Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such “background variance heterogeneity” (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene’s test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term “mean-variance QTL mapping”, to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight. A standard modeling assumption in quantitative trait locus (QTL) mapping is that all individuals, regardless of differences in their phenotypic mean, have the same residual variance. In reality, the residual variance—sometimes termed the environmental variance, and in general relating to the apparent noisiness of the phenotype—can differ between individuals. These differences in residual variance can arise from many sources, both extrinsic, such as environmental factors, and intrinsic, such as sex, or, more broadly, genetics. Environmental sources of residual variance heterogeneity have been well-documented, and include, for example, soil nitrogen and irrigation , temperaThe presence of residual variance heterogeneity, however, regardless of its source, can be problematic for analysis protocols that disregard it. Differences in residual variance between groups of individuals affect the precision of estimated means and, in turn, tests of significance or association . In the e.g., sex, housing, strain, a vQTL, etc.) implies that inferences about any other effect  occur against a backdrop of systematically heterogeneous residual variance. This “background variance heterogeneity” (BVH) acts to disrupt the natural observation weights: rather than every individual being subject to equal noise variance and therefore meriting equal weight, with BVH present some individuals’ phenotypes are inherently more (or less) noisy and so due less (or more) weight. And just as reweighting accordingly should lead to a more powerful analysis, then assuming all weights are equal  risks overleveraging outliers and increasing the potential for both false negatives and false positives. This is likely to be true not only for studies detecting mQTL but also those detecting vQTL, which rely on the accurate attribution of residual noise.Specifically, the background presence of a major variance-controlling factor  of these tests—a standard asymptotic evaluation and three procedures that reasonably could be expected to provide protection against violations of model assumptions.This section reviews the tests and evaluation procedures that we studied through simulation. First, we describe eight statistical tests that can be used to model the effect of a single locus on phenotype mean and/or variance: the standard linear model, Levene’s test, Cao’s three tests, and three DGLM-based tests. We also describe four procedures for evaluating the statistical significance : effects on the phenotype variance that arise from the locus under consideration ;Background Variance Heterogeneity (BVH): effects on the phenotype variance that arise from outside of the focal locus, e.g., from another locus or an experimental covariate.T on the observed data followed by an estimation of statistical significance based on a conception of T’s distribution under the null. BVH, however, will often constitute a departure of distributional assumptions, and in any rigorous applied statistical analysis when departures are expected it would be typical to consider protective measures such as, for example, transforming the response to make asymptotic assumptions more reasonable, or the use of computationally intensive procedures to evaluate significance empirically, such as those based on bootstrapping or permutation.In comparing different statistical tests and their sensitivity to BVH, namely the effect of BVH on power and false positive rate (FPR), it is important to acknowledge that various measures could be taken to make significance testing procedures more robust to model misspecification in general and to BVH specifically. The significance testing methods considered here are frequentist, involving the calculation of a test statistic i.e., the p-value for a single hypothesis test) is evaluated using four distinct procedures. The first two rely on asymptotics:Nominal significance : As for standard, except observed phenotypes T is recomputed as T under the randomized null. Two alternative randomizations are considered:The second two determine significance empirically based on randomization: the test statistic 3. Residperm: we generate a pseudo-null response l model, , a proce4. Locusperm: we leave the response intact, instead permuting the rows of the design matrix (or matrices) that differentiate(s) the null from alternative model.L loci. Then for each randomization R such permutations is then used to fit a generalized extreme value distribution (GEV) , the probability of making at least one false positive finding across the whole genome. This is done following the general approach of μ is the intercept, The standard model of quantitative trait mapping uses a linear regression based on the approximation of The test statistic for an mQTL is based on comparing the fit of the full model, acting as an alternative model, with that of a null that omits the locus effect, namely,T proceeds as follows. We first fit the null model  . Note ththe test , and repr, the group labels are permuted among the individuals, which is equivalent to replacing all instances of r applies the same permutation Levene’s test does not lend itself naturally to the residperm approach because it does not explicitly involve a null model to split the data into hat values and residuals. We therefore use the null model from the SLM  to approi.e.,ith individual. These are defined in what we term the “full model” asi belongs, and M test involves an LRT between Cao’s full model and Cao’s no-mQTL model:The CaoTL model . The locTL model , leavingV test involves an LRT between Cao’s full model and Cao’s no-vQTL model:The Cao the SLM .V, pseudo-null phenotypes are generating using To execute the residperm procedure for CaoTL model . The locTL model , leavingMV test involves an LRT between Cao’s full model and Cao’s no-QTL model:The Cao the SLM .MV, pseudo-null phenotypes are generated using To execute the residperm procedure for CaoTL model . The loci. In what we term the “DGLM full model”, these are specified asμ is the intercept, The DGLM models the phenotype As with Cao’s full model, the DGLM full model can be compared, in a likelihood ratio test, with various null models to test for mQTL, vQTL , or mvQTFor detecting mQTL, we use an LRT of the DGLM full model in To execute the residperm procedure for DGLMFor detecting vQTL, we use an LRT of the DGLM full model in To execute the residperm procedure for DGLMFor detecting mvQTL, we use an LRT of the DGLM full model in To execute the residperm procedure for DGLMThe eight methods and four significance testing procedures described in the previous section, amounting to 32 test-procedure combinations in total, were compared by simulation. The simulations examined the performance of each combination, in terms of false and true positive rate, under eight distinct scenarios relating to the presence or absence of a QTL , and the presence or absence of BVH. We describe the general simulation setup below, followed by a detailed description of the eight scenarios and then describe the metrics by which performance was judged.Each simulated experiment consisted of 300 individuals, where each individual was defined by one single-locus genotype, one covariate, and one phenotype.i, denoted by i, denoted The genotype for individual We conducted simulated experiments under eight different scenarios. These scenarios varied conceptually across two dimensions. First, we considered four types of locus:null locus: The locus has no effect on phenotype.pure mQTL: The locus has an additive effect on the phenotype mean.pure vQTL: The locus has an additive effect on the log of the residual phenotype variance.mixed mvQTL: The locus has both an additive effect on phenotype mean and an additive effect on the log of residual phenotype variance.i.e.:Then, we considered whether or not BVH was present, BVH absent: The covariate does not influence the residual variance of the phenotype.BVH present: The covariate influences the residual variance of the phenotype .i.e., all combinations) were realized in silico with three parameters: the effect of the locus on phenotype mean (α), the effect of the locus on phenotype variance (θ), and the effect of the covariate on phenotype variance  when 300 individuals are simulated, a typical population size for QTL mapping experiments.pure vQTL: vQTL analysis is much less established, so the vQTL effect size was chosen to match the detectability of the mQTL. Thus, the vQTL effect size was defined such that the traditional vQTL test (Levene’s test) has 70% power at 5% FPR in a population of 300 individuals in the absence of BVH.MV and DGLMMV with 70% power at an FPR of 5% in a population of 300 individuals in the absence of BVH.mixed mvQTL: The mvQTL effect sizes were chosen such that the mean and variance signals are equally detectable, and the aggregate signal is detectable by Caoθ combine additively on the log standard deviation scale and result in fifteen unique variances as detailed in the Supplementary Materials.The values of s, the phenotype for individual i, denoted For each of the eight scenarios, we conducted To each simulated experiment, eight tests were applied, and four procedures were used to assess the statistical significance of each test, for a total of 32 test-procedures.V, DGLMV; and two for detecting mvQTL: CaoMV and DGLMMV. These tests are detailed in the Statistical Methods and summarized in The eight tests comprise three tests for detecting mQTL: SLM, CaoThe four procedures for evaluating the statistical significance of results were: standard, RINT, residperm, and locusperm, as described in the Statistical Methods. The RINT procedure was selected because it returns any phenotype distribution, no matter how exotic, to a standard normal distribution. The fact that it is commonly used in genetics research demands that its properties, and its effects on QTL mapping, be better understood. The residperm was selected because it was recently proposed for use in mQTL, vQTL, and mvQTL mapping studies  and power at a nominal FPR of 0.05. The empirical FPR of a given test-procedure combination in a given scenario was taken as the fraction of null simulations (where the phenotype was simulated with no dependence on genotype) that resulted in The above evaluations focused only on the cutoff of This mapping population has been studied for association with several phenotypes: asymmetry of mandible geometry , 2007, aAnalyses were conducted in the R statistical programming language . The simhttps://doi.org/10.25387/g3.7290146.The reanalyzed dataset is available on the Mouse Phenome Database .These results reflect the reality that, when a simple model is exactly true, a more elaborate model tends to be less powerful. Additionally, they highlight the capability of the permutation-based procedures to accurately control FPR even when the standard and RINT procedures fail to do so , but accurately controlled FPR under the permutation-based procedures , standard error of the positive rate at V and DGLMV were both slightly anti-conservative under the standard procedure and conservative under the RINT procedure.In the absence of BVH, all vQTL tests had nearly-accurate FPR control . All tesV and DGLMV had more power to detect vQTL than Levene’s test under all procedures. Specifically, under the well-calibrated  procedures, CaoV and DGLMV had power in the range , while under those same well-calibrated  procedures, Levene’s test had power in the range   DGLMV-standard, however, comes with a caveat: if there were some additional, unknown (and therefore unmodeled) BVH-driving factor, DGLMV-standard with BVH present. The locusperm procedure, in contrast, ensures accurate FPR control whether all BVH-driving factors are modeled (as in DGLMV) or not (as in CaoV). DGLMV-locusperm therefore emerges as the most robust test-procedure for vQTL mapping in the presence of BVH.Direct interpretation of these results might lead one to consider the trade-off between DGLMFor each vQTL test-procedure combination, the AUC (Table S1), standard error of the positive rate at MV-RINT slightly conservative and DGLMMV-standard slightly anti-conservative , standard error of the positive rate at A consistent feature of the simulations involving detection of variance effects, whether vQTL or mvQTL, is that FPR control and power is affected, for better or worse, by applying the RINT to the response.M under the standard procedure was anti-conservative  and the RINT procedure slightly over-corrected it (FPR = 0.046). Similarly, the RINT procedure disrupted DGLMV, which was modestly anti-conservative under the standard procedure, causing overly conservative behavior (FPR = 0.023).In the case of vQTL detection in the presence of BVH, CaoMV and DGLMMV were anti-conservative under the standard procedure, illustrating their relations to CaoV and DGLMM respectively. In the case of CaoMV, the RINT procedure corrected the FPR, but in in the case of DGLMMV, it resulted in an over-correction into the realm of over conservatism (FPR = 0.049 and 0.038 respectively).As always, in the presence of BVH, the mvQTL tests exhibited a mixture of the patterns observed in mQTL tests and vQTL tests. Both CaoIn summary, the RINT procedure was unhelpful in the context of the DGLMTo understand the impact of BVH on mean and variance QTL mapping in real data, we applied both traditional QTL mapping, using SLM, and mean-variance QTL mapping, using Cao’s tests and the DGLM, to body weight at three weeks in the mouse backcross dataset of We first used a traditional, linear modeling-based QTL analysis, with sex and father as additive covariates and genomewide significance based on 1000 genome permutations . AlthougNo associations rose above the threshold that controls family-wise error rate to 5% .The same phenotype was analyzed with the DGLM-based tests. In a baseline fitting of the DGLM, sex was found not to be a statistically significant predictor of mean or residual variance  and downweighted . This weighting gave the DGLM-based mapping approach more power to reject the null as compared with the SLM.Some fathers, for example fathers 2 and 7, appear to have offspring with less residual variance than average, whereas others, for example father 1, seem to have offspring with more residual variance than average. The DGLM captured these patterns of variance heterogeneity, and estimated the effect of each father on the log standard deviation of the observations . Based oFor brevity, we described in detail only the results of the DGLM-based analysis of body weight at three weeks; but, of the eight phenotypes from this cross available on the Mouse Phenome Database, the mean-variance approach to QTL mapping discovered new QTL in four. Five of the eight phenotypes — body weight at twelve days, three weeks, and six weeks, as well as subcutaneous and gonadal fat pad thickness — exhibited BVH due to father, and for each we performed both traditional QTL mapping using the SLM and mean-variance QTL mapping using the DGLM. This reweighting changed the results in three cases: For body weight at three weeks  and six weeks , we identified one new mQTL and two new vQTL respectively. For subcutaneous fat pad thickness, we discovered one mQTL and “undiscovered” one mQTL . That is, after reweighting the observations based on the observed variance of each father, one locus that was overlooked by SLM was identified as an mQTL and one locus that was identified by SLM as an mQTL was no longer found to have a statistically significant association with the phenotype.Since the recognition that variance effects can be attributable to individual genes, a growing body of research has asked questions about the prevalence of such effects . These set al. dataset demonstrated that accommodating BVH can lead to detection of mQTL that would otherwise be overlooked.Our simulation studies showed that modeling BVH when it is present increases power to detect mQTL, vQTL and mvQTL. Our reanalysis of the Leamy e.g., the recent Bayesian hierarchical model of In both cases, of the methods compared, the most powerful were those based on the DGLM, with the most robust versions of those using the locusperm significance procedure. These results should not be too surprising. The DGLM was the only method examined that can accommodate variance effects arising from both the locus and from other covariates; and the locusperm method  is least reliant on parametric assumptions. We would expect other methods that allow flexible modeling of covariate effects on variance to be competitive in these regards, Beyond advocating any particular method, however, our results can be used to draw attention to a number of more general points about 1) the relationship between increased residual variance, observation weighting and downstream inference; 2) how knowledge of variance effects can be exploited in experimental design, analysis and reanalysis; 3) the sensitivity of variance effect detection to distributional assumptions and how this can be mitigated by strategies such as variable transformation or permutation; and, 4) how to report quantitative genetic parameters under heteroskedasticity.M under BVH stems entirely from its greater ability to capture this additional information, and thereby give more credence to phenotype values that are more precise.The additional power of mean-variance QTL mapping to detect mQTL in general—and of DGLMe.g., We note a related approach to correcting inference of mean effects in the face of heteroskedasticity not considered here is the use of heteroskedastic consistent covariance matrix estimators (HCCMEs) e.g., . HoweverThe possibility that some individuals could be predictably more variable than others has clear implications for experimental design. A key parameter in the design of experiments is the number of replicates, typically specified to provide adequate precision of, and thereby power to detect, an estimated effect. But foreknowledge that residual variance will differ for certain groups suggests a more nuanced approach that explicitly weighs replicates against intrinsic variability.For example, when designing an experiment on a population that happens to have a known, segregating vQTL that is not itself the focus of interest but would induce BVH, it may be preferable to allocate a disproportionate share of the replication to individuals in the high-variability genotype class. In such cases, it then becomes additionally helpful to understand at what level(s) the heterogeneous variance manifests. Specifically, increased variability could arise from greater between-individual variation or greater within-individual variation , the use.g., replacing the standard 0.05 with 0.02.Because this procedure involves three families of tests rather than one family as would be typical with an SLM-based analysis, an additional correction may be desired to control experiment-wise error rate. DGLMal tests , but DGLIn summary, we demonstrate the effect of BVH on QTL mapping of both mQTL and vQTL, and the value of accommodating it using the DGLM. In doing so, we propose a standard protocol for mapping mQTL, vQTL and mvQTL in standard genetics crosses.


Imaging of joint inflammation provides a standard against which to derive an updated DAS for RA. Our objectives were to develop and validate a DAS based on reweighting the DAS28 components to maximize association with US-assessed synovitis.n = 434 and n = 117) and a clinical trial (n = 59) were assessed at intervals up to 104 weeks from baseline; all US scans were within 1 week of clinical exam. There were 899, 163 and 183 visits in each cohort. Associations of combined US grey scale and power Doppler scores (GSPD) with 28 tender joint count and 28 swollen joint count (SJC28), CRP, ESR and general health visual analogue scale were examined in linear mixed model regressions. Cross-validation evaluated model predictive ability. Coefficients learned from training data defined a re-weighted DAS28 that was validated against radiographic progression in independent data .Early RA patients from two observational cohorts . The re-weighted two-component DAS28CRP outperformed conventional DAS28 definitions in predicting GSPD , Larsen score and presence of erosions.Of the conventional DAS28 components only SJC28 and CRP were associated with GSPD in all three development cohorts. A two-component model including SJC28 and CRP outperformed a four-component model (A score based on SJC28 and CRP alone demonstrated stronger associations with synovitis and radiographic progression than the original DAS28 and should be considered in research on pathophysiological manifestations of early RA. Implications for clinical management of RA remain to be established. Tender joint count and general health visual analogue scale are not associated with imaging-detected inflammation.A two-component DAS28 shows closer association with inflammation and structural damage than the original score.This new score should be considered in research on pathophysiological manifestations of early RA.The DAS, introduced in 1990 . HoweverThe DAS was developed using rheumatologists’ treatment decisions to define periods of high and low disease activity. Of the DAS components, Ritchie articular index and 44 swollen joint count (SJC44) made the largest contribution to discriminating between these states, suggesting that the rheumatologists placed more importance on joint assessments than blood markers of inflammation (ESR) or patient subjective effects . Updated DAS definitions have since been introduced; to reduce assessment time, 28-joint counts  may be uet al. , Health Assessment Questionnaire Disease Index (HAQ-DI) score, age and sex. Predictive mean matching was used to impute all variables; results from 20 imputed datasets were combined according to Rubin’s rules.Conventional DAS28 scores were calculated as follows :4C-DAS28CRP = (0.56 × √TJC28) + (0.28 × √SJC28) + (0.36 × ln(CRP+1)) + (0.014 × GHVAS) + 0.964C-DAS28ESR = (0.56 × √TJC28) + (0.28 × √SJC28) + (0.70 × ln(ESR)) + (0.014 × GHVAS)3C-DAS28CRP = [(0.56 × √TJC28) + (0.28 × √SJC28) + (0.36 × ln(CRP+1))] × 1.10 + 1.15In addition we calculated partial simplified disease activity index (SDAI) clinical disease activity index (CDAI) scores, which excluded physician VAS, as follows :Partial SDAI = TJC28 + SJC28 + CRP + GHVASPartial CDAI = TJC28 + SJC28 + GHVASR2) between predicted and observed US scores, concatenating all test folds into a single dataset. The strength of evidence favouring one model over another was evaluated as the difference in test log-likelihoods. For each individual in the test dataset, the log of the multivariate Gaussian likelihood of the model given the residuals was calculated, and this test log-likelihood was summed over all individuals in all test folds. The difference in test log-likelihood can be interpreted directly as a measure of the strength of evidence favouring one model over another. The asymptotic equivalence of model choice by leave-one-out cross-validation and Akaike’s information criterion (AIC) [P = 0.01.We modelled GSPD as a function of individual DAS28 components in linear mixed models, using 20-fold cross-validation to evaluate predictive performance on data not seen before; multiple imputation was not nested within training folds. Predictor performance was evaluated as the squared Pearson correlation (on (AIC)  implies To account for differences in scaling of the GSPD outcome, ratios between coefficients obtained from each cohort were weighted by the number of cases with non-missing acute phase measurements to produce the final definition for the re-weighted DAS28. For example, the ratio of the coefficient for SJC28 to the coefficient for CRP from each cohort was multiplied by the number of observations, then the values from the three cohorts were added together and divided by the total number of observations to give the final ratio to be used in the score. We did not attempt to scale the final score to maintain compatibility with the conventional DAS28 definition(s) because they were derived using entirely different methods and existing cut-offs for remission or severity of disease activity would not be valid for use with the new score.As previously described , 28, 31,Analyses that included US were conducted in R version 3.3.1 ; analyses of radiographic outcomes were carried out in Stata v14 .Baseline characteristics of patients are provided in The median of values imputed in IACON using robust regression on order statistics for values of CRP < 5 was 1.88. We therefore imputed 2 for observations of CRP < 5. See Associations between DAS28 components and GSPD were only investigated in IACON, IDEA and PEAC; NOAR was excluded to provide external validation of the new scale against radiographic progression.In four-component (4C) models that included CRP, in all three cohorts only SJC28 and CRP were independently associated with GSPD. Results for models with two or four individual components, using either CRP or ESR, are presented in Repeating this analysis with complete case data, within each cohort, did not alter the conclusions (data not shown).R2 and test log-likelihood values favoured the two-component (2C) model that included only SJC28 and the acute phase reactant over the models containing all four components of conventional DAS28 , 0.78 (IDEA) and 1.16 (PEAC). Weighting for the number of non-missing CRP observations , the coml scores , and larWhile we consider 2C-DAS28CRP the most appropriate measure of global synovitis, to permit analysis of historical datasets with only ESR available we constructed 2C-DAS28ESR, using the same method to combine SJC28: ESR coefficient ratios across the three cohorts:Results of analyses testing the association of DAS28 components with radiographic outcomes are presented in Conventional 3C-DAS28CRP was significantly associated with Larsen score; however, the association was stronger with re-weighted 2C-DAS28CRP . FurtherConventional 3C-DAS28CRP was not associated with the presence of erosions. However, re-weighted 2C-DAS28CRP was significantly associated with the presence of erosions. Model fit was again better with the re-weighted 2C-DAS28CRP score.This is the first study to produce a 2C DAS weighted against US-detected inflammation and demonstrate that it outperforms the conventional definitions in the strength of association both with synovitis and radiographic progression. The resulting 2C-DAS28CRP is a potential tool for assessing one pathophysiological manifestation of RA, i.e. synovitis, consistent with the OMERACT core set definition .RA DASs were intended to capture the severity of the patient’s inflammatory symptoms. The 2C-DAS28CRP was developed to better reflect the pathophysiological manifestation of synovial inflammation and not a more global construct of disease activity, as reflected in the breadth of the OMERACT core set. Consequently, we would not recommend replacing the conventional DAS28 with the 2C score in clinical trials or clinical practice without due consideration of how to measure the other core set areas. The 2C-DAS28CRP score will therefore be useful in studies that focus on the pathophysiological manifestations of RA: these include (pharmaco)genetic/genomic studies. It could be argued that to identify biological factors associated with disease activity or response to therapy, US inflammation should be used directly as the outcome. This would have the advantage of allowing us to separate synovitis from tenosynovitis, although it is likely that these both fall into a broader construct of ‘RA inflammation’ which would remain an appropriate target for such research. However, it is not currently possible to acquire and score images in routine clinical settings, from which most of the large (pharmaco)genetic/genomic datasets have been compiled to date and are likely to be compiled in the near future. The 2C-DAS28CRP will therefore aid the (re)analysis of (existing) large-scale datasets and may yield larger effect sizes for any putative associations in comparison with existing DAS28CRP.Our findings show that if TJC28 and patient GHVAS are removed from the DAS28, the association with the pathophysiological mechanism targeted by DMARDs, namely synovial inflammation, is improved. The association was stronger for CRP-based models than for ESR-based models, consistent with CRP being a more sensitive and responsive measure of inflammation than ESR, which is an indirect measure of multiple plasma proteins and influenced by age, gender, haemoglobin and serum immunoglobulin levels, among other confounders .et al. [In previous work Baker et al.  found thIn a previous attempt to update the DAS28 using US-detected synovitis , TJC28 aad hoc approaches such as complete case analysis [There are a number of limitations to this study. A limited amount of missing covariate data was addressed using multiple imputation, which has been shown to reduce imprecision and bias in the estimation of coefficients in comparison to analysis . We did In summary, a re-weighted DAS28 equation including only SJC28 and CRP was more closely associated with US-detected synovitis than definitions that also included TJC28 and GHVAS. CRP-based DASs were more closely associated with synovitis than ESR-based counterparts. The 2C-DAS28CRP showed stronger association with burden of radiographic damage than the conventional 3C-DAS28 including TJC28. The improved association with both synovitis and erosion demonstrates that the novel 2C-DAS28 is a more appropriate measure of pathophysiology in early RA compared with conventional DAS28.kez049_Supplementary_DataClick here for additional data file.


N = 217,568). Two patterns emerge: (1) anorexia nervosa, schizophrenia, obsessive-compulsive disorder, and education years are negatively genetically correlated with body fat % and fat-free mass, whereas (2) attention-deficit/hyperactivity disorder (ADHD), alcohol dependence, insomnia, and heavy smoking are positively correlated. Anorexia nervosa shows a stronger genetic correlation with body fat % in females, whereas education years is more strongly correlated with fat mass in males. Education years and ADHD show genetic overlap with childhood obesity. Mendelian randomization identifies schizophrenia, anorexia nervosa, and higher education as causal for decreased fat mass, with higher body fat % possibly being a causal risk factor for ADHD and heavy smoking. These results suggest new possibilities for targeted preventive strategies.Body composition is often altered in psychiatric disorders. Using genome-wide common genetic variation data, we calculate sex-specific genetic correlations amongst body fat %, fat mass, fat-free mass, physical activity, glycemic traits and 17 psychiatric traits (up to  Psychiatric disorders are often accompanied by alterations in BMI and body composition due to changes in eating behaviour and physical activity. Here, Hübel et al. study the genetic overlap between these traits and find that genetic correlations between psychiatric disorders and body composition are sex-specific and evident only in adulthood. Genome-wide association studies (GWASs) of psychiatric disorders have identified more than 300 independent genomic loci4, informed biological follow-up studies5 and may deliver promising targets for drug discovery and repurposing8. Genome-wide summary statistics generated by GWASs can be used in several different ways9, including estimating single-nucleotide polymorphism-based heritability , which estimates the genetic overlap (i.e. the shared genetic effects) between two traits12. Such GWAS-based genetic correlation analyses have shown substantial genetic overlap among psychiatric disorders13, providing evidence for an underlying “p factor” representing general liability for psychiatric illness15. For instance, genomic structural equation modelling16 of GWAS summary statistics for schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder and anxiety showed that they load onto one shared latent factor with loading estimates between 0.29 and 0.8616. However, marked differences in the clinical presentation of psychiatric disorders exist for psychotic experiences or dysfunctional reward systems, suggesting the existence of additional disorder-specific genetic effects16.Psychiatric disorders are complex traits influenced by thousands of genetic variants that act in concert with environmental factors17, most prominently observed in eating disorders, such as binge-eating disorder and anorexia nervosa18. Such severe weight dysregulation typically reduces patients’ quality of life and is associated with excess morbidity and mortality19. Body composition traits, including body fat % (BF%) and fat-free mass (FFM), are also complex, with substantial twin heritabilities of ~70%21. Body mass index (BMI) is the most commonly studied body composition phenotype and its associated genetic variants have been found to be significantly overrepresented in genes and genomic regions active in brain cell types22, suggesting it may be a partially behavioural trait. Several studies have also shown negative genetic correlations of BMI with anorexia nervosa and schizophrenia25 and positive genetic correlations of BMI with attention-deficit/hyperactivity disorder (ADHD) and major depressive disorder27. These observations suggest that an in-depth investigation of the shared genomics between psychiatric and body composition traits is needed.Clinically, many psychiatric disorders are accompanied by disturbances in appetite regulation, eating behaviour and altered physical activity. These disturbances can alter body composition and result in comorbid overweight or underweight2)30. Sex differences are not limited to body composition: major depressive disorder31 and anxiety32 are more common in females, whereas ADHD33 and autism spectrum disorder34 occur more often in males. Sex differences in body composition, psychiatry and their interplay are not fully understood. Hormones and sex chromosomes have clearly been demonstrated to play a role35, but are insufficient to fully explain the sex differences36.In addition, both extreme overweight and extreme underweight show a clear sex difference: females are not only disproportionately affected by anorexia nervosa (with ratios up to 15:1) but also by obesity  and five behavioural traits with sex-specific GWASs of body composition traits derived from a healthy and medication-free subsample of the UK Biobank  and anorexia nervosa, but with no other psychiatric disorder. In addition, glycaemic traits show significant genetic correlations only with anorexia nervosa and years of education, which positions anorexia nervosa as unique among the psychiatric disorders we investigate. These findings encourage a deeper investigation of metabolic pathways that may be implicated in psychiatric disorders to identify potential targets for preventive strategies.pBonferroni = 0.05/28 = 0.002. We detected a genetic correlation between males and females in BF% that was significantly different from 1 . Sensitivity analyses using Haseman–Elston regression38 confirmed these results  = 0.4–0.8%, LDSC; Supplementary Table p value threshold for the genetic correlations with body composition traits was pBonferroni = 0.05/190 = 2.6 × 10−4 using matrix decomposition of the genetic correlation matrix to identify the number of independent tests to adjust the threshold using Bonferroni correction39.In the genetic correlations of the psychiatric disorders and behavioural traits with body composition and physical activity, distinct patterns emerged resulting in two groups Table . In the rg = −0.44, s.e. = 0.04, LDSC) was stronger than with BF% in males  with a significant difference of δrg = −0.17 . Conversely, education years showed a stronger genetic correlation with FM in males than in females , which was also seen with FFM . No other sex differences were observed  that was more pronounced in females  than in males . This mirrored the observed genetic correlations. Additionally, GSMR showed evidence for a 3.7 kg decrease in FM per year of education . Furthermore, GSMR showed a 0.88 kg decrease in FM  and a 0.58 kg decrease in FFM  per standard deviation of liability to schizophrenia . However, GSMR showed evidence in the reverse direction with a 1.05-fold increase in risk for ADHD per kg FM  as well as a 1.03-fold increase in risk for ADHD per kg FFM  and a 1.04-fold increase in heavy smoking per kg FM  and anorexia nervosa  correlated positively with objectively measured physical activity, whereas education years showed a significant correlation with physical activity only in females  nor any other trait in the second group correlated with physical activity  and education years  correlated genetically negatively with fasting insulin concentrations. Accordingly, anorexia nervosa  and education years  also showed negative genetic correlations with insulin resistance. In addition, education years showed a negative genetic correlation with fasting glucose concentrations , whereas heavy smoking showed a positive genetic correlation with fasting glucose concentrations , between childhood and later adulthood  and between adolescence and later adulthood . The genetic correlation of FFM between childhood and adulthood was also significantly different from one . As above, multiple psychiatric disorders and traits showed significant positive and negative genetic correlations with adult BMI and FFM. However, neither BMI in childhood, adolescence or young adulthood, nor FFM in childhood, showed significant genetic correlations with any of the psychiatric disorders or behavioural traits  correlated negatively with obesity in childhood. In the second group, ADHD was the only psychiatric disorder that showed a significant positive genetic correlation with obesity in childhood . GSMR analyses gave evidence for a 1.42-fold increase for ADHD per kg/m2 increase in childhood BMI .As a secondary aim, we explored the developmental dependence of genetic correlations of BMI and FFM at different ages with psychiatric disorders and behavioural traits Fig. . We used22, suggesting that BMI may be a metabo-behavioural trait. This spurred our in-depth investigation of the shared genetics of psychiatric traits and body composition. We were able to show that five psychiatric disorders—anorexia nervosa, OCD, schizophrenia, ADHD and alcohol dependence—as well as three behavioural traits—education years, insomnia and heavy smoking—show significant genetic correlations (i.e. shared genetics) with body composition in two distinct patterns.Symptomatically, psychiatric disorders are often accompanied by alterations in energy intake, energy expenditure and body composition. Recent genetic analyses of BMI found an important role for genes expressed in the brain and specific brain cell types42 is likely to represent medication effects43 given our observations of a putative causal effect of schizophrenia on lower FM and FFM. This finding reiterates the pressing need for the development of new antipsychotic medications with more favourable weight-related side effect profiles.The first group of psychiatric disorders and behavioural traits included anorexia nervosa, OCD, schizophrenia and education years, and was characterized by genetic correlations with genomic variants predisposing to lower BF% and FFM. The second group comprised ADHD, alcohol dependence, heavy smoking and insomnia, and had genetic correlations with genomic variants predisposing to higher BF% and FFM. Our Mendelian randomization analyses used significant genetic variants as instrumental variables and found that anorexia nervosa, schizophrenia and education years showed evidence consistent with a negative causal effect on FM and, in the reverse direction, higher BF% appeared to be a risk factor for both ADHD and heavy smoking. Our results also suggested that the overweight seen in individuals with schizophrenia in epidemiological studies41. These findings suggest that anorexia nervosa and BF% may share a sex-dependent set of genomic variants potentially contributing to the marked sex bias in the prevalence of anorexia nervosa. Education years showed a stronger genetic correlation with FM in males than in females. However, the GSMR analysis showed a more pronounced protective effect of education years on FM in females than in males in line with a large epidemiological study44. This suggests that the stronger genetic association between education years and FM in males may be driven by a set of pleiotropic variants.In our analysis, anorexia nervosa showed a stronger correlation with BF% in females than in males. This phenomenon was not observed for other traits genetically associated with anorexia nervosa, such as neuroticism, anxiety, major depressive disorder, OCD or schizophrenia45 have been termed “genetic innovation”46 and represent the effects of partially different, age-dependent sets of genomic variants on body composition regulation at certain periods of life45. Some of the psychiatric disorders, such as ADHD and anorexia nervosa, typically have their onset in childhood or adolescence with preceding symptoms or behaviours that implicate neurodevelopmental components. We used the available life-stage GWASs of body composition and did not find genetic overlap between childhood or adolescence/young adulthood BMI with psychiatric disorders, but instead found significant genetic correlations of psychiatric disorders with later adult BMI and BF%. Our analyses also show that genetic variants associated with obesity before the age of ten were positively correlated only with ADHD and negatively only with education years. The relatively specific positive genetic correlation of childhood obesity with ADHD recapitulates a large body of clinical evidence of high phenotypic comorbidity47, also shown in family studies48. Overweight may represent a difficult but potentially intervenable risk factor at a young age.From a developmental perspective, it is striking that GWASs of body composition across ages do not genetically correlate perfectly with each other. These varying genetic effects across the lifespanOur finding of a genetic overlap between ADHD and obesity in childhood may implicate shared biological pathways between both traits. Given our other results, it appears that this shared component is unlikely to be related to physical activity or glycaemic traits. Instead, speculatively, a central nervous system pathway that is dysregulated by increased body mass in childhood may increase the liability to develop ADHD.We also investigated body composition-related traits, including physical activity, fasting insulin and fasting glucose concentrations. Physical activity showed a positive genetic correlation with anorexia nervosa and OCD, which themselves were negatively genetically correlated with BF%. Carrying genetic variants that predispose to higher physical activity may be associated with the relationship between lower BF% and psychiatric traits. Higher physical activity, therefore, should be carefully assessed in the treatment of patients with compulsive psychiatric disorders like anorexia nervosa and OCD as it may be a genetically mediated behaviour, as indicated by our analysis.49 and is likely to have an alternative cause, such as insufficient inhibitory control as observed in paediatric clinical samples with ADHD50, healthy adult population samples51, and in a large longitudinal developmental cohort study52.Contrary to our expectations, ADHD did not show a genetic correlation with physical activity. This suggests that hyperactivity in ADHD may not originate from biological liability for higher accelerometer-measured physical activity53, opening an avenue for deeper investigation of metabolic components in psychiatric disorders. The genetic correlations of ADHD with glycaemic traits were not significant, implying that these traits play a smaller role in ADHD than in anorexia nervosa, given the comparable sample size of the GWASs on both psychiatric disorders26. Genetic associations of physical activity and glycaemic traits with body composition and psychiatric traits in plausible directions render them interesting candidates for formal mediation analyses as they may be actionable targets54.Our analyses showed that anorexia nervosa and education years have a negative genetic correlation with fasting insulin concentrations and insulin resistance, positioning anorexia nervosa as a special case within the psychiatric disorders and potentially differentiating it from OCD. These negative correlations with fasting insulin concentrations mirrored the negative genetic correlations between anorexia nervosa, education years and BF%. The potential involvement of metabolic hormones like insulin in anorexia nervosa underscores the relationship of brain and body and their reciprocal regulation37, with body composition or glycaemic traits, ADHD, education years, schizophrenia or heavy smoking as exposure were sufficiently powered; however, the analyses with anorexia nervosa, insomnia or OCD as exposures should be regarded as exploratory in nature because p value thresholds were lowered to include at least 10 single-nucleotide polymorphisms (SNPs) in the instrument variable.Our study represents the largest investigation of sex- and age-dependent effects in the genomic overlap of body composition and psychiatric traits. Although our analyses drew on the largest available GWASs, some phenotypes still had relatively small sample sizes for genomic investigations of common variants in complex traits, especially for our sex-specific analyses. These should be repeated when sample sizes have increased, especially for OCD as its currently available GWAS sample size is particularly modest. All Mendelian randomization analyses, using GSMR56 and two molecular genetic studies45, which enabled our examination of their relationship with psychiatric traits. Most importantly, shared biological pathways and common environmental factors influencing both body composition and behavioural traits should be studied as potential targets for interventions.Finally, the age-dependent genetic influences we observed between psychiatric traits and body composition suggests that future research could focus on a developmental approach to GWAS analyses of body composition, to capture age- and sex-dependent differences. These differences have already been suggested by larger twin studiesnrelated = 7765) European subsample 57 of the genotyped UK Biobank participants  is a prospective cohort sampled from the general population between 2006 and 2010. All participants were between 40 and 69 years old, were registered with a general practitioner through the United Kingdom’s National Health Service, and lived within travelling distance of one of the assessment centres.We performed GWASs on an unrelated  with a minor allele frequency >1%, imputation quality scores >0.8, and that were genotyped, or present in the HRC reference panel64 and used an additive model on the imputed dosage data provided by UK Biobank, using BGENIE v1.265. We accounted for underlying population stratification by including the first six principal components, calculated on the genotypes of our European subsample using FlashPCA266. We performed GWASs including incremental numbers of principal components and checked each GWAS for inflation by calculating its LDSC intercept. We identified six principal components as the optimal number to adjust for population stratification within the European subsample and to not overcorrect the analysis retaining the greatest signal. Additionally, we included assessment centre as a covariate to adjust for population stratification. We then meta-analysed the sex-specific GWASs using METAL67 (URLs) applying an inverse variance-weighted model with a fixed effect, to obtain sex-combined results.The continuous body composition traits—BF%, FM, FFM and BMI—were measured using the validated bioelectrical impedance analyser Tanita BC-418 MA  at every assessment centrep < 5 × 10−8) were considered as potential index SNPs. SNPs in LD (r2 > 0.2) with a more strongly associated SNP within 3000 kb were assigned to the same locus using FUMA (URLs)68. Overlapping clumps were merged with a second clumping procedure in FUMA, merging all lead SNPs with r2 = 0.1 to genomic loci. After clumping, independent genome-wide significant loci (5 × 10−8) were compared with entries in the NHGRI-EBI GWAS catalogue69, using FUMA68.Significantly associated SNPs .To ensure the robustness of our results, we applied multiple approaches to calculate heritability estimates and genetic correlations. We used BOLT-LMM27, schizophrenia3, anorexia nervosa25, bipolar disorder75, ADHD76, alcohol dependence77, autism spectrum disorder78 and PTSD79, were provided by the PGC (URLs), for OCD81 by International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS), for borderline personality disorder82 by the German Borderline Genomics Consortium, for cannabis use by the International Cannabis Consortium83, for anxiety84 by our own group, for insomnia85 by the Complex Trait Genetics group at VU University Amsterdam (URLs), for heavy smoking86 by University of Leicester available from the UK Biobank (URLs), for the behavioural traits years of education87 by the Social Science Genetic Association Consortium (SSGAC) (URLs), for neuroticism41 by our own group . Glycaemic traits’90 summary statistics were provided by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), whereas childhood obesity91 results were provided by the Early Growth Genetics  Consortium, BMI in young adulthood by Graff et al.92 and physical activity by our group41.All of the following traits were used for the sex-specific and age-dependent analyses  to examine the genetic overlap of body composition and glycaemic traits with psychiatric and behavioural traits and disorders in a sex-specific manner. Differences in genetic correlations were calculated and their s.e.’s were calculated using a block jackknife approach as previously described41.Using an analytic extension of LDSC37. Mendelian randomization is a method that uses genetic variants as instrumental variables, which are expected to be independent of confounding factors, to test for causative associations between an exposure and an outcome93. Mendelian randomization can be used to infer credible causal associations when randomized-controlled trials are not feasible or are unethical93. GSMR performs a multi-SNP Mendelian randomization analysis using summary statistics. Let z be a genetic variant (e.g. SNP), x be the exposure (e.g. psychiatric disorder) and y be the outcome (e.g. body composition trait). First, GSMR is based on the premise that several nearly independent SNPs (z) are associated with the exposure (x). Second, it assumes that the exposure (x) has an causal effect on y. If both assumptions hold true, the SNPs that are associated with the exposure (x) will exert an effect on the outcome (y) via the exposure (x). If in this instance no pleiotropy is present, the estimate (bxy) at any of the SNPs that are associated with the exposure (x) should be highly similar, because each effect of all SNPs on the outcome (y) will be mediated through the exposure (x). With the help of a generalized least squares (GLS) model, the estimates of bxy of each SNP that is associated with the exposure (x) can be combined, resulting in higher statistical power94. The GSMR method essentially implements summary data-based Mendelian randomization analysis for each SNP instrument individually, and integrates the bxy estimates of all the SNP instruments by GLS, accounting for the sampling variance in both bzx and byz for each SNP and the LD among SNPs. We used individual-level genotype data from a subsample of the anorexia nervosa GWAS to approximate the underlying LD structure to account for LD between the variants in the multi-SNP instrument. Pleiotropy is an important potential confounding factor that could bias the estimate and often results in an inflated test statistic in Mendelian randomization analysis. We also removed potentially pleiotropic SNPs (i.e. SNPs that have effects on both risk factor and outcome) from this analysis using the heterogeneity in dependent instruments outlier method95 that detects pleiotropic SNPs at which the estimates of bxy are significantly different from expected under a causal model. The power of detecting a pleiotropic SNP depends on the sample sizes of the GWAS data sets and the deviation of bxy estimated at the pleiotropic SNP from the causal model. Based on this, the overall bxy can be estimated from all the instruments remaining using a GLS approach that takes the LD between the variants and the correlations between their effect sizes into account by modelling them in a covariance matrix. Additionally, GSMR uses the intercept of the bivariate LD score regression to account for potential sample overlap between the GWASs used as instruments for the exposure or outcome12. Estimates with binary exposures were converted to the liability scale40. Some of these analyses are exploratory because a few utilised GWASs were underpowered  and we therefore lowered the p value threshold for inclusion, in order to include at least 10 independent SNP instruments as previously recommended37.We investigated putative causal bidirectional relationships between these traits using GSMRp value threshold accordingly. The first matrix of the main analysis contained all 17 psychiatric traits, all four body composition traits, physical activity and childhood obesity  and adjusted our http://csg.sph.umich.edu/abecasis/metal/; for FUMA, see http://fuma.ctglab.nl/; for SSGAC, see https://www.thessgac.org/; for Complex Traits Genetics lab, see https://ctg.cncr.nl; for International Headache Genetics Consortium, see http://www.headachegenetics.org/; for the MAGIC, see https://www.magicinvestigators.org/; for UK Biobank, see https://www.ukbiobank.ac.uk/; for the PTSD working group of the Psychiatric Genomics Consortium, see https://pgc-ptsd.com/; for the Psychiatric Genomics Consortium, see http://www.med.unc.edu/pgc; for the R project, see https://www.r-project.org/; for the EGG Consortium, see https://egg-consortium.org/.For METAL, see Further information on research design is available in the Peer Review FileSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Reporting SummaryDescription of Additional Supplementary Files


Although oat cultivation around the Mediterranean basin is steadily increasing, its yield in these regions lags far behind those of Northern Europe. This results mainly from the poor adaptation of current oat cultivars to Mediterranean environments. Local landraces may act as reservoirs of favorable traits that could contribute to increase oat resilience in this region. To aid selection of suitable agro-climate adapted genotypes we integrated genome-wide association approaches with analysis of field assessed phenotypes of genetic variants and of the weight of associated markers across different environmental variables. Association models accounting for oat population structure were applied on either arithmetic means or best linear unbiased prediction (BLUPs) to ensure robust identification of associations with the agronomic traits evaluated. The meta-analysis of the six joint environments (mega-environment) identified several markers associated with several agronomic traits and crown rust severity. Five of these associated markers were located within expressed genes. These associations were only mildly influenced by climatic variables indicating that these markers are good candidates to improve the genetic potential of oat under Mediterranean conditions. The models also highlighted several marker-trait associations, strongly affected by particular climatic variables including high rain pre- or post-heading dates and high temperatures, revealing strong potential for oat adaptation to specific agro-climatic conditions. These results will contribute to increase oat resilience for particular climatic conditions and facilitate breeding for plant adaptation to a wider range of climatic conditions in the current scenario of climate change. Avena sativa L.) is a major cereal and fodder crop grown worldwide over approximately 9 million hectares. A major division of the crop is into white oats (A. sativa subsp. sativa L.), used for human consumption and fodder, and preferred for milling, and red oats (A. sativa subsp. byzantina (K. Koch) Romero Zarco) preferred for forage  causes high losses in yield and grain quality worldwide . Following data curation a total of 1712 markers were used for association purposes.Genotyping of the collection was performed by Diversity Arrays P/L, using the high density oat array  as described in -2. Sowings took place between November and December, according to local practices, except in Córdoba during the season 2010–2011 in which, due to intense rain levels, the sowing took place in January . No irrigation or artificial inoculation were performed at any location, crown rust infection occurring naturally. Hand weeding was carried out when required, and no herbicides or fertilizers were applied. Trials were hand-harvested” , heading date , and crown rust severity together with a description of the genotype by environment (GxE) interactions have been reported in Marker-phenotype associations were estimated with the software package TASSEL 4.1.27 . Initialp value for each marker, which is the probability of a greater F value under the null hypothesis that the polymorphic loci is unrelated to the phenotype. To assess the correct fitting of the data and the control for type I error, a combined quantile-quantile plot that compare the observed distribution of –log10(P) values for each marker-trait association with the cumulative was drawn for all models and methods. Selection of the optimal model was performed by estimating the genetic inflation factor λmedian, as follow:General linear models procedures tested fixed-effect models in which mean phenotypes of a given trait were predicted by the independent variables. Tests were run with 1000 permutations allowing determination of the site-wise A genetic inflation factor higher than 1.25 (λ > 1.25) indicates data stratification even after structure corrections. Therefore all models with λ > 1.25 were discarded and were not included in subsequent meta-analysis to avoid false positives .p values for assessing the significance of associations were corrected for multiple comparisons based on the false discovery rate (FDR) criteria . Humidity refers to mean percentage of relative humidity during the growing season. To evaluate the influence of environmental factors on the significant markers, data were subjected to non-metric multidimensional scaling ordination (NMDS)  during pre-heading, at heading and post-heading period, and humidity] were obtained from the AEMET,n (NMDS) . This orn (NMDS)  allowingn (NMDS) . Venn diNMDS) , and ME. Most models showed a genetic inflation value very close to 1 suggesting appropriate adjustment for potential substructure. Models that did not fulfill the expectation were discarded from the analysis. Supplementary Figure S3 depicts as an example the distribution of p values obtained with these models for rust severity. As stated, the degree of deviation from the red solid line representing the null expectation (absence of type I error) measured formally by the λ-statistic  as cofactors to take population structure into account (GLM-Q), a GLM model using the PCAs covariates as cofactors (GLM-PCA), a GLM model using both Q matrix and PCAs covariates (GLM-Q-PCA) and a MLM using both the percentages of admixture and the kinship coefficients as cofactors (Q and K matrices). In addition, we ran the models using both the means and the BLUPs. Out of all models tested, the GLM-PCA and the MLM models yielded the lowest overall genetic inflation factors and were chosen for further analysis. tatistic  was veryFigure 1A) and data had a good fit with the assumed normal distribution, confirmed for instance by low values of the Anderson-Darling statistic (as observed for Córdoba and Escacena data), markers were associated with higher probabilities and had a higher weight in the analysis of the ME. However, the representativeness of the particular environment on the average environment, which can be derived from heritability-adjusted genotype plus G×E (HA-GGE) biplot analysis , were more likely to appear significant in the meta-analysis. For instance two out of the seven markers significantly associated with crown rust severity in Córdoba, were also highly significant in the ME whereas none of the significant markers from Escacena or Salamanca were significantly associated with crown rust severity in the ME . Regarding the type of estimates used in the models, data showed that significant markers derived from BLUPs-based models were also significantly associated with the trait in means-based models. However, although BLUPs improved the adjustment of data to normality, they greatly reduced the variance, which usually reduced the number of significantly associated markers .To uncover the biological significance of the identified marker-trait associations, the association study was performed not only for the joint data (ME) but also for each environment and locality. Interestingly, some significantly associated markers in a particular environment were also significantly associated in the ME while many others were not. In addition, the association study from the ME data reveal novel significant association not detected for any of the single environments or localities. The former occurred when a marker was highly significant in a particular environment but not significant in the others while the latter occured when a marker was nearly significant in most single environments. We observed that the phenotypic data distribution is key for the strength of association but is not the only parameter to take into account. Thus, when phenotypic data were represented in a Q–Q plot . We found a total of five markers associated with yield, three with biomass, four with HI, five with heading date and seven associated with rust severity, respectively. Interestingly, marker Opt-8261 located onto Mrg23 (Table 3). As expected, most associated markers had no homology to any known gene confirming that the majority of the significantly associated markers are not located within the causative gene. However, significant homology to previously identified genes was detected for a handful of associated markers (Table 3). For example, oPt-10734 (accession number FI158728), located on Mrg05 (A. sativa. oPt-12924 (accession number FI157582), located on Mrg12, matched with a cinnamoyl-CoA reductase 1 of Aegilops tauschii. oPt-8261 (accession number FI158468), which was associated for both yield and HI had some similarity to a Hordeum vulgare RNA sequence encoding the 30S ribosomic S17 protein. oPt-15271 marker (accession number FI157794) which is included in marker Merge 82, associated with heading date, matched with a sequence encoding a predicted non-specific lipid transfer protein. Interestingly, marker oPt-11494 (accession number FI159112), located on Mrg01 and associated with rust severity, matched to a nucleotide-binding site leucine-rich repeat (NBS-LRR) disease resistance protein homologue (rga S-226) of Hordeum vulgare.The meta-analysis of the six joint environments for association of the main agronomic traits with genomic regions highlighted several significant markers  defining the relevant agronomic traits were the maximum temperatures, mainly pre- and post-heading, the minimum temperatures during the whole season and the rain during the post-heading period. Escacena was characterised by relatively high temperatures during the whole season [with positive projections (depicted) over these vectors] and low levels of rain post-heading [with negative projections (non-depicted) on that vector]. Salamanca followed the opposite pattern with high post-heading levels of rain and the coldest temperatures, although Sa10 showed a milder climate. Córdoba showed average conditions, with most projections falling near to the vectors origin, compared with the other two sites, confirming that it was more representative of the average environment.First, we performed an NMDS analysis in order to better understand the climate parameters contributing to different environments at the various locations. Supplementary Table S2). Figure 3 depicts the influence of the different climate variables on the phenotypic variance explained by significantly associated markers in the models. We observed that the phenotypic variance explained by the markers was highly influenced by climate variables, with many markers located in the extreme of the climate vectors. For yield, most markers, including the significant markers in the meta-analysis, grouped between the vectors of rain and maximum temperature post-heading, which are key factors determining yield . For biomass, markers highlighted as significant in the ME were near to the vector origin  indicating the lowest influence of climate variable in them. However, other markers explained a higher proportion of the phenotypic variance under more extreme climate conditions such as high levels of rain  or higher temperatures . Interestingly, for crown rust severity, vectors related to temperatures fell opposite to rain post-heading vector confirming that moderate temperatures and humidity favor this disease. The plot shows that again most of the markers highlighted as significant in the ME (Table 2) tend to localize near to the vector origin suggesting that they were less influenced by particular climatic variables. By contrast several markers such as Merge 11, 2, 3, 429 123, and 343, or oPt-13149 explained higher phenotypic variance under higher temperatures and low rain level or vice-versa.We then performed CCA in order to reveal the climate variable effect on the phenotypic variance explained by those markers that had been highlighted as significant in particular environments  and red  oat types forecasting the existence of a population structure which have to be accounted. The collection contains approximately 10% of the polymorphic markers found in the global oat reference panel  and was tua type  and withtua type  and morptua type , the idep value should be distributed according to the null hypothesis of no marker-trait association except for a reduced number of markers that are in true association with the trait. However, potential genotyping errors, cryptic relatedness and/or the stratification of the population might distort artificially the marker allele frequencies leading to the inflation of the test statistics across the whole genome  in addition to the overall ME analysis . This was done in order to find more reliable markers and to understand the relationship between the markers identified as significant in the ME and those identified in each locality. The rationale behind this was that better distribution of data in a particular site or data from a site with climatological conditions more similar to the average environment could influence the significance of the marker-trait association found in the meta-analysis . We observed that, as expected, under a high variance and optimal distribution of data, the significance of the marker-trait association was higher. In addition, associated markers highlighted as significant in those sites with environments that better reflected the average environment had a higher probability to be significant in the ME. On this basis, markers that arise in a particular site but not in the ME could be particularly influenced by the climatic variables of that site. To explore this possibility we performed NMDS and CCA . Interesability” . This miability” .Previous studies have identified several QTL for the traits studied here. A dense hexaploid oat consensus map based on 12 bi-parental populations is now available  permittiAlthough most significantly associated markers were expected to be separate from the causative variation, five of them appear to encode for functional genes. Thus, marker oPt-10734 was identified as an iron/siderophore transporter. Interestingly, graminaceous plants, such as oats, use a chelation strategy as a mechanism for iron acquisition. These plants secrete phytosiderophores to solubilize soil iron, and uptaking the resulting iron-phytosiderophore complexes . PhytosiThe results provided here will allow the use of the genetic resources included in this study in breeding programs based on sound information of their adaptation to different environments and on the genetic regions more influenced by particular climatic variables. This knowledge may help to improve the oat crop in current but also future growing conditions.NR conducted most of the bioinformatic analysis and modeling. GM-B and JS-M conducted most work related to genotyping and phenotyping, respectively. DR contributed to the stress resistance aspects. FF contributed to statistical aspect of the work. CH and TL contributed to genetic aspects. EP steered the research, designed experiments, and interpreted the results and drafted the manuscript. All authors participated in the critical reading and writing of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.