to interpret. To assist interpretation, we described the overall net benefit as a difference of two complementary proportions. In the special case where the harms of a false positive equal the harms of a false negative (the threshold being t p= 0.5), the overall net benefit equals (a + d) - (b + c), which is simply the percentage of those correctly classified minus the percentage of those incorrectly classified. An overall net benefit of 0.2, for example, means that 60% of the subjects are correctly classified while 40% are not, the net benefit hence being 60% - 40% = 20%. In the more general case where different personal values are assigned to profits and losses, this difference of complementary proportions is weighted accordingly, such that a net benefit of 0.2 keeps the same "clinical value" in the context described above with t p= 0.5, or in another context.We note that it might be difficult to define a measure of the utility of a model which is invariant with respect to the coding of the outcome, which only depends on the relative expression In summary, we propose to use the overall net benefit instead of the benefit for the treated in a decision curve analysis when the issue is to assess and compare several concurrent prediction models with respect to their utility. We also took the opportunity to recall the important difference which may arise between the notions of accuracy and utility of a prediction model, as illustrated by Figure ip: probability that an individual i has (or will develop) a disease of interest; tp: threshold probability above which one opts for treatment; L: loss caused by treating an individual without the disease of interest; P : profit obtained by treating an individual with the disease of interest; a: proportion of individuals with i p>t pwith the disease of interest; b: proportion of individuals with i p>t pwithout the disease of interest; c: proportion of individuals with i p<t pwith the disease of interest; d: proportion of individuals with i p<t pwithout disease of interest; overallu: overall net benefit; treatedU: benefit for the treated; treatedu: net benefit for the treated; untreatedU: benefit for the untreated; untreatedu: net benefit for the untreated; ROC: receiver operating characteristic; π: observed prevalence of the disease of interest; π0: true prevalence of the disease of interest.The authors declare that they have no competing interests.This paper is the result of numerous discussions between VR and TZ. Both authors participated actively in the writing and editing of the manuscript. Both authors read and approved the final draft.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6947/11/45/prepub


GPC-B1. Bulked segregant analysis (BSA) was used to define a subset of putative SNPs within the candidate gene region, which were then used to fine-map GPC-B1.Next generation sequencing (NGS) technologies are providing new ways to accelerate fine-mapping and gene isolation in many species. To date, the majority of these efforts have focused on diploid organisms with readily available whole genome sequence information. In this study, as a proof of concept, we tested the use of NGS for SNP discovery in tetraploid wheat lines differing for the previously cloned grain protein content (GPC) gene GPC-B1 locus. After discriminating for SNPs between the two homoeologous wheat genomes and additional quality filtering, we identified inter-varietal SNPs in wheat unigenes between the parental lines. The relative frequency of these SNPs was examined by RNAseq in two bulked samples made up of homozygous recombinant lines differing for their GPC phenotype. SNPs that were enriched at least 3-fold in the corresponding pool  were further evaluated. Marker assays were designed for a subset of the enriched SNPs and mapped using DNA from individuals of each bulk. Thirty nine new SNP markers, corresponding to 67% of the validated SNPs, mapped across a 12.2-cM interval including GPC-B1. This translated to 1 SNP marker per 0.31 cM defining the GPC-B1 gene to within 13-18 genes in syntenic cereal genomes and to a 0.4 cM interval in wheat.We used Illumina paired end technology to sequence mRNA (RNAseq) from near isogenic lines differing across a ~30-cM interval including the This study exemplifies the use of RNAseq for SNP discovery in polyploid species and supports the use of BSA as an effective way to target SNPs to specific genetic intervals to fine-map genes in unsequenced genomes. Triticum turgidum ssp. durum), which is normally used for pasta-making, and hexaploid bread wheat (T. aestivum), which is used for bread and biscuit-making. Both of these polyploid species have large genome sizes (5.3 Gb per haploid genome) . RSL65 cA total of 25.6 and 31.7 million paired end 120 base reads were obtained for LDN and RSL65, respectively, after excluding low quality reads and trimming for adaptor contamination and low quality regions. There is currently no reference genome sequence available for wheat, therefore individual reads from each parent were aligned to the NCBI wheat transcriptome [RNA was not normalized in this study as the objectives were both SNP discovery and subsequent identification of allele frequencies between bulked samples. To examine the consequences of this approach on the sequencing of highly expressed transcripts, the relative expression level of unigenes was estimated by calculating the transcript abundance expressed as reads per kilobase per million mapped reads (RPKM) . Unigene2 = 0.85, Figure Despite this and the incomplete reference used, there was sufficient sequencing depth in a single Illumina lane to align reads (with at least 8-fold coverage) to 24,083 and 25,080 unigenes in LDN and RSL65, respectively. This represents approximately 60% of the unigene reference set that was present in the parental data. Comparison of unigene transcript levels (expressed as RPKM) between parental lines showed a high correlation  and hexaploid wheat  as the homoeologous genomes share sequence identities of ~96-98% . These Sca napus ,28.In this approach, it is expected that reads originating from homoeologous genes (A and B genomes in tetraploid wheat) will be mapped to the same unigene reference. Maq (default parameters) was usedThe implementation of this two-step pipeline identified 3,963 putative varietal SNPs across 2,520 unigenes between LDN and RSL65. The vast majority of the SNPs discovered (80.8%) were of the hemi-SNP type, manifested as ambiguity codes and signifying co-expression of homoeologous genes, which have a particular nucleotide polymorphism in one parent only. In these circumstances, the hemi-SNP then becomes allelic and serves as a genetically tractable marker.GPC-B1 region [A concern when working with non-sequenced and complex genomes such as those of wheat is the presence of closely related paralogues or pseudogenes that could confound SNP discovery and downstream marker design and mapping. Evidence of paralogues being mapped to the same reference sequence would be revealed as unigenes with high SNP density. Therefore, SNP densities were calculated for the 2,520 unigenes with at least one varietal SNP and their frequency distribution examined. The average SNP density was 1.80 SNPs/kb (± 1.46), and decayed rapidly after 3 SNPs/kb. This value is similar to that calculated empirically (2.2 SNPs/kb) by sequencing 6.8 kb from exons and UTRs of four genes across the 1 region . Based oVisual examination of several SNPs revealed that Maq was not able to properly call SNPs occurring close to homoeologous SNPs or within a 10-bp sliding window. This meant that some IHPs were being called as varietal hemi-SNPs because of their close proximity. Therefore, the default Maq parameters were relaxed by setting the maximum summed quality score of mismatched bases to 120 (default 70) at each step in the workflow (referred to as Maq-120 hereafter). This allowed SNP haplotypes occurring within the 120 base reads to align with higher mapping quality and reduce these errors.The Maq-120 analysis produced some improvements in terms of total reads mapped to the unigene reference , although the percentage of reads that mapped in pairs remained constant (Table Xwms508 -Xwms193) [GPC-B1 to confirm their genotype. All lines produced the expected results, except RSL135, which was heterozygous for all markers and was therefore excluded from downstream work. Equal amounts of total RNA from the 14 individuals previously classified as having high protein content were mixed to produce the high protein RNA bulk, whereas 15 recombinant lines were used for the low protein bulk . For each bulk, two libraries with slightly different average insert size (250-bp and 400-bp) were constructed and sequenced in individual Illumina lanes .Twenty eight homozygous lines with recombination events across a ~12-cM interval , although a smaller number of reads aligned in pairs  with respect to RLS65 (C), the signal from the informative base (G) is partially masked by the homoeologous (non-informative) base (C) Figure . TherefoBrachypodium Bradi3g03340, rice LOC_Os02g04500, and sorghum Sb04g003030, which are all in the corresponding collinear regions to wheat chromosome arm 6BS, where GPC-B1 maps.The approach focused exclusively on the putative SNPs previously identified between LDN and RSL65 in the initial experiment and did not seek to identify SNPs in the complex bulk RNA mixtures. Of the 3,427 SNPs  identified in the Maq-default analysis, 1,619 SNPs were recovered and had sequence coverage of at least 8-fold in both bulks  were also examined in the bulks. In this new pipeline, the frequencies and ratios of multiple SNPs within a single unigene were estimated independently and thus all SNPs were considered. Over 71% of SNPs  had at least 8-fold coverage and could be detected in both bulks  were first examined. Fifteen SNPs were either located too close to the ends of the available sequence or their assigned unigenes were repetitive as determined by high number of hits to the 5× Chinese Spring wheat genomic sequence  . TherefoThe subsequent Maq-120 analysis yielded a total of 212 putative SNPs with BFRs above 3.0, and 41 SNPs (19.3%) were in common with the Maq-default analysis. Examination of these common putative SNPs showed that of the 39 successful assays previously developed, 30 were polymorphic between LDN and RSL65 (76.9%), whereas only nine were monomorphic. This was a large increase with respect to the average polymorphic rate in the initial Maq-default analysis (58.5% polymorphic)  to those of the common SNPs, comparable to the validation rate of the Maq-default unique SNPs (41.5%). In total, 82 putative SNPs were successfully screened from the Maq-120 analysis (39 common and 43 unique) with 46 SNPs being validated as polymorphic (56.1%) and 36 confirmed monomorphic. These results suggest that the Maq-120 analysis is able to identify putative SNPs with a similar conversion rate (56.1%) to that of the Maq-default analysis (58.5%), but that the SNPs common between both analyses produces the putative SNP set with the highest validation percentage (76.9%).In summary, 64 SNPs  from a total of 125 putative SNPs were confirmed (51.2%) as polymorphic in the parental lines.The 64 validated SNPs were screened in the individual RSLs that comprised the high and low protein bulks. The SNPs from the Maq-default analysis were screened by SSCP (42 SNPs) and direct sequencing of PCR products (6 SNPs), whereas the confirmed SNPs from the Maq-120 analysis  were screened using KASPar assays (16 SNPs). All confirmed SNPs could be scored in the 29 RSLs and parental lines, except for one SNP (Ta#S32606580), which was very difficult to score unequivocally by SSCP and was therefore removed from further analysis.GPC-B1, defined by Xwms508-Xwms193 , to one SNP marker every 0.19 cM in the proximal end (GPC-B1-Xwms193). Almost all recombination events across the region were identified, including additional ones that were previously not resolved. The recombination events not identified correspond to those just flanking the GPC-B1 gene on either side, between Xucw79 and Xucw71. The composition of the 40 SNPs was similar to that of the overall Maq-default and Maq-120 analysis, with a higher number of hemi-SNPs (33) than simple SNPs (7), and a similar proportion coming from each parent (16 LDN and 17 RSL65 SNPs). The SNP marker details are provided in Additional File A total of 40 SNPs mapped to the 12.2 cM region encompassing Brachypodium and rice genomes, whereas 31 unigenes had unique orthologues in sorghum. Based on the established syntenic relationships, 22, 18 and 20 of these orthologues were located on the corresponding syntenic regions in Brachypodium, rice and sorghum, respectively. This implies that between 56% (rice) and 69% (Brachypodium) of the mapped unigenes are syntenic. These collinear regions encompass between 5.5 and 6.8 Mb and include 665, 989 and 607 genes in Brachypodium, rice and sorghum, respectively. The distribution of the orthologous syntenic cereal genes was examined to estimate the number of genes between each SNP in the sequenced genomes. On average, the orthologous syntenic genes were separated by 32, 59 and 32 genes in Brachypodium, rice and sorghum, respectively. This average includes markers on adjacent genes to markers separated by at most 103 genes in Brachypodium and sorghum and 248 genes in rice. The remaining unigenes have orthologues that map elsewhere in these sequenced grass genomes and there is no apparent pattern except for two unigenes, which are linked in wheat and map closely in Brachypodium (Bradi1g67540 and Bradi1g67570), rice (LOC_Os03g15390 and LOC_Os03g15350) and sorghum (Sb01g040520 and Sb01g04530).Unique orthologues could be determined for 32 of the 40 wheat unigenes in the sequenced GPC-B1 gene was mapped using the phenotypic information previously published for the RSLs [Brachypodium, rice and sorghum provides an immediate location for GPC-B1 within a region including approximately 18, 16 and 13 genes, respectively. Table GPC-B1 genetic map, suggest that the enrichment via BSA was very effective at identifying closely spaced markers, enabling the mapping of the gene to a narrow genetic interval.The the RSLs ,14 withiBrachypodium, rice and sorghum, but only one had a SNP between LDN and RSL65 (BFR 1.9) and was confirmed to be monomorphic. We next developed an additional nine gene models in wheat for genes that had no corresponding unigene in the reference set used to map the Illumina reads. Again, this search was unsuccessful, as no additional parental SNPs were found based on our mapping criteria. Regardless of these specific results, the ability to examine the data in an iterative manner should be useful for other gene targets.To further refine this position, we examined the parental SNP data to identify cases where a collinear cereal gene did not have a corresponding wheat unigene, or to assess if any parental SNPs in candidate genes were not identified in the bulks. We analyzed 23 wheat unigenes with homology to genes within the intervals defined in GPC-B1 target interval. In summary, increasing coverage from 8-fold to 16-fold reduces the total number of putative SNPs identified in the bulks by 60%, increases the validation rate from 57%-83%, but does not affect the percentage of validated SNPs mapping to the target interval (approximately 67-72%).Increasing the depth threshold applied to the whole SNP calling process serves to increase the probability that reads from each homoeologue are sampled. We therefore examined the effect of different minimum depth thresholds  on the false positive rate of SNPs identified under the Maq-120 analysis. First, the total number of SNPs with a BFR above 3.0 decreased from 212 using 8-fold coverage, to 121 (12-fold coverage) and 84 putative SNPs using 16-fold coverage. The subset of 82 SNPs from the Maq-120 analysis, which was experimentally tested, was further examined  to focus the SNP discovery on a specific chromosome interval. We hypothesized that the majority of SNPs would map to the ~30 cM interval on chromosome 6B (including both the short and long arms) that was segregating between these lines. The direct analysis of our results in wheat was not possible due to the absence of an assembled wheat genome sequence. Therefore, we conducted this analysis using the most closely related sequenced cereal, stachyon -33.Brachypodium regions were identified as being over-represented  and continues in the long arm of wheat 6 and the Brachypodium interval starting at approximately 47.4 Mb (Bradi3g45420) and finishing at the end of the chromosome  [Three mosome 3 ,34. The Brachypodium chromosome 2 (Bradi2g15900-Bradi2g20570) that was unexpectedly enriched for SNPs as its overall syntenic relationship is to wheat chromosome arm 1 L [Brachypodium chromosome 3, where 351 SNPs were identified and over 13% (46 of 351) were enriched in the bulks. This suggests either the presence of an additional wild emmer chromosome fragment in RSL65, which was previously not identified by molecular markers, or a small insertion of genes into wheat 6B. Overall, the approach was successful at identifying SNPs in the corresponding collinear regions, despite a variable level of background noise across all Brachypodium chromosomes. When analyzing the 12-fold and 16-fold coverage data, the background noise decreased, while the three previously identified regions remained consistently above background level  and hexaploid  wheat even with the current sequencing capabilities. Therefore, alternative approaches must be used. Genome capture  is curreIn this study, we used RNAseq as the strategy for complexity reduction in tetraploid wheat. This method has been successfully implemented for SNP discovery in several species and focuses on just a fraction of the complete genome. For example, the NCBI reference has a gene space of 31,671,110 bp per genome equivalent, accounting for less than 1% of the complete tetraploid genome. We also consciously used non-normalized RNA samples as the downstream BSA requires quantitative estimates of frequency ratios, which would be perturbed by a normalization procedure. However, this generates a major drawback as 50% of all mapped reads correspond to just a minute fraction of very highly expressed unigenes (< 1% in LDN and < 2.5% in RSL65). After accounting for these 'lost' reads and those not mapping to the NCBI reference, less than 30% of all raw reads generated (~15 million reads) were used for the vast majority of the SNP discovery and BSA analysis. This though still provides an average coverage of 23-fold per genome for each unigene.GPC-B1 . Expression differences between homoeologous genomes led to SNPs being identified in the RNAseq data, but these putative SNPs were not identified subsequently in the DNA samples. This confounded our results and was further accentuated by the low coverage used in the SNP detection . The Maq-120 analysis provided the ability to detect varietal SNPs close to IHPs  suggests a relative balance between the lines.The ability to detect rare transcripts is an important aspect of the SNP discovery and BSA strategy, especially in polyploid species with hemi-SNPs. The Lander-Waterman Model  providesOur results confirmed that the 8-fold coverage was not sufficient to account for sampling variation within the bulk samples. The composition of the examined Maq-120 SNPs suggests that hemi-SNPs had a lower validation rate than simple SNPs at 8-fold coverage. This is expected from the discussion above, as simple SNPs can be considered as single copy genes that would follow the Lander-Waterman Model more closely. The majority of the putative hemi-SNPs, which were later confirmed to be monomorphic, appeared to be represented by only one homoeologous transcript in the bulk alignment, as determined by the absence of linked IHPs within the read. With increased coverage, the percentage of validated hemi-SNPs increased from 52%-83%, whereas simple SNPs increased marginally from 75%-83% Figure . A similSNP validation was greatly facilitated by the development of novel genotyping technologies that are flexible and allow a rapid assay design at a relatively low cost. We initially validated SNPs using the SSCP method, as it allows the resolution of polymorphisms in co-amplified homoeologues from a PCR reaction. This circumvents the design of homoeologue specific assays, which are normally required for most genotyping platforms. Although this method proved reliable, it has limitations in that the putative SNP is not directly assayed and that it is an overnight gel-based system that is not high-throughput (roughly 60 samples per gel). Therefore, for the Maq-120 putative SNPs we used the KASPar platform, which is a PCR based system that only requires a three minute end-point fluorescence analysis and which has a higher throughput than SSCP. KASPar allows the direct assay of the putative SNP as the two alternative bases are positioned in the 3' end of the two competing PCR primers . Regardless of these factors, most genetic maps in wheat have at best an average mapping resolution of 5-10 cM. This includes maps between durum and emmer wheat using a combination of SSR and DArT markers (average 7.5 cM between markers ), as welArabidopsis re-sequenced DNA from large bulks of ~500 F2 individuals to map specific EMS mutations [2 individuals by exploiting a larger number of SNPs [Arabidopsis, focus on specific EMS-induced transitions that lead to amino acid changes in proteins, and benefit from the low mutation densities in Arabidopsis  [Arabidopsis, mapping a mutation to within 300-400 kb should suffice as the number of candidate SNPs corresponding to EMS mutations and leading to amino acid changes should be very small. In maize, several recessive mutant phenotypes have been mapped using a BSA and quantitative genotyping (Sequenom) approach [2 individuals, and mapping intervals of several cM were achieved.The first NGS studies in utations . Recent  of SNPs . Both ap~1-2 cM) . This meapproach . In this3 recombinant lines per bulk. These lines originate from a large screen of ~4,500 F2 plants and were selected based on the presence of recombination across a 12.2 cM interval to which GPC-B1 was previously mapped. Therefore, although the absolute number of individuals is low with respect to other studies, the amount of information in terms of recombination is high. Considering equal probability of recombination for all 27 individuals across the 12.2 cM interval, we expected to achieve a mapping resolution of 0.45 cM.In our study, we used bulks composed of a seemingly small number of individuals; 14 and 13 homozygous FGPC-B1 to a 0.3 cM interval defined by rice genes Os02g04520 and Os02g04630  developed for the cloning of GPC-B1, and the distal flanking marker Ta#S37941845 is homologous to Os02g04490, just five genes away in rice to the distal end of the physical map, Os02g04550.In wheat, a bespoke physical map is required for each positional cloning project making the ability to rapidly achieve sub-cM intervals critical for success. Based on previous experience, one BAC clone is roughly equivalent to 0.1 cM, therefore small genetic distances (0.3-0.5 cM) are usually required before initiating the physical map to reduce the number of chromosome walking steps. Using a traditional mapping approach with individual marker development based on collinearity, we had previously mapped This very narrow mapping interval was achieved in a matter of months as opposed to years, although it is important to consider that we benefitted from high quality phenotypic data and germplasm to assemble the bulks. The new genotyping and sequencing technologies will reduce the time and effort required for many steps such as screening for recombinant plants, identifying polymorphic markers, and genotyping the individuals from the bulks. However, the reliable phenotyping of plants will remain a major determinant in the successful outcome of any BSA and mapping project. Having said this, these new technologies now allow for several genes to be targeted in parallel by a single person and for BSA to be conducted in species with no genome sequence information.Ae. tauschii raw sequences [Our results suggest that NGS-enabled genetics should be feasible in polyploid species and highlight several areas for further improvement in wheat genomics. An important first step would be to produce a more comprehensive and definitive unigene set for wheat. There are several ongoing efforts which should be coordinated to develop a publicly available gene set for all wheat researchers. We expect a catalogue of IHPs to be available for the three wheat genomes and an initial assignment of SNPs should be possible based on the diploid progenitors. This strategy has been used successfully before  and the equences  are a goequences  and grouequences  chromosoequences . CombiniGPC-B1) to a 0.4 cM interval. The large number of SNPs also generated a high density haplotype across the region, which in future projects will be useful for breeding purposes.In this study, we outline a method that combines SNP discovery by RNAseq with BSA for fine-mapping genes in polyploid wheat. A balanced set of validated SNPs from both parents was generated and ~70% mapped within the targeted interval. The SNPs were well distributed and allowed the identification of almost all recombination events, successfully mapping a gene  and a recombinant substitution line RSL65) from the cross LDN (DIC-6B) × LDN . The hom from theth leaf was collected for DNA extraction, whereas the bottom third was collected for RNA extraction (the middle third was kept as back-up).To minimize differences in growth conditions between plants with an opposite phenotype, pairs of high and low protein RSLs were grown together in 2 L pots and properly labelled. Four biological replicates of each high-low pairing were grown, but only one pot was selected for sampling based on visual comparison between the high and low RSLs. The top third of the 5GPC-B1 interval using markers Xuhw89 , Xucw71  and Xucw101  using published conditions [th leaf in liquid nitrogen and extracting RNA using TRIzol (Invitrogen) according to the manufacturer's protocol. RNA concentration was measured using 1 μL of each RNA sample on the NanoDrop ND-1000 Spectrophotometer. RNA quality was assessed by running 1 μL of each RNA sample on an Agilent RNA 6000 n LabChip . Samples with an RNA Integrity Number (RIN) value greater than eight were deemed acceptable according to the Illumina mRNA-Seq protocol. Equal amounts of RNA from the 14 individuals previously classified as high protein were mixed to produce the high protein RNA bulk. The low protein RNA bulk was constructed using the RSLs described above except for RSL 135, which was found to be heterozygous in the DNA marker analysis and therefore excluded. To maintain a balanced set of alleles at the flanking loci, we added double the amount of RNA from RSLs 77 and 78 to the low protein bulk, which therefore included RNA from 15 RSLs (13 distinct genotypes).DNA from individual samples was prepared as described previously  and analnditions ,14. TotaThe Illumina mRNA-Seq 8-Sample kit  was used according to the manufacturer's protocol with the following modifications. In brief, poly-A containing mRNA molecules were purified from 5 ug total RNA using poly-T oligo attached magnetic beads. The purified mRNA was fragmented by addition of 5× fragmentation buffer  and was heated at 94°C in a thermocycler with 2 different times (2 min and 5 min). The fragmentation time of 5 min is the standard time used in the protocol, which yields fragments of ~250 bp. The shorter fragmentation time was used to yield slightly larger library fragments of 350-400 bp. First strand cDNA was synthesised using random primers to eliminate the general bias towards 3' end of the transcript. Second strand cDNA synthesis was done by adding GEX second strand buffer , dNTPs, RNaseH and DNA polymerase I followed by incubation for 2.5 h at 16°C. Second strand cDNA was further subjected to end repair, A-tailing, and adapter ligation in accordance with the manufacturer supplied protocols. Purified cDNA templates were enriched by 15 cycles of PCR for 10 s at 98°C, 30 s at 65°C, and 30 s at 72°C using PE1.0 and PE2.0 primers and with Phusion DNA polymerase . The samples were cleaned using QIAquick PCR purification columns and eluted in 30 μl EB (Elution Buffer) as per manufacturer's instructions . Purified cDNA libraries were quantified using Bioanalyzer DNA 100 Chip .Parental libraries were normalized to 7.5 nM in EB (Qiagen). Samples were then diluted to 1.5 nM with NaOH  and left at room temperature for 2 min before transferring 4 μL into 496 μL of HT1  to give a final concentration of 12 pM. Each bulk library was normalised to 10 nM in EB, diluted to 2 nM with NaOH and 2.5 μL transferred into 497.5 μL HT1 to give a final concentration of 10 pM. 120 μL of normalised library was then transferred into a 200 μL strip tube and placed on ice before loading onto the Cluster Station, each library being run on a single lane. Flow cells were clustered using Paired-End Cluster Generation Kit V4, following the Illumina PE_amplification_Linearization_Blocking_PrimerHyb_v7 recipe. Following the clustering procedure, the flow cell was loaded onto the Illumina Genome Analyzer GAIIx instrument following the manufacturer's instructions. The sequencing chemistry used was v4  using software SCS 2.6 and RTA 1.6. Each parental library was run in a single lane for 120 cycles for each paired end, and each bulk library for 80 cycles. Illumina base calling files were processed using the GERALD pipeline to produce paired sequence files containing reads for each sample in Illumina FASTQ format.Brassica napus [After first converting the Illumina FASTQ files to Sanger FASTQ format, initial alignment of paired reads from single lanes was conducted using Maq v0.7.1  against ca napus ,28. CrucA new Perl script bulk_frequencies.pl , together with a measure of transcript abundance, expressed as reads per kilobase per million mapped reads .Unigenes that showed apparent SNP densities of greater than 5 SNPs/kb were considered artefactual  and excluded from the analysis . The BFR results were organized in a spreadsheet format to aid further inspection and sorting. Ancillary synteny data for each unigene was added, including the best hit for the unigene from pre-computed BLASTN analysis against 2O, 30 μl common primer (100 μM), and 12 μl of each tailed primer (100 μM)) [® II 480 qPCR machine. Fluorescence was detected at ambient temperature  with four detection steps per °C. If the signature genotyping groups had not formed after the initial amplification, additional amplification cycles  were applied, and the samples were read again. Data analysis was performed manually using the inbuilt Roche Lightcycler® 480 software (Version 1.50.39). A full list of primers is provided  . Sequenc100 μM)) . Assays Short read sequence data reported here have been deposited at the Sequence Read Archive (SRA) under the accession code ERA050658.Triticum turgidum ssp. dicoccoides chromosome 6B substitution line; DNA: Deoxyribonucleic acid; EST: Expressed sequence tag; gDNA: Genomic DNA; GPC-B1: Grain protein content gene; IHP: Inter-homoeologue polymorphisms; KASPar: KBiosciences competitive allele specific PCR genotyping system; LDN: Langdon; NCBI: National Center for Biotechnology Information; NGS; Next-generation sequencing; PCR: Polymerase chain reaction; RNA: Ribonucleic acid; RNAseq: mRNA sequencing; RPKM: Reads per kilobase per million mapped reads; RSL: Recombinant substitution line; SHOREmap: Short read mapper; SNP: Single nucleotide polymorphism; SSCP: Single strand conformation polymorphism; SSR: Simple sequence repeat.BAC: Bacterial artificial chromosome; BFR: Bulk frequency ratio; BSA: Bulked segregant analysis; cM: centi-Morgan; DArT: Diversity array technologies; DIC6B: MT and CU conceived and designed the experiments; MF performed library construction, NMA, SGM, and CCJ carried out the SNP validation and mapping experiments; MT and CU performed data analysis; MT performed bioinformatic analysis; MT, NMA, CCJ, and CU drafted and revised the manuscript. All authors read and approved the final version of the manuscript.Figure S1: Flow-chart summarizing the main steps of the NGS-BSA approach. Figure S2: Chromatograms of LDN and RSL65 for Ta#S32574498. Figure S3: BFR of validated and mapped SNPs across the GPC-B1 interval. Figure S4: Mapping of wheat unigenes with putative SNP to the Brachypodium genome. Figure S5: View of MAQ alignment of Ta#S37941845 reads for LDN and RSL65 encompassing the hemi-SNP (R = A/G) at position 582. Figure S6: Visualization of fluorescence output for two hemi-SNPs Table S1: Breakdown of SNPs lost during the validation and mapping process Table S2: Collinearity between most closely identified markers in the present study and previous markers used for physical map construction.Click here for fileCharacteristics of all putative SNPs identified in the bulk samples by Maq-default analysis.Click here for fileCharacteristics of all putative SNPs identified in the bulk samples by Maq-120 analysis.Click here for fileGPC-B1 regionCharacteristics of SNPs mapped to the .Click here for fileSNP_parser.pl Perl script.Click here for filebulk_frequencies.pl Perl script.Click here for filePrimers used in this study and characteristics of all SNPs tested.Click here for file


Podosphaera plantaginis, an obligate fungal pathogen of Plantago lanceolata. This interaction has been intensively studied in the Åland archipelago of Finland with epidemiological data collected from over 4,000 host populations annually since year 2001.Molecular tools may greatly improve our understanding of pathogen evolution and epidemiology but technical constraints have hindered the development of genetic resources for parasites compared to free-living organisms. This study aims at developing molecular tools for A cDNA library of a pooled sample of fungal conidia was sequenced on the 454 GS-FLX platform. Over 549,411 reads were obtained and annotated into 45,245 contigs. Annotation data was acquired for 65.2% of the assembled sequences. The transcriptome assembly was screened for SNP loci, as well as for functionally important genes . A genotyping assay of 27 SNP loci was designed and tested on 380 infected leaf samples from 80 populations within the Åland archipelago. With this panel we identified 85 multilocus genotypes (MLG) with uneven frequencies across the pathogen metapopulation. Approximately half of the sampled populations contain polymorphism. Our genotyping protocol revealed mixed-genotype infection within a single host leaf to be common. Mixed infection has been proposed as one of the main drivers of pathogen evolution, and hence may be an important process in this pathosystem.The developed SNP panel offers exciting research perspectives for future studies in this well-characterized pathosystem. Also, the transcriptome provides an invaluable novel genomic resource for powdery mildews, which cause significant yield losses on commercially important crops annually. Furthermore, the features that render genetic studies in this system a challenge are shared with the majority of obligate parasitic species, and hence our results provide methodological insights from SNP calling to field sampling protocols for a wide range of biological systems. Molecular tools in parasite species can address a range of exciting questions both from evolutionary and ecological perspectives Podosphaera plantaginis, formerly Sphaerotheca plantaginis,  is an obligate powdery mildew pathogen of the ribwort plantain Plantago lanceolata L. (Plantaginaceae). The occurrence of this pathogen has been intensively studied since 2001 in the Åland archipelago, south-west Finland, with more than 4,000 host plant populations surveyed each year by 40–70 students recording the presence-absence of the pathogen  can only be elucidated with suitable molecular tools. This is non-trivial, as co-infection is considered to be a key force driving pathogen evolution. Theoretically competition for limited host resources under co-infection is expected to promote more aggressive growth and reproduction of co-occurring strains than under conditions of single infection i.e. transmission during epidemics Po. plantaginis also allows the description of the metapopulation genetic structure and the study of molecular epidemiology whereby past disease spread scenarios may be reconstructed and future risks of disease spread assessed through molecular characterization of the pathogen. Other exciting venues of research that open up include study of the genetic structure driving adaptive patterns, as well as the investigation of the reproductive strategy of this plant pathogen.The development of genetic resources for Blumeria graminis was recently released Golovinomyces orontii and Erysiphe pisi, Max Planck Institute for Plant Breeding Research, Cologne). Transcriptome sequencing was also performed in B. graminisG. orontiiErysiphe necatorPo. plantaginis. This is surprising considering the agronomical importance of this group encompassing very important crop pathogens  compared to other fungi. Sequencing the transcriptome instead of the genome appears to be a good strategy to reduce data complexity, while including functionally important genes Single Nucleotide Polymorphism (SNP) markers have become the genetic marker of choice in ecology and evolution as a consequence of their numerous advantages: wide and putatively random distribution in the genome, simple and low cost genotyping allowing high-throughput screening, co-dominant inheritance, high between-lab repeatability and well-defined mutation model de novo assembly and annotation of the transcriptome data and subsequent identification of SNP loci. It finally led to the design of a set of 27 SNP markers that can be simultaneously genotyped. Although the SNP array designed in this study would mostly be relevant to Po. plantaginis in the Åland archipelago because of ascertainment bias and lack of cross-amplification Po. plantaginis and in the powdery mildew fungi in general. We then use our newly developed molecular tools to estimate levels of mixed-genotype infection at the leaf level, i.e. various pathogen strains occurring on the same host leaf  in this haploid organism, a phenomenon assumed to be crucial for the evolution of pathogen virulence (see above).Here, we describe a method to develop molecular tools in an obligate plant pathogen that may be used to gain insights into the ecology and evolution of a wild host-pathogen interaction. First, the development of molecular resources included the 454 sequencing of a pooled RNA sample, Pl. lanceolata leaves infected with Po. plantaginis was performed in September 2008. Neither the host plant nor the pathogen is protected species and Finnish legislation (“Jokamiehenoikeus”) allows the sampling of wild species to everyone. The aim was to generate a sample comprising much of the variation present in the pathogen metapopulation in the Åland archipelago, and hence strains from 16 different populations across the archipelago  was preheated to 65°C. The samples were dissolved to 300 µl of lysis buffer followed by incubation at 65°C for 1 hr with occasional mixing. Large aggregates were removed by centrifugation in Eppendorf centrifuge . The supernatants were extracted with an equal volume of hot (80°C) phenol:cloroform:isoamyl alcohol . The phenol extraction was repeated 2–3 times, followed by a cloroform:isoamyl alcohol (24∶1) extraction and EtOH precipitation. The pellet was dissolved in 12 µl Hwww.evrogen.com), as described previously Normalized cDNA libraries were produced by Evrogen Inc . To compare similarity of GO annotations between species we also analysed all the proteins and GO annotations of Magnaporthae oryzae, Sclerotinia sclerotiorum and Tuber melanosporum from UniProt database. GO classes were narrowed down to Protein Information Resource (PIR)-slim GO vocabulary for better comprehension of data. Ratios were calculated as follows: GO classes under e.g. Biological Process category were divided by the total count of Biological Process annotations in the datasets. GO classes under Molecular Function and Cellular Component categories were done in similar manner. The visualization of some representative GO classes from each category is shown in The assembled contigs were annotated with descriptions and gene ontology (GO) classes by using the PANNZER tool  could be found in our contigs. We also described a set of candidate secreted effectors proteins (CSEPs), as peptides having a signal peptide (as thus excreted from the cell) but no transmembrane domains  and no orthologous sequences outside the powdery mildews http://frodo.wi.mit.edu/primer3/). DNA was extracted using E.Z.N.A. plant DNA kit  following the manufactureŕs instructions, with the final elution performed in 100 µl. PCR reactions were performed in 20 µl final volume containing 2 µl of DNA, 0.5 µM of each primer, 800 µM of deoxyribonucleotides (dNTPs) and 0.6 U Taq polymerase  in appropriate 1x buffer. Samples were subjected to an initial denaturation at 94°C for 2 minutes, followed by 35 cycles of denaturation at 94°C for 30 s, annealing at 54°C for 30 s and extension at 72°C for 20 s, with a final extension step of 10 min at 72°C. PCR products were purified using ExoSAP-IT . Sequencing reactions and capillary electrophoreses were performed by the Finnish Institute for Molecular Medicine .The contigs were screened for possible SNPs with a custom script . The criteria to detect probable polymorphic SNP were: 3× occurrence of a minor allele, 7× minimum total coverage at the position and minor allele frequency more than 20%. To validate that the identified SNPs were highly polymorphic in our setting, we sequenced a subset of field samples collected in 2010 (randomly taken from the sampling described below) using Sanger technique. Primers were designed to amplify a 350–600 bp region flanking the expected SNP site using Primer 3  was used to visualize the results.The program MassARRAY Assay Designer  was used to design the SNP panel, which was then validated by genotyping in a global sample collected in the Åland archipelago in September 2010. In that year, a total of 175  patches , dependiPodosphaera plantaginis is a haploid fungus and consequently, one allele per sample is expected in case of single-genotype infection whereas both alleles can eventually be found in case of mixed-genotype infection. To validate such expectations under co-infection, we experimentally produced mixed-genotype infection by mixing nine combinations of two different strains in different proportions . 452,711 of the reads exceeded our quality threshold and were assembled into 45,245 contigs of average length 943 nt. The NPo. plantaginis transcriptome sequences. First, we searched for homologs to proteins already described as potentially important for fungal pathogenesis. According to PANZER annotation tool, 83 contigs were described as putative virulence effectors from their homology with virulence effectors described in other fungal species. When searching for homologs of the AVRk1 and AVRa10 sequences (the EKA family) −5 for at least one of the two effectors. Finally, we searched for homologs of the 491 proteins identified as CSEPs in Blumeria graminis in our dataset using TBLASTN and obtained 58 hits with e-values lower than 10−5. These hits corresponded to 45 different contigs. Second, we attempted to identify new CSEPs from our sequences. From our dataset of 24 323 predicted peptides longer than 70 amino acids, we had 320 peptides having a signal peptide, 251 of them lacking a transmembrane domain and 19 of them having no orthologous sequences except within the powdery mildews. A list of 19 candidate effectors not yet described was thus identified from our dataset. All the contigs identified as coding for potential effectors are listed in the Supplementary File 2 and a Neighbor-joining tree of these contigs is represented in the Supplementary Blumeria CSEPs.The study of potential avirulence genes, as well as effector proteins would be highly interesting in the context of host-pathogen coevolution and we consequently applied four different methods to identify such candidate genes of interest in the Erysiphe necator in our library using TBLASTN and found the following orthologs to all five searched genes: MAT1-1-1 , MAT1-2-1 , MAT1-1-3 , SLA2 , and APN2 .The mating-type (MAT) genes were recently described in few Erysiphales species Using the criteria previously described, we identified 4200 SNPs in 1806 contigs. The SNP occurrence rate was 0.03 SNP/100 bp over the 14.33 Mb of the 7475 contigs having a maximum coverage of at least seven (seven reads is the minimum coverage needed to observe a SNP according to our criteria). As most of the re-sequenced contigs did not present the expected polymorphism, we finally designed primers for 165 contigs for Sanger sequencing. We obtained high quality sequence for 99 loci (60% of the investigated loci) but only 20 amplified regions presented polymorphism for the expected site . While examining the Sanger sequencing results, we found 13 additional polymorphic sites located in eight different contigs (8.1% of the contigs with good sequence). These 13 newly identified SNPs had variable status in the original 454 transcriptome dataset: five of them were monomorphic, five SNPs showed some polymorphism but did not fulfil our minor allele frequency criteria (minimum 3 reads), two SNP sites had too low sequencing depth and finally, one SNP was found in an intron. The SNP occurrence rate over the whole re-sequencing dataset (39.5 kb) was 0.081 SNP/100 bp. Overall, we found 33 polymorphic SNP loci within 27 contigs see .Po. plantaginis haploid strains, our genotyping produced either only one allele or both alleles for each locus  significantly increased with the frequency of the minor allele  contained no genetic diversity at all: only one multilocus genotype (MLG) was detected . When considering only the patches presenting some diversity, the proportion of leaf samples with mixed-genotype infection within a patch varied between 0 and 1 , we obtained a total number of 85 MLG, with an uneven distribution across the metapopulation . Vast maPo. plantaginis transcriptome, and subsequently developed SNP panel, reveal genetic diversity in the pathogen metapopulation and a high frequency of co-infection. The developed SNP panel proved powerful for detecting genetic variation in this pathosystem, and to date we’ve uncovered other genetic resources that may be important tools for studying the evolution and epidemiology of this system, including virulence effectors and mating type genes. Given the vast information on the epidemiology of this pathogen – recorded since 2001 in over 4000 host populations annually – this holds promise for a fascinating model system for molecular epidemiology studies.Our 454 sequencing of the Po. plantaginis.The project was initiated by 454 sequencing of a pool of RNA samples originating from various location within the Åland archipelago. Obtained sequences were assembled into 45245 contigs, with median length of 1063 nucleotides and median coverage of 1.76. Taxonomic annotation showed that 58.5% of our sequences are homologous to previously described fungal proteins. We cannot expect to obtain an exhaustive representation of the proteins from our species in this low-coverage assembly, as some proteins may be missing or truncated. Moreover, RNA was extracted from scraped material from infected leaves and thus likely contains mostly conidia and mycelia, and little haustoria or cleistothecia. However, functional analysis shows that the distribution of functional annotation is similar to other fungal species . This inPo. plantaginis; such exhaustive view could only be obtained from much deeper sequencing of cDNA from various tissues, especially haustoria B. graminisG. orontiiMating-type genes were found, facilitating projects aiming at a better understanding of the sexual stage of this species. We also identified candidates for proteins involved in pathogenicity, with 145 orthologous proteins to already described fungal effectors and 19 newly described candidate effectors. The list of potential effectors obtained from this study is likely not to represent an exhaustive view of the possible avirulence genes of The SNP discovery process revealed to be more difficult than expected; indeed, as much as 83% of the 4200 SNPs discovered initially were shown to be monomorphic through Sanger re-sequencing. Previous SNP discovery though NGS attempts have reported highly variable frequency of SNP validation, with for example 48% of the SNPs validated in the rainbow trout, The use of a pooled RNA sample originating from 16 patches widely distributed within Åland archipelago should avoid the ascertainment bias problem Po. plantaginis genetic diversity within Åland archipelago is likely to be low. Indeed, we found a SNP occurrence rate first of 0.03 SNP/100 bp in the assembly, but this estimate includes non-validated SNPs and 0.081 SNP/100 bp within the re-sequenced regions, which are expected to be biased towards polymorphism as we sequenced regions expected to be polymorphic. Previous studies reported SNP rates ranging between 0.33/100 bp Overall We tested our SNP genotyping panel in a dataset of 380 samples originating from 80 patches (4–5 samples per patch) throughout the Åland archipelago. The genotyping method was very efficient . Extracting both plant and pathogen DNA into the same sample appears thus as a successful strategy: increased DNA amount by including plant DNA likely increased the DNA recovery and therefore the downstream amplification success. Furthermore, SNP markers were specific enough so that the plant DNA did not affect the SNP marker amplification. The practical implications of this cannot be understated, as it enables large-scale field sampling to be carried out when the time consuming purification of samples is not required. Approximately half of the sampling sites presented no diversity at all, with only one multilocus genotype (MLG) found in all four or five samples analysed per site. More intensive sampling was performed for a subset of the patches (up to 30 infected leaves collected) and, although less frequent than in this study, absence of diversity within a patch was also commonly found . Lack of diversity is thus not only an artefact resulting from the low within-patch sampling effort of this study. Lack of genetic variability in some patches is likely to prevent the pathogen populatiońs evolution Microbotryum violaceumMycosphaerella graminicolaTeratosphaeria nubilosaOur genotyping method allowed the detection of mixed-genotype infection at the leaf level as, even in the case of unequal mixing of two strains mimicking the field sampling procedure, both alleles were detected in 82.3% of the samples. Mixed-genotype infection appears to be quite common in this system. Indeed, even when applying a conservative detection method, 40% of the samples were mixed-infections in those patches that contain polymorphism. Co-infection levels vary greatly between patches as some patches presenting various MLGs contain no mixed-genotype samples while in some patches all the samples represented co-infection. The few available studies that have investigated mixed genotype infections have detected up to 70% of infections containing more than one pathogen strain  and restricted to only one patch. The distribution was highly uneven, with three MLG each representing 5–10% of the samples and found in seven patches, some of them being far away from each other. This wide distribution of a few MLG likely reflects past stepping-stone dispersal. In accordance with previous experimental findings of differences in infectivity between strains, these MLG may then have been maintained through natural selection. This hypothesis could be tested by assessing the link between the genotype and the phenotype through experimental infections of collected samples.This set of 27 SNP loci consequently opens new research perspectives in this system for which epidemiological data have been collected over 10 years. Our current validation of this SNP panel provides valuable guidelines for sampling intensity for future studies. Combining population genetics and disease surveys into the molecular epidemiology approach Figure S1Distribution of (1a) the contig length in base pair (bp) and (1b) the average coverage over the contig. Each histogram bar is divided into annotated contigs in white and unannotated contigs in grey.(TIF)Click here for additional data file.Figure S2Neighbor-joining tree of the contigs identified as coding potential effector proteins.(TIF)Click here for additional data file.Figure S3Genotyping results for the experimental mixed-genotype samples. Each graph correspond to a particular combination of two different strains Click here for additional data file.Table S1Results of the genotyping of the experimental mixed-genotype samples.(DOCX)Click here for additional data file.File S1Sequence of the 33 SNPs found from the transcriptome sequences of Po. Plantaginis.(FAS)Click here for additional data file.File S2List of the contigs identified as coding potential effector proteins.(XLSX)Click here for additional data file.


P ≤ 0.003) with average daily gain (ADG) and 3 of these were also associated (P ≤ 0.002) with average daily feed intake (ADFI) in a population of crossbred beef cattle. The SNP were clustered in a 570 kb region around 38 Mb on the draft sequence of bovine chromosome 6 (BTA6), an interval containing several positional and functional candidate genes including the bovine LAP3, NCAPG, and LCORL genes. The goal of the present study was to develop and examine additional markers in this region to optimize the ability to distinguish favorable alleles, with potential to identify functional variation.In a previously reported genome-wide association study based on a high-density bovine SNP genotyping array, 8 SNP were nominally associated (NCAPG-LCORL locus displayed significant association with both ADFI and ADG even after stringent correction for multiple testing (P ≤ 005). These markers were evaluated for their effects on meat and carcass traits. The alleles associated with higher ADFI and ADG were also associated with higher hot carcass weight (HCW) and ribeye area (REA), and lower adjusted fat thickness (AFT). A reduced set of markers was genotyped on a separate, crossbred population including genetic contributions from 14 beef cattle breeds. Two of the markers located within the LCORL gene locus remained significant for ADG (P ≤ 0.04).Animals from the original study were genotyped for 47 SNP within or near the gene boundaries of the three candidate genes. Sixteen markers in the NCAPG-LCORL locus were significantly associated with feed intake and body weight gain phenotypes. These markers were also associated with HCW, REA and AFT suggesting that they are involved with lean growth and reduced fat deposition. Additionally, the two markers significant for ADG in the validation population of animals may be more robust for the prediction of ADG and possibly the correlated trait ADFI, across multiple breeds and populations of cattle.Several markers within the  Feed intake and average daily gain are economically important phenotypes in beef cattle. The ability to genetically select for animals that better suit a producer's needs in terms of the amount of feed an animal consumes and the gain that can be expected would benefit the industry. However, it is critical that markers used to select for feed and growth traits do not adversely affect other traits including meat quality, carcass traits or reproduction.Quantitative trait loci (QTL) for cattle growth traits such as birth weight, body length, carcass weight, and longissimus muscle area (LMA) have been detected on bovine chromosome 6 in several different populations of animals. Nkrumah et al.  discoverLAP3), mediator complex subunit 28 (MED28), LOC523874, C6H4Orf30, non-SMC condensin I complex, subunit G (NCAPG), and LOC540095 . LAP3 is an aminopeptidase that catalyzes the removal of amino acids from intracellular proteins and peptides. LAP3 is most highly expressed in the bovine pineal gland, kidney, skin, intestine, mammary, and adipose tissues . MED28 is a subunit of the Mediator complex that interacts with RNA polymerase II and can interact with co-activators and co-repressors [MED28 is most highly expressed in the bovine pineal gland, lyphoreticular tissue, uterus, abomassum, and expressed in lower quantities in the liver, reticulum, intestine and kidney . LOC523874 is homologous to the human family with sequence similarity 184, member B (FAM184B) for which there is little information. Recent SNP association studies suggest that NCAPG has a function in cattle growth . LCORL has recently been associated with human skeletal size [LCORL is also expressed in the liver, intestine in the bovine fetus and calf, and in the extraembryonic tissue of the bovine fetus . Based on this information, we chose to evaluate LAP3, NCAPG and LCORL as potential positional candidate genes for cattle feed intake and growth.We have also detected a region on chromosome 6 that is correlated with feed intake and gain located within 37.96 - 38.53 Mb in a crossbred population of cattle [pressors . MED28 i growth  Animal Care and Use Committee. Procedures for handling cattle complied with those specified in the Guide for the Care and Use of Agricultural Animals in Agricultural Research and Teaching .1, born in 1999, 2000, and 2001. Female F1 and the 2001-born F1 males  were kept for breeding, and mated in multiple-sire pastures to produce 2-, 3-and 4-breed cross progeny designated F12. The F12 calves were born in March through May of 2003 to 2007, from 3-year-old and older dams. Male calves were castrated within 24 h after birth. Calves were weaned in September at approximately 165 d of age [The breeding scheme used for the SNP discovery population of animals with feed efficiency phenotypes is described in Snelling et al. . BrieflyLAP3, NCAPG, and LCORL were selected from the discovery population of animals. These animals represented extreme feed intake and gain phenotypes.Animals (n = 24) used for resequencing of candidate genes Validation animals (n = 406) were derived from progeny in the USMARC continuous Germplasm Evaluation Program . These pDiscovery population calves were familiarized to the test ration as described in Snelling et al. . Briefly12 steers (discovery population) are described in detail in Snelling et al. [Feed efficiency phenotypes for Fg et al. . Brieflyg et al. .The steers used for the validation study were fed for either 84 or 148 d. Feed intake for those fed for 84 d was measured using an Insentec system . Diets were similar to those used in the discovery population. Dry matter intake was equal to the total DMI consumed for either the 84- or 148-d period and averaged to a daily basis. A linear regression was used to predict BW in steers fed for 84 d and a quadratic equation was used to predict BW in steers fed for 148 d. Gain for both groups was calculated for the respective feeding periods as described in the discovery population above.12 steers were serially harvested at a commercial processing plant as described by King et al. [th rib region. For slice shear force (SSF), steaks were thawed at 5°C for 24 h then cooked on a conveyorized electric belt grill to a final internal temperature of 71°C as described by Wheeler et al. [Fg et al. . USDA yig et al. . A poster et al. . SSF wasr et al. .DNA was isolated from whole blood or buffy coats collected from steers selected from the discovery population of animals. Animals (n = 24) with extreme ADG or ADFI phenotypes were chosen for SNP discovery. Twelve animals with low ADG or ADFI values and twelve animals with high ADG or ADFI values were used. All seven breeds were represented in the animals with high and low phenotypes. None of the breeds were over-represented in the high and low groups. Angus and Hereford breeds each represented ~30% of the breed composition in both groups. Simmental represented 12.5% in the low group and 16% in the high. Composition of the other breeds  ranged from 3-8%.LAP3, NCAPG and LCORL from genomic DNA were designed using Primer 3 [http://frodo.wi.mit.edu). Cattle sequences used as templates for primer design were obtained from the chromosome 6 Btau 4.0 assembly. Oligonucleotide primers were synthesized by IDT . PCR was performed in a DNA engine Dyad® peltier thermal cycler . PCR reactions included 0.25 U Hot Star Taq polymerase ; 1× supplied buffer; 1.5 mM MgCl2; 80 μM dNTPs; 0.33 μM each primer; and 25 ng genomic DNA in 12 μl reactions. Three microliters of the PCR reaction were electrophoresed in 2% agarose gels to determine quality of amplification, and the remainder was prepared for sequencing after treatment with 0.1 U exonuclease I . Sequencing reactions were precipitated with 70% isopropanol and sequenced with an ABI 3730 capillary sequencer . Bases were called with Phred and assembled into contigs with Phrap. Polymorphisms were identified using Polyphred and assessed using Consed http://www.phrap.org.Primer pairs for amplification of regions of Primer 3  . Assays were designed for 47 SNPs in and around the LAP3, NCAPG, and LOC540095 (LCORL) gene loci. Each amplification primer contained a 10-base tag to yield amplification products with different masses. Amplicon lengths were between 90 and 120 bp. Reaction conditions were performed as recommended by Sequenom. A total of 1,137 animals with dry matter intake and average daily gain phenotypes and their sires were genotyped and used in the analyses after exclusion.Multiplex assays for the Sequenom MASSARRAYP-values by multiplying the P-value by the number of markers tested (n = 47). Analyses of significant SNP in the validation population followed the same model definition.The data were analyzed using an animal model (MTDFREML)  that inc2) was defined for the 47 SNP on chromosome 6 using Haploview 4.0 software [http://www.broad.mit.edu/mpg/haploview/index.php. Blocks of LD were based on pairwise LD values. Haploview settings were as follows: the exclusion of animals with > 50% missing genotypes, ignoring pairwise comparisons of markers > 500 kb apart, the percentage of genotypes ≥50% and minimum minor allele frequency of 0.001.Linkage disequilibrium . A 3 base pair deletion resulting in an in-frame deletion of a glutamic acid (p.E47del) and a non-synonymous alteration (p.D92E) were detected in LAP3 , NCAPG (n = 8) and LCORL (n = 4) genes were multiplexed into Sequenom genotyping assays. In addition to these, another 26 publicly available SNP located up- and down-stream of these genes that were not used on the BovineSNP50 BeadChip [A total of 21 out of the 39 SNP identified within the BeadChip , were alP-values obtained from the genotyping analysis  to obtain a corrected P. After correction, 16 markers between 38.16 and 38.37 Mb were significantly associated with ADFI (P < 0.05) and 20 markers between 38.16 and 38.41 Mb were associated with ADG (P < 0.05).Marker associations for ADFI and ADG are presented in Table is Table . P-valueNCAPG gene locus were associated (after multiple correction) with ADFI (P ≤ 0.02) and seven were associated with ADG , adjusted fat thickness (AFT), marbling score (MARB), ribeye area (REA), and SSF. The phenotypic data is presented in Table LAP3 gene locus, 6 in NCAPG and 9 in the LCORL gene loci. Of the 20 markers, 13 and 17 were associated with ADFI and ADG, respectively, after Bonferroni correction in the discovery population. In addition to these 20 markers, six markers from the Illumina BovineSNP50 BeadChip that were nominally significant ADG  in the discovery population were also genotyped in the validation population of animals. Association analysis produced two markers  that were associated with ADG in these animals (Table LCORL gene locus. Marker 83714_224 (or p.I422M) [NCAPG was not significant in this population (P = 0.09). Genotypes were analyzed with Haploview for linkage disequilibrium (LD) in this population of animals  ; however, they are in complete LD  with ADG and three SNP with significant association with ADFI (P ≤ 0.002) [P, nominal = 4.8 × 10-6) and the most significant marker for ADFI was Hapmap31285-BTC-041097 . Of the markers presented in this study, 14 are more significant for ADFI  and 11 were more significant for ADG  than the markers on the BovineSNP50 BeadChip.A previous single nucleotide polymorphism association analysis identified a 570 Kb region on BTA6 from 37.96 to 38.53 Mb containing eight SNP with significant association  involved in growth and/or appetite pathways, these functions have yet to be elucidated.NCAPG-LCORL locus has been interrogated in other populations of cattle for growth, meat quality and carcass traits. The most significant marker tested within the NCAPG-LCORL region in Japanese Black and Japanese Brown cattle for carcass weight, longissimus muscle area and subcutaneous fat thickness was the I422M marker (or marker 83714_224 in this study) in NCAPG [LCORL gene locus were even more significant. The only markers tested in all three of these studies were in the NCAPG gene. Only one marker within the LCORL gene locus (HapMap28154-BTC-041134) was tested in our population and in the study by Eberlein et al. [LCORL region difficult. In our population of cattle, we detected 14 SNP in the NCAPG-LCORL locus that were significant for ADFI, ADG and the carcass/meat quality traits HCW, AFT and REA. All of these SNP are in LD (r2 ≤ 0.73), thus it is unclear which of these SNP and genes may be responsible for the phenotypic variation within this population of animals.The in NCAPG . In our LCORL gene and suggests that these SNP may be robust among differing breeds of cattle for the prediction of weight gain. The LD in the validation population of animals in this region differs from the LD in the discovery population of animals. An example is that the LD for the NCAPG SNP 83714_224 (I422M) marker and the most significant SNP for ADG in our study (HapMap28589-BTC041045) was r2 = 0.96 in our SNP discovery population and r2 = 0.91 in the validation population.In an attempt to better define which SNPs are responsible for the phenotypic variation, we performed a validation of these markers in another crossbred population of animals consisting of 14 breeds of cattle. This produced two SNP associated with ADG located in the intronic regions of the th rib at BTA6:36. These QTL plus the numerous QTL for growth and carcass weight seem to be harboring a gene or genes that are responsible for lean growth and fat deposition. Moreover, the NCAPG I442M SNP was associated with plasma arginine, symmetric dimethylarginine (SDMA), and linoleylcarnitine (C18:2) levels in a recent metabolomic study [NCAPG and LCORL do not currently seem to suggest a direct interaction with the metabolites identified in Weikard et al. [NCAPG and/or LCORL in growth.The SNPs tested in our study and in previous work by Setoguchi et al.  are signic study . The allic study . The rolic study ,30, and ic study ,31,32. Td et al. , and theIn summary, we have identified several markers that are highly significant for steer feed intake and gain phentoypes. Moreover, several of the markers evaluated in this study are more significant for ADFI and ADG than the markers on the BovineSNP50 BeadChip. Genetic selection with these markers is likely to have an impact on HCW, REA and AFT with little influence on marbling or tenderness. It is not known how these markers will affect reproductive traits. These markers need to be evaluated on additional populations of animals to ensure that they maintain utility across breeds. We were able to confirm that two of these markers were significant for ADG in a validation population of crossbred cattle that included 14 breeds. These two markers may be particularly effective for the prediction of weight gain across various populations and breeds of cattle.ALP conceived of the study, carried out molecular genetic studies, and drafted the manuscript. AKS participated in the project design and carried out molecular genetic studies. LAK performed statistical analysis of genotypic data. WMS provided statistical advice. TPLS assisted with SNP selection. DAK, SDS and TLW managed meat quality and carcass phenotypic data collection. CLF, TGJ, HCF and LAK collected and provided feed efficiency phenotypic data. All authors read and approved the manuscript.


Thirteen SNPs were found in more than one category. We found two correlated and predicted non-benign coding variants (rs8100241 in exon 2 and rs8108174 in exon 3) of the gene, ANKLE1. Most putative functional LD SNPs, however, were found in either epigenetically defined enhancers or in gene TSS regions. Fifty-five percent of these non-coding SNPs are likely functional, since they affect response element (RE) sequences of transcription factors. Functionality of these SNPs was assessed by expression quantitative trait loci (eQTL) analysis and allele-specific enhancer assays. Unbiased analyses of SNPs at BCa risk loci revealed new and overlooked mechanisms that may affect risk of the disease, thereby providing a valuable resource for follow-up studies.Breast Cancer (BCa) genome-wide association studies revealed allelic frequency differences between cases and controls at index single nucleotide polymorphisms (SNPs). To date, 71 loci have thus been identified and replicated. More than 320,000 SNPs at these loci define BCa risk due to linkage disequilibrium (LD). We propose that BCa risk resides in a subgroup of SNPs that functionally affects breast biology. Such a shortlist will aid in framing hypotheses to prioritize a manageable number of likely disease-causing SNPs. We extracted all the SNPs, residing in 1 Mb windows around breast cancer risk index SNP from the 1000 genomes project to find correlated SNPs. We used FunciSNP, an R/Bioconductor package developed in-house, to identify potentially functional SNPs at 71 risk loci by coinciding them with chromatin biofeatures. We identified 1,005 SNPs in LD with the index SNPs (r BRCA1 & 2 genes Apart from a few examples of genetic mutations with high penetrance, such as found in In contrast to Mendelian disorders, where most disease-causing mutations result in absent or non-function proteins, many complex disease-associated variants, such as for BCa are mainly found in non-coding regions of the genome. Since >90% of the genome is non-coding and risk mechanisms of complex diseases are likely due to subtle regulation of gene expression, risk-SNPs are more often found in non-coding regions. Knowledge of the non-coding regions is rudimentary compared to the protein coding part. However, recent ENCODE data dramatically demonstrated that the non-coding part of the genome is much more than simply ‘junk’ DNA and contains well-demarcated gene regulatory regions, in particular enhancers We have recently formulated a roadmap to address the functionality of risk SNPs in non-coding regions by characterizing gene regulatory regions with nucleosome and transcription factor occupancy and histone modifications In the present study, we addressed the hypothesis that BCa risk SNPs reside in functional genomic regions such as coding exons, TSS regions, and enhancers. In order to identify potentially functional SNPs, we conducted a comprehensive analysis on 656,895 SNPs from the 1000 genomes project data released in May 2012, at the 71 BCa risk loci by measuring LD and annotating them with 11 NGS datasets, all in primary breast epithelial cells. Thus, we found 1,005 potentially functional high LD SNPs. From these, we were able to frame specific hypotheses involving 547 SNPs in terms of novel biological mechanisms; 2 SNPs were at non-benign codon changes in one gene, 42 and 503 SNPs were within response elements of known transcription factors in TSS regions and enhancers, respectively. This shortlist of potentially functional SNPs will not only aid in prioritizing a manageable number of likely functional SNPs, but also reveal hidden biological mechanisms for the etiology of breast cancer.BRCA2 gene) 2<0.1), 11,438 low LD (0.1≤r2<0.5), and 3,508 high LD (r2≥0.5) SNPs at the 71 BCa risk loci (in a 1 MB window surrounding each index SNP)  .Functional Integration of SNPs) analysis In order to identify potentially functional SNPs, we hypothesized that risk SNPs occur at sites with functionality of some form or another. Candidates are in coding exons, regulatory regions near TSS (TSS regions), and enhancers. To assist in assigning potential functionality, we performed a FunciSNP  signals] and/or histone modifications as diagnostics of promoters  Coding exon data were downloaded from the UCSC genome table browser 2 and distance to the associated index SNP. Among 322,954 correlated SNPs (r2>0), 22 percent were at biofeatures (2≥0.5); 21 in exons, 76 in TSS regions, and 921 in enhancers  features . Severalnhancers  at 60 of2≥0.5) were annotated in exons (ANKLE1 at locus 19p13) (2 = 0.94) with the original GWAS index SNP, rs2363956, which in turn also results in another non-benign amino acid change (L184W) in exon 5 of ANKLE1 as revealed by PolyPhen analysis  , are pres 19p13) . The firanalysis . Thus, tanalysis  with mosin silico searches of finding TF REs by utilizing 4 different softwares: HOMER (ChIP-seq known motifs), FIMO, Genome Trax (ChIP-seq TFBS), Haploreg  Next, we studied 76 high LD SNPs, which resided at TSS regions of 25 genes . Fifty-t2 = 0.81) with a known index SNP, rs1353747, which is located 22 kb downstream from it. The correlated SNP is located in the promoter region of Inositol-3-phosphate synthase 1 (ISYNA1) gene, which catalyzes the de novo synthesis of myoinositol 1-phosphate from glucose 6-phosphate , which likely alters a SP1 RE. This SNP is highly correlated . As an example, rs832552 (at MAP3K1 promoter region) changes the expression level of C5orf35 gene in estrogen receptor positive breast cancer tissues as its allele changes  analyses were performed to examine whether these TSS regional SNPs are associated with messenger RNA (mRNA) level by using publicly available datasets  changes .2≥0.5) were annotated at enhancers , and named them breast cancer enhancer 1 (BCE1) through BCE11  and normal breast epithelial cells (HMEC) (Nine-hundred-and-twenty-one high correlated SNPs (rnhancers . To verigh BCE11 . By perfgh BCE11  and S11.s (HMEC) .2≥0.5) at the active regulatory element within BCE5   . In orde0087810) . Note thin vitro enhancer assays by generating plasmids, which contain different versions of each SNP in BCE5 region   . The tope below) . It also2 = 0.88) with a GWAS index SNP, rs1353747, which is located 58 kb upstream from it. This correlated SNP is located in the large intron 10 of the PDE4D gene. PDE4D encodes for an enzyme that has 3′, 5′-cyclic-AMP phosphodiesterase activity and degrades cAMP, resulting in regulation of multiple signaling pathways and metabolism  PDE4D gene is large, 140 kb in length and contains several histone marks of enhancers with nucleosome depletion signals (i.e. DNaseI and FAIRE). The rs76969790 is in close proximity with FAIRE and DNase1 signals and coincides exactly with enhancer histone marks  (One example of a likely TAL1-affecting SNP is rs76969790 at the 5q11 risk locus . The SNP H3K9ac) .Additionally, expression quantitative trait locus (eQTL) analyses on the 921 high LD SNPs in enhancers were conducted using published data as we described above for TSS regions BRCA2 gene, in which rs11571833, a nonsense index SNP resided, we executed an ingenuity pathway analysis . When we examined interactions among these genes and/or their protein products by using data from published papers, we found only one direct interaction and two indirect interactions In order to investigate the interactions among genes at the breast cancer risk loci, we further highlighted 32 genes, which contained functional SNPs either in their exons or within their TSS regions. Using these 32 genes plus the FOSL1 gene, whereas SP1 binds directly to another BCa risk protein, BRCA2 We next analyzed the relationships among the top 18 TF motifs affected by 10 or more enhancer SNPs and proteins encoded by the above 33 genes. Although some of genes have been understudied and currently lack information about their functions and locations, we observed that a number of proteins interacted with each other, and these TFs mediated interactions among the 33 BCa risk genes/proteins . For insh in our priority list  Recently, it was reported that the average number of distal elements interacting with a TSS was 3.9, and the average number of TSSs interacting with a distal element was 2.5 Newly identified regulatory elements, coinciding with high LD SNPs are not necessarily targeting protein-coding genes. For instance, they can interact with long noncoding RNAs (lncRNA) −8 for over 250 traits. Most of the identified index SNPs from GWASs are in noncoding DNA regions, making the assignment of functionality difficult Since 2005, over 1,600 variants have been identified at p-value ≤5×10HMEC cells were obtained from Lonza  and cultured under recommended conditions. MDAMB231, MCF10A and MCF7 cells were obtained from American Type Culture Collection . MDAMB231 and MCF7 cells were cultured in DMEM with 5% FBS. MCF10A cells were cultured in DMEM/F12 with 5% horse serum, 100 units/ml penicillin, 0.1 mg/ml streptomycin, 0.5 µg/ml hydrocortisone, 100 ng/ml cholera toxin, 10 µg/ml insulin, and 20 ng/ml epidermal growth factor (EGF).FAIRE assays were performed as described http://biowhat.ucsd.edu/homer) −9 were used as a cut off to select significantly enriched peaks. FAIRE-seq data within 3 kb windows centered on the annotated TSS of genes were used to define TSS regions. The data >1.5 kb from TSS were utilized to define enhancer regions for the FunciSNP analysis.Each bam file was filtered using a quality filter score of 30 after removing PCR artifacts and duplicates by the Samtools http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeBroadHistone).Histone modification ChIP-seq data  in HMEC were obtained from accession number [GSE29611] through the NCBI Gene Expression Omnibus portal. [GSE29611] was published as part of the ENCODE project. ChIP assay protocol as well as data processing details may be seen here . Detailed protocols may be seen at following websites (http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=307403817&c=chr1&g=wgEncodeOpenChromDnase and http://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=307403817&c=chr1&g=wgEncodeUwDnase). NGS data within 3 kb windows centered on the annotated transcription start sites of genes were used to define TSS regions for FunciSNP analysis. For putative enhancer regions, NGS data >1.5 kb from TSS were utilized.DNaseI-seq data in HMEC were obtained from accession number [GSE32970] through the NCBI Gene Expression Omnibus portal. Additional DNaseI-seq data generated by University of Washington as part of the ENCODE project were downloaded from here ). FunciSNP version 0.99 was used to find correlated SNPs, which coincide with 11 independent ChIP-seq/FAIRE-seq/DNaseI-seq data sets in TSS regions and putative enhancer regions. All the SNPs from the 1000 genomes project (up to May 2012 data release) 2 value 0.5 as a cut-off  were amplified from genomic DNA using High Fidelity Platinum Tag DNA polymerase . The amplified sequences were then subcloned using SacII, EcoRI, BglII or KpnI restriction sites upstream of a thymidine kinase (TK) minimal promoter-firefly-luciferase vector. All clones were confirmed by sequencing. The primer sequences for subcloning are listed in PCR reactions were performed on FAIRE-isolated and input DNA using High Fidelity Platinum Taq DNA polymerase  for 15 cycles after which products were purified and re-PCRed for 20 cycles to minimize the PCR artifacts due to over-cycling. Purified DNA from these reactions was sequenced, using primers near the SNP locations by the DNA Core Facility at the University of Southern California . Each exPoint mutations were introduced to create enhancer-reporter constructs with specific SNP allele using QuikChange site-directed mutagenesis kit . In order to avoid the bias from miniprep procedures, six independent clones of each construct were made and confirmed by sequencing. Each of the six independent clones of each construct were transfected in four wells and two luciferase assays per well were performed in order to record luciferase-reading variation. Allele-specific fold activities were presented and values shown are means ± SEM of the six independent clones of each allele. The analysis of variance statistical test (ANOVA) was used to confirm the difference and two-side p-values between alleles were calculated using the student t-test.−4, fold change >2.0, within top 10% gene rank. The result of this analysis for the genes, which high LD TSS regional SNPs reside in, is listed in TAL1 gene expression level change between normal and breast cancer tissues were shown in detail as boxplots , University of Chicago eQTL Browser (http://eqtl.uchicago.edu), the Genevar (http://www.sanger.ac.uk/resources/software/genevar/), and The Cancer Genome Atlas (TCGA) breast cancer datasets in 15 breast cancer risk loci −5 was used as a cut-off (We performed expression quantitative trait locus (eQTL) analyses on FunciSNP identified SNPs to examine whether these SNPs were associated with messenger RNA (mRNA) level of nearby genes. We assessed eQTL for all SNPs by using the RegulomeDB, the GTEX database ( cut-off  and S14.http://tools.genouest.org/tools/meme/meme-download.html) −8 was applied for the PWM score of each instance for Haploreg. The change in log-odds (LOD) score as alleles change was calculated and listed in In order to annotate SNP effects on regulatory motifs, sets of position weight matrices (PWMs) were used from FIMO, HOMER (ChIP-seq known motifs), Genome Trax (ChIP-seq TFBS), Haploreg  www.ingenuity.com). IPA Path Explore tools were used to identify direct and indirect interactions among molecules. IPA Path Designer tools were utilized to map the annotated subcellular location of each molecule.We obtained information of the top 18 TFs and 33 genes/proteins using an Ingenuity Pathway Analysis  Linkage Disequilibrium block (in EUR) showing two high LD SNPs, rs8100241 and rs8108174, and index SNP, rs2363956, found in exons of ANKLE1. (B) Haplotypes of these SNPs (in EUR) and protein isoforms, containing different amino acid compositions. Antoniou et al reported that T allele of rs2363956 is associated with breast cancer risk (TIF)Click here for additional data file.Figure S2The UCSC genome browser near the ANKLE1 gene, showing breast epithelial cell RNA-seq data. Long RNA-seq from ENCODE/Cold Spring Harbor Lab in HMEC and MCF7 cells were used (TIF)Click here for additional data file.Figure S3Overlap count keys for FunciSNP results. The name of correlated SNP is colored based on the number of biofeatures. (A) Overlap count key for FunciSNP results for TSS regions. (B) Overlap count key for FunciSNP results for enhancers.(TIF)Click here for additional data file.Figure S4RAD51L1 gene expression value in HMEC and MDAMB231.RAD51L1 gene expression value for HMEC and MDAMB231 were obtained from accession number [GSE33167]. Three replicates for each cell type were generated by using the affymetrix HG-U133 plus2 arrays (TIF)Click here for additional data file.Figure S5TAL1 expression level in breast tissues. The expression value of TAL1 gene was obtained from The Cancer Genome Atlas (TCGA) breast tissues TAL1 expression level comparison between normal breast tissues and invasive breast carcinoma (B) comparison between normal breast tissues and invasive ductal breast carcinoma (C) comparison between normal breast tissues and mixed lobular and ductal breast carcinoma (D) comparison between normal breast tissues and invasive lobular breast carcinoma. The analysis was performed by using the Oncomine database (TIF)Click here for additional data file.Figure S6HMEC FAIRE peaks from two replicates.(TIF)Click here for additional data file.Table S171 Breast cancer risk index SNPs and high LD SNPs genomic locations.(DOC)Click here for additional data file.Table S2Protein function prediction results for missense variants of high LD SNPs.(XLS)Click here for additional data file.Table S3Protein function prediction of index SNPs in exons.(XLS)Click here for additional data file.Table S4FunciSNP results for TSS regional high LD SNPs.(XLS)Click here for additional data file.Table S5Differential expression analysis of the genes, which high LD TSS regional SNPs reside in.(XLS)Click here for additional data file.Table S6TSS regional high LD SNP motif analysis result.(XLS)Click here for additional data file.Table S7Top 10 TF motifs for TSS regional high LD SNPs.(DOC)Click here for additional data file.Table S8eQTL analyses on 76 TSS regional high LD SNPs.(DOC)Click here for additional data file.Table S9FunciSNP results for high LD SNPs in enhancers.(XLS)Click here for additional data file.Table S10Breast Cancer Enhancer (BCE) regions used for luciferase assays.(DOC)Click here for additional data file.Table S11Oligonucleotide sequences used for cloning and qPCR.(DOC)Click here for additional data file.Table S12high LD SNPs in enhancer motif analysis result.(XLS)Click here for additional data file.Table S13Top 18 TF motifs for high LD SNPs in enhancers.(DOC)Click here for additional data file.Table S14eQTL analyses on high LD SNPs in enhancers.(DOC)Click here for additional data file.Table S15lncRNA which intersect with high LD SNPs in regulatory elements.(XLS)Click here for additional data file.Table S16FAIRE-seq statistics.(XLS)Click here for additional data file.