Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits.DOI:http://dx.doi.org/10.7554/eLife.00299.001 Vaccines increase resistance to disease by priming the immune system to respond to specific viruses or microorganisms. By presenting a weakened (or dead) form of a pathogen, or its toxins or surface proteins, to the immune system, vaccines trigger the production of antibodies against the virus or microorganism. If a vaccinated individual then encounters the pathogen, their immune system should be able to recognize and destroy it. Many vaccines also include a secondary agent, known as an adjuvant, to further stimulate the immune response.Influenza, an RNA virus commonly referred to as the ‘flu’, is an infectious disease that affects both birds and mammals. Seasonal epidemics occur each year affecting 2–7% of the population. According to the World Health Organization, influenza leads to nearly 5 million hospitalizations each year and causes up to half a million deaths. Vaccination is a primary strategy for the prevention of seasonal influenza, but responses to the vaccine vary markedly, partly because of variation in the genetic makeup or genotype of individuals. However, the details of how genes influence response to vaccination, and indeed susceptibility to influenza, remain unclear.To investigate the genetic basis of variation in the immune response of healthy adults to the seasonal influenza vaccine, Franco et al. combined information about the genotypes of individuals with measurements of their gene transcription and antibody response to vaccination. They identified 20 genes that contributed to differential immune responses to the vaccine. Almost half of these encode proteins that are not specifically associated with the immune system, but have more general roles in processes such as membrane trafficking and intracellular transport.Focusing on these genes may enable researchers to spot those individuals who are less likely to respond to a vaccine. It could also open up new avenues of research for vaccine development: rather than designing adjuvants that target known immune mechanisms, researchers should develop adjuvants that target the proteins encoded by these 20 genes.DOI:http://dx.doi.org/10.7554/eLife.00299.002 Influenza remains one of the major threats to human health worldwide and is responsible for an estimated 250,000–500,000 deaths each year . AttemptGene expression is strongly controlled by common genetic variants  with botcis-acting eQTL), and correlation between gene expression and the magnitude of the antibody response.We immunized an ethnically homogeneous group of 119 healthy adult male volunteers with licensed trivalent influenza vaccine. DNA was obtained from peripheral blood and genome-wide SNP genotyping was performed. We also measured global transcript abundance in peripheral blood RNA specimens before and at three time points  after vaccination. Type-specific antibody measurements  were made in serum samples before and at two time points (days 14 and 28) after vaccination. An identical study was then carried out with an independent validation cohort of 128 ethnically homogeneous healthy adult female volunteers. This experimental design allowed us to search for loci that show evidence of a transcriptional response to vaccination, genetic regulation of gene expression  and concomitant evidence of a transcriptional response to the vaccine (day effect p<0.01). Of these, 2606 SNP-transcript pairs, corresponding to 256 genes, were validated in the independent cohort of female volunteers (genotype effect p<0.05 and day effect p<0.01). When more stringent thresholds were applied, 756 SNP-transcript pairs, corresponding to 114 unique genes, exhibited significant genotype-expression association (genotype effect p<5 × 10−8) and concomitant evidence of a transcriptional response to the vaccine (day effect p<0.01) in the discovery cohort. Of these, 654 SNP-transcript pairs, corresponding to 93 genes, were validated in the second cohort (genotype effect p<0.05 and day effect p<0.01). A majority of these  would pass equally stringent thresholds in both cohorts . A Manhattan plot of these results is presented in Interactive Results Tool . These SNP-transcript pairs correspond to 543 unique genes. We then identified the SNP-transcript pairs in which the expression variance explained was most strongly increased after vaccination . This analysis revealed multiple loci at which the genetic effect was either enhanced or only apparent after the experimental perturbation. An example is presented in NECAB2 locus before and 3 days after vaccination in both cohorts.We hypothesized that, at some loci, the magnitude of the genetic effect could be different before and at different time points after vaccination. This type of effect, which would not be observed in a cross-sectional study design, could be directly examined with our serial expression data. We analyzed the additive effect of genotype on expression at each day in the study. Using a cis-acting eQTL and a transcriptional response to vaccination, calculating the relative magnitude of slope and within-genotype variance between the pre-vaccination and maximal Rg2 time points. g2, and that this amplitude change was a general feature of the loci in which we observed both a strong cis-acting eQTL and a transcriptional response to the vaccine stimulus. The delta-Rg2 values were consistent between the cohorts when evaluated by Spearman’s rank correlation analysis using all SNP-transcript pairs . To select a conservative set of candidate loci based on this property for further analysis, we identified the SNP-transcript pairs that were in the top 1% of the delta-Rg2 distribution and also showed evidence of a strong cis-acting eQTL (genotype effect p<5 × 10−8), in both cohorts. Data for the resulting set of 146 SNP-transcript pairs, including Rg2 values, are provided in Table 2 via the Interactive Results Tool  showed significant enrichment for genes involved in antigen processing and presentation, cytotoxic T-lymphocyte-mediated apoptosis of target cells, dendritic cell maturation and function, and membrane trafficking .10.7554/Interactive Results Tool , a significant correlation between the transcriptional and antibody responses (expression-antibody effect p<0.05), and either a transcriptional response to the vaccine (day effect p<0.01) or evidence of a change in the magnitude of the genetic effect after vaccination (top 1% of the delta-Rg2 distribution) in the two independent cohorts. These loci have the strongest evidence of genetic variation influencing the immune response to the vaccine, and include TAP2, SNX29, FGD2, NAPSA, NAPSB, GM2A, C1orf85, JUP, FBLN5, CHST13, DIP2A, PAM, D4S234E, C3AR1, HERC2, LST1, LRRC37A4, OAS1, RPL14, and DYNLT1. Remarkably, seven of these encode proteins involved in intracellular antigen transport and processing , gene expression to antibody titer, and genotype to antibody titer (QTL). We now considered alternative models for the relationships between these distinct types of association , and we The results provide an unbiased integrative survey of the genetic and transcriptional components of the humoral immune response to influenza vaccination in humans. They suggest that variation at the level of genes involved in antigen processing and intracellular trafficking is an important determinant of vaccine immunogenicity. Even in healthy, young individuals, there are a significant number of people who do not develop a protective antibody response after influenza vaccination. If these individuals could be identified prior to vaccination, modifications to the type or dose of vaccine could be attempted, with the goal of reducing the number of unprotected vaccinated individuals. The genes identified in this study as playing a role in variation in the humoral response to vaccination would be a logical starting point for the development of DNA- or RNA-based predictive biomarkers. Prospective evaluation of such biomarkers would be the next step towards clinical implementation.Understanding the mechanisms that underlie variation in response to the vaccine may also direct modification of factors that enhance the response. Most of the efforts to date have focused on vaccine adjuvants that activate known immunologic mechanisms. Surprisingly, many of the genes identified in this study encode proteins that are not specifically immune but play a more general role in membrane trafficking and intracellular transport. Interventions aimed at increasing vaccine antigen affinity to these proteins or altering their intracellular concentrations could represent new avenues in vaccine development.More broadly, the results demonstrate that a longitudinal, integrative genomic analysis study design, applied to a clinical intervention, is an efficient alternative to cross-sectional methods for the identification of genes involved in medically relevant complex traits. By making repeated measurements on the same individual over time after a controlled experimental perturbation, we were able to account for individual variation in a way that would not have been possible otherwise. The dynamic nature of the measurements also allowed us to uncover genetic effects that are either enhanced by or only evident after the experimental perturbation. The specificity of gene identification in this study emerges from the genome’s acute response to the perturbation, which cannot be assessed by a cross-sectional eQTL analysis or a genome-wide association study. This approach could be used for a broad variety of medically important problems whenever there is the opportunity to test a well-controlled intervention such as drug, dietary, or vaccine responses.Several limitations of the study are worth noting. First, we studied two samples of healthy young adults, thereby excluding the segments of the population that are most likely to have a poor response to influenza vaccination: children, the elderly, and individuals with severe illnesses. Second, in order to minimize the risk of false associations related to population stratification, we studied an ethnically homogeneous group of individuals. Third, while an interesting aspect of our study design is that it could open the door for direct comparisons of causal and reactive models, the sample size in this study was not sufficient to establish whether or not there is a causal relationship between the loci for which an association was identified and the antibody response to the vaccine. Finally, while antibody titers have historically been used to evaluate vaccine responsiveness, it is clear that they do not capture the complexity of the human immune response to vaccination. Additional studies would be necessary to determine whether the genes identified are also related to variation in influenza vaccine responses in groups other than the one chosen for this study, whether there is a causal relationship between these genes and the antibody response, or whether they also influence the cell-mediated immune response to the vaccine.A visual representation of the study design, the resulting data sets, and the integrative analysis scheme, is presented in Healthy volunteers ages 18 to 40 years were enrolled. Individuals who were known to have received an influenza vaccine in the previous 3 years or who had signs or symptoms of an active infection at the time of enrollment were excluded. To minimize false-positive results related to population stratification, enrollment was limited to individuals of self-reported Caucasian ancestry. Enrollment, vaccination and sample collections were conducted at a university campus. The initial (discovery) cohort was restricted to males. The validation cohort was enrolled approximately 18 months after the initial cohort and was restricted to females. The protocol was approved by the institutional review boards of all participating institutions. Informed consent was obtained from each subject prior to enrollment.Study participants were immunized on day 0. Those enrolled in the initial cohort received the 2008–2009 inactivated trivalent influenza vaccine . The validation cohort received the 2009–2010 vaccine, which came from the same manufacturer and included (A/Brisbane/59/2007), (A/Brisbane/10/2007[H3N2]), and (B/Brisbane/60/2008) strains.Whole blood samples (7 ml) for DNA purification were collected in Vacutainer acid citrate dextrose (ACD) tubes , on day 1 after vaccination. DNA was purified using Qiagen Gentra Puregene Blood Kits . Quantitation and quality control were performed with a NanoDrop-1000 spectrophotometer  and using the Quant-iT PicoGreen dsDNA Reagent  in a Tecan GENios microplate reader .Peripheral blood samples for RNA purification were obtained immediately before (day 0) and on days 1, 3, and 14 after vaccination. To minimize changes in gene expression induced by sample handling and processing, whole-blood samples (2.5 ml) were collected in PAXgene RNA stabilization tubes  and frozen at −80°C. RNA purification was performed using the PAXgene Blood RNA system (Qiagen Inc.). All RNA samples from an individual were consistently purified in the same batch. Spectrophotometry  and microfluidic electrophoresis  were used for quality control.Genotyping was performed on Illumina HumanOmniExpress microarrays  following the manufacturer’s instructions. The arrays include 730,525 SNP markers. Basic quality control of the genotyping data was performed on GenomeStudio software, version 2010 . All microarrays had call rates >0.99.In vitro transcription was performed using Ambion Illumina TotalPrep RNA Amplification Kits . cRNA was hybridized onto Illumina HumanHT-12v3 or HumanHT-12v4 Expression BeadChips , following the manufacturer’s protocol. All samples for a given individual were processed on the same slide. The arrays have 48,742 (v3) and 47,301 (v4) reporters, representing approximately 25,000 genes and non-annotated gene candidates.Whole blood (10 ml) was collected in Vacutainer Serum Separator Tubes (Beckton–Dickinson). Serum was separated by centrifugation prior to storage at −20°C. Hemagglutination inhibition (HAI) tests were performed as previously described , except HAI and neutralizing antibody titers were measured for each viral antigen included in the vaccine. For all antigens, the change in antibody titer post-vaccination is known to be negatively correlated with the pre-vaccination titer. The responses to the individual viral antigens were correlated within individuals. For each individual, we computed a Titer Response Index (TRI), as previously described . The TRIlumi package , unique reporter identifiers (ProbeID and nuID) for the 9809 reporters selected in the discovery cohort were used to subset the data from the validation cohort. This data set was then used for eQTL analysis in the validation cohort.The analysis of correlation between gene expression and antibody titer in the discovery cohort was previously published . As part2 < 1 × 10−7 were removed prior to analysis. For local eQTL mapping, we restricted the analyses to SNPs within 1 Mb (500 kb upstream or downstream) of each unambiguously mapped expression reporter sequence. We used the genotype information to reliably establish that the individuals in each cohort were indeed unrelated and ethnically matched. For this, we estimated pairwise identity-by-descent (IBD) metrics on the basis of identity-by-state (IBS) information from their genome-wide SNP genotyping results, followed by multidimensional scaling (MDS) analysis on the resulting matrix of pairwise distances, using PLINK . Default algorithms were used to normalize, generate SNP clusters, and make genotype calls. SNPs with minor allele frequency (MAF) <0.05 and Hardy-Weinberg Equilibrium (HWE) χng PLINK . The firng PLINK  and are Y = observed expression value, i = day , j = individual, D = day effect, g = genotype effect, P = person effect (random), and e = random error. We fit this model using the R package nlme and the estimation method of the same name. The day effect (D) is a shift term which considers time-ordered change in expression. The genotype effect (g) is a main effect for genotype that considers the expression differences between genotypes to be additively related to the number of copies of a reference allele. The day–genotype interaction allows the additive effect to vary by day (see P) is modeled as a random effect; this term helps to efficiently account for the fact that the same individual appears in each time point, without overfitting the expression model to the particular sample ascertained in our study. We explored different parametrizations, such as allowing the person effect to vary by day, and found that the simplest model with a single term for each person was effective. This person effect can account for inter-individual or batch differences which are stable within individuals for the duration of the study.Analyses were carried out separately for each cohort and in the combined data set. Because we have repeated gene expression measurements on the same individuals over a series of time points, we performed a random effects linear model analysis. For each SNP-transcript pair, we fit a model with terms for day, additive effects for genotype and day–genotype interactions, and a random effect for each person:Where Yj denotes the matrix of expression traits of individuals j at a given time point  and Gi,k is a matrix of genotypes for individuals j at SNP locus k such that each element is assigned 0, 1 or 2 according to the number of minor alleles at the kth locus of the jth individual. This allowed estimates of the proportion of expression variance explained by individual genotypes from the model’s coefficient of determination (R2g). This model permitted a more detailed examination of the changes in the strength of the genotypic association with expression at each time point. To detect SNP-transcript pairs where the magnitude of the genotype effect varied after immunization, we took the difference in R2g measures (ΔR2g) from pairwise linear models fitted after  and before vaccination (day 0). We then took the top 1% ΔR2g values between day 0 and a later time point. This cis-acting eQTL subset represents the loci that show significant changes in the genetic effect as a result of the vaccine.In addition to the random effects linear model, we analyzed each expression trait with respect to genotype at each time point usingwhere The titer response index was treated as a dependent variable and modeled as a linear combination of expression values for each transcript across the time course. A single F-statistic p-value was determined to evaluate the explanatory value of the expression data using all days. Separate evaluations were made in each cohort and in the combined data. The 301 genes displayed in Enriched biological and functional pathways were analyzed using DAVID Bioinformatics Resources , and IngT is trait, E is gene expression, and G is genotype at a locus. In our experiment, the term E includes a separate value for each day. The term G has a separate effect on the trait under the independent null model, while under the causal alternative, where it acts through gene expression, its influence is entirely transmitted through E. The statistic used to assess the influence of G is the partial F reported by the ‘aov’ function in R (version 2.15.1). The analyses utilize an equivalence test approach, where the null hypothesis is that the second independent variable has an effect on the response conditional on the first variable, while the alternative is complete co-linearity in the associations. The null distribution of the F statistic–which under parametric assumptions would have a non-central F distribution—is derived using a permutation test procedure, as follows: The relationship between E and T is decoupled by regressing E on G, permuting the resulting residuals, then adding the permuted residuals to the predicted values to arrive at E*, which is independent of T but maintains the marginal variance and G-correlation of E. This procedure enforces the assumption of the independent model, while leaving other properties unchanged. The partial F statistic for G inrepresents a sample from the distribution under the independent null hypothesis. This process, repeated 1000 times, provided a null distribution. The p-value for the observed F is the proportion of the null distribution that is below the observed value.To evaluate the relationship between the associations identified in our study, we extended a recently published analysis framework for causal modeling in eQTL data . The relThe reactive model is evaluated using the same approach, but with the expression and trait terms swapped, that isThis scheme tests the extent to which the trait value mediates the relationship between genotype and gene expression. In this analysis, the permutation step decouples the expression-to-trait relationship from the eQTL association and asks if the eQTL association disappears after accounting for the expression-to-trait association.G is simulated as binomial, while E and T are normal, with parameters chosen to match the minor allele frequency, variance, and correlations observed in our data set. The power to reject the false (independent) model is shown using a threshold of p<0.05. The power properties for the tests used to evaluate both the reactive and causal models are equivalent because the model structures and r2 relationships are symmetric between the two alternatives.Power analyses for these models apply the same scheme to simulated data. The term  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been evaluated by a Senior editor and 2 reviewers, one of whom, Manolis Dermitzakis, is a member of our Board of Reviewing Editors.Thank you for choosing to send your work entitled “Integrative genomic analysis of the human immune response to influenza vaccination” for consideration at The Reviewing editor and the other reviewer discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments based on the reviewers' reports.The manuscript describes a project that aims to integrate genetic, gene expression, and vaccination data, together with immunological traits. The design is innovative and promises interesting discoveries, some of which are described in the submission. Some key concerns were raised that need to be addressed before a final decision can be made:1) The eQTL discovery was performed in a cohort comprised of males only and the replication was performed in a cohort comprised of females only. What was the purpose of this design? The authors do not address the issue that discovery and replication are confounded by sex.2) We suspect that an interaction term in the models would actually be more efficient in detecting genetic effects modified by the time point after vaccination. Why was this not used (instead a simple comparison among time points was made)?3) The fact that there is enrichment in the QQ plots on 4) The study design used two different array versions: Illumina HT-12v3 and HT-12v4. The authors should describe how they combined data from these arrays, particularly for transcripts where the probe sequence differs between versions.5) The expression data processing should be described in more detail to make it clear how normalization, and so on, was performed given the different time points, array versions, and discovery/replication cohorts.6) The authors mention that the person effect within their model would account for differences in cell populations among individuals, given that these are stable within individuals during the sampling timeframe. No data or references are provided to support this claim. Immune cell relative proportions can change rapidly.2 metric to identify eQTLs that differ between time points or in response to the vaccination. It seems that the authors could have performed the analysis using the delta of transcription(time point x)−transcription(baseline) as a trait. This normalizes to each individual's starting point. Furthermore, a difference of r2 between time points can have multiple causes (as the authors point out). It seems to us that what they are most concerned with is cases where the effect size (beta) of a given eQTL differs between time points, or where the actual response (transcription time point x−transcription baseline) differs between individuals. The authors reasoning behind these choices should be made clearer.7) The authors use the delta r-8. Indeed for a GWAS of common variation, this may be appropriate for a single trait, but the authors should account for testing nearly 10K transcripts.8) For the trans-eQTL analysis, the authors consider as genome-wide significant any SNP-transcript pair with p<5×102. The case is still not convincing because only 146 of the 541 cases were validated. It is not clear why the difference in r2 is necessarily interpreted as a significant change when only 30% of the cases are validated.A) The revision is not satisfactory for point 7, above, on the delta rB) The modelling as previously discussed in point 3 is somewhat weak and causality is not as well demonstrated, as the authors want to claim. The fact that the association with the residuals is lower than with the original values is not direct but indirect evidence that there are casual relationships. Also, how were the “random” SNPs chosen? Establishing a relevant null is important here. Also, direct causal models would help in establishing the relationships rather than the indirect approach the authors use. 1) The eQTL discovery was performed in a cohort comprised of males only and the replication was performed in a cohort comprised of females only. What was the purpose of this design? The authors do not address the issue that discovery and replication are confounded by sex.Many loci show sex-specific effects at the level of gene expression. The study design for the discovery cohort, therefore, sought to minimize the effects of suspected variables, such as previous vaccine exposure, ethnicity, age, and sex. The follow-up study was designed to fill the resulting gap in information about responses to the seasonal influenza vaccine in women. We agree that the consequence of this feature of the data is confounding of potential sex-specific effects with other technical variables in the trials. However, it should not result in confounding with other biologically important variation, particularly in the validation of eQTLs. The literature is inconsistent with respect to terminology, but we prefer to make the distinction between the terms “validation” and “replication” .Validation involves a confirmation attempt on a sample from a different population, where systematic variability  would be expected in addition to random variability. If a genetic association can be validated, it shows greater generalizability than a replicated association, in which the confirmation sample is drawn from the same population as the discovery sample. We agree that future studies should include both men and women as a way to identify potential sex-specific differences in transcriptional response to vaccine. Additional trials will also be required to assess the effects of age, other risk-increasing clinical variables, such as compromised immunity, and ethnic/racial origins.2) We suspect that an interaction term in the models would actually be more efficient in detecting genetic effects modified by the time point after vaccination. Why was this not used (instead a simple comparison among time points was made)?We agree with the reviewers and did attempt to investigate this. The model we employed for the analysis is as follows:igj) for the change in the coefficient for the additive effect of genotype by day. In the loci showing significant transcriptional response to the vaccine, we found that the main effect of genotype was far stronger than these interaction effects. The sign of the additive effect does not change across the days, but the magnitude tends to increase after vaccination. We therefore speculate that our sample size does not provide sufficient power to exploit the interaction terms. Future larger studies may allow more precise estimates of the time × gene interaction and allow identification of additional vaccine response loci beyond those detected here.This model includes interaction terms (β3) The fact that there is enrichment in the QQ plots on .We agree with the reviewers that the enrichment shown in the QQ plots does not provide evidence of causality. As noted, the results do support the possibility that the selected markers are enriched for true associations to the humoral immune response to influenza vaccine. We have revised the manuscript to clarify this point and have separated 2 between SNP  and antibody response using an additive model. We then performed a regression analysis predicting antibody response from gene expression and we used the residuals from that analysis to perform another association analysis. We observed that the association between SNP and antibody, as measured by non-zero R2 values, disappeared after accounting for gene expression. This reduction in R2 was not observed in randomly-sampled sets of SNPs. The preceding data showed that the SNPs used are eQTL and that gene expression changes  precede the change in antibody titers (day 28). Taken together, these observations support the interpretation that the predictive information in the genotype for the change in antibody titer is mediated by gene expression. This analysis supports a causal role for these genes in the change in antibody titer.A potential causal relationship between the genes identified and the antibody response is supported by the conditional independence analysis shown in 4) The study design used two different array versions: Illumina HT-12v3 and HT-12v4. The authors should describe how they combined data from these arrays, particularly for transcripts where the probe sequence differs between versions.The sub-heading “Expression data processing and quality control” in the Materials and methods section has been re-written to more clearly explain how the expression microarray data were processed. In summary, for the initial part of the study (the eQTL component), the two cohorts were analyzed independently. Because of the difference in array versions, unique reporter identifiers (Illumina ProbeIDs and sequence-based nuIDs) were used to ensure that the same set of reporters was being analyzed in each of the cohorts. The two resulting expression data sets, which included data from the same set of 9,809 reporters, were then combined for the integrative genomic analysis component of the study.5) The expression data processing should be described in more detail to make it clear how normalization, and so on, was performed given the different time points, array versions, and discovery/replication cohorts.The sub-heading “Expression data processing and quality control” in the Materials and methods section has been re-written to more clearly explain how the expression microarray data were processed. The data for all individuals and all time points for a particular cohort were processed together. In the eQTL components of the analysis, the two cohorts were analyzed independently. Background adjustment, variance stabilization transformation, and normalization (VST-RSN) were carried out using identical parameters for each cohort. When the two data sets were combined for the integrative genomic analysis component, an additional quantile normalization was performed to adjust for batch effects between cohorts.6) The authors mention that the person effect within their model would account for differences in cell populations among individuals, given that these are stable within individuals during the sampling timeframe. No data or references are provided to support this claim. Immune cell relative proportions can change rapidly.We agree with the reviewers. Our model has a random-effect term to account for the fact that individuals were serially sampled. This person effect could account for systematic variation that was stable within an individual over the duration of the study (days 0–14), but would not account for changes in cell composition following vaccine administration. The corresponding paragraph in the manuscript has been corrected to reflect this.2 metric to identify eQTLs that differ between time points or in response to the vaccination. It seems that the authors could have performed the analysis using the delta of transcription(time point x)−transcription(baseline) as a trait. This normalizes to each individual’s starting point. Furthermore, a difference of r2 between time points can have multiple causes (as the authors point out). It seems to us that what they are most concerned with is cases where the effect size (beta) of a given eQTL differs between time points,7) The authors use the delta r or where the actual response (transcription time point x−transcription baseline) differs between individuals. The authors reasoning behind these choices should be made clearer.2 explained on each day, and we analyzed this change in R2 to show that (a) in principle, R2 for the explanatory power of the genotype increases in the days after vaccination, and (b) this change is due to an increase in the magnitude of the additive effect, and not in a change in the dispersion of the errors across days.We attempted analyses with an approach similar to that proposed by the reviewers in the course of our work, but found that the approach that was ultimately chosen to explore the day differences was the most powerful. Specifically, utilizing a pairwise comparison (difference) between time points as the substrate for the eQTL analysis would lead to an increase in the technical variance of the phenotype, as the sum of two independent  errors has twice the variance of an individual measurement. We agree with the reviewer that our interest is in investigating the source of changes in R-8. Indeed for a GWAS of common variation, this may be appropriate for a single trait, but the authors should account for testing nearly 10K transcripts8) For the trans-eQTL analysis, the authors consider as genome-wide significant any SNP-transcript pair with p<5×10.cis-eQTL analysis within an interval of 1 Mb centered on the reporter sequence. Positive associations in this analysis are most likely to result from direct allele-specific effects on transcription rate or transcript stability. A p-value threshold of 5 x 10-8 would, therefore, be conservative based on either number of SNPs or LD structure around the transcript. Future studies with much larger sample sizes might allow a full trans-eQTL analysis in which one would seek to identify genetic variants at other loci that indirectly affect transcript abundance measures. We have removed the term “genome-wide significance”, in reference to the p-value, from the relevant section of the Results.In this study we performed only 2. The case is still not convincing because only 146 of the 541 cases were validated. It is not clear why the difference in r2 is necessarily interpreted as a significant change when only 30% of the cases are validatedA) The revision is not satisfactory for point 7, above, on the delta r.2) because it is evident from our data . This information has been added to the manuscript. To select a conservative set of candidate loci based on this phenomenon, we chose to impose a stringent intersection rule, requiring a threshold of the top 1% of the delta-Rg2 distribution in both cohorts, in addition to evidence of a strong cis- acting eQTL (genotype effect p < 5 × 10-8) in both cohorts. The manuscript was modified to clarify that the 146 SNP-transcript pairs that were selected for further analysis comprise the set that passed this strict intersection rule.The Reviewing editor has raised concern about the validation of the delta-RgB) The modelling as previously discussed in point 3 is somewhat weak and causality is not as well demonstrated, as the authors want to claim. The fact that the association with the residuals is lower than with the original values is not direct but indirect evidence that there are casual relationships. Also, how were the “random” SNPs chosen? Establishing a relevant null is important here. Also, direct causal models would help in establishing the relationships rather than the indirect approach the authors use.We agree with the Reviewing editor and, as stated above, spent considerable time reconsidering our approach to the evaluation of causal versus alternative association model comparisons.Our work considered three distinct types of associations in the vaccine response data: genotype to expression (eQTL), expression to antibody response and genotype to antibody response. An important advantage of experimental designs such as ours is the opportunity to consider relationships between these distinct types of association. To respond to the reviewing editor's questions and to more formally evaluate alternative models, we have now adapted the approach recently published by Eric Schadt and colleagues , which provides a statistical testing framework for so-called causal versus reactive model analyses. This work is related to ideas and methods developed by Judea Pearl and others, but more specifically applied to the context of eQTL data. Under this approach, a formal statistical hypothesis test is developed for evaluating three possible scenarios, referred to as independent, causal and reactive models.This concept is now depicted in Finally, we performed a power analysis to examine the sample size that would be required to establish statistical significance under this methodology when effect sizes and errors are consistent with our data. These analyses suggest that a sample size of 200 – which corresponds to the combined sample size of our two cohorts – has 50–60% power to detect deviations from the independence model if they are present . This re


Triticum turgidum ssp), which included naked and hulled accessions, we analysed the pattern of LD considering 26 simple sequence repeats and 970 mostly mapped diversity array technology loci. In addition, to validate the potential for association mapping in durum wheat, we evaluated the same genotypes for plant height, heading date, protein content, and thousand-kernel weight. Molecular and phenotypic data were used to: (i) investigate the genetic and phenotypic diversity; (ii) study the dynamics of LD across the durum wheat genome, by investigating the patterns of LD decay; and (iii) test the potential of our panel to identify marker–trait associations through the analysis of four quantitative traits of major agronomic importance. Moreover, we compared and validated the association mapping results with outlier detection analysis based on population divergence. Overall, in tetraploid wheat, the pattern of LD is extremely population dependent and is related to the domestication and breeding history of durum wheat. Comparing our data with several other studies in wheat, we confirm the position of many major genes and quantitative trait loci for the traits considered. Finally, the analysis of the selection signature represents a very useful complement to validate marker–trait associations.Association mapping is a powerful tool for the identification of quantitative trait loci through the exploitation of the differential decay of linkage disequilibrium (LD) between marker loci and genes of interest in natural and domesticated populations. Using a sample of 230 tetraploid wheat lines ( Determination of the genetic basis of agronomic traits has been one of the major scientific challenges in the process of crop improvement. In plants, linkage mapping is currently the most common approach for detection of quantitative trait loci (QTLs) that correspond to agronomic traits. In linkage mapping, linkage disequilibrium (LD) is generated by establishing populations that are derived from bi-parental crosses, and co-segregation of alleles of mapped marker loci and phenotypic traits allows the identification of linked markers. In durum and bread wheat, QTL mapping has been used frequently to dissect out the genetic architecture of agronomic and quality traits using doubled haploids, recombinant inbred lines, and backcross populations. Several main-effect QTLs have been identified for yield To overcome the constraints inherent to linkage mapping, a complementary strategy is based on the correlation of genotype with phenotype in domesticated and natural populations, and this is commonly referred to as association mapping (or LD mapping), where the focus turns from families to populations. The principle that underlies this approach is that LD tends to be maintained between linked loci over many generations. High LD is expected between loci in tight linkage, while recombination should have eliminated LD between unlinked loci Q), which is incorporated into a general linear model (GLM) for testing associations. This has been widened to a mixed linear model (MLM), which also includes the kinship relationships of the samples, and offers improved control of type I error rates, which accounts for the population structure and relatedness in association mapping A major problem of association mapping is the high probability of false-positive associations between a marker and a trait (type I error) Triticum turgidum ssp. turgidum var. durum) is of significant commercial importance, because of its end-use products: pasta, cous-cous, bread and bulgur. For this species, the combined effects of domestication and modern plant breeding have contributed to its greatly reduced genetic diversity in comparison to the wild forms Triticum turgidum ssp.), and other related wild species might have crucial roles in breeding programmes, given their wide variability in terms of their many phenological, morphological, abiotic, biotic and quality traits. Here, the data used are from an association-mapping panel that is composed of 230 tetraploid wheat accessions  that have been fingerprinted according to 26 simple sequence repeats (SSRs) and 970 diversity array technology (DArT) markers, and evaluated for plant height (PH), heading date (HD), protein content (PC), and thousand kernel weight (TKW). These data are used to: (i) investigate the genetic and phenotypic diversity; (ii) study the dynamics of LD across the durum wheat genome, by investigating the patterns of LD decay; and (iii) test the potential of our panel through the analysis of four quantitative traits of major agronomic importance, to identify marker–trait associations (MTAs) using both GLMs and MLMs.Durum wheat  collection of 230 inbred lines (durum sub-sample), and on two subgroups identified through the population structure analysis  as reported by Laidò et al. This study evaluated a tetraploid wheat  were excluded from all of the present analysis. The genetic diversity, or heterozygosity (H), was investigated within the whole collection and within the durum sub-sample for each chromosome, by calculation of the unbiased estimator of genetic diversity /HE(durum)), where HE(wc) and HE(durum) are the genetic diversity for the whole collection and for the durum sub-sample, respectively.Alleles that occurred at low frequency (rsity HE. To meas2r) estimates P values) was computed using 1,000 permutations. The LD statistics were calculated per chromosome, and subsequently aggregated over all of the chromosomes of the A and B genomes. The LD was calculated separately for loci on the same chromosome  and for unlinked loci . From the unlinked loci, a critical value of 2r was estimated following Breseghello and Sorrells 2r values and taking the 95% percentile of this distribution as the threshold beyond which the LD is probably caused by a real physical linkage. The intrachromosomal 2r values were plotted against the genetic distance, and a smooth line was drawn using second-degree locally weighted polynomial regression (LOESS) R (http://www.r-project.org), to determine at which distance the LOESS curve intercepts the critical 2r, to see how rapidly the LD decay occurs. The LD analysis for the mapped markers on the durum wheat consensus linkage map was performed separately for the whole collection, the durum sub-sample, and the two subgroups identified with the genetic structure analysis . The LD was also estimated among the unmapped MTAs and mapped markers, although only for the whole collection.The LD between the mapped DArT markers on the durum wheat consensus linkage map constructed by Marone et al. 2. According to the standard agronomic practices in the study area, fertiliser applications were made at pre-sowing  and top dressing  at Zadoks growth stage 2.2 and 3.1 To test the potential of our panel to perform association studies, we investigated four quantitative traits of major agronomic importance. In particular, the tetraploid wheat collection of 230 inbred lines was evaluated for PH, HD, PC and TKW in a field trial conducted in Foggia  over one season (2007/2008). The field trial was sown on 20 December, 2007, in clay-loam soil (typic Chromoxerert), in a randomised block design with four replicates. The plots were 2.0 m×1.5 m, with a seed density of 350 germinated seeds/mca. 50% of the tillers had emerged from the flag-leaf sheaths by approximately half of their length  and for the durum sub-sample (K = 3 with SSR markers). In the MLMs, the Kinship matrix (K matrix) was used in addition to the genotypic data, phenotypic data and Q matrix, to correct for both population and family structure. In total, the genotypic score was available for 984 loci in the whole collection (958 DArT markers and 26 SSR loci), and for 871 markers in the durum sub-sample (845 DArT markers and 26 SSR loci). To generate a marker similarity matrix containing all of the lines (K matrix) using the TASSEL software, the trimmed marker datasets were used. TASSEL calculates the kinship as the proportion of alleles shared between each pair of lines. Once this matrix was calculated, the numbers were rescaled, so that they were between 0 and 2 P values for the assessment of the significance of the MTAs were calculated separately for each trait, based on a false discovery rate (FDR) of 0.05, which is defined as the expected proportions of the true null hypotheses that are rejected The TASSEL software, version 3.0.115, was used to calculate the associations between the markers and each trait in turn. Both GLMs and MLMs were used in the association analysis for the whole collection and for the To compare our data with data obtained in other studies, we considered the most recently published information on genes and QTL mapping for the traits . All of the significant MTAs, identified using the MLM model, located on the durum wheat consensus map within short map intervals (5–10 cM) were grouped into a single putative QTL.Fst-outlier detection method, and using 100,000 simulations, 211 loci under selection were identified, of which 109 markers were mapped on the durum wheat consensus map and used to validate the MTAs, or the region containing the MTAs, that were identified for each trait by association mapping.As reported in Laidò et al. durum sub-sample were >0.05. Although these only represented half of the markers used in the present study, they covered an estimated 2,247 cM, which represents approximately 73.5% of the durum wheat consensus map From the 970 polymorphic DArT markers, 592 were genetically mapped on the durum wheat consensus linkage map constructed by Marone et al. durum sub-sample, we evaluated the genetic diversity (HE) and the loss of diversity for each chromosome , with HE values for the whole collection and the durum sub-sample, of 0.40 and 0.31, respectively . Pairwise LD was estimated using the squared-allele frequency correlations (r2). As reported in r2 of 0.02  as a function of genetic distance in centiMorgans (cM) indicated a clear decay of the LD with genetic distance, and also suggested that the LD is dependent on the population structure (r2 of 0.08) showed LD estimates that were largely influenced by their physical distances  was calculated from the interchromosomal LD analysis (r2 = 0.120), beyond which the LD is assumed to be caused by physical linkage   to less than 6.0% . All of the loci pairs that are in complete LD are spaced at a genetic distance <10 cM, except for two loci pairs at genetic distances from 10 cM to 20 cM.The plots of the LD estimates (tructure . As expeistances . Due to  linkage . Four cl of 0.26 . For themarkers)  and the markers) . Also, fdurum sub-sample is given in r2 and lower numbers of significant pairs, compared to the whole collection. The Q1 group shows a mean r2 and number of significant pairs similar to the durum sub-sample , compared to 192 in the whole collection and 201 in the Q2 group     and the = 0.142) . The durt ∼18 cM ; the Q2 t ∼18 cM .durum sub-sample , compared to only 27.1% and 16.2%, respectively, for the MLM  are specific for a single trait , while 27 are multi-trait MTAs. Overall, PH is involved in the highest number of multi-trait MTAs (15) identified in the two samples, followed by HD (13), TKW (12) and PC (11).There were 51 loci (11 SSRs and 40 DArTs) identified as MTAs for the same trait in both the whole collection and the durum sub-sample (corresponding to 94 loci). For the whole collection, out of the 111 loci (14 SSRs and 97 DArTs) for which there is at least one MTA, 86.5% (96 loci) are associated with only one trait, while the rest consist of associations of up to three traits (15 loci). In the durum sub-sample, out of the 94 loci (11 SSRs and 83 DArTs), 85.1% (80 loci) are single-trait MTAs. Among these 111 (whole collection) and 94 (durum sub-sample) loci, 16 and 15, respectively, are putatively under selection.There were 128 mapped MTAs  and 3 inThe MTAs are located in all of the chromosomes of both genome A and B. The highest number of MTAs identified in the two samples are on chromosome 3A (32 MTAs for 17 loci), followed by chromosome 4A (30 MTAs for 18 loci) and chromosome 2B (27 MTAs for 17 loci), with the lowest on chromosome 1A (6 MTAs for 5 loci) and chromosome 4B (5 MTAs for 2 loci). Considering the homoeologous groups, group 3 contains the highest number of MTAs (53 for 36 loci), followed by group 2 with 38 MTAs (27 loci); with the lowest numbers for groups 5 (25 MTAs) and 6 (27 MTAs), for 14 and 22 loci, respectively. Altogether, the A-genome has a higher number of MTAs (127), for 83 loci, compared to the B-genome with 122 MTAs, for 84 loci.Ppd), vernalisation (Vrn), earliness per se (Eps), dwarfism (Rht), grain protein content (Gpc), and domestication syndrome traits .The MTAs that are identified for each trait are presented in durum sub-sample) MTAs were identified , was identified in both the whole collection and the durum sub-sample in the genomic region harbouring the growth habit gene Rht-B12R values (from 6% to 9%) are located on chromosome arms 1BS, 4AL and 7BL, and are tagged by the markers wPt-0308, Xgwm937 and wPt-5138, respectively  and 22  and the durum sub-sample . The Xcfd15 marker is also associated to multi-trait MTAs for PH and HD. In this chromosome region, Maccaferri et al. The first putative QTL was identified by the DArT marker wPt-0308, which is also putatively under selection durum sub-sample, with 2R of 6% and 9%, respectively. This marker is also associated to a multi-trait MTA for PH, HD and TKW. At 7 cM from this MTA, the wPt-4660 locus is associated with PH in the whole collection, and in the same region, Maccaferri et al. The second putative QTL is associated to the Xgwm937 SSR marker, and was identified in both the whole collection and the 2R, 2%) and the durum sub-sample . In the same region, the wPt-9746 (258.9 cM) marker is also putatively under selection, and is associated to a MTA only in the durum sub-sample . In the same chromosome region, three MTAs  located at 151 cM that span an interval map of 0.2 cM are associated with PH only in the durum sub-sample . In a panel of 154 common wheat accessions, Zhang et al. The third QTL is in the distal region on chromosome 7BL and was identified by the wPt-5138 (244.6 cM) marker, associated in both the whole collection (durum sub-sample) MTAs. These MTAs (corresponding to 47 loci) detected 30 putative QTL regions  locus was identified as a MTA for HD only in the whole collection. This MTA is close on the interval map, among the Xgwm359 (38.9 cM) and Xgwm122 (25.4 cM) markers, within which Griffiths et al. Ppd-A1) using meta-QTL analysis. Then on chromosome arm 2BS, Le Gouis et al. Ppd-B1 locus close to the wPt-6932 marker, located at 66.5 cM on the durum wheat consensus map. In the present study, at a distance of ∼10 cM from the wPt-6932 marker, one HD-related QTL was detected from the wPt-2600 and wPt-3720 markers, only in the durum sub-sample, with 2R for both of 4% , in the present study, there are HD-related QTLs only in the chromosome regions harbouring the Vrn-B1 and Vrn-A3 loci, which are located on chromosome arms 5BL and 7AL, respectively, as reported by Le Gouis et al. 2R of 5%, is close to the Vrn-B1 locus. Then, on chromosome arm 7AL and very near to the Vrn-A3 locus, there are three MTAs  associated to HD only in the whole collection, with a maximum 2R of 3%.The HD is associated to 31 (whole collection) and 23  locus is associated to HD in both the whole collection  and the durum sub-sample , and the wPt-0302 locus is putatively under selection. The wPt-0302 marker was associated with components of earliness in an association mapping study conducted by Le Gouis et al. 2R of 4%) and the durum sub-sample , which is a multi-trait MTA associated to the other traits considered in the present study. Close to this MTA, Blanco et al. On chromosome arm 3BS in an interval map of ∼16 cM, 7 loci were identified as MTAs for three HD-related QTLs , with R2durum sub-sample (durum sub-sample (2R from 2% to 4%). The putative QTL detected in this chromosome region is close to markers identified for significant associations reported by Maccaferri et al. In the distal region on chromosome arm 4AL in an interval map of 6.6 cM, the present study identified 4 MTAs, each of which explain 4% of the phenotypic variation, where the wPt-1007 DArT locus was detected in both the whole collection and the b-sample . In thisdurum sub-sample) MTAs. These MTAs (corresponding to 58 loci) detected 39 putative QTL regions  DArT MTA , the PC-related QTL was identified in the genomic region harbouring the Gpc-B1 gene, among the Nor2 (77.3 cM) and Xgwm193 (107.8 cM) markers, as reported by Uauy et al. The PC is involved in 38 (whole collection) and 26  on chromosome arm 2BS. In the same chromosome region, the second PC-related QTL was identified by two multi-trait MTAs  and is associated only in the durum sub-sample, with 2R of 4%. The other two PC-related QTLs were identified on chromosome arm 2BL in an interval map of ∼10 cM, and were detected from 6 MTAs (2R from 2% to 7%), of which three were identified as putatively under selection , for which two DArT markers  are putatively under selection. In the same chromosome region, and using linkage mapping analysis for kernel characteristics in a segregating population of common wheat, Tsilo et al. In an interval map of ∼10 cM on chromosome arm 6AL, two PC-related QTLs were identified from 6 MTAs only in the whole collection (durum sub-sample) MTAs. These MTAs (corresponding to 65 loci) detected 42 putative QTL regions  and was detected for the whole collection and in the durum sub-sample, with 2R from 3% to 6%  and in the durum sub-sample  (The TKW is associated with 33 (whole collection) and 39  . The put2R from 3% to 8%) were identified, of which four are detected only in the durum sub-sample dataset. In particular, in the distal region of this chromosome arm, there are 3 putative QTLs identified by 9 MTAs, of which three markers  are putatively under selection, and two  are multi-trait MTAs associated also to PC and HD (durum sub-sample (2R from 2% to 4%). The putative QTLs detected in this chromosome region are close to markers identified as having significant associations in others studies of QTL mapping, as reported by Maccaferri et al. On chromosome arm 3AL, five putative TKW-related QTLs , 38 loci were detected as MTAs only in the durum sub-sample (13 putatively under selection), and 21 are MTAs in both samples (5 putatively under selection).Three SSRs and 378 DArT markers were not located on the durum wheat consensus map. The three SSR loci  were identified as multi-trait MTAs, and the first two were detected in both the whole collection and the durum sub-sample) and 592 DArT loci mapped on the durum wheat consensus map. For 11 of these unmapped MTAs that are associated with all of the traits except PH, strong or complete LD was detected for the mapped markers, and therefore they are considered to be mapped in the same region. Five and 6 MTAs are located on genomes A and B, respectively, and are trait-specific MTAs, except for the wPT-9577 locus on chromosome arm 3BL, which is associated with PC and TKW.Linkage disequilibrium analysis was performed between 114 unmapped DArTs identified as MTAs  that is constituted by a large set of durum wheat varieties and by a representative sample of The analysis of the literature on QTL mapping for the same four traits confirms many of our MTAs and clearly indicates that our approach was successful for the detection of relevant QTLs for the traits investigated. Most likely, these positive results are also associated with the high heritability observed for the target traits. Considering the whole collection, the heritabilities for all of the traits exceeded 0.85 (except for PC at 0.67). Indeed, association mapping is strongly influenced by the heritability and by the quality of the phenotypic data Our study was greatly aided by the availability of the consensus map that was reported in Marone et al. durum sub-sample compared with that observed in the whole collection , with a relatively strong reduction in diversity in the durum sub-sample for chromosome 1A. The genetic determinants of PC and TKW have been mapped previously for chromosome 1A We were able to analyse the genetic diversity of our samples using mapped markers that are well distributed among the chromosomes, with a density of 1 marker every 4.0 cM. Our data indicate that the level of diversity among the chromosomes is heterogeneous in the The durum wheat consensus map developed by Marone et al. 2r of all of the interchromosomal pairs in the whole collection and in the durum sub-sample are 0.023 and 0.029, respectively, which are slightly higher than the 0.022 described by Breseghello and Sorrells The means of durum sub-sample, the LD decay  was at ca.18 cM, while in the whole collection the decay was more rapid, at 14 cM. The level of LD in our durum sub-sample was larger than that observed in durum wheat by Maccaferri et al. durum sub-sample and the Q1 group follow the same pattern in the decay of the LD, whereas in the Q2 group, the LD falls at a distance of 5 cM. Overall, the level of LD was greater in the durum sub-sample compared to the Q2 group, which includes wild and domesticated accessions.For the intrachromosomal LD, in each dataset the percentage of locus pairs showing significant LD is highest for adjacent locus pairs (<10 cM), lower for locus pairs on the same chromosome (>10 cM), and lowest for locus pairs on independent chromosomes. In the durum sub-sample compared to the wild and domesticated accessions (the Q2 group) can be explained by the different levels of historical recombination in the two samples, and because of the effects of the bottlenecks of domestication and breeding, as increases in the level of LD. Our data can be explained under just a neutral model, where the effective recombination rate due to the effective population size along with the physical and recombination distance between the loci is the major determinants of the pattern of LD. For this reason, using the LD pattern Overall, this picture indicates that in tetraploid wheat the pattern of LD is extremely population dependent and related to the process of domestication. This has also been reported in other autogamous species, such as barley, where the genome-wide LD extended from 10 cM to 15 cM 2r>0.75) with three co-mapping DArT markers  at 18.3 cM in all of the datasets, with the exception of the Q2 group. In the durum sub-sample, we observe other cases of long-range LD between loosely linked markers on chromosomes 6A and 3B  at a distance of ∼14 cM.However, we cannot exclude the role of selection in favouring the conservation of haplotype blocks, along with the possible role of inversions and other chromosomal mutations. On the other hand, we do not observe much evidence of such phenomena, and only in a few cases did we observe evidence of long distance or interchromosomal LD, as has been reported for other species where epistatic selection might have been the cause of the observed patterns er). Indeed, the LD decay in the whole collection is different for the single chromosomes. For some of the chromosomes, the LD decay is in the same range as for the whole collection, with the exceptions of chromosomes 4A and 3B , in which the decay is at 5 cM and 8 cM, respectively, and of chromosomes 1B and 6A, with the decay at 18 cM and 20 cM.However, in some cases, the pattern of LD might be indicative of areas of the genome where selection and chromosomal mutations might have reduced the effective recombination rate , vernalisation (Vrn), earliness per se (Eps), dwarfism (Rht), and grain protein content (Gpc). By comparing our data with several other studies in wheat, we were able to confirm the positions of many major genes and QTLs for the considered traits, while obtaining a validation of our results for the novel QTLs that were detected.Overall, our data provide insight into the genetic architecture of important agronomic traits for durum wheat . In total, we identified 89 QTLs for these traits, from 25 QTLs for PH, to 42 QTLs for TKW. Some genomic regions harbour QTLs for more than one trait, and based on the map comparisons, 44 QTLs concur with previously mapped QTLs in various studies of association mapping and linkage mapping in durum and common wheat, and with genes for photoperiod insensitivity  shared by both studies. Interestingly, this MTA is very close (1.6 cM) to a marker that is putatively under selection (wPt-7489).Similarly for the PC; in the same genomic region where Uauy et al. Drosophila by Schwarzenbacher et al. The exploitation of the signature of selection on the structure of the molecular diversity durum sub-sample (2R = 7%) by the wPt-0308 MTA putatively under selection. At ∼14 cM, we detected a novel multi-trait QTL for PH and HD; in particular, for PH, Xcfd15 MTA was identified in both the whole collection and the durum sub-sample. In this region, using LD mapping, only Maccaferri et al. durum sub-sample. This latter marker co-segregates with two other DArTs, all of which are identified as putatively under selection.Considering PH, three new QTLs were identified on chromosomes 1B, 3A and 7B. In the telomeric region of the chromosome arm 1BS, the first QTL was identified in the 2r = 0.95).A new HD-related QTL in the telomeric region of chromosome 1AS was identified only in the whole collection dataset, through two MTAs detected in the LD mapping analysis that were also putatively under selection. In particular, the first of these MTAs (wPt-4735) was located on the consensus map , whereas the second one (wPt-3560) was mapped in the same position using the LD pattern , which were putatively under selection. In the telomeric regions of chromosome arms 3AL and 3BL, two PC-related QTLs were identified for the durum sub-sample . On chromosome arm 7AL, only two co-segregating DArT markers (wPt-1976 and wPt-3403) were detected as putatively under selection, the first of which was identified as a MTA for PC.In the present study, considering the PC trait, two novel QTLs were detected on chromosome 1B and on the group 3 chromosomes, and one on chromosome arm 7A. For the two QTLs on chromosome 1B, the first was detected in the telomeric region on chromosome arm 1BS by three MTAs, all of which were identified in the durum sub-sample by two MTAs, of which the wPt-1888 marker is putatively under selection. In this centromeric region of 13 cM on chromosome arm 3AL, six DArT markers were mapped, with all of these identified as MTAs for TKW, with 2R from 2% to 6%. The second QTL was mapped on linkage group 5B-3, from two MTAs, of which wPt-7605 was putatively under selection. The last QTL on chromosome arm 6BS was detected by three MTAs, of which wPt-4742 was identified in both the whole collection and the durum sub-sample. Between these MTAs, we detected two DArT markers  that are putatively under selection, so this QTL was identified by two different approaches.Three novel TKW-related QTLs were detected on chromosomes 3A, 5B and 6B. On chromosome arm 3AL, a TKW-related QTL was identified in both the whole collection and the Our data have important implications in relation to the development of association mapping studies in durum wheat, which suggests that whole-genome scan approaches are feasible. Moreover, with careful assessment of the population structure, it will be possible to choose appropriate populations to obtain different resolutions in association studies in tetraploid wheat. Additional LD studies that focus on a few genomic segments will be needed to obtain a more complete picture of the level and structure of the LD, and to conduct LD mapping based on a candidate gene approach using specific tetraploid wheat populations .By the genotyping of collections of accessions and cultivars with a large number of markers, association studies provide the means to improve the genetic characterisation of the germplasm. Markers associated with genomic regions that control traits of agronomic interest improve our understanding of the genetic value of the individual genotypes within germplasm collections. Moreover, as similar studies are completed, the genetic characterisation of many different germplasm sets will provide researchers with a comprehensive global perspective of the tetraploid wheat gene pool. The current study is amongst the first to assemble and analyse a population that represents all of the tetraploid wheat sub-species, among which 108 durum wheat accessions (96 mainly as elite cultivars) are representative of the Italian durum wheat breeding programmes over the last 100 years. The information that we have generated is also valuable for the choice of parental lines to use in crosses  to complement genome-wide association mapping data and to analyse some of the novel associations in more detail.Figure S1Overview of the LD parameter 2rof the intrachromosomal pairs in the single chromosome with a large number of DArT markers. The scatterplots show the distribution of the LD parameter 2r according to the genetic distance.(PDF)Click here for additional data file.Figure S2Phenotypic distribution for plant height (PH), heading date (HD), protein content (PC), and thousand kernel weight (TKW) in the whole collection.(PDF)Click here for additional data file.Figure S3Phenotypic distribution for plant height (PH), heading date (HD), protein content (PC), and thousand kernel weight (TKW) in the durum sub-sample.(PDF)Click here for additional data file.Table S1Overview of the LD in the intrachromosomal pairs for the whole collection, the durum sub-sample, and the two groups (Q1 and Q2).(DOCX)Click here for additional data file.Table S2List of all of the MTAs identified for the four traits  using the GLM and MLM.(XLSX)Click here for additional data file.


The environment can moderate the effect of genes - a phenomenon called gene-environment (GxE) interaction. Several studies have found that socioeconomic status (SES) modifies the heritability of children's intelligence. Among low-SES families, genetic factors have been reported to explain less of the variance in intelligence; the reverse is found for high-SES families. The evidence however is inconsistent. Other studies have reported an effect in the opposite direction (higher heritability in lower SES), or no moderation of the genetic effect on intelligence.Using 8716 twin pairs from the Twins Early Development Study (TEDS), we attempted to replicate the reported moderating effect of SES on children's intelligence at ages 2, 3, 4, 7, 9, 10, 12 and 14: i.e., lower heritability in lower-SES families. We used a twin model that allowed for a main effect of SES on intelligence, as well as a moderating effect of SES on the genetic and environmental components of intelligence.environment-environment interaction.We found greater variance in intelligence in low-SES families, but minimal evidence of GxE interaction across the eight ages. A power calculation indicated that a sample size of about 5000 twin pairs is required to detect moderation of the genetic component of intelligence as small as 0.25, with about 80% power - a difference of 11% to 53% in heritability, in low-  and high-SES (+2 SD) families. With samples at each age of about this size, the present study found no moderation of the genetic effect on intelligence. However, we found the greater variance in low-SES families is due to moderation of the environmental effect – an In a UK-representative sample, the genetic effect on intelligence is similar in low- and high-SES families. Children's shared experiences appear to explain the greater variation in intelligence in lower SES. However, if the effects of genes and environments do not simply “add up”, i.e., if there exists a GxE interaction, heritability will depend on the level of the moderating environment.A key construct for understanding the interplay between nature and nurture is genotype-environment (GxE) interaction: Genes can have different effects on a phenotype depending on the environment, and environments can have different effects depending on genes g) by their parents' SES increased with SES. Focusing on early cognitive development, the study found an increasing heritability of the change in IQ between the ages of 10 months and 2 years as a function of SES. Although SES was measured as a continuous variable, the magnitude of genetic moderation found suggested an increase in the heritability of IQ from 5% in low-SES families , to 50% in high-SES families (+2 SD).The education, occupation and income of parents – indices of the families' socioeconomic status (SES) – have been found to moderate the heritability of their children's intelligence bioecological modelenvironmental disadvantage hypothesisreal world, but the interactions reported are statistical and model-dependent It is reasonable to consider the possibility that heritability of intelligence is higher in higher SES families because such families seem likely to provide more opportunities to realize differences in children's genetic potentials. Conversely, in lower SES families, genetic differences might be restrained by poverty. Two theories, the At least three design differences could play a role in the inconsistent findings: first, statistical GxE interaction has been investigated with a variety of methods with different power to detect an interaction; second, the age range investigated has covered infancy  may be particularly important in explaining the inconsistent reports of GxE interaction. The continuous moderator model, used by several of the studies investigating GxE interaction, has demonstrated low power to distinguish between moderation of the genetic (A) and shared environmental (C) variance components. Purcell Aβ, Cβ, and Eβ) interactions jointly contributed significant variance to differences in (IQ), the models were less able to distinguish which of the individual interactions with A, C, and E was most important.”  Nonetheless, the full model, which simultaneously takes into account all influences on a trait , tends to recover the true parameter values in simulated data For several power-related reasons, the moderation of environmental factors  twin sample, with longitudinal data on IQ from infancy to adolescence, we aimed to address these age, population, and power concerns. We set out to replicate the finding that SES modifies the genetic effect on children's intelligence with three indices of SES: parental education and occupation measured when the twins were 18 months old; the same composite of education and occupation measured when the twins were 7 years old; and family income measured when the twins were 9 years old. The possibility that the environmental disadvantage hypothesis applies to academic achievement and reading measures has also been studied. However, because achievement and reading are quite different from IQ, and studies of them are no more conclusive about the presence or absence of GxE interaction, in the present study we choose to focus on IQ only. Given the inconsistency in the literature, we hypothesized that we would not find consistent GxE interaction from childhood to adolescence.This study used as its sampling frame the ongoing Twins Early Development Study, TEDS g) at ages 2, 3, 4, 7, 9, 10, 12, and 14. Analyses were performed on a subsample of 8716 twin pairs (2996 monozygotic (MZ); 5720 dizygotic (DZ)) for whom we had IQ data for at least one twin (at any one age), and with at least one index of SES. Subsets of these data were assessed at each age. In the analyses described below, we used all the available data with full-information maximum likelihood procedures.The present study investigated the moderating role of parental SES on children's intelligence or IQ ; SES index 2, parental education and occupation acquired at age 7; and SES index 3, parental income assessed at age 9. All composites were created as a unit-weighted sum of the contributing scales, i.e., first mapped to a standard normal distribution with the rank-based van der Waerden transformation As indices of SES, we used parental education, occupation, and family income. We assessed parental education and occupation  at first contact with the families, when the twins were 18 months old, and again when the twins were 7 years old; we assessed family income at age 9. To explore the possibility that the inconsistency in the literature is due to different measures of SES, we tested three indices: g. We mapped all verbal and nonverbal cognitive tests to a standard normal distribution g composite. This score was identical to a first principal component extracted from the balanced test battery.At all ages, a unit-weighted composite of verbal and nonverbal cognitive tests was used as an index of In early childhood, parent-administered tests and parent-reported observations were used to assess verbal and nonverbal cognitive abilities at each age. These measures have been validated against standard tests administered by a trained tester Nonverbal performance: Nonverbal cognitive performance was assessed using age-appropriate versions of the Parent Report of Children's Abilities PARCA; . The PARVerbal performance: The verbal component of the early childhood battery included vocabulary and grammar as assessed by parent reports for the CDI-III, an extension of the short form of the MacArthur Communicative Development Inventories: Words and Sentences At age 7, verbal and nonverbal abilities were tested by telephone Nine-year-old participants received a test booklet containing two verbal and two nonverbal tests that, like the tests in early childhood, were administered under the supervision of the parent (guided by an instruction booklet rather than a telephone interview). The verbal tests comprised vocabulary and general knowledge tests adapted from the multiple-choice version of the WISC-III-UK Children at age 10 participated in web-based testing. Widespread access to inexpensive and fast internet connections in the UK has made online testing an attractive possibility for collecting data on the large samples necessary for genetic research. The advantages and potential pitfalls of data collection over the Internet have been reviewed At age 12 we again used Web-based assessment of general cognitive ability. The tests administered were updated versions of the same tests used at age 10, with the addition of more difficult age-appropriate items. We administered two verbal ability tests: WISC-III-PI Information Multiple Choice  and WISC-III-PI Vocabulary Multiple Choice At age 14 we measured general cognitive ability with one verbal and one non-verbal Web-based test. The verbal test used was WISC-III-PI Vocabulary Multiple Choice The correlations between these eight IQ scores are shown in The twin design compares the phenotypic resemblance of identical  twins to the phenotypic resemblance of non-identical  twins in order to partition the variance on a trait into sources of genetic and environmental variation. The coefficient of genetic relatedness is 1.0 between MZ twins, and on average 0.5 between dizygotic twins, who share 50% of their segregating alleles. The twin model attributes the similarity of reared-together twins to additive genetic (A) and shared environmental (C) factors, and the differences between them to nonshared environmental (E) factors www.R-project.org; χ2) which provides a goodness-of-fit test. The χ2 can be converted to Akaike's information criterion  on the trait is assessed by estimating the value of Mβ. In the present study the trait is IQ and the moderator is SES. The residual variance in the trait is then partitioned into latent A, C and E components; the effect of each of these components on the trait is also expressed as a linear function of the moderator. For example, the additive genetic path coefficient is made up of both an unmoderated element (a) and a moderated element (AMiβ), where Mi represents the family-wide moderator value for the ith twin pair. The significance of the moderating effect of SES is tested by asking whether Aβ is significantly different from zero. Likewise, the C and E path coefficients (Cβ and Eβ respectively) indicate the moderating effect of SES on the shared and nonshared environmental components of the residual variance in IQ and their significance is tested against zero.The mean of trait (T) is given by Mβ term in the means model).One limitation of the basic GxE model is that it cannot detect potential moderation of any genetic variation in common between the measured environment and the trait, and SES is phenotypically correlated with IQ. It is well established that ‘environmental’ measures are to some extent heritable – a phenomenon known as genotype-environment correlation www.R-project.org; We used exact data simulation with the continuous moderator model to estimate power to detect GxE moderation, and in particular, moderation of the latent genetic (A) component. For all power calculations we used the MASS N random values for MZ pairs and N for DZ pairs from a standard normal distribution. This was our obligatorily-shared moderator (SES in the present study). Then, for each level of the moderator we drew a single pair from a multivariate standard normal distribution. The variance-covariance matrix for each randomly sampled pair was specified by (the covariance structure of the basic continuous moderator model), MZ twin pairsMi was the value of the moderator for the ith twin pair; a, c, and e are the unmoderated path coefficients; and Aβ, Cβ, Eβ, and are the moderated path coefficients. (R script available from authors upon request.)In order to generate outcome data under continuous moderation, we first sampled The means, standard deviations, and analysis of variance by sex and zygosity for IQ at every age are presented in Because similarity due to age and sex can contribute to phenotypic similarity and inflate estimates of C, as is standard practice in twin analyses SES index 1, Parental education and occupation acquired at first contact (age 18 months); SES index 2, Parental education and occupation at age 7; and SES index 3, Parental income at age 9. At the end of this section, we present results for a discontinuous analysis, i.e., IQ as a function of stratified SES.Below, results are presented for continuous moderation analyses of IQ moderated by three indices of SES: Phenotypic correlations between SES  and IQ are presented in greater in low-SES families.Second, except for a large drop in the A contribution with increasing SES at age 10, we found no substantial change in A across the eight ages: little or no change at ages 2, 3, 9, and 14, and small increases with increasing SES at ages 7 and 12. This suggests no consistent GxE interaction. Moreover, it should be noted that the only substantial GxE interaction at age 10 is in the opposite direction from that suggested in the literature: heritability is Third, differences in C were somewhat more consistent: at ages 2, 4, 7, 9, and 12, there was a drop in C with increasing SES. This suggests the presence of greater C in low-SES families.Intra-class correlations 2/((a+AβM)2+(c+CβM)2+(e+EβM)2). . At age 4, the best-fitting model was one with moderation of both A and C terms . At age 9, moderation of only the C term was significant (Cβ = −.06). Age 10 showed a significant decrease in A with increasing SES (Aβ = −.10). At age 14, the best fitting model, as indicated by AIC, suggested significant moderation of the C term (Cβ = −.19).At ages 3, 7, and 12 the best-fitting model, as indicated by AIC, was one with no moderation of either genetic or environmental components. At age 2, the best fitting model, as indicated by AIC, was one with no genetic moderation. The p-value showing model fit for individually dropped parameters suggests only moderation of the C term is significant  and IQ show a similar pattern of increasing correlation with age, and are in the range 0.22–0.33 . Intra-cA graphical summary of the continuous moderation analyses is presented in Cβ = −.06, Cβ = −.05, and Cβ = −.09 respectively).At ages 7 and 14, the best fitting model as indicated by AIC was one with no moderation of genetic or environmental components of intelligence. At all other ages  the best fitting model included only moderation of the C component (Phenotypic correlations between SES (family income at age 9) and IQ are presented in A graphical summary of the continuous moderation analyses at ages 9, 10, 12 and 14 is presented in Cβ = −.05, Cβ = −.04, Cβ = −.16, and Cβ = −.16 respectively).At all ages , the best fitting model as indicated by AIC includes (in addition to the main effect of SES) only moderation of the shared environmental component (g at age 10 (higher heritability in low-SES families) disappears with the more proximal measures of SES at ages 7 and 9. Second, the best fitting model indicates no interaction of any kind at three ages for SES index 1 , and for two ages for SES index 2 (ages 7 and 14). Third, moderation of the shared environmental component of g is indicated at four of eight ages for SES index 1, three of five ages for SES index 2, and four of four ages for SES index 3. Thus, the most consistent result across ages and across the three indices of SES is moderation of the influence of shared environment on children's intelligence - an environment-environment interaction.Summarized in a = c = e = 1; Cβ = Eβ = 0. We simulated a range of genetic moderation (Aβ) between 0.05 and 0.50. We generated 1000 replicates for a range of sample sizes, with equal numbers of MZ and DZ twin pairs. In order to estimate power of the continuous model to detect genetic moderation under conditions of genetic moderation only, we set parameters as follows; a = c = e = 1; and, Cβ = Eβ = Aβ = a range of values between 0.05 and 0.50. Again, we generated 1000 replicates for each sample and effect size, and estimated the model's ability to detect the presence of the genetic moderation only, i.e. a 1df test. Aβ = 0.30*a) the model does not perform well when assessing the significance of just the genetic moderation (a 1df test). Purcell's Second, to explore how the model performed when moderation of all three terms is present, we simulated data with parameters set as follows: The simulations summarized in heterogeneity analysis. Although these discontinuous analyses have usually ignored variance differences between groups by using twin correlations (which standardize variances between groups), heterogeneity analysis provided components of raw variance which we present along with the standardized estimates to highlight the difference between components of raw and standardized variance.Because several studies explored GxE interaction by comparing ACE estimates, or twin correlations, in low-and high-SES groups see , we compWe present results for age 9 IQ, which showed the most consistent C interaction across the three SES indices. We split the sample into quartiles and compared the variance components derived for the top and bottom 25% of the SES distribution. In In summary, this discontinuous analysis of low-SES versus high-SES groups generally confirms the results of our continuous moderator analysis for the largest interaction effect, despite a great loss in power for the discontinuous analysis gene-environment interaction across childhood and adolescence. At only one of the eight ages, age 10, did we find a significant moderation of the genetic contribution to IQ. However, the GxE interaction was in the opposite direction from that predicted by the environmental disadvantage hypothesis, and moreover, was not significant with a more proximal measure of parental education and occupation. Instead, using three different indices of SES, at eight ages from infancy through adolescence the emerging pattern appears to be one of environment-environment interaction rather than gene-environment interaction: shared experiences explain more of the variance in children's performance on IQ tests in more disadvantaged backgrounds.We attempted to replicate the finding that parental SES moderates the heritability of children's intelligence, with a greater genetic contribution to IQ in high-SES families compared to low-SES families. In a large UK-representative sample, we did not find evidence for the presence of such a relative contribution of genes – heritability or h2 = A/(A+C+E)) – is lower in low-SES families because the shared environmental effect increases.How can the present finding of SES moderation of the shared environmental effect on IQ, be reconciled to the reports of SES moderation of the genetic component of IQ? An increase in the contribution of C in lower-SES families would seem to require a reduction in the relative contribution of A because environmental and genetic variance components are complementary, and explain 100% of the variance. However, this is only the case for standardized components that are forced to sum to 100% regardless of total variance differences. Our most consistent finding is that total IQ variance is greater in lower-SES families, which must be caused by greater A, C, or E components of variance in lower-SES families. Although the power demands are daunting to disentangle A and C sources of this increased variance in lower-SES families, data from our large sample suggests that the source is C rather than A. The genetic effect does not differ for low- and high-SES groups using unstandardized estimates  that take into account the greater total variance in the low-SES group, but the Children from low-SES families face many physical and psychosocial environmental handicaps for their cognitive development What factors contribute to the inconsistency in the literature ? We suggDifferences between countries is another possible sampling issue for two overlapping reasons: the relationship of the SES measures to each other, and their relationship to IQ. First, the traditional measures of SES – family income, parental education, and occupational status Second, the magnitude and nature of the effect of SES on children's IQ could differ in different countries We believe that sample age is a particularly important factor in the inconsistent findings. Because heritability increases and shared environmental influence decreases from childhood to adulthood Detecting modest shared environmental effects in the presence of larger genetic and nonshared environmental effects requires large twin samples statistical in nature, they are necessarily dependent on measurement scale, analytical model, and the assumptions underlying the model. Establishing a mechanism for moderation of the effect of genes, such as a change in gene expression, is several steps removed from finding moderation as a latent genetic population variance component Several quantitative genetic approaches have been used to investigate moderation of the genetic effect on IQ. These include the heterogeneity model , regression models with an interaction term (e.g. extended DF regression), and the continuous moderator model. The continuous moderator model is the most powerful approach, allowing the use of full information maximum likelihood to estimate potential moderation of latent variance components while simultaneously controlling for the confounding effects of gene-environment correlation. Because the interactions tested by the various approaches are The notion that heritability may be lower in lower-SES families is appealing, in part because of its environmental implications: If heritability is lower in lower-SES families, it suggests that environmental interventions might be more effective in boosting cognitive development for children in lower-SES families. The present study, which is based on a large UK-representative sample of children followed longitudinally, leads to a similar implication. Although the genetic influence on IQ is the same in lower-SES families, shared environmental influence appears to be greater in lower-SES families, suggesting that family-based environmental interventions might be more effective in these families. However, two further aspects of the results temper the policy implications of this finding. First, shared environmental influence is found in both lower- and higher-SES families and the difference in shared environmental influence between them is modest. Second, shared environmental influences on IQ decline from childhood to adulthood so that these influences might not have an impact in the long run.Table S1Continuous moderator model fit – SES index 1. Model fit for twins with parental education and occupation at 18 months. Bold rows show best fitting model as indicated by AIC.(DOC)Click here for additional data file.Table S2Continuous moderator model fit – SES index 2. Model fit for twins with 7-year parental education and occupation. Bold rows show best fitting model as indicated by AIC.(DOC)Click here for additional data file.Table S3Continous moderator model fit – SES index 3. Model fit for twins with 9-year family income at age 9. Bold rows show best fitting model as indicated by AIC.(DOC)Click here for additional data file.