P<1.51E-06, Bonferroni correction) association with egg production and quality traits under the Fisher's combined probability method. Some significant SNPs are located in known genes including GRB14 and GALNT1 that can impact development and function of ovary, but more are located in genes with unclear functions in layers, and need to be studied further. Many chromosome-wise significant SNPs were also detected in this study and some of them are located in previously reported QTL regions. Most of loci detected in this study are novel and the follow-up replication studies may be needed to further confirm the functional significance for these newly identified SNPs.Availability of the complete genome sequence as well as high-density SNP genotyping platforms allows genome-wide association studies (GWAS) in chickens. A high-density SNP array containing 57,636 markers was employed herein to identify associated variants underlying egg production and quality traits within two lines of chickens, i.e., White Leghorn and brown-egg dwarf layers. For each individual, age at first egg (AFE), first egg weight (FEW), and number of eggs (EN) from 21 to 56 weeks of age were recorded, and egg quality traits including egg weight (EW), eggshell weight (ESW), yolk weight (YW), eggshell thickness (EST), eggshell strength (ESS), albumen height(AH) and Haugh unit(HU) were measured at 40 and 60 weeks of age. A total of 385 White Leghorn females and 361 brown-egg dwarf dams were selected to be genotyped. The genome-wide scan revealed 8 SNPs showing genome-wise significant ( In addition, through making association test based on direct genotypic effects of markers within or near candidate genes, several polymorphisms were revealed relevant to egg production and quality traits Modern egg layers have been selected for egg production and quality for decades. A breeding program for egg-type chickens has to face two major problems, the measurement of phenotypic data for individual hens and the efficient selection of cockerels which do not have records on laying performance and egg quality. Although great success has been achieved in layer breeding over the past decades, it is becoming more and more difficult with traditional breeding programs to make improvement in egg production and quality traits. With advances in technologies of molecular genetics and availability of DNA markers, identifying QTL controlling egg production and quality traits for application in marker-assisted selection has been progressing rapidly Although many studies have successfully identified lots of QTL and a few causative genes, application of these results in commercial lines is still infeasible due to the precision of mapping. Identification of numerous single nucleotide polymorphisms in animal genome The blood samples were collected from brachial veins of chickens by standard venipuncture along with the regular quarantine inspection of the experimental station of China Agricultural University, and the whole procedure was carried out in strict accordance with the protocol approved by the Animal Welfare Committee of China Agricultural University (Permit Number: XK622).Two experimental lines of egg-type chickens, White Leghorn (WL) and dwarf brown egg layer (DW), have been selected for egg production for over 10 years in the experimental station of China Agricultural University. These two lines were employed as two different experimental populations in the study. For each line, 600 hens from 40 families were kept in individual cages and their egg productions were recorded daily from 21 to 56 weeks of age. Production traits including total number of eggs from 21 to 56 wk of age (EN), age at first egg(AFE), first egg weight (FEW) were summarized. Individual data for egg quality traits including egg weight (EW), eggshell weight (ESW), Yolk weight (YW), eggshell thickness (EST), eggshell strength (ESS), albumen height(AH) and Haugh unit(HU) were measured with conventional methods. Eggs were collected in 3 consecutive days when hens were 40 weeks and 60 weeks old. The average for 3 d was taken as phenotypic value of each trait for every hen. As egg production decreased with age, some hens laid only one egg or even none within the 3 test days at 40 or 60 wks, and therefore no egg quality data were available for those layers at the respective age. All phenotypic values of traits in genotyped individuals were tested for normality, and some abnormal values extremely deviating from normal distribution were deleted. Box-Cox transformation was made for traits apart from normal distribution before conducting association tests.Genomic DNA was isolated from blood sample by using standard phenol-chloroform extraction. Within family, two full-sib individuals with the same dam ID were randomly selected for SNP genotyping. In total, 385 White Leghorn females and 361 dwarf dams were genotyped for 57,636 markers by using 60K SNP Illumina iSelect chicken array. These markers cover twenty-nine autosomes including GGA1 to 28 and GGA 32, two linkage groups containing E22C19W28_E50C23 and E64, and two sex chromosomes . The genxL = contrast to estimate the genetic effect, is = a function of marginal and conditional allelic and genotypic frequencies for estimating additive or dominance effect, y = vector of phenotypic values, X = the design matrix, n = number of observations, k = rank of XAssociation tests were performed based on generalized least squares (GLS) testing to account for sib correlation by using EPISNP computer package P values originally proposed by Fisher from independent tests of significance is adopted herein to address the heterogeneity of raw data. This strategy has also been successfully applied to GWAS and expression arrays elsewhere Pwl and Pdw be the P value for significance of SNP effect (additive or dominance) obtained from association tests in White Leghorn and dwarf layer population separately, and s = −2ln(Pwl+Pdw). Then under H0, Pwl(dw)∼unif . Hence s∼χ2(df = 4). H0 can be rejected at α level of significance if s>χ2. Bonferroni method was adopted to adjust for multiple testing from the number of SNP markers detected. A significant SNP was declared if its combined P value<0.05/N, here N is the number of SNP markers tested in combined analyses.As White Leghorn and dwarf layers are different in their genetic background and phenotypic values , it is nDescriptive statistics of phenotypic measurements of egg quality and production traits in White Leghorn and dwarf layers used for GWAS were given in P values from the two independent analyses for WL and DW were combined under the Fisher's method by using 33,068 markers shared in the two populations. Taking 1.51E-06  as the genome-wise significance level with Bonferroni correction, it was revealed that 8 additive SNP effects showed significant association with egg production and quality traits including AFE, EN, ESW40, ESW60, EST40 and YW40. No dominance effects reached genome-wise significance. The profiles of P values, in terms of −log (P), of all tested SNPs after combining for different traits are shown in P value in each population, combined P values, and candidate genes are summarized in Association tests were performed separately in White Leghorn and dwarf layer populations, and all results obtained from EPISNP were re-analyzed with PLINK. As PLINK also uses least squares regression analysis for quantitative traits, the results from PLINK were almost the same as those from EPISNP. Subsequently, P = 1.42E-06), and it was located in the intron2 of odd Oz/ten-m homolog 2 gene (ODZ2). For EN, one SNP at 21.67 Mb on GGA7 with combined P value of 3.97E-07 showed association, and it was located in the intron12 of growth factor receptor-bound protein 14 gene (GRB14).A SNP on GGA13 was found to be significantly associated with AFE (combined P = 5.85E-09) and the other on GGA3 (combined P = 1.41E-07). The significant SNP on GGA2 is located in the intron2 of UDP-N-acetyl-alpha-D-galactosamine:polypeptide N-acetylgalactosaminyltransferase 1 gene (GALNT1). The significant SNP on GGA3 is at site of 100.09 Mb and 21,753 bps away from the nearest known gene BLK. An association signal was also detected in analysis of ESW60, which is at about 9.59 Mb on GGA11 and in the intron3 of zinc finger protein 536 gene (ZNF536). For YW40, only one SNP at 184.63 Mb on GGA1 reached the genome-wise significant level, which is located in ataxia telangiectasia mutated gene (ATM).As egg weight has a great impact on eggshell and yolk weight, it was taken as a covariant in the association test for ESW and YW. Two significant SNPs were detected by combined analysis for ESW40, one on GGA2 (combined P method. One of them (combined P = 9.21E-07) is located in a predicted gene LOC418918, and the other one (combined P = 1.89E-07) is about 22,428 bps away from a known gene ENOX1.Two SNPs located within an 8.12 Mb segment (between 171.22 Mb to 179.35 Mb) on GGA1 were found to be significantly associated with EST40 by using combined P values of association tests did not reach the genome-wise significant level, but some SNP-trait combinations showed suggestive evidence with chromosome-wise significant level ; genes related to immune system; a gene plays a role in the calcium homeostasis(EFHC1). Functions of most genes mentioned above are not fully understood in chickens and need to be studied further, although they have been studied extensively in human.In addition to egg production, egg quality is another major selection criterion in poultry breeding, especially eggshell quality. Good eggshell quality is not only important for reproductive performance but also for human consumption. Eggshell weight, eggshell strength, and eggshell thickness are important indications of eggshell quality. We identified several significant SNPs influencing eggshell weight at different age. One significant SNP associated with ESW40 is in the intron2 of et al. found that in selected lines, similar populations as used in the present study, correlations between early and late traits were weak P = 2.04E-06) and ESS40 (combined P = 1.72E-06), and it is located in the intron3 of ZNF536 gene that encodes a kind of DNA binding protein and functions as a transcriptional repressor ZNF536 may affect egg shell weight in chickens.Significant SNPs differed over time for egg shell weight, and a SNP on GGA11 was found to be significantly associated with ESW60. Fairfull and Gowe noted high correlations over ages in egg quality traits LOC418918 and the other in a known gene ENOX1. The region harboring these two SNPs ranges from 171.22 Mb to 179.35 Mb, which may be a novel QTL for eggshell thickness and about 70 Mb away from the QTL reported by Sasaki et al.Lots of QTL regions affecting eggshell thickness have been detected by previous linkage studies and they distribute on GGA1, GGA2, GGA5, and GGA7 ATM gene on GGA1. ATM can regulate a wide variety of downstream proteins mainly related to genome stabling and cell cycle controlling Internal quality is becoming another focus of attention in improving egg quality traits, especially as egg consumption is changing from shell eggs toward egg products. Yolk weight is an important indication of internal egg quality. In this study, an association signal at genome-wise significant level was found for YW40, located in et al. and Tuiskula-Haavisto et al.et al.In order to avoid the extreme conservation induced by Bonferroni correction and increase probabilities of finding potential genetic variant impacting egg production and quality traits, association tests were also performed for markers on each chromosome individually. Some chromosome-wise significant SNPs were found in locations within the previously reported QTL regions. On GGA4, there were four QTL regions affecting egg weight, and their range span 51.6–52.6 Mb, 46.7–46.8 Mb, 70.9–80.3 Mb and 61.5–81.3 Mb As large differences of genetic background and phenotypic distribution exist between White Leghorn and dwarf layers, data from individual studies of these two populations cannot be analyzed together in a single association test. Otherwise it would induce false positive caused by population stratification. Therefore, we conducted association tests separately and then performed a meta-analysis to increase the statistical power in estimating the true effect signals. Fisher's combined probability method, although proposed decades ago, is a simple but elegant technique for meta-analysis. This method is appropriate to combine the results from several independent tests bearing upon the same overall hypothesis. For the present study, association tests in two different experimental populations were under the same null hypothesis (H0): no SNP associated with the trait. Furthermore, as the basis of the combined probability method should be one-sided test, and in order to avoid the heterogeneity, we rejected SNP markers with opposing direction of effect in separate studies, and combined ones with additive effect in the same direction. Therefore, Fisher's method is particularly well suited as a meta-analysis tool for this study that performed association tests across different experimental populations.The number of identified SNPs is limited and these loci might not fully describe genetic diversity underlying traits in this study. Furthermore, the genetic mechanisms of quantitative traits might involve complex interactions among genes and between genes and environmental conditions, or epigenetic mechanisms which cannot be captured by additive models. Therefore, increasing density of markers in the genotyping panels and improving genetic models and statistical methods may benefit the detection of causative genetic variability for quantitative traits in domestic animals.In summary, the current study revealed 8 genome-wise significant and 95 chromosome-wise significant SNPs for egg production and quality traits in White Leghorn and brown-egg dwarf layers by using the high-density SNP array and association analysis based on least squares regression. Some SNPs are located in possible causative genes or within the previously reported QTL region, but most of the significant SNPSs are reported, for the first time, to be associated with egg production and quality traits. To our knowledge, this is the first publication of GWAS on egg production and quality traits in chickens and our findings lay a preliminary foundation for follow-up studies to identify causal mutations by enriching markers within the identified intervals and functional studies on related genes, and subsequently they may be applied in marker-assisted selection program on egg layers.Table S1Chromosome-wise significant threshold for each chromosome.(DOC)Click here for additional data file.Table S2Chromosome-wise significant trait-SNP combinations.(DOC)Click here for additional data file.


Several quantitative approaches for benefit-harm assessment of health care interventions exist but it is unclear how the approaches differ. Our aim was to review existing quantitative approaches for benefit-harm assessment and to develop an organizing framework that clarifies differences and aids selection of quantitative approaches for a particular benefit-harm assessment.We performed a review of the literature to identify quantitative approaches for benefit-harm assessment. Our team, consisting of clinicians, epidemiologists, and statisticians, discussed the approaches and identified their key characteristics. We developed a framework that helps investigators select quantitative approaches for benefit-harm assessment that are appropriate for a particular decisionmaking context.Our framework for selecting quantitative approaches requires a concise definition of the treatment comparison and population of interest, identification of key benefit and harm outcomes, and determination of the need for a measure that puts all outcomes on a single scale . We identified 16 quantitative approaches for benefit-harm assessment. These approaches can be categorized into those that consider single or multiple key benefit and harm outcomes, and those that use a benefit-harm comparison metric or not. Most approaches use aggregate data and can be used in the context of single studies or systematic reviews. Although the majority of approaches provides a benefit and harm comparison metric, only four approaches provide measures of uncertainty around the benefit and harm comparison metric . None of the approaches considers the actual joint distribution of benefit and harm outcomes, but one approach considers competing risks when calculating profile-specific event rates. Nine approaches explicitly allow incorporating patient preferences.The choice of quantitative approaches depends on the specific question and goal of the benefit-harm assessment as well as on the nature and availability of data. In some situations, investigators may identify only one appropriate approach. In situations where the question and available data justify more than one approach, investigators may want to use multiple approaches and compare the consistency of results. When more evidence on relative advantages of approaches accumulates from such comparisons, it will be possible to make more specific recommendations on the choice of approaches. Some decisions on health care interventions are straightforward because the benefits clearly outweigh the harms or vice versa. Many decisions, however, require careful balancing of the benefits and harms. For example, in order to decide on the use of aspirin for the prevention of myocardial infarction, one would typically consider the risk reduction for myocardial infarction over a certain period of time e.g. 10 years) as well as the increased risks for hemorrhagic stroke and gastrointestinal bleeding 0 years a. As withIn situations where multiple outcomes, patient profiles and also patient preferences need to be considered, it is challenging to compare benefits and harms without a quantitative approach. Without a quantitative approach, it is not verifiable if and how different outcomes, patient profiles, patient preferences, and sources of evidence were considered and weighted. Non-quantitative assessments of benefits and harms may lead to inappropriate decisions for or against treatments. This lack of transparency may be less problematic in an individual decisionmaking context, but it seems unacceptable when major regulatory decisions or clinical guideline recommendations are at stake. A number of quantitative approaches have been developed and applied to handle the multidimensionality of a benefit-harm assessment -8. But tSuch an organizing framework could be particularly attractive for organizations or investigators who conduct or use systematic reviews because the literature is mostly silent about the use of quantitative benefit and harm assessment approaches in the context of a systematic review. Familiarity with available approaches and their key characteristics will help systematic reviewers to develop protocols that specify all sources and types of data needed for benefit-harm assessments. For example, additional database searches may be needed to identify evidence on baseline risks, harms or patient preferences that would be missed by standard searches that commonly focus on randomized trials. Therefore, our aim was to review existing quantitative approaches for benefit-harm assessment and to develop an organizing framework that helps understanding of differences among methods and their selection for a specific clinical question.Our purpose was to review quantitative approaches for benefit and harm assessments that use formulas or graphical displays to compare the benefits and harms. We evaluated methods feasible for use in systematic reviews or to use the data synthesized in systematic reviews. We did not consider theoretical frameworks and qualitative approaches for benefit and risk assessment, nor approaches that have not been used in the medical field. It is important to note that our review focused on quantitative assessment and not on the entire process of a benefit-harm assessment  that includes quantitative and qualitative processes ,11. Alsohttp://www.ahrq.gov/clinic/epc/) and of the Cochrane Collaboration.We began our search for quantitative benefit and harm approaches with key articles culled from the investigators’ reference libraries, including prior work on approaches for assessing benefits and harms. We looked for articles that were written for quantitative benefit and harm assessment, which included consideration of at least one outcome for both benefit and harm of a medical or public intervention. We included approaches that analyzed benefit and harm outcomes entirely separately as well as approaches that provided a benefit and harm comparison metric where single or multiple benefit and harm outcomes are put on the same scale, which we will call the benefit-harm comparison metric . We screened the reference lists of all included articles for more relevant articles. The group then discussed each article that seemed potentially relevant, as described below. We also reviewed the manuals of the Evidence Based Practice Center program of the Agency for Healthcare Research and Quality (AHRQ)  or a particular clinical population (e.g. patients with manifest coronary heart disease). Secondly, the key benefit and harm outcomes of interest for which the evidence and the benefit-harm comparison is sought should be identified. The figure shows that there might be a single benefit and a single harm outcome that are of interest, or there could be multiple outcomes. As is noted in literature on the more general processes of assessing the benefits and harms of interventions in guidelines and systematic reviews, patient-important outcomes might be preferred as key outcomes but, sometimes, a surrogate outcome may be considered if there is a strong correlation with patient-important outcomes or if no evidence on patient-important outcomes is available. Finally, one should decide whether a benefit-harm comparison metric is desired that puts all outcomes on a common scale so that a single number will inform about the comparison of benefits and harms. The decisionmaking context is likely to be important to decide on the need for a benefit-harm comparison metric because the output of the available quantitative approaches differ substantially and may not be appropriate for all decisionmakers such as patients and their health care providers, clinical or public health guideline developers, regulatory agencies, policymakers, or payers.We identified 16 approaches, which can be grouped into two broad categories  and approaches that use modeling (stochastic) where data are not described by unique values, but rather by probability distributions.2. The type of benefit-harm comparison metric: Researchers may use absolute and relative metrics as well as QALYs.3. Assumptions: In benefit-harm analyses researchers need to make a number of assumptions. For example, if researchers use a benefit-harm comparison metric, the assumption is that it is justifiable that outcomes are combined on a single scale. Other assumptions relate to the joint occurrence of separate outcomes. Some approaches assume that separate outcomes occur independently, which may be justifiable in some instances.4. Consideration and incorporation of patient preferences: Some quantitative approaches explicitly consider patient preferences for different outcomes in order to weight the benefits and harms.5. Types of presenting benefit risk comparisons: Researchers can use various formats to present the results of a quantitative benefit-harm assessment. Researchers may present the benefit-harm comparison as a difference in the number of events between a treatment or no treatment, or as a ratio. They may also express the comparison by the time gained or lost without symptoms through a treatment. Or, researchers can use graphics that depict the benefit-harm comparison for patients at different outcome risks or based on other patient characteristics.6. In Table The main finding of our review of quantitative approaches for benefit and harm assessment used in the medical literature is a simple algorithm that categorizes existing quantitative approaches broadly into approaches that consider single or multiple benefit and harm outcomes and into approaches that use a benefit-harm comparison metric or present outcomes side by side. We also found that for most approaches, researchers use aggregate data so as to make the approaches suitable for systematic reviews even if that is not their intended purpose. Interestingly, only few approaches provide measures of uncertainty and none of the approaches considers a potential correlation between benefit and harm outcomes (joint distribution).We identified a number of assumptions that researchers make when applying some of the quantitative approaches: First, for some approaches researchers assume that one or more benefit and harm outcomes can be put on the same scale to calculate a benefit and harm comparison metric. Challenges for putting different outcomes on the same scale include their relative importance to decisionmakers, simplification of the outcomes , or different methods and timing in the ascertainment of different outcomes.However, the advantages of a benefit-harm comparison metric may be substantial, for example, in the context of complex situations where multiple outcomes are important and where patient, provider, and policymaker preferences vary . It is aSecond, we were surprised to see that there were no quantitative approaches that considered or even discussed the joint distribution of benefit and harm outcomes, even when individual patient data were available. The joint distribution describes the correlation between benefit and harm outcomes. Trial reports commonly describe standard errors and confidence intervals for the benefit and harm outcomes separately, but rarely describe the joint distribution of the effects of the treatment on the benefit and harm outcomes. Without the joint distribution of all the effects, we have to assume independence of the benefit and harm effects. This may not yield a valid estimate of the uncertainty of the benefit-harm balance metric. Changes in reporting practices, such as online journal appendix materials or online repositories of covariance data for later data synthesis, could address this limitation. Systematic reviewers should keep in mind the limitation of not considering the joint distribution when interpreting results from a quantitative benefit and harm assessment.The figure and table show a number of characteristics that help distinguish various existing quantitative approaches. While these characteristics are important for the selection of an appropriate quantitative approach, there are a number of additional considerations that researchers need to make because they have implications regarding the type of evidence included in the benefit-harm assessment. For example, clinical trials are commonly designed to provide high-quality evidence and sufficient power for benefit outcomes. Harms often receive much less attention in terms of accurate and valid methods of measurement . Such asIn contrast to the framework developed here that focused entirely on the quantitative assessments, Lynd and others developed criteria that apply to the entire process of a benefit-harm assessment. This usually requires that researchers consider both quantitative and qualitative approaches to make conclusions regarding benefit-harm comparisons of health care interventions ,11,41,42Our review showed that current quantitative approaches for benefit-harm assessment might need some further development. Firstly, many quantitative approaches identified here focus on binary outcomes that occur just once, with or without consideration of time to event. Current methods need extensions that also consider different types of data. Some patient-important outcomes, such as quality of life or symptoms, cannot be expressed appropriately as binary outcomes without substantial loss of information. Some benefit and harm events can occur several times so that the number of events per person-time needs to be considered rather than the proportion of persons with at least one event. Secondly, uncertainty estimates for the benefit and harm comparison metric  are likely to be of key importance for decisionmakers and organizations making treatment recommendations. Researchers do not commonly report estimates of uncertainty that arises from sampling variability. In addition, none of the methods considers the joint distribution of benefit and harm outcomes. Researchers should develop statistical methods for considering joint distributions when estimating standard errors for benefit-harm comparison metrics. For systematic reviews it would be valuable to develop approaches for making assumptions about joint distributions because covariance matrices are rarely available from reports of primary studies and it may be challenging to request them from authors of primary studies. Thirdly, researchers should develop systematic approaches for sensitivity analyses that assess the influence of the various assumptions commonly made. One approach would be to agree on a list of standard sensitivity analyses for key aspects of a benefit-harm assessment. For example, data for estimating baseline risks (e.g. probability of outcome without treatment) can come from different sources . The best available evidence on treatment effects may sometimes come from single randomized trial or observational study rather than from meta-analyses. Researchers may be able to derive patient preferences by different eliciting techniques. A systematic outline of these options , would make benefit-harm assessments transparent and give users of the evidence a sense for how sensitive the results are to different assumptions.A strength of our review is the collaborative effort of clinicians, epidemiologists, and statisticians that helped us to develop a comprehensive framework for characterizing quantitative approaches for benefit-harm assessment. Some may perceive it as a limitation that we did not conduct a separate formal systematic review but we capitalized on an existing, recent review . Also, wWe developed a framework for the use of quantitative approaches for benefit-harm assessment that can help researchers select specific approaches. We do not make recommendations for or against specific approaches. It is too early to make such recommendations because of the lack of evidence from studies that directly compare quantitative approaches applied to a specific question. The adequacy of approaches depends on the specific benefit-harm question and on the amount and quality of data that determine how justifiable certain assumptions are. In some situations, there may be a single approach that appears to be most appropriate. But commonly, there will be several approaches that are reasonable options given the question, the goal of the benefit-harm assessment, and the available data. In such situations, we suggest that investigators use several approaches, as commonly used in other areas ,44, whicThe authors declare that they have no competing interests.All authors read and approved the final manuscript.This work was funded by a contract to AHRQ, Rockville, MD (Contract No. HHSA 290-2007-10061-I). The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.Dr. Boyd was supported the Paul Beeson Career Development Award Program .The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2288/12/173/prepub


Background: Nearly all fish consumption advisories for methylmercury (MeHg) are based only on risk. There is a need to also address benefits, especially those from polyunsaturated fatty acids (PUFAs), in neurodevelopmental function and cardiovascular health. However, because MeHg and PUFA generally act on these same end points, disentangling risk and benefit is challenging.Objectives: We propose an approach for balancing risk and benefit that is based on the use of statistically dissociated measures of risk and benefit.Discussion: Because of mutual coexposure of MeHg and PUFAs in population-based studies and their opposite effect on many of the same end points, MeHg risk and PUFA benefit are tightly linked statistically, which results in mutual (negative) confounding. Thus, neither MeHg risk nor PUFA benefit can be accurately quantified without taking the other into account. A statistical approach that generates unconfounded risk and benefit coefficients for each end point can permit their subsequent recombination to describe the overall risk–benefit profile of each species of fish or fish diet. However, it appears that some end points may be adversely affected by MeHg without experiencing counterbalancing benefit from PUFAs. Such end points may drive consumption advisories and may preclude balancing of risk and benefit on the basis of other end points.Conclusions: Our thinking about fish consumption advisories now recognizes the need to balance risk and benefit. However, although statistical analysis of the appropriate data can eliminate mutual confounding, care is required to address the most sensitive end points that may be sensitive to risk and not benefit. After observations of frank toxicity in the mass poisoning episodes in Japan in the 1950s and in Iraq in the 1970s , there wIn proposing an approach for balancing MeHg and PUFA benefit in the crafting of fish consumption advisories, we focus here on neurodevelopmental risks and benefits because that is the end point addressed by the current U.S. EPA RfD (2001). However, the same considerations would apply in balancing cardiovascular risks and benefits. It is also important to note that for the purposes of this discussion, we are focusing only on MeHg risk and omega-3 benefit. There are other potential contaminants in fish for which the balancing of risks and benefits is not necessarily addressed by the same considerations that apply to MeHg and PUFAs. Likewise, there are potentially other beneficial constituents of fish for which data on benefits are not necessarily confounded by co-occurring contaminants. Because much of the practical discussion about balancing MeHg risk against the benefits of fish consumption has focused on PUFA benefits , as haveGiven that MeHg and PUFA operate largely in different directions on the same end points, the analysis of data on risk and/or on the benefit of fish consumption to derive practical fish consumption advisories is not straightforward. This is because in studies of fish-consuming populations, data on the adverse effects of MeHg will be confounded by the simultaneously competing PUFA benefits, and data on PUFA benefits will be confounded by competing MeHg risks. In contrast to classic statistical confounding in which two or more variables affect the outcome in the same direction, this type of confounding with two variables affecting the outcome in opposite directions has been referred to as negative confounding . BecauseClearly, some combinations of MeHg and omega-3s in fish lend themselves to obvious consumption advice. Fish with characteristically high levels of PUFAs and low MeHg, such as anchovies, sardines, herring, and salmon, will pose net benefits. Conversely, fish with characteristically high levels of MeHg and low levels of PUFAs, for example, swordfish and shark, will pose a net risk. The difficulty arises when we think about how to craft advisories for fish with intermediate levels of MeHg and PUFAs, including tuna, snapper, bluefish, sea bass, freshwater bass, pike, and walleye.Several studies have regressed data on health outcomes against fish consumption as a whole rather than against PUFAs and MeHg , 2008b. Another possibility is to obtain risk information from one study that quantifies MeHg exposure but not PUFA intake and benefit information from a different study that provides data on PUFA intake but not MeHg intake data. Under such a scheme, regression coefficients, BMDLs (benchmark dose–low), or plateau values in a dose–response curve for adverse effects and benefits could be compared. The problem with this approach is that, as represented in Instead, risk data and benefit data are needed that are derived from the same study in the same population and not obscured by each other. We can think of these unconfounded estimates as naked risk and benefit data.x IQ points above the mean score. Although there has been no consistent evidence of a MeHg–PUFA statistical interaction, such interactions may ultimately be present for one or more end points. If statistical interaction occurs, the relationship shown in The data necessary to generate unconfounded risk and benefit information can be derived from well-designed population-based epidemiological studies that generate MeHg and PUFA intake estimates by subject. Intake can be derived from accurate dietary survey data or from MeHg and PUFA biomarker measurements from which intake can be estimated by applying pharmacokinetic models. Models are available that relate MeHg biomarkers (hair and blood) to intake . Relevanx IQ points). Thus, each possible combination of MeHg and PUFA intake corresponds to a specific level of IQ performance. The dashed lines present three of these possible combinations, each corresponding to the MeHg and PUFA intake resulting from consumption of, for example, 8 oz/week of a hypothetical fish species (or a specific fish diet). For fish 1, with a relatively low MeHg concentration and a relatively high PUFA concentration, weekly intake of 8 oz/week would be expected to result, on average, in an increase in IQ score relative to the mean population score. On the other hand, for fish 3, with a relatively high MeHg concentration and a relatively low PUFA concentration, the same intake would, on average, result in a decrease in IQ score of equal magnitude. An appropriate consumption advisory for fish 3 might caution against consumption of 8 oz/week. In theory, such data can be combined in any combination to reflect the mix of MeHg and PUFA characteristics of any given species of fish or fish diet. The intake rate of that fish could then be adjusted to result in an overall benefit or, at least, the prevention of an overall negative outcome.This type of relationship allows us to predict the value for a dependent outcome variable, such as IQ, that would result from independent values of MeHg and PUFA. A given combination of MeHg exposure and PUFA intake can be chosen that reflects the characteristics of a particular consumption pattern for a specific species of fish, for example, 8 oz of largemouth bass per week. Data on MeHg [U.S. Food and Drug Administration (FDA) 2009] and PUFA concentrations  in commoa priori reason to assume that a MeHg–PUFA regression relationship derived for one of these end points will hold for any other. Thus, a range of end points should be evaluated, and advisories should be based on the end point most sensitive to MeHg risk. In addition, there is evidence that some end points respond negatively to increasing MeHg exposure but do not respond positively to increasing PUFA exposure  models to produce adjusted estimates of benefit and risk. If PUFA–MeHg interactions occur, nonlinear relationships among PUFA and MeHg intakes and outcomes will need to be addressed. This approach should be applied to studies of neurological development and to studies of cardiovascular effects. Particularly for neurodevelopmental outcomes, it will be necessary to generate unconfounded risk and benefit relationships for multiple developmentally significant outcomes to identify those that are most sensitive to MeHg risk and least subject to PUFA benefit. Particular attention should be given to identifying end points that may experience risk but no compensating benefit.a priori representative end point that can be used arbitrarily to construct risk–benefit guidance, even given unconfounded coefficients for PUFA and MeHg. At least for neurodevelopment, it may be necessary to generate PUFA and MeHg coefficients for each end point deemed to have independent public health significance to identify the most risk-sensitive end point for use in constructing fish consumption advisories.Evidence for the beneficial effects of PUFAs in fish for cardiovascular and developmental end points and for the subtle, subclinical adverse effects of MeHg has been accumulating for more than a decade. Initially, the tendency was to assume that either risk or benefit was the most important parameter when considering fish consumption advisories. There is now a growing recognition that the most useful fish consumption advisories need to simultaneously address both PUFA benefit and MeHg risk and that both are tightly linked through fish consumption. For at least some important end points, the risks and benefits are mutually confounding, and neither can be adequately understood in isolation. It appears that the only way to untangle this confounding is through statistical approaches that reveal the naked (unconfounded) risk and benefit coefficients (βs) for each end point. Subsequent recombination of the risk and benefit coefficients can then describe the overall risk–benefit profile of each species of fish for a given end point. However, close reading of the literature suggests that different end points have different coefficients for risk and benefit and that some end points may be adversely affected by MeHg without experiencing beneficial effects of PUFA. This implies that there may be no such thing as an We encourage future researchers to generate data that relate both MeHg and PUFA intake to a range of end points in a way that allows the risks and benefits to be statistically disentangled and recombined to facilitate generalizable consumption advice.


Lens comprises a range of closely related species within the galegoid clade of the Papilionoideae family. The clade includes other important crops (e.g. chickpea and pea) as well as a sequenced model legume (Medicago truncatula). Lentil is a global food crop increasing in importance in the Indian sub-continent and elsewhere due to its nutritional value and quick cooking time. Despite this importance there has been a dearth of genetic and genomic resources for the crop and this has limited the application of marker-assisted selection strategies in breeding.The genus L. culinaris) and wild (L. ervoides) genotypes together with the utilization of a bioinformatics workflow enabled the identification of a large collection of SNPs and the subsequent development of a genotyping platform that was used to establish the first comprehensive genetic map of the L. culinaris genome. Extensive collinearity with M. truncatula was evident on the basis of sequence homology between mapped markers and the model genome and large translocations and inversions relative to M. truncatula were identified. An estimate for the time divergence of L. culinaris from L. ervoides and of both from M. truncatula was also calculated.We describe here the development of a deep and diverse transcriptome resource for lentil using next generation sequencing technology. The generation of data in multiple cultivated (The availability of the genomic and derived molecular marker resources presented here will help change lentil breeding strategies and lead to increased genetic gain in the future. Lens culinaris Medik.) is the world’s fifth largest pulse crop [Lentil (lse crop  with annlse crop . Lentil lse crop  primarillse crop ) of the lse crop . Lentil Medicago truncatula, concerted efforts were initiated to develop genetic marker technology platforms for lentil. These technologies will be of great benefit to future genetic improvement efforts and to future understanding of the relationships between the cultivated lentil and its wild relatives. These benefits have already been demonstrated to a limited extent by Alo et al. [Medicago EST sequences to develop primers that amplified conserved sequences in Lens spp. for diversity analysis. Application of second-generation sequencing technologies to lentil has also been reported by Kaur et al. [Until recently, molecular markers for use in lentil genetic mapping have been limited to anonymous markers such as RAPDs, ISSRs and AFLPs -11 and lo et al.  who usedr et al. , using 4Recent advances in the development of genetic marker technologies have mainly been based on the availability of large numbers of single nucleotide polymorphisms (SNPs) between different genotypes of targeted organisms. The drive to implement single nucleotide variations as markers has come from the arena of human genetics where large numbers of SNPs have been identified in recent years . The impSNP marker technology is characterized by a large portfolio of genotyping methodologies that range from single-plex assays for interrogating individual loci to highly parallel multiplex assays, which can be used in various applications. For example, single-plex assays can be used for routine marker-assisted selection of thousands of individual plants for a targeted trait  while paLens species and genotypes, the identification and characterization of SNPs from this data, the selection of a subset of SNPs for the development of a 1,536 SNP Illumina GoldenGate array and the use of the array to generate a comprehensive linkage map for a L. culinaris genetic mapping population.In this paper we describe the development of a collection of 3′-cDNA 454 reads derived from multiple L. culinaris genotypes and two L. ervoides wild accessions were selected for targeted 3′-cDNA transcript profiling using 454 pyrosequencing technology (Table 6 reads) compared to the other genotypes and thus enable the development of a reference de novo assembly. This process produced a base assembly consisting of 50,146 contigs which were then filtered based on duplication, overlap and size to produce a reference assembly of 27,921 contigs. The relatively large number of identified contigs can be explained by the targeted 3′ nature of the 454 transcript sequence data that enables a high read depth representation of the 3′ ends of genes. This avoids the issue of limited read depth across the entire gene space that is commonly observed with shotgun cDNA 454 sequence data, and that consequently limits the ability to identify robust sequence variation across multiple genotypes. The high average read depth for all the different genotypes demonstrates the success of this strategy (Table In order to identify a breadth of nucleotide diversity across the genic regions of the lentil genome a selection of nine gy Table . One gen6 reads), but identified only 15,354 contigs [In contrast, an assembly of such shotgun 454 data from six different lentil genotypes in a previous study used approximately the same amount of 454 data  that contained such a repeat and represent a resource for potential genetic marker development. This will complement identified repeats from other initiatives using similar sequencing strategies in this crop [An his crop .M. truncatula and soybean [M. truncatula = 12,821 independent contigs; Glycine max = 4,729 independent contigs. Additional file M. truncatula represents 30.4% of the total annotated genes  in this species and represents the limitation of transcript profiling in only a small set of plant tissues. It also reflects the 3′ nature of the contigs that just represent the 3′-UTR of genes in a significant proportion of cases, limiting the effectiveness of identifying orthologous genes. The same analysis was carried out for just the 11,050 (39.6%) polymorphic, non-redundant contigs and identified 8,696 (78.6%) contigs with orthologous copies in the related model legume genomes, and accounted for 36,893 (82.2%) of the total set of non-redundant SNPs. The classification of the gene models identified for contigs from the total set and contigs from just the polymorphic contigs was also generated according to the available and well established Arabidopsis gene ontology (GOSlim) categories for cellular, molecular function and biological processes  total and 2,354 (21.4%) polymorphic lentil contigs with no orthologous loci in either model is not surprising considering the 3′ nature of the contigs; however, it is also possible that a lack of an orthologous gene is due to a high degree of divergence between orthlogous genes in the different species, the presence of lentil specific genes, or an incomplete representation of genes in the current model genome assemblies. The higher percentage of total contigs with no orthologous counterparts compared to polymorphic contigs can also be explained by a lower average contig length of the total set (451 bp) compared to the polymorphic set (666 bp), thus reducing the amount sequence available for comparative analysis of this nature.M. truncatula and soybean for polymorphic contigs also enabled SNPs to be classified as putative protein coding (exonic) or non-coding (3′-UTR) in nature. This revealed 17,748 SNPs as potentially exonic in nature and 5,039 potentially in 3′-UTRs. The classification of SNPs as exonic allows the potential for the impact of the observed variation on the codon reading frame and an estimate of time of divergence between L. culinaris and L. ervoides based upon the observed synonymous substitution rate  enabled an estimate of the time of divergence at 677,000 years ago. Synonymous changes observed between L. culinaris and M. truncatula led to an estimate of divergence between these two species at 38 Million Years Ago (MYA) which is similar to existing estimates between Lens and Medicago[L. culinaris and L. ervoides genotypes. Of these, 8,963 SNPs encoded a synonymous codon change and 12,548 SNPs a non-synonymous codon change. For the latter class 7,714 SNPs encoded a different amino acid property , 4,834 SNPs resulted in a non-synonymous codon change with the same properties and 147 SNPs that would result in a truncated protein due to the presence of a stop codon. Interestingly, only a few less synonymous  and non-synonymous  changes were observed when only comparing amongst the L. culinaris genotypes, revealing, at least in the coding regions of this subset of genes, very little difference in observed polymorphism between L. culinaris and L. ervoides. The large number of the same changes in both species relative to Medicago further reflects the significant time of divergence from a common ancestor as well as the very recent divergence of L. culinaris from L. ervoides. Since the number of genotypes from each species in this study is relatively small we cannot comment on the occurrence of discrete artificial selection sweeps during the domestication and adaptation of L. culinaris as observed in other domesticated species [L. culinaris in the relatively near future, as well as the availability of increasing amounts of sequence data from multiple L. culinaris genotypes and other Lens species, will help to further elucidate the nature of the domestication process for this crop.Annotation of the gene models in  Medicago. In tota species , or the in silico mapping of M. truncatula homologues, the 8,060 polymorphic contigs, including the 1,536 selected for SNP array development, are evenly distributed across the genome  had no predicted exon/intron boundaries and it worked in some, but not all, genotypes. Of the remaining, all worked in most of the 11 genotypes and none gave erroneous results across all genotypes. There were several instances where there was a mismatch but this could be due to the inherent heterogeneous nature of lentil cultivars.KASP assays were initially designed around 28 SNPs within unique contigs for in-house validation in the 11 genotypes  of the loci were polymorphic and could be used for mapping purposes. This was in line with what was expected based on the 454 SNP allele calls. Of the remaining 1,052 assays, 848 (81%) were monomorphic in this population, 154 (14.6%) failed completely, 20 (2%) had a pattern where one of the alleles failed to amplify but the other did . Specifically, the assembled 454 sequence contigs are available for search by name or through the BLAST tool provided by KnowPulse. This allows researchers to find contigs with high sequence similarity to their gene of interest and thus narrow the set of polymorphic loci to those most likely to be linked to their trait of interest. The identified sequence loci known to be polymorphic between at least two of the lines are listed on each contig with their location and any known allele calls. These loci can be inspected by contig or as an independent set in the genotype listing tool where potential markers are shown as rows in a table with the known allele calls in user selected germplasm comprising the columns. This genotype listing can be narrowed to only those potential markers expected to be polymorphic  between any two user-selected genotypes allowing researchers to inspect the total known differences between the two. The loci included on the Lc1536 GoldenGate OPA and full assay details, including primers for any KASP assays designed, are presented via this portal informing researchers of already developed markers and providing all the information necessary for the integration of these markers directly into a research project or breeding program.All data presented in this study are available on our publically searchable database KnowPulse: Pulse Crop Breeding and Genetics  population LR-18 that was used for genetic mapping. CDC Robin is a small red cultivar released in 1999  and 964aB method .Five kinds of tissue samples were collected individually: (1) 2-week old leaf, (2) stem before flowering, (3) 1-week-old etiolated seedling, (4) mixed flower stages, and (5) developing seed at mixed stages. Total RNA from leaves was extracted using the RNeasy Plant Mini Kit (Qiagen) including on-column DNase digestion. Total RNA from other tissues was extracted using the CTAB method described by Meisel et al.  and thenAciI as the restriction enzyme used to generate 3′ cDNA fragments of the optimal size range for amplification during 454 Titanium chemistry sequencing [AciI as the appropriate enzyme was determined by in silico digestion of ESTs collected from chickpea . First-strand cDNA was generated from ~5 ug of total RNA or RiboMinus RNA  using the ArrayScript kit (Ambion) according to manufacturer’s instructions. The synthesis was carried out using a biotinylated oligo(dT) fused to the 454 B-adapter primer, 5′ –biotin-CCTATCCCCTGTGTGCCTTGGCAGTCTCAG(T)12VN-3′. First-strand cDNA was then treated with 2 ul of RNase Cocktail Enzyme Mix (Ambion) for 30 min at 37°C. Double-strand cDNA was synthesized immediately by addition of 30 U of E. coli DNA Polymerase I and 1 U of E. coli RNase H (Fermentas Canada Inc.) to the first-strand synthesis reaction, following the manufacturer’s suggested protocol for second-strand cDNA synthesis.3′-anchored cDNA libraries for 454 sequencing were prepared based on a protocol described in Eveland et al. [quencing . The useAciI (New England Biolabs) to create 2-base 5′CG overhangs for 454 A-adaptor ligation. The A-adapter was prepared by annealing 1μl each of top and bottom strand oligos , 1 μl 10X Annealing Buffer , and 7μl dH2O for a total volume of 10 μl. The annealing mixture was heated at 55°C in a heating block for 5 min followed by slow cooling to room temp of the block together with samples. 30 μl dH2O was then added to achieve a final concentration of 2.5 pmol/μl for A-adaptor stock.The double stranded cDNA product was purified using MiniElute PCR Purification Kits (Qiagen Inc.), and then digested with the restriction enzyme AciI-digested cDNA was treated with Agencourt AMPure Beads (Beckman Coulter Inc.) to remove smaller fragments (< 250 bp). The 3′-fragments of cDNA were recovered using DynBeads M-270 streptavidin (Invitrogen) and then ligated with A-adaptor. Unligated adaptors were removed by washing beads twice with 1× B&W Buffer , and then fill-in reactions for repairing the nicks were done by using Bst DNA Polymerase, Large Fragment (New England Biolabs). To isolate the single-stranded AB adapted library, the immobilized beads were washed twice with 1× B&W Buffer and twice with Molecular Biology Grade water (Sigma). The desired 5′-A-cDNA-B-3′ template strand was eluted with 125 mM NaOH, neutralized, and concentrated on a column from the MinElute PCR Purification Kit (Qiagen Inc.).The The quantity and quality of the resultant single-stranded DNA library was assessed using a Q-PCR strategy  by usingRoche 454 Titanium sequencing of the titrated single strand DNA libraries was carried out following the procedure described by Margulies et al.  with modde novo assembly of the CDC Redberry 454 reads. Parameters used for the de novo assembly included: Min/Match Percent = 90; Max454 Sequence Length = 600; Repeat Handling On; and Expected Coverage = 20. Processing was carried out on a Dell R910, 2 × 2.40 GHz, 48GB RAM Windows 64Bit server. Following assembly, repeat class contigs were removed as well as any remaining contigs of <200bp. Sequence data from the other genotypes was then assembled against the CDC Redberry de novo reference assembly using NGen (DNAStar). To ensure removal of the 454 key sequence from reads, 10bp were trimmed from the 5′ ends and screened for contamination using the vector/adapter file. Adapter screening was used to remove the wobble primer, the adapter, and any poly-A tail. Contaminant filtering was implemented for a set of seven ribosomal sequences that were found to remove the majority of contaminant reads.NGen (DNAStar) software was used for The identification of candidate SSRs was carried out using the software QDD  in the cL. culinaris contigs, their L. ervoides orthologues and the corresponding M. truncatula genes were aligned using ClustalW and those without indels  were used to estimate evolutionary divergence time. Ks values were obtained with the codeml program in the PAML program suite [-3 substitutions/synonymous site/Myr for the legume lineage [The am suite . The est lineage .Seqman Pro (DNAStar) was used to identify SNPs relative to CDC Redberry. Individual reports were parsed into spreadsheet format to compare using a custom Perl pipeline. Only transitions and transversions were reported; indels were ignored as the nature of the pyrosequencing technique makes them less robust. The final spreadsheet report indicates if the SNP is the same as the reference; the alternate allele, or if there is no sequence data at that position .GO categorization was assigned based on the high-quality annotation information available for A set of 28 SNPs were chosen for initial validation  for design of additional KASP assays. Of these, it was possible to design assays for 124 SNPs and genotyping was carried out at KBioscience on 144 LR-18 RILs and on 10 individuals from the SNP discovery panel. The resulting data were visually inspected for allele calling errors using SNPviewer software (KBioscience) and corrected when deemed necessary.L. culinaris genotypes and revealed polymorphism between at least two L. culinaris genotypes and the CDC Redberry reference were kept for further interrogation. Sequence data for the contigs surrounding the SNPs was checked for the number of base pairs to the end of the contig, to the next SNP, or to the closest splice site. Those with less than 60 bp in this flanking region were eliminated since they cannot be used for Illumina GoldenGate arrays . All remaining candidate SNPs were submitted to Illumina for assay design and a total of 7,229 SNPs were returned with design scores greater than 0.4; preferential selection was given to ones scoring above 0.6 as recommended by Illumina.All SNPs in the collection  were surveyed and only those that revealed polymorphism amongst M. truncatula gene models. This final set of 1,536 was submitted to Illumina for design and synthesis of the Lc1536 OPA.To reduce the number of SNPs to 1,536 for final array design, only one SNP per contig or one per predicted SNP haplotype was selected. Only one SNP assay for duplicated contigs or single assay for each duplicate was selected and the final selection was based on even spread across the genome by inference of distribution from contig homology to http://www.illumina.com/technology/goldengate_genotyping_assay.ilmn). Products generated by this assay were read with an Illumina HiScan  and the resulting data were clustered for allele calling using GenomeStudio software version 2010.3 . Allele calls were visually inspected for errors in automatic allele calling and corrected where deemed necessary. Any calls that were not clearly one allele or the other were reported as missing data to avoid errors. Five individuals were removed before mapping due to too many missing data points, leaving 139 individuals for map development.A total of 144 RILs from the LR-18 population were genotyped using the Lc1536 OPA and a standard Illumina GoldenGate assay protocol  is a repository of legume genetic and genomic data. The assembled 454 sequencing data for the lentil reference line (CDC Redberry) is available with all polymorphic loci and associated markers (KASP and Illumina Golden Gate arrays) indicated. Researchers can use a BLAST interface to identify potential homologues of their gene of interest based on sequence similarity. This allows them to both find existing markers associated with their gene of interest, as well as export information on specific loci in a variety of formats to aid in marker design. A comparative GBrowse (Generic Model Organism Database (GMOD), http://gmod.org) with a M. truncatula genomic backbone graphically displays sequence similarity-based homology between legume species providing an alternative approach to finding candidate markers. For an overview of all data made available through this project, visit the project page .KnowPulse: Pulse Crop Breeding and Genetics (Sequence data has been deposited in the NCBI Sequence Read Archive (SRA) under the study accession SRP019372.The authors would like to declare that they have no financial or non-financial competing interests in the publication of this manuscript.KEB, AGS and AV were the co-PIs on the projects that led to this manuscript; they conceived of the study, participated in its design and coordination and co-wrote the manuscript. AGS oversaw the 3′ cDNA library construction and sequencing portions of the work. KEB oversaw the genotyping and mapping portions of the work. AV selected the germplasm used for sequencing and developed the LR-18 mapping population. MJF carried out the genetic mapping of LR-18. LAS prepared the data for dissemination through KnowPulse and the additional files and provided bioinformatic support throughout the project. RL provided input in terms of RNA extraction and established the adapted form of the 3′ cDNA profiling methodology. PV participated in the design and coordination of the study. SK provided methodologies for estimates of the time of divergence between species. LR provided bioinformatics support from SNP selection through data analysis and prepared several figures. WEC developed the SNP discovery pipeline. All authors read and approved the final manuscript.Bioinformatics workflow for lentil SNP discovery and selection for the Illumina genotyping platform.Click here for fileContig and SNP data including lists of all contigs, all SNPs, gene homology with Medicago or soybean, SNPs selected for KASP and GoldenGate assays, and the results of these assays on a panel of 11 lines.Click here for fileClassification of the gene models according to Arabidopsis GO Slim categories.Click here for fileKs plots representing the frequencies of substitutions per synonymous site for aligned othologous contigs between M. truncatula and L. culinaris (a) and between L. culinaris and L. ervoides (b).Click here for file


Genome-wide association studies (GWAS) have become an effective tool to map genes and regions contributing to multifactorial human diseases and traits. A comparably small number of variants identified by GWAS are known to have a direct effect on protein structure whereas the majority of variants is thought to exert their moderate influences on the phenotype through regulatory changes in mRNA expression. MicroRNAs (miRNAs) have been identified as powerful posttranscriptional regulators of mRNAs. Binding to their target sites, which are mostly located within the 3′-untranslated region (3′-UTR) of mRNA transcripts, they modulate mRNA expression and stability. Until today almost all human mRNA transcripts are known to harbor at least one miRNA target site with an average of over 20 miRNA target sites per transcript. Among 5,101 GWAS-identified sentinel single nucleotide polymorphisms (SNPs) that correspond to 18,884 SNPs in linkage disequilibrium (LD) with the sentinels ( The state of knowledge regarding development, progression and inheritability of human diseases has long since outrun the classical understanding. There are only few disorders where monogenic causes could be determined. Also, the genetic links to disease incident are missing to a substantial part and the complexity of pathways encompassing pathogenic effects can still not be limited upwards. In recent years, more and more evidence is emerging that microRNAs (miRNAs), a class of small non-coding RNAs, play an important role in human traits. Databases collecting information on miRNAs mediating human disease such as miR2Disease MiRNAs are key posttranscriptional regulators of most known cellular processes and have been associated with cell fate decision, development, and stress response. Additionally, miRNAs have been identified to be usable as biomarkers for human diseases As soon as the importance of miRNA functioning for human health was realized, approaches were undertaken with the objective to identify potential interrelations of miRNA dysregulation and genetic variation. However, until now neither the data on trait-associated polymorphisms nor experimentally verified miRNA targeting information provided a sufficient basis for such analyses. For instance, mutations in the 3′-UTR, the major target of miRNA-mediated regulation, have long been (and are still) neglected for the most part in association studies. Therefore, only few particular cases of polymorphisms affecting miRNA regulation pathways have been identified, yet The success of genome-wide association studies (GWAS) in determining the genetic causes of some common diseases led to an immense increase of the efforts put on screening common alleles for disease involvement. Since then, more and more loci associated with trait susceptibility are detected. GWAS can, however, neither identify causal genes in associated loci nor provide functional mechanisms behind observed association signals. Consequently, many identified GWAS signals are awaiting mechanistic characterization and the rate at which GWAS signals are currently discovered necessitates systematic and scalable functional approaches cis-acting regulatory effects.When it became clear that only few diseases are due to common risk alleles, sequencing endeavors have been performed to identify putatively rare variants hiding behind the signals detected in GWAS. In some cases these approaches successfully identified rare causative variants predisposing to disease. However, the expected breakthrough failed to appear as in several cases sequencing of risk loci did not provide further knowledge rs10923 in the 3′-UTR of SMC4 we show how our findings can be utilized in the biological interpretation of GWAS results in a systems biological manner.We assess the potential impact of trait-associated SNPs on miRNA regulation pathways using publicly available GWAS data r2 thresholds.We compared the amount of trait-associated variants within the predefined five function classes of SNPs , coding sequence (CDS), and 3′-UTR) to examine a potential location bias of these markers. Of 18,884 SNPs contained in the extended GWAS-SNP set . To investigate if 3′-UTR SNP enrichment values hold only for specific MAFs, we recalculated the ORs against the HapMap-SNP set in dependency of the MAF. The ORs resulting for the five MAF intervals follow roughly the pattern of over−/underrepresentation observed in the comparison with the other extended GWAS-SNPs but never lose significance or fall below an OR of 2.0 . By analyzing the MAF, we found the extended GWAS-SNPs to hold a commonly higher MAF than the HapMap-SNPs, regardless of their chromosomal location. However, the comparison of the MAF distribution of the 3′-UTR SNPs to the MAF distribution of other extended GWAS-SNPs revealed a slight trend of 3′-UTR SNPs towards moderate MAF frequencies between 0.1 and 0.4. This trend becomes more explicit when comparing the 3′-UTR SNPs to the combined extended GWAS-SNPs in the other two exonic regions (i.e. 5′-UTR and CDS). In comparison, 3′-UTR SNPs show underrepresentation of the intervals 0.0–0.1 (OR = 0.88), 0.2–0.3 (OR = 0.70) and 0.4–0.5 (OR = 0.78) whereas the other two intervals are significantly  revealed only four significantly enriched  reported in the study is in high LD  is associated with the damaging of the small bile ducts and is mediated by auto-antibodies rs10923 is localized within an experimentally validated RISC binding site hsa-mir-299-5p, a miRNA that has been shown to be up-regulated in PBC patients rs10923 disrupts the 6mer seed complementary region  and thus the ability of the miRNA to bind the transcript , expression data from the MuTHER study SMC4 expression in lymphoblastoid cell lines. We suggest that, by this process, rs10923 contributes to the phenomenon of DNA repair perturbation in cirrhosis SMC4 in the generation of the Condensin I-PARP1-XRCC1 complex. Furthermore, PBC patients present elevated risk to develop different types of cancer The SNP y region  and thusy region  and thusy region  and thusAlthough thousands of SNP-trait associations have been published and the applicability of GWAS is broadly appreciated, the associations identified by GWAS hold predictive power for only a small fraction of disease incidence so far Here, we suggest that associated common or unknown rare variants in the 3′-UTR depict posttranscriptionally regulatory variants which, to differing extent, affect disease development and progression by interfering with miRNA regulatory networks. The differing impact of 3′-UTR SNPs can be explained with two facts: first, human mRNAs are mostly targeted by multiple miRNAs at different target sites which may allow for compensating the loss of a single binding site; and second, the extent to which mRNAs are silenced by miRNAs depends on multiple factors such as tissue-specific miRNA expression, the binding affinity of the RISC and the degradation rate of the mRNA rs2168518) was found to be located in the hairpin of hsa-mir-4513. But we observed a significant overrepresentation of trait-associated SNPs in the 3′-UTR that strongly suggests a functional coherence between genetic variants and miRNA regulation pathways on the cis-regulatory level.Based on recently available data, our results clearly show that several lines of evidence become obvious that miRNAs play an important role in genetically determined posttranscriptional disease development and progression. There is no evidence for considerable direct interference of miRNA processing: only one SNP  signals, our extensive analysis of the potential impact of GWAS-identified SNPs on functional elements in the 3′-UTR revealed several mechanisms whereby variants may affect miRNA-mediated regulation. The smallest fraction of potentially functional 3′-UTR SNPs affects 3′-UTR splicing. These SNPs are predicted to mediate miRNA target site loss, mostly through the gain of acceptor splice sites (donor splice site gain only occurred once) resulting in shortened 3′-UTRs. That altered splicing is the rarest found mechanism can be explained by the huge impact it mediates on miRNA targeting which manifests in the high fraction of target site loss (46% on average) for the affected transcripts. The second mechanism is the SNP-mediated alteration of RNA secondary structure of a RISC binding region. The impact of RNA folding on the binding affinity of RBPs has already been described In general, our findings indicate that miRNAs play an important role in genetic variants causing trait development. Therefore, novel aspects not only for the interrelations in pathogenic disturbance of cellular processes, but also for the coherence of different traits can be addressed. For instance, differential tissue-specific expression patterns of miRNAs in combination with genetic variants may shed light on the still unknown functions driving the same cellular pathways to feature different effects in diseased and healthy individuals. Thus, closing the gap between trait-associated mutations and impaired miRNA-mediated gene regulation may lead the way to a new understanding of the interconnection of those functional entities.SMC4 we show that the incorporation of miRNA related information can be used to construct models of potential disease-driving mechanisms in a systems biological fashion. In this case, we present a very simple one-gene/one-miRNA scenario. However, up to now SMC4 and PBC have never been connected, as well as an association of PBC and cancer only exists in clinical ratios. But our suggestion of a highly active, SMC4-dependent DNA repair machinery is not only a likely cellular response mechanism to apoptosis signals in cirrhotic tissue. It also provides the link to cell transformation and cancerogenesis. Therefore, in the context of miRNA biology plausible coherences can be established for common variants with unknown effect. This also points out that, beyond statistical evaluations, case-specific detailed analyses may greatly outrange global approaches in the biological interpretation of GWAS results.We found 326 candidate genes, and on the example of Our findings highlight numerous starting points for biomedical research. To advance the potential of conclusion drawing in GWAS results interpretation, it may be invaluable to overcome the GWAS bias towards the coding sequence and extend approaches based on 3′-UTR mutations. Conversely, more comprehensive data on miRNA binding sites might enhance our understanding of miRNA regulation functioning. It may also be of interest to investigate the most suspicious cases described in this work further. Moreover, determination of miRNA regulation via RISC binding to other mRNA locations than the 3′-UTR might shed light on the effect of coding-synonymous trait-associated alleles. The combination, i.e. extensive measurements of miRNA-mediated gene regulation in patients with traits for which a plausible model of miRNA involvement can be created in the context of associated mutations in the 3′-UTR, may further provide new perspectives of disease progression. But the implications of our findings go beyond GWAS and miRNAs. Considering the advances made in the exploration of the still poorly understood elements of the genome, the impact of the presented results are pointed out. Just recently, the approaches of the modENCODE project We downloaded the Catalog of Published Genome-Wide Association Studies As background distribution for localization enrichment we used the 2.7 million SNPs from the CEU panel of the joint HapMap Phases I, II and III  for which genotype information was available r2 values in the localization enrichment analysis was performed accumulatively in 5% steps. For binning of HapMap-SNPs into LD blocks, we searched for all SNP sets with an all-vs.-all We mapped all SNPs on genomic locations of protein-coding genes and miRNA genes. The genome annotations were obtained from the UCSC Table Browser We used the algorithm PhastCons from the PHAST package Argonaute and TNRC6 in human HEK293 cells To analyze the single nucleotide mutation effect on miRNA targeting, we used the high-throughput transcriptome-wide CLIP-Seq interaction maps describing sites of the RBPs We obtained chromosomal positions of poly(A) signals from the PolyA DB for mRNA polyadenylation sites We applied the NNSplice algorithm from the Berkeley Drosophila Genome project . All SNPs inducing a minimal correlation coefficient of less than 0.55 between wild-type and mutated structure were filtered.To account for structural changes caused by SNPs we used the RNAfold algorithm from the Vienna RNA Package version 1.8.5  To find all possible MREs we searched for sites complementary to a canonical miRNA seed sequence We evaluated the enrichment of functional annotations of the 326 genes containing 3′-UTR SNPs using DAVID For retrieval of phenotype-specific microRNA expression alterations we used PhenomiR Table S1SNPs predicted to affect 3′-UTR splicing. The first column lists the SNP rs-numbers, in the second column the respective transcripts are given and the third column contains the genomic locus of the SNP including strand information of the transcripts. The fourth column shows the conservation of the SNP in binary code, i.e. 1 means conserved, 0 means not conserved. The fifth column gives the type of the gained splice site, the score column contains the likelihood of NNSplice and the last column provides the percentage of lost RBP binding sites for the accordant transcripts.(XLS)Click here for additional data file.Table S2SNPs predicted to affect 3′-UTR secondary structure. The first column lists the SNP rs-numbers, in the second column the respective transcripts are given and the third column contains the genomic locus of the SNP including strand information of the transcripts. The fourth column shows the conservation of the SNP in binary code, i.e. 1 means conserved, 0 means not conserved. The fifth column lists the correlation coefficient of the wild-type structure to the mutated structure of the respective transcripts.(XLS)Click here for additional data file.Table S3SNPs disrupting existing MREs. The first column lists the SNP rs-numbers, in the second column the respective transcripts are given and the third column contains the genomic locus of the SNP including strand information of the transcripts. The fourth column shows the conservation of the SNP in binary code, i.e. 1 means conserved, 0 means not conserved. The fifth column lists the miRNAs the MREs of which are affected.(XLS)Click here for additional data file.Table S4SNPs creating new MREs. The first column lists the SNP rs-numbers, in the second column the respective transcripts are given and the third column contains the genomic locus of the SNP including strand information of the transcripts. The fourth column shows the conservation of the SNP in binary code, i.e. 1 means conserved, 0 means not conserved. The fifth column lists the miRNAs for which MREs are created.(XLS)Click here for additional data file.


To aid the identification of these enhancers, we performed genomewide ChIP-seq for H3K27-acetylation, a mark of actively engaged enhancers, as well as the transcription factor TCF7L2. We analyzed in depth three variants in risk enhancers, two of which show significantly altered androgen sensitivity in LNCaP cells. This includes rs4907792, that is in linkage disequilibrium (Genome-wide association studies (GWAS) have revolutionized the field of cancer genetics, but the causal links between increased genetic risk and onset/progression of disease processes remain to be identified. Here we report the first step in such an endeavor for prostate cancer. We provide a comprehensive annotation of the 77 known risk loci, based upon highly correlated variants in biologically relevant chromatin annotations— we identified 727 such potentially functional SNPs. We also provide a detailed account of possible protein disruption, microRNA target sequence disruption and regulatory response element disruption of all correlated SNPs at  In the following work we provide a complete summary annotation of functional hypotheses relating to risk identified by genome wide association studies of prostate cancer. In addition, we present new genome-wide profiles for H3K27-acetylation and TCF7L2 binding in LNCaP cells. We also introduce the concept of a risk enhancer, and characterize two novel androgen-sensitive enhancers whose activity is specifically affected by prostate-cancer risk SNPs. Our findings represent a preliminary approach to systematic identification of causal variation underlying cancer risk in the prostate. The basic goal of research into human genetics is to connect variation at the genetic level with variation in organismal and cellular phenotype. Until recently, inferences about such connections have been limited to the kind associated with heritable disorders and developmental syndromes. Such variations often turn out to be the result of disruptions to protein coding sequences of critical enzymes for an affected pathway. Recent advances in genomics and medicine have begun to illuminate a sea of variation of a more subtle variety, not always the result of mutation of protein coding sequences. In particular, genome-wide association studies (GWAS) have identified thousands of variants associated with hundreds of disease traits Lymph Node Cancer of the Prostate (LNCaP) At present, little is known regarding the functional mechanisms of the common variant susceptibility loci in non-coding regions. For one, there are many genetically correlated variants that—to varying degrees—may account for the risk associated with each index-SNP. It is unclear whether more than one variant carries functional consequences relevant to the risk that was reported. In addition, we are only beginning to understand the nature of non-coding regions as revealed by histone modifications and other chemical signatures on chromatin. Efforts to fill this void are underway, notably by the ENCODE consortium Funci–SNP}www.1000genomes.orgFunci–SNP} and similar tools such as RegulomeDB We propose that by identifying all the variants that are in linkage disequilibrium with GWAS SNPs and subsequently filtering down to those present within genome-wide functional annotations we will identify the most likely causal susceptibility variants within regulatory elements that can be tested for their functional significance. We previously developed the R-Bioconductor package Funci{SNP} and genome-wide chromatin biofeature data for breast epithelia-derived cell lines as described above, but this level of detail is lacking for prostate cancer. In that study, we catalogued and assessed the correlated functional variants at 72 breast cancer risk loci and performed preliminary enrichment analysis of motifs. We identified over 1,000 putative functional SNPs, most of which were in putative enhancers. We provide here a similar analysis for prostate cancer, extending the previous work and introducing some improvements to the downstream analyses. We also present some new ChIP-seq datasets to add to ENCODE.Post-GWAS analyses of breast cancer Funci{SNP} In order to identify variants that are in linkage disequilibrium with 77 prostate cancer risk loci  followed by exposure to vehicle control or physiological levels of the androgen dihydrotestosterone (10 nM DHT). For LNCaP treated with vehicle (minus DHT) we observed 57,623 peaks, with an average peak height of 32 tags and median height of 22 tags, and a range of 9 to 212 tags. The average peak width was 2,233 bp. For LNCaP post-androgen stimulation, we observed 60,752 peaks, with an average peak width of 2,267 bp. Overall the relative tag density and peak width distribution was extremely similar between the two conditions , FOXA1 and NKX3-1, data for TCF7L2— another transcription factor with a proposed role in prostate- and other cancers Funci{SNP}, we identified 49,305 SNPs that were correlated in the population in which the original index SNP was reported within prostate epithelial chromatin biofeatures, of which only 727 had an http://genome.ucsc.edu/cgi-bin/hgTracks?hgS_doOtherUser=submit&hgS_otherUserName=hazelett&hgS_otherUserSessionName=pca.Using l to 0.5 . The mosl to 0.5 . A complAfter identifying SNPs in primary biofeatures, we grouped them according to putative functional classes for further analysis. We identified 30 SNPs in putative promoter regions −1000 bp to +100 bp relative to transcription start sites, 663 SNPs in putative enhancer regions, 4 SNPs in microRNA target sequences within ng exons .To directly observe the relationships of the annotations to each SNP across the entire set, we performed unsupervised clustering on the matrix of biofeatures and SNPs . The resWe identified 27 exon SNPs in linkage disequilibrium with index SNPs for prostate cancer . Of thesMDM4 gene. This SNP was previously reported in functional annotation of iCOGS HAPLN1, SLC22A3, and FOXP4 genes, and are also of potential interest (see We next identified 29 http://genome.ucsc.edu/cgi-bin/hgTracks?hgS_doOtherUser=submit&hgS_otherUserName=hazelett&hgS_otherUserSessionName=pca). For both promoters and enhancers we also identified a subset of disruptive variants that target response elements for factors of special interest to prostate cancer, namely AR, FOXA1, NKX3-1, TCF7L2, MYC, GATA1 and GATA3. There were 6 SNPs in promoters and 177 in enhancers for this short list of PCa-specific factors. These findings for PCa response elements are summarized in In order to identify putative functional variants within proposed enhancer and promoter regions, 663 SNPs from enhancers and 30 SNPs from promoters were queried against 87 positional weight matrices (PWM) compiled from Factorbook POU5F1B coding region, and with both the MYC and FAM84B genes. Another locus, the PCAT1 non-coding gene, has several SNPs affecting MYC, ETS1 and TCF7L2 candidate binding sites that potentially interact with the MYC gene locus  do not overlap any biofeatures or chromatin 5C capture data, whereas the correlated enhancer SNPs with response element disruptions do. These variants cluster within 5C-interacting regions despite having been filtered with LNCaP biofeatures, which are distributed evenly throughout the region  and AR  binding sites in putative enhancers. Therefore, we hypothesize that all three index SNPs in this region are correlated to these other functional SNPs as the primary source of risk, and that together they constitute a single independent risk locus  clearly indicated significant deviation from the line of unity, suggesting greater similarity of the GWAS correlated SNPs to random LNCaP biofeature SNPs compared to background, consistent with our hypothesis. A Shapiro-Wilk test for normality revealed that the Second, we hypothesized that LNCaP cell-specific enhancer regions might differ from random SNPs in the relative abundance of some motifs, and therefore might be a more appropriate background. To test this, we repeated the procedure of random selection of SNPs, this time filtering by the same genomic regions used in our Prostate cancer is driven by androgen receptor signaling We first took an inventory of nearby genes to the 77 risk loci see  and analWe also detected enrichment for genes involved in male gonad and sex differentiation , IGF2R, CHMP2B, BMPR1B, and the cell cycle reglator Cyclin D1 (CCND1). in vitro heterologous enhancer-reporter luciferase assay in LNCaP cells. In the absence of good prior information, we could not predict the magnitude of the effect of a variant at a single nucleotide in a strong consensus binding site on enhancer activity. In order to obtain reliable inference on basal enhancer activity and response to androgen for possibly very slight changes, we eliminated other sources of variation such as plasmid preparation, batch and transfection effects. Thus, we sampled evenly over this parameter space  in basal activity between the G and the A allele  are present, hence we believe that many important risk variants will be identified within enhancer regions.One of our primary motivations for using There are at least two other types of potential regulatory variation that are difficult to capture with this type of analysis. One is alterations to the primary sequence that, by mechanisms which have yet to be elucidated, alter the pattern of nucleosome spacing or histone modification. It is known that some sequences contribute to nucleosome positioning in chromatin e.g. MYC), such disruptions are necessarily tissue specific and mild so as to confer slightly elevated risk over a lifetime, and perhaps with cumulative effects or environmental interaction.Another issue is that many identified GWAS associations consist of common variants with only slightly elevated risk , but the causal variants aren't biased toward disruption of a particular factor. Thus, any factor that disrupts the activity of a particular androgen-sensitive enhancer might be suspect. Biologically this makes some sense, since we expect the target gene to be more important than components of the regulatory network. It has long been known that transcription factor motifs cluster in regulatory regions in vivo is consistent with this latter observation. Finally, we note that even enrichment for androgen-bound mechanisms does not preclude a subset of loci having androgen-independent risk.An association was reported for GWAS loci LD-blocks and genome-wide androgen receptor bound regions Funci{SNP} identifies these SNPs plus a handful of false positives. We would more likely detect true enrichment if we restricted our analysis to the set of true causal risk SNPs. On the other hand, it is possible that clouds of functional variants in correlation with the index  affect the tissue-specific expression of a single key effector gene as in . There iWe have characterized two SNPs, rs4907792 and rs10486567, with highly significant effects in a heterologous reporter assay. These SNPs affect response elements of factors widely thought to be drivers in the progression of prostate cancer. It is interesting to compare and contrast the different effects we observed for the SNPs.Rs4907792, which is located in the enhancer near NUDT11, directly changes a computationally identified AR response element. We observed little basal activity for this enhancer, but a 7.8-fold activation in response to DHT. We detected an 80% difference in the level of activation between the two alternate versions of the SNP, consistent with our hypothesis that the SNP itself affects a critical residue in a true androgen receptor response element.The SNP at rs4907792 is in linkage disequilibrium with index SNPs rs5945572 .LNCaP cells were cultured as described previously Enhancers were amplified by polymerase-chain-reaction using primers listed in Funci{SNP}, available at Bioconductor.org Funci{SNP} was run with the following settings: a window size of 1 Mb around the index SNP was used, and Funci–SNP}-generated SNPs potentially affect the binding of known transcription factors, PWMs were employed from To integrate chromatin biofeature annotations with rted see . AnalysiThe We assumed There were typically 6 plasmid preps for each enhancer, and 4 transfections of each plasmid prep in each batch where that plasmid was measured. Each sample was replicated twice on the plate. The negative controls and PSA positive control were run on each batch.The Bayesian model and subsequent inferences were fitted via the Metropolis algorithm Figure S1Funci{SNP} analysis.Histogram of H3K27Ac peaks. Peak height plotted as a function of peak number for both charcoal stripped serum (css) and DHT treatment (dht) in LNCaP cells. The dotted line indicates the cutoff top 25 k peaks used as biofeatures for (EPS)Click here for additional data file.Figure S2H3K27Ac Overlap of peaks +/− DHT.(EPS)Click here for additional data file.Figure S3Enrichment of TCF7L2 binding sites within ChIP-seq peaks. Average number of TCF7L2 motifs as a function of distance from center of peak. Red line: top 20 k peaks. Blue line: top 10 k peaks. Green line: top 5 k peaks.(EPS)Click here for additional data file.Figure S4rs12621278 in two populations. (EPS)Click here for additional data file.Figure S5rs7584330 in two populations. (EPS)Click here for additional data file.Figure S6rs17021918 in two populations. (EPS)Click here for additional data file.Figure S7rs7679673 in two populations. (EPS)Click here for additional data file.Figure S8rs12653946 in two populations. (EPS)Click here for additional data file.Figure S9rs1983891 in two populations. (EPS)Click here for additional data file.Figure S10rs339331 in two populations. (EPS)Click here for additional data file.Figure S11rs9364554 in two populations. (EPS)Click here for additional data file.Figure S12rs10486567 in two populations. (EPS)Click here for additional data file.Figure S13rs6983267 in two populations. (EPS)Click here for additional data file.Figure S14rs7127900 in two populations. (EPS)Click here for additional data file.Figure S15rs10896449 in two populations. (EPS)Click here for additional data file.Figure S16rs11228565 in two populations. (EPS)Click here for additional data file.Figure S17rs8102476 in two populations. (EPS)Click here for additional data file.Figure S18Comparison of relative risk in different cancers. GWAS odds ratios of SNPs reported for various cancers for comparison with prostate cancer (red).(EPS)Click here for additional data file.