The vertebrate stress response comprises a suite of behavioural and physiological traits that must be functionally integrated to ensure organisms cope adaptively with acute stressors. Natural selection should favour functional integration, leading to a prediction of genetic integration of these traits. Despite the implications of such genetic integration for our understanding of human and animal health, as well as evolutionary responses to natural and anthropogenic stressors, formal quantitative genetic tests of this prediction are lacking. Here, we demonstrate that acute stress response components in Trinidadian guppies are both heritable and integrated on the major axis of genetic covariation. This integration could either facilitate or constrain evolutionary responses to selection, depending upon the alignment of selection with this axis. Such integration also suggests artificial selection on the genetically correlated behavioural responses to stress could offer a viable non-invasive route to the improvement of health and welfare in captive animal populations. Stress responses comprise suites of physiological and behavioural traits that enable individuals to cope with adverse conditions . Some inG;Natural selection does not act on single traits in isolation, but rather on multivariate phenotypes . This isOncorhynchus mykiss  through quantitative genetic modelling provides a complementary strategy that also allows investigation of exactly how multivariate genetic variation is structured within populations. In the context of the stress response, this should provide insights into both how selection has acted in the past  selection β . This enables us to determine not only (i) whether these components are genetically integrated on the major axis of genetic (co)variation , but also (ii) whether the structure and orientation of this axis suggests variation in overall stress responsiveness and/or ‘coping style’ . We predicted that individual traits will be heritable and that G will contain strong genetic correlation structure between behavioural and physiological components of the stress response consistent with genetic integration. We also predicted that both behavioural and endocrine components of the stress response would load on the major axis of genetic variation in multivariate trait space, gmax. The ‘stress coping style’ model  and ‘shoaling trials’ (ST) to characterise among-individual and genetic variation in the behavioural stress response. Second, we characterise the physiological stress response and its rate of habituation by assaying GC levels following first and third exposure to a handling and confinement stressor . Utilising repeated behavioural and physiological testing of individual fish within a known pedigree structure, we are able to estimate the repeatable  component of phenotypic (co)variance in these stress response traits, and then determine the additive genetic contribution to this (e’ model  predictse’ model . That is1 and Cortisol3). Full details of husbandry, phenotyping and analysis are provided in Materials and methods.In total, we obtained multivariate behavioural data from 5966 trials  on 1384 individual fish. The number of individuals phenotyped  and the mean number of observations per fish  varied across the behavioural data types. All fish were contained within a genetic pedigree structure comprising maternal full-sibships nested within paternal half-sibships. This structure was produced via multiple rounds of breeding work and has a maximum depth of five ‘generations’. Some of the OFT data have already been used in studies of the evolutionary genetics of personality , but heremergence time’) was extracted from ETs and natural log (ln) transformed for analysis, while shoaling tendency was calculated from STs as the time spent in the third of the tank closest to a same-sex shoal  minus the time spent in the third of the tank farthest from the shoal. The OFT, ET, and ST testing paradigms are all considered to assay behavioural components of the stress response in the broad sense, as each test starts with the capture and transfer of the focal fish into a novel and brightly lit arena away from their home tank and familiar tank mates.Behavioural data were extracted from OFTs, ETs, and STs using video tracking of fish (as described in track length (distance swum), area covered (as a proportion of the arena floor area), the number of freezings , and were very highly correlated with a measure of average distance from the wall at the observation level . All these OFT traits are repeatable and heritable in this population  with track length. A possible explanation is that a long track length arises sometimes from a (putatively) less stressed fish exploring the arena (fish one in area covered and time in the middle (exploration) or the converse (flight response).The absence of a strong positive correlation between  covered  is notabrelative area covered’. We used a simple simulation procedure  to predict expected area covered for any given track length under a null ‘random swim’ within the arena  while controlling statistically for social housing group and non-genetic sources of among-individual variance . These confirmed the presence of significant additive genetic variation for the relative area covered trait, as well as for track length, time in the middle  are low to moderate .Pedigree-based ‘animal models’  were usence time . With thtendency , despite1 = 8.47 ± 0.05, Cortisol3 = 8.02 ± 0.05, Wald F1,38.8 = 110.0, p < 0.001; see Materials and methods for explanation of units). We first modelled Cortisol1 and Cortisol3 as distinct response variables in a bivariate framework assuming no GxE . This model revealed a significant additive genetic component to variation among individuals in their cortisol levels following stressor exposure .Using a series of nested bivariate animal models, we tested for additive genetic variance in cortisol levels (ln-transformed) following stressor exposure (handling and confinement) and for genotype-by-environment interaction (GxE). In this context, the environment (E) is the trial number in each fish’s stress trial series . Any GxE present can therefore be interpreted as genetic variance for habituation to the stressor, given that the average cortisol level was lower following exposure to the third stressor than the first , meaning GxE is present. This can be viewed as genetic variance for habituation to repeated stressor exposure, or as a change in genetic variance for cortisol from the first to the third sampling (A-Cortisol1 = 0.081 ± 0.029) and the third (VA-Cortisol3 = 0.041 ± 0.025) arises because genotypes with higher-than-average levels for 1Cortisol habituate more rapidly (i.e. have more negative reaction norm slopes). Note however that allowing the cross-context genetic correlation to deviate from +1 does not significantly improve the model fit : thus the rank order of the genotypes does not appreciably change across the two contexts .Expanding the model to allow GxE by estimating separate genetic variances for Cortisolsampling . These aGroup-Cortisol1 = 0.038 ± 0.014, VGroup-Cortisol3 = 0.032 ± 0.012), but that residual (unexplained) variance is greater after the third stressor exposure . In combination, the changes in both additive genetic and residual variance between the two contexts lead to appreciably higher heritability for cortisol levels following the first stressor exposure relative to the third .In this model we also find that variance in cortisol explained by housing group effects is similar across contexts . Although the above analysis demonstrates GxE for cortisol, the strong positive cross-context genetic correlation justifies collapsing Cortisol1 and Cortisol3 into a single trait to maximise statistical power to detect any genetic covariance with behaviour.There is strong evidence for phenotypic integration of vels see . To testrelative area covered, time in the middle, track length, (square root-transformed) freezings, (ln-transformed) emergence time, and (ln-transformed) Cortisol (now treated as two repeats of a single trait). We standardised all traits to standard deviation units (after transformation where applicable) to assist multivariate model fitting and to prevent eigenvectors of G (see below) being dominated by traits with higher variance in observed units. To simplify interpretation of G, we also multiplied emergence time by –1 after transformation. Thus, high values denote rapid emergence from the shelter.Our final model contained six response traits: G . We found a large number of strong genetic covariance/correlation estimates between trait pairs that were deemed statistically significant . The 4 OFT traits  show strong patterns of pairwise genetic correlation. Track length shows significant negative genetic correlation with relative area covered, time in the middle, and √freezings . Relative area covered and time in the middle show significant positive genetic correlation with one another, and √freezings shows significant positive correlation with time in the middle. The only genetic correlation among OFT traits that is not significant is between √freezings and relative area covered. The speed at which individuals emerge from the shelter during the emergence trials, -ln emergence time, shows significant positive genetic correlation with track length . There are no significant pairwise genetic correlations between –ln emergence time and the other OFT traits, but all correlation estimates are negative. Pairwise genetic correlations between ln cortisol and all behavioural traits are illustrated in cortisol shows significant negative genetic correlations with both time in the middle and √freezings. Genetic correlations between ln cortisol and other OFT traits were not significantly different from zero , with correlations estimated as negative with relative area covered and positive with track length. The genetic correlation between –ln emergence time and ln cortisol is also not significant, and is estimated at very close to zero.The resultant estimate of G  containsG provides a more holistic view of the genetic covariance structure and the level of integration among traits. Here, the major axis maxg  explains 59.5%  of the genetic variance in multivariate phenotype. Subsequent axes necessarily capture declining proportions of the multivariate genetic variance  type of behavioural stress response to the OFT assay . This behavioural profile is associated with ‘stronger’ physiological responses to stress (i.e. producing higher cortisol levels in response to the stressor).Eigen decomposition of  on gmax . Relativresponse , where ggmax of the genetic variance-covariance matrix G we suggest that it is more readily interpreted as an axis of genetic variation in stress coping style than in stress responsiveness, although we acknowledge that any distinction may be somewhat subjective. Overall, by estimating the genetic covariance structure among traits we find the first quantitative genetic support to date for the hypothesis of evolutionary integration between behavioural and endocrine components of the stress response.In this study, we sought to determine whether – and to what extent – there exists genetic variation for, and integration between, behavioural and physiological (endocrine) components of the stress response. Our results provide three main novel insights. First, we find that genetic variation does underpin individual differences in both behavioural and physiological components of the stress response. Second, we find genetic covariance structure among these behavioural and physiological traits, indicating that they are indeed genetically integrated. Thirdly, having identified the structure of the major axis relative area covered. The latter trait, derived by considering an appropriate biological null model of the relationship between track length and (absolute) area covered, serves as a useful proxy for exploratory behaviour. Here, we demonstrate an axis of repeatable and heritable variation that spans from ‘freeze’ type responses characterised by less active but more exploratory swimming patterns  through to a ‘flight’ type response characterised by higher activity coupled with thigmotaxis and low exploration . Given the wide use of OFTs in biomedical research  may have broad applicability for discriminating between exploration and stress/anxiety-related patterns of behaviour. The OFT paradigm is also widely applied to studies of ‘shy—bold’ type personality variation in fishes , emergence time, an OFT trait that is commonly considered to reflect ‘boldness’ (Burns 2008), was found to be heritable but did not load significantly on gmax  could perceive the shelter area as less safe than the open arena . Indeed, in an earlier study some guppies decreased (rather than increased) shelter use following simulated predation events , including the derived trait ch e.g.,  as well n fishes  and othen fishes . The extn fishes  should bn fishes . While wgmax see . This con events .G.Tendency to shoal varies among individuals but is not detectably heritable. Although not generally considered a stress-response trait per se, shoaling is an anti-predator behaviour in guppies . We had 2 = 0.31) constituting over 80% of the individual–level repeatability (R = 0.37) for ln-transformed Cortisol. In addition, by adopting a reaction norm approach to modelling stress physiology, as recently advocated by ourselves  for cortisol levels following the first exposure to the stressor than following the third. This pattern, which occurs because genotypes that produce the highest cortisol response at first exposure also show the most marked habituation, is consistent with the idea of cryptic genetic variance being ‘released’ by exposure to novel, and so potentially stressful, environments . All elsG shows evidence of genetic integration between behavioural and endocrine components of the stress response. The ends of the major axis of G (gmax) are largely consistent with expectations given genetic variance in ‘stress coping styles’  produce higher levels of cortisol following the handling and confinement stressor, while genotypes showing (putatively) more reactive ‘freezing’ type behaviour in the OFT  produce lower levels of cortisol following the same stressor. Were one to accept this view, the trait loadings on gmax would suggest that thigmotaxis and freezing behaviour are strong indicators of stress coping style. Notably, here we find continuous variation along this major axis of genetic variation (see maxg represents variance in stress response ‘magnitude’ rather than coping style per se. This is because the putatively ‘reactive’ end of gmax might simply reflect a low magnitude stress response rather than a particular style of ‘coping’ with a stressor. Such an interpretation would help explain why fish (or genotypes) that produce lower levels of cortisol following handling and confinement also have more ‘exploratory’ movement (higher relative area covered) and reduced thigmotaxis (i.e. increased time in the middle) in the OFT. We note that the distinction between ‘style’ and ‘responsiveness’ may be rather moot if, for example, fish with proactive styles are also more responsive.Considering all traits together,  styles’ . Genotyption see , rather tion see . An alteG among traits analysed here . In principle, disentangling these dimensions should be possible by characterising stress response through within-subject comparison of behaviour and endocrine state between stressed and ‘baseline’ (unstressed) states. In practice, we found here that genetic variation in GC responsiveness  is very strongly correlated with the average (or intercept). We found similar results for behavioural responses to increased stressor severity in another study of this population, where there exist strong among-individual correlations between the intercept and slope of the plastic response  could be viewed as a second dimension of variation, distinct from differences in the ‘type’ (or style) of response . It is nresponse . While wG should shape  future evolutionary responses to selection—whether natural or artificial. Here we have no direct knowledge of how contemporary selection is acting in the wild or whether it might be changing as a consequence of anthropogenic stressors. Nor do we know exactly how well our estimate of G will match that which may be found in the wild . Thus, we cannot comment directly on how G will shape future evolution of the guppy stress response beyond noting that selection on behaviour will cause correlated evolution of endocrine physiology (and vice versa). Nonetheless, while it seems reasonable to expect that current integration of stress response in natural populations should be broadly adaptive, this seems less likely in captive populations . We know that prolonged, chronic activation of stress response pathways (notably the HPA/I axis) frequently disrupts health and survival in captive animals (The genetic integration of behaviour and physiology detected here is consistent with (but not proof of) the hypothesis that correlational selection in the past has led to the coevolution of these components of the stress response. This hypothesis assumes that correlational selection favours any combination of trait values that yield higher fitness, creating a genetic correlation as mutations that generate such combinations will be recruited into the population while those that do not will be selected out . While r animals . It may  animals . For insG to be broadly consistent with the widely invoked ‘reactive—proactive’ model of variation in stress coping style  and/or stress contexts (i.e. exposing the subjects to different types of stressor). With respect to the latter, here we observed behavioural and GC responses to two different stressors separated in time. We think that our demonstration of genetic correlation structure between behaviours tested in one context and physiology assayed in a different one adds at least some support to the idea that the integration characterised here may ultimately prove generalisable across stress contexts.Here, we find evidence for genetic variation in – and integration of – behavioural and physiological (endocrine) components of the stress response. Overall, we consider the structure of ng style . This invice versa. Determining contemporary selection on the stress response, and testing the possibility that its underlying genetic variation is maintained by fitness trade-offs, is thus an obvious – if empirically challenging – next step to understanding the functional importance of genetic variation in the stress response in wild populations. In a more applied context, integration of behavioural and endocrine stress response components at the genetic level has potential utility for artificial selection to improve resilience to chronic stressors in managed populations  Act 1986 under licence from the Home Office (UK), and with local ethical approval from the University of Exeter.We used fish taken from our captive population housed at the University of Exeter’s Penryn campus, which is descended from wild fish collected in 2008 from the lower Aripo River in Trinidad. This population has been maintained at a population size of several thousand and has undergone no deliberate selection or inbreeding. All fish are fed to satiation twice daily (0800–1000 hr and again at 1600–1800 hr) using commercial flake food and live To create our pedigreed sub-population, female fish were sampled haphazardly from the stock tanks and kept in female-only groups for 3 months. Isolation from male contact minimised the chance of females carrying viable sperm from previous matings. For the first generation of offspring, we used a group breeding design  and brood size. We kept juvenile fish in full-sib family groups in 2.8 L tanks before moving them to 15 L ‘growth’ tanks at an average age of 56 days. At an average age of 133 days (range 59–268), we tagged individuals and placed them into mixed family groups of 16–20 adults , kept in 15 L tanks. Note that variation in tagging age arose largely because groups were necessarily established sequentially as sufficient individuals from multiple families reached a large enough size that we deemed the procedure to be safe. Each adult group comprised a mix of fish from different families, reducing the potential for common environment effects to upwardly bias our genetic parameter estimation.Behavioural phenotyping commenced at least one week after tagging. In all trials, we filmed movement behaviour of individual fish using a Sunkwang video camera equipped with a 6–60 mm manual focus lens suspended over the tank. We used the tracking software Viewer II (BiObserve) to extract behavioural data from each recording (detailed below). The tank was lit from below using a light box and screened with a cardboard casing to prevent external visual disturbance. After each behavioural trial, the individual tested was weighed and then moved to a temporary ‘holding tank’. Once a full group (as described above) had been tested, all were moved from the holding tank back to their home tank. We replaced the water in the testing and holding tanks between groups to reduce the build-up of hormones or other chemicals. The first offspring generation experienced four repeat open field trials (OFTs) over a 2-week period, with at least 48 hr between trials. Subsequent generations experienced four repeat behavioural trials, alternating 2 OFTs with two emergence trials (ETs). For the final two generations, we extended the OFTs by including a shoaling trial (ST) at the end of each OFT.Open field trials (OFT) followed the methodology described by track length , area covered , time in middle , and number of freezings  were appended to a subset of OFTs, by positioning a small tank containing 10 stock fish (of same sex as the test subject) next to one end of the OFT tank but with visual cues blocked by a cardboard divider. At the end of the normal OFT, we removed this divider slowly, allowing the focal animal to have visual contact with the shoal. We began recording the shoaling trial 30 s after removing the divider in order to limit any artefacts of slight disturbance. . We then recorded behaviour of the test fish for an additional 3 min. We characterised shoaling tendency via the tracking software by subdividing the tank area into three equal-sized rectangular areas: one next to the tank holding the group of same-sex fish, one farthest from this group, and the central area. We then calculated shoaling tendency as the time spent in the 1/3 area closest to the same-sex group after subtracting the time spent in the 1/3 area farthest away. The decision to use a single-sex shoal aimed to reduce any effects of  mate preference and/or avoidance, but also this necessitated replicate arena setups allowing male and female individuals from each group to be tested in the OFT/ST concurrently. We randomised which tank was used for each sex in each group and recorded this information.Emergence trials (ET) followed the methodology described by Time to emerge (in seconds) was recorded by the tracking software automatically as the fish exited the shelter area and emerged into the open tank section. Trials were ended either at emergence or at 15 min if the fish had not emerged by that point; in the case of non-emergence, fish were given the maximum value (i.e. 900 s).area covered’ variable assayed in the OFT is calculated in BiObserve by dividing the arena  into 1cm x 1cm grid squares. The path taken by the fish during observation is then used to determine what proportion of these grid squares the fish entered. However, we sought to derive a measure of ‘relative area’ that describes whether a fish covers a large, or small area relative to its observed track length.The ‘track lengths. We first selected 40 OFT results at random from our total data set and extracted the coordinates of the fish in each frame from the raw tracking file, creating a set of x and y movements and their associated distances. As original coordinates were recorded in pixels we used the calibration of the software to convert to cm units. We then use a ‘random walk’ algorithm to select a movement (i.e. step size and direction) from this observed distribution at random and calculate the new coordinates. If the movement keeps the ‘fish’ within the bounds of the ‘tank’ (i.e. defined as a 20cm x 30 cm arena), the movement is accepted and coordinates added to a movement matrix; if not, a new movement is drawn from the distribution. If the movement is greater than 1 cm in distance, we break the movement into a number of smaller parts to be added to the matrix . Once the total distance of the random walk reached or exceeded the track length set as the simulation objective, the path is terminated and the area covered is calculated by counting the number of unique grid squares in the matrix of coordinates and dividing by the total number possible.To do this, we simulated ‘random swims’ within the arena across the observed range of track length , we modelled (simulated) area covered as a fourth order polynomial function of track length. Using this regression model (which explained 97.8% of the variance in simulated data), we calculated the relative area for each actual OFT trial as the observed area covered minus the area covered under a random swim, as predicted from our regression model and the observed track length.After simulating random walks across 500 values of On completion of behavioural data collection, individuals entering the endocrine testing program were left undisturbed for a minimum of two weeks. Waterborne hormone sampling was then conducted over a 5-day period that included three handling and confinement stressor exposures with 48 hr between each. We followed the method described by We collected samples between 1200 and 1400 hr to control for diel fluctuations in GC levels. For each sample, we netted an entire group from their home tank quickly using a large net, transferring them to two holding tanks (containing water from the home tank supply) for moving to an adjacent quiet room (performed within 20 s of the net first hitting the water). We then transferred fish to individual Pyrex beakers containing 300 ml of clean water from the main supply (which serves the main housing units), which had been warmed to the appropriate temperature . Beakers were placed within cardboard ‘chambers’ to prevent fish from seeing each other or experiencing outside disturbance. One fish was transferred every 30 s, alternating across holding tanks, such that all fish were in their beakers within 10 min of the initial netting. After 60 min in the beaker, each fish was removed by pouring its sample through a clean net into a second beaker, with the fish then quickly checked to confirm ID and returned to the holding tank until the entire group could be returned to its home tank.12 = 0.029, p = 0.97; June, female:: t12 = 0.343, p = 0.74; December, male:: t12 = 0.119, p = 0.91; December, female:: t12 = 0.224, p = 0.83). The serial dilution curves also identified 1:32 as an appropriate dilution to ensure that all samples fell on the linear phase of the standard curve. A total of 37 96-well plates were used, and the pooled sample was included at the beginning and end of each plate. Intra-assay coefficients of variation ranged from 0.12% to 19.83% with a median of 3.08%; the inter-assay coefficient of variation was 19.22%. Cortisol is presented and modelled in (ln-transformed) units of ng/hr to reflect the 1 hr sampling duration.We immediately filtered each water sample using Grade one filter paper (Whatman), then passed them slowly through solid phase C18 extraction columns  via high-purity tubing  under vacuum pressure . Columns2nDF as twice the difference in model log likelihoods, with the number of degrees of freedom (n) equivalent to the number of additional parameters in the more complex model. When testing a single random effect (variance component), we assumed the difference to be asymptotically distributed as an equal mix of χ20 and χ21 , we used the random effects specification to partition phenotypic variation  into the effects of additive genetics (Va), permanent environment defined as the non-(additive) genetic component of among-individual differences, (Vpe), and housing group (Vgroup), as well as residual variation . We natural log-transformed emergence time prior to analysis to meet assumptions of residual normality and homoscedasticity. For all behavioural traits, we included fixed effects of assay repeat, the order within each group in which the fish was trialled (mean-centred continuous predictor), temperature , time , age , sex, and the generation from the breeding population. The order caught predictor in particular was used to control statistically for variation in disturbance over the course of measuring a group . We tested the significance of genetic variance for each behaviour by LRT comparison of the corresponding full model to one in which the (additive) genetic random effect was excluded.For each OFT, ST, and ET behaviour in turn . Random effects were first used to partition phenotypic (co)variance  into among-group and residual components. Fixed effects included the context-specific means, and overall effects of the order in which the fish was caught from each group for assay (mean-centred continuous predictor), temperature , time of day , age , and sex. In addition, we included fixed covariates of body mass  and a sex by body mass interaction (see aV) we compared this first bivariate model to a second formulation that also included the (additive) genetic merit, but under the assumption that this is invariant with context within an individual . We then test for the GxE by comparing the second model to a third in which we allow GxE (i.e. the context-specific genetic variances are free to differ and the cross-context genetic correlation can be <+1).Cortisol data were also natural log (ln) transformed for analysis. We formulated a bivariate model to test for both additive genetic variation and genotype-by-environment interaction (GxE) in cortisol levels across the two ‘contexts’ , emergence time (ln transformed), and Cortisol (ln transformed). We multiplied (transformed) emergence time by –1 to simplify interpretation . We also scaled all (transformed) response variables to standard deviation units. This was to facilitate model fitting, and also prevent scale effects complicating interpretation of eigenvectors of G. Fixed and random effects were fitted on each trait as specified for the univariate models. Note that one exception to this is that we elected to treat Cortisol as a single repeated-measures trait here  such that a permanent environment effect was now included. Fixed effects estimates are reported in the supplementary information , permanent environment (PE), group (GROUP), and residual (R) covariance structures as unstructured matrices to be estimated. Note that R partitions observation-level covariances  that are not definable or statistically identifiable if traits are not measured at the same time . Where this was the case we constrained specific covariance terms in R to equal zero. Estimates of PE, GROUP and R are provided in G was specified as a diagonal matrix . To aid the interpretation of covariance terms contained in G, we calculated the corresponding genetic correlations ar from the full model. For any pair of traits , ar = aCOVa/ (√(a(x)V) × √(a(y)V)). We also subjected our estimate of G to eigen decomposition to determine the proportion of additive genetic variation captured by each principal component and assess whether the major axis of variation (gmax) could indeed explain most of the genetic variance in the multivariate phenotype. We estimated uncertainty on the trait loadings associated with each principal component (eigenvector), as well as on the variance explained by each eigenvector, using a parametric bootstrap approach as described by We specified additive genetic . Best linear unbiased predictors (BLUPs) are used for visualisation only, not for any statistical analysis  at the among-individual level, we also built a multivariate model as above with the addition of shoaling tendency and without estimating additive genetic effects. The estimates of all among-individual (co)variances are provided in To test for associations between all traits  feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Molly Przeworski as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Joel McGlothlin (Reviewer #1).Thank you for submitting your article "Evolutionary genetic integration of behavioural and endocrine components of the stress response" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this summary to help you prepare a revised submission.Essential revisions:1. Extract measures of passive (freezing) versus active (flight/escape) behavioral responses from the video recordings and include them in the analyses of heritability and genetic covariance. Evaluate whether these alternative behavioral measures provide improved discriminatory power between a "reactive-proactive" model versus continuous variation in stress responsiveness.2. Clearly state the criteria that define a "single major axis of genetic variation," or revise to avoid this strong claim.3. Provide a complete table of all the eigenvectors and eigenvalues for the eigendecomposition analysis of G.4. Address the potential effects of using non-standardized stressors across phenotypes on variance component estimation .Reviewer #1 (Recommendations for the authors):Overall, I thought this paper was fantastic. My public review reflects my enthusiasm for the work. Most of my points below amount to writing suggestions.My only substantive comment is that I think it would be helpful to show a table of all the eigenvectors and eigenvalues, not just the figure showing the first eigenvector loadings. This could be put into the supplement with the other tables.70-71: Need some more citations here: Maybe Cheverud 1982 (which is cited later), Roff and Fairbairn 2012143: This is just a style preference, but I always prefer past tense when it comes to the word "predict" in the introduction. By the time the paper is being written, the prediction is in the past. There are some places in the results like this where it is written in present tense but seems like it should be in past tense.146-147: This sentence is perhaps too informal.162: Multivariate doesn't need to be in parentheses.314-327: The organization of this paragraph could be improved a bit. It was tough to follow the results and I had to read it over again to get it.Figure 3: Would these make more sense with cortisol on the X? I know it's not a regression, but people tend to think of the path hormone -> behavior.479: I think it might be useful to have one more logical step linking the results to potential past correlational selection. Specifically, some explanation about how these traits might interact to influence fitness would help. It's not entirely clear whether the authors are envisioning correlational selection on a suite of behaviors or correlational selection on hormone levels plus one or more of the behaviors. I'm obviously sympathetic to the idea that correlational selection influences hormone-mediated suites, but I do worry that others might see discussing of it here as too speculative (see below). The authors don't necessarily need to talk about it to explain the significance of their results.507: I may be misunderstanding this, but wouldn't the reactive-proactive model be consistent with a single axis as well? Reactive would be on one end of the spectrum and proactive would be on the other. This goes back to lines 150-159. The way it is described sounds like a single axis. Is the difference just that the two ends of the continuum are more extreme in the reactive-proactive model?514: Following on the comment above, I like the idea of correlational selection explaining the effect, but it might be too much of a speculation to lead off the last paragraph of the paper with it. In other words, I think the results are consistent with it, but don't quite suggest it.Reviewer #3 (Recommendations for the authors):Title: I am not sure what "evolutionary genetic integration" means. "(Quantitative) Genetic integration" sure, but what does the "evolutionary" add? All biological states are the product of evolution, so in a sense "evolutionary" could apply to all biological objects but be quite pointless. The study describes the current state of G in the population but does not describe an evolutionary process (past or on-going).L.69-73 I agree that past correlational selection would have tended to shape the G matrix along a major axis, but it is difficult to predict a priori what a G matrix should look like if you do not know the variance-covariance of mutational (and migration) input was or what were the exact properties of correlational selection. So "should" we really expect genetic and phenotypic integration? The expectation is not clear to me. Maybe make clearer what the expectation assumes.L.72 First occurrence of the word "integration" in the main text. Maybe it would be good to formally define this concept that is central to the paper.L.96 Is Thomson et al., 2011 the correct reference? The sentence was about great tits, the paper is about trouts.L.105 Clarify what integration looks like in term of G properties.L.145-147 "We also predict that G will be dominated by a single major axis of genetic variation in multivariate trait space, but are more circumspect about how that might look." is a vague and problematic prediction; in fact I think it is the weakest point of the paper. What does define a single major axis? Would you consider this prediction correct if you had one axis explaining 51% of variation and another one 49%? What about one axis at 49% and others around 10%? Is the absolute proportion captured by the first axis what matters, or is it the difference between the proportion captured by different axes? What values should the absolute or relative proportions captured by the first axis take for your prediction to be confirmed? What is the null distribution of joint-proportions captured by each axis and do empirical estimates deviate from that null? Without a clearer expectation I find the prediction not very useful and it is difficult to interpret the eigen decomposition of G . If a clear prediction cannot be formulated I think the author should refrain from making a prediction at all and instead say they will describe the pattern of eigen components.L.335 Similarly: "This structure is suggestive of a single major axis…" Maybe, but without a definition of what defines a "single major axis", this conclusion is void.L.514 "Our results suggest that correlational selection in the past has likely shaped the multivariate stress response". I think this conclusion is incorrect and unrelated to the results. The G matrix is not only shaped by selection, but also by mutational input (in your case it is not difficult to imagine that mutations have pleiotropic effects on behaviour and cortisol levels), migration (and drift). In itself, an instantaneous picture of G says little about processes that have shaped it in the past. Reviewer #1 (Recommendations for the authors):Overall, I thought this paper was fantastic. My public review reflects my enthusiasm for the work. Most of my points below amount to writing suggestions.My only substantive comment is that I think it would be helpful to show a table of all the eigenvectors and eigenvalues, not just the figure showing the first eigenvector loadings. This could be put into the supplement with the other tables.We are very grateful to Reviewer 1 for their positive comments, and we respond point-by-point below. As requested, we have included a supplemental table of the eigen vectors and values .70-71: Need some more citations here: Maybe Cheverud 1982 (which is cited later), Roff and Fairbairn 2012Thank you for the suggestions – now inserted.143: This is just a style preference, but I always prefer past tense when it comes to the word "predict" in the introduction. By the time the paper is being written, the prediction is in the past. There are some places in the results like this where it is written in present tense but seems like it should be in past tense.Agreed, and we have made several edits towards the end of the introduction accordingly.146-147: This sentence is perhaps too informal.max.”Now changed to “We also predicted that both behavioural and endocrine components of the stress response would load on the major axis of genetic variation in multivariate trait space, g162: Multivariate doesn't need to be in parentheses.Parentheses removed.314-327: The organization of this paragraph could be improved a bit. It was tough to follow the results and I had to read it over again to get it.We have made edits to this paragraph, and hope that its readability has improved.Figure 3: Would these make more sense with cortisol on the X? I know it's not a regression, but people tend to think of the path hormone -> behavior.We have updated Figure 3 to put cortisol on the x axis .479: I think it might be useful to have one more logical step linking the results to potential past correlational selection. Specifically, some explanation about how these traits might interact to influence fitness would help. It's not entirely clear whether the authors are envisioning correlational selection on a suite of behaviors or correlational selection on hormone levels plus one or more of the behaviors. I'm obviously sympathetic to the idea that correlational selection influences hormone-mediated suites, but I do worry that others might see discussing of it here as too speculative (see below). The authors don't necessarily need to talk about it to explain the significance of their results.We have inserted some text about the assumptions here as regards selection favouring combinations of trait values that yield higher fitness, and that our interpretation is necessarily speculative.507: I may be misunderstanding this, but wouldn't the reactive-proactive model be consistent with a single axis as well? Reactive would be on one end of the spectrum and proactive would be on the other. This goes back to lines 150-159. The way it is described sounds like a single axis. Is the difference just that the two ends of the continuum are more extreme in the reactive-proactive model?We think this was just a slight misunderstanding of our intended meaning , however this section has now been rewritten in the light of new results including the number of freezings (as advised by reviewer 2).514: Following on the comment above, I like the idea of correlational selection explaining the effect, but it might be too much of a speculation to lead off the last paragraph of the paper with it. In other words, I think the results are consistent with it, but don't quite suggest it.We agree and have removed this  – please see response to reviewer 3 where we address this in more detail.Reviewer #3 (Recommendations for the authors):Title: I am not sure what "evolutionary genetic integration" means. "(Quantitative) Genetic integration" sure, but what does the "evolutionary" add? All biological states are the product of evolution, so in a sense "evolutionary" could apply to all biological objects but be quite pointless. The study describes the current state of G in the population but does not describe an evolutionary process (past or on-going).“Evolutionary genetic” is often used in our field in the context of polygenic traits studied within an explicitly evolutionary framework; however, we agree for a general audience this is not particularly meaningful. We have therefore removed ‘evolutionary’ from the title .L.69-73 I agree that past correlational selection would have tended to shape the G matrix along a major axis, but it is difficult to predict a priori what a G matrix should look like if you do not know the variance-covariance of mutational (and migration) input was or what were the exact properties of correlational selection. So "should" we really expect genetic and phenotypic integration? The expectation is not clear to me. Maybe make clearer what the expectation assumes.We have modified the text here to be more circumspect about this point, and highlighted relevant assumptions about mutational input:et al., 2008). In the context of the stress response we should therefore expect genetic – as well as phenotypic – integration of behavioural and physiological traits .”“Evolutionary theory predicts that correlational selection will shape the structure of multivariate quantitative genetic variance , contingent on the distribution of new pleiotropic mutations that generate multivariate genetic variance as well as the selection that depletes it . In general, correlational selection should (under certain assumptions) have direct and strong effects on genetic covariances , behavioural ecology (Sih and Bell 2008)), but has not been tested for explicitly using quantitative genetic approaches.”L.96 Is Thomson et al., 2011 the correct reference? The sentence was about great tits, the paper is about trouts.Thank you for catching this – the sentence was also meant to refer to trout (as per the citation). Amended to reflect this.L.105 Clarify what integration looks like in term of G properties.We have inserted the following sentence here:“Integration in G is manifest as genetic correlations between trait pairs, and also at the level of the whole matrix by an overdispersion of eigenvalues—indicating that there are fewer effective dimensions of genetic variation than there are traits measured (Blows 2007).”L.145-147 "We also predict that G will be dominated by a single major axis of genetic variation in multivariate trait space, but are more circumspect about how that might look." is a vague and problematic prediction; in fact I think it is the weakest point of the paper. What does define a single major axis? Would you consider this prediction correct if you had one axis explaining 51% of variation and another one 49%? What about one axis at 49% and others around 10%? Is the absolute proportion captured by the first axis what matters, or is it the difference between the proportion captured by different axes? What values should the absolute or relative proportions captured by the first axis take for your prediction to be confirmed? What is the null distribution of joint-proportions captured by each axis and do empirical estimates deviate from that null? Without a clearer expectation I find the prediction not very useful and it is difficult to interpret the eigen decomposition of G . If a clear prediction cannot be formulated I think the author should refrain from making a prediction at all and instead say they will describe the pattern of eigen components.post hoc context). We have therefore amended the statement to emphasis that our goal is to describe the patterns. Nevertheless, we do think that it is appropriate to provide some predictions—even if qualitative—to help a reader unfamiliar with eigen decomposition of covariance matrices to understand the connection to the biology we describe. Rather than predicting a ‘single’ major axis, we now explicitly predict that the major axis of genetic variation  will load on both behavioural and endocrine components . We return later to the theme of just how dominant this axis is, using the confidence intervals around the eigen values in our post hoc interpretation of results.We accept this as a valid criticism. To our knowledge there are no formal definitions of what comprises a ‘single’ major axis and so we are reluctant to introduce one de novo  describes multivariate genetic variation in integrated stress response because both behavioural and endocrine components load on it.Agreed – we have removed ‘single’ but the point  still stands that the major axis of genetic variation , migration (and drift). In itself, an instantaneous picture of G says little about processes that have shaped it in the past.We agree that the first part of this sentence was overstating the conclusions that can be drawn from our results, and have removed the clause about correlational selection here. We also edited the earlier line on this topic to be explicit that our results are consistent with but not proof of the idea that correlational selection in the past has led to coevolution of these stress components.


Genomic characterization is playing an increasing role in plant germplasm conservation and utilization, as it can provide higher resolution with genome-wide SNP markers than before to identify and analyze genetic variation. A genotyping-by-sequencing technique was applied to genotype 541 soybean accessions conserved at Plant Gene Resources of Canada and 30 soybean cultivars and breeding lines developed by the Ottawa soybean breeding program of Agriculture and Agri-Food Canada. The sequencing generated an average of 952,074 raw sequence reads per sample. SNP calling identified 43,891 SNPs across 20 soybean chromosomes and 69 scaffolds with variable levels of missing values. Based on 19,898 SNPs with up to 50% missing values, three distinct genetic groups were found in the assayed samples. These groups were a mixture of the samples that originated from different countries and the samples of known maturity groups. The samples that originated from Canada were clustered into all three distinct groups, but 30 Ottawa breeding lines fell into two groups only. Based on the average pairwise dissimilarity estimates, 40 samples with the most genetic distinctness were identified from three genetic groups with diverse sample origin and known maturity. Additionally, 40 samples with the highest genetic redundancy were detected and they consisted of different sample origins and maturity groups, largely from one genetic group. Moreover, some genetically duplicated samples were identified, but the overall level of genetic duplication was relatively low in the collection. These findings are useful for soybean germplasm management and utilization. Genomic characterization of ex situ plant germplasm is playing an increasingly important role in germplasm management and utilization ,2,3,4. CGlycine spp.] germplasm  and to g et al. ; Cho et g et al. ; Oliveirg et al. ; Kaga etg et al. ; Priollig et al. ). These g et al.  reporteds et al.  demonstrorldwide . Little Plant Gene Resources of Canada  maintains a soybean germplasm collection of 1031 accessions. These accessions were acquired largely from Canadian soybean breeding programs over the last 50 years and from the USDA-ARS soybean collection and N.I. Vavilov All-Russian Institute of Plant Genetic Resources for accessions with known early maturity over the last 15 years. Little is known about the genetic diversity and structure of the PGRC collection. Thus, a genomic characterization of the collection was initiated in 2017 with the objective of collecting information to enhance soybean germplasm management and utilization. To make the characterization more relevant to soybean germplasm utilization, we also included 30 soybean cultivars and breeding lines developed and released from the Ottawa soybean breeding program of Agriculture and Agri-Food Canada (AAFC). The specific objectives of the characterization were to: (1) apply a genotyping-by-sequencing technique to genotype 541 PGRC soybean accessions and 30 Ottawa breeding lines; (2) analyze the patterns of genetic diversity in the assayed samples; and (3) identify accessions with the most genetic distinctness and redundancy.The soybean collection conserved at PGRC has 1031 accessions, but only 743 accessions were available for seed distribution in 2016. For this study, we selected 541 accessions from the 743 accessions representing 28 countries of origin and one group of unknown origin, based on the log proportion of the group size with respect to the 29 groups involved. Fortunately, the selection also included 269 accessions with three known maturity groups (MG)  . Note thTM PicoGreen® dsDNA Assay Kit  and adjusted to 20 ng/µL with water.Approximately five seeds were randomly selected from each accession and planted in the greenhouse at the Saskatoon Research and Development Centre. Leaf tissue was collected at the 2–4 leaf stage separately for each plant, freeze-dried for 1–2 days in a Labconco  Freeze Dryer System, and stored at −20 °C. DNA was extracted from 12–15 mg of freeze-dried leaf tissue from one randomly selected plant per accession using the Qiagen BioSprint 96 DNA Plant Kit , following the product handbook, with the exception of DNA elution in 100 µL rather than 200 µL of water. DNA quality was assessed using a 260/280-nm ratio from the Thermo Scientific Nanodrop 8000, and DNA was quantified using the Invitrogen Quant-iTTM Library Quantification Kit for Illumina TruSeq  and diluted to 6 pM. Libraries were sequenced by NRC  on a HiSeq 2500 using Rapid Mode, 1 × 250 bp . The sequencing generated 1152 sequence read files in FASTQ format and these raw sequences were deposited into the SRA database of the National Centre for Biotechnology Information (NCBI) under BioProject ID of PRJNA705793.We screened genome-wide genetic variability of soybean germplasm using the genetic diversity-focused GBS (gd-GBS) protocol described by Peterson et al. . This prAll FASTQ files were first assessed with FastQC  to deterSNP calling was made for each sample from the trimmed FASTQ files against the soybean genome assembly of Wm82.a2.v1 ,26. The ® file.SNP genotype data with up to 50% missing values were firstly cleaned with six control samples ; only those markers having the same marker values for six control samples were kept. There was further removal of the markers with singletons, with the same genotype across the assayed samples and with more than three genotypes per sample. Minor allele frequency, the extent of missing SNPs, SNP distribution with respect to chromosome, country of origin and maturity were generated in a Microsoft ExcelThe genetic structure of the 571 samples was analyzed based on the SNP data of 19,898 markers (or M50 dataset) using a model-based Bayesian method available in the program STRUCTURE version 2.2.3 . The STRThe inferred genetic structure was further compared for consistency with the genetic relationships of individual samples obtained from two commonly applied approaches. A principal coordinate analysis (PCoA) of all 571 samples was performed using the R routine, AveDissR, for assessing genetic distinctness and redundancy ,32 and pAn analysis of molecular variance (AMOVA) was performed with Arlequin version 3.01  on 19,89We applied the same approach as proposed by Fu  that wasExtra effort was also made to generate a pairwise dissimilarity matrix for all 571 samples based on 19,898 markers using AveDissR with modification to output the matrix file. This analysis helped identify the sample pairs with the lowest pairwise dissimilarity values for the assessment of genetically duplicated samples.The HiSeq run of 576 soybean samples  yielded The Bayesian inference of genetic structure by STRUCTURE without consideration of accession information revealed three optimal clusters of the 571 soybean samples with a strong delta K support . ClusterThe PCoA plot also revealed three distinct genetic groups of the 571 soybean samples . The memThe NJ clustering revealed the same three distinct groups of all 571 soybean samples  as thoseThe APD estimates for the 571 assayed samples ranged from 0.0986 to 0.2522 with a mean of 0.1284 and standard deviation of 0.018 . The freAs the APD value of a particular sample measures the overall genetic difference of the sample against the remaining samples of the collection, we selected 40 more genetically distinct samples with APD estimates greater than 0.1565 and 40 more genetically redundant samples with APD estimates lower than 0.1076 for further germplasm management . Note thWe also identified 16 sample pairs with the lowest pairwise dissimilarity values that were roughly equivalent to 37 or fewer  SNP markers showing genetic differences between two samples . Such exThe germplasm characterization presented here revealed some interesting patterns of genetic variation in the soybean collection held at Plant Gene Resources of Canada. First, there were three distinct genetic groups present in the assayed samples, and each group was a mixture of the samples originating from different countries and with different known maturity groups . Second,Three different diversity analyses  revealedWe applied pairwise dissimilarity to identify genetic duplicates and average pairwise dissimilarity to identify the samples with the most genetic redundancy. Overall, the extent of genetic duplication in the PGRC soybean collection was relatively low . This fiOur characterization also generated a unique set of genomic resources for genetic analyses of soybean germplasm. The acquired SNP genotype data had an adequate genome sampling across the 20 chromosomes A and canThe revealed patterns of genetic variation have implications for managing soybean germplasm. The genetic variation was relatively low with respect to sample origin and maturity group, but considerably larger among three distinct genetic groups . Thus, sThe findings presented here also have some implications for germplasm utilization in soybean breeding. Breeders searching for germplasm with early maturity need to screen all the soybean accessions in the collection, as the inferred genetic groups were not unique to specific maturity groups, nor to specific sample origins. A field evaluation of maturity traits is needed. Identification and exploration of genes associated with early maturity traits can proceed with any genetic group, but would be more fruitful with the group in which the majority of the 30 Ottawa breeding lines resided.The genotyping-by-sequencing generated abundant SNP markers across the 20 soybean chromosomes. The diversity analysis revealed three distinct genetic groups present in the soybean collection held in Plant Gene Resources of Canada. These groups were a mixture of the samples originating from different countries and samples of known maturity groups. The 30 Ottawa breeding lines were clustered with two of the three groups. The analysis also identified 40 samples with the most genetic distinctness and 40 samples with the highest genetic redundancy and showed that the extent of genetic duplication in the collection was relatively low. These findings are useful for soybean germplasm management and utilization.


A new generation of KOR antagonists with rapid onset and shorter duration of action can potentially decrease the effects of stress exposure in translational models, and may be of interest for medication development. This study examined the rapid onset anti-stress effects of one of the shorter acting novel KOR-antagonists -2-pyridin-3-ylpyrrolidin-1-yl)methyl) phenoxy)benzamide)) in a single-session open space swim (OSS) stress paradigm (15 min duration), in adult male and female C57BL/6 J mice. LY2795050  had rapid onset (within 15 min) and short duration (<3 h) of KOR-antagonist effects, based on its blockade of the locomotor depressant effects of the KOR-agonist U50,488 (10 mg/kg). LY2795050 (0.32 mg/kg), when administered only 1 min prior to the OSS stress paradigm, decreased immobility in males, but not females. With a slightly longer pretreatment time (15 min), this dose of LY2795050 decreased immobility in both males and females. A 10-fold smaller dose of LY2795050 (0.032 mg/kg) was inactive in the OSS, showing dose-dependence of this anti-stress effect. Overall, these studies show that a novel KOR-antagonist can produce very rapid onset anti-immobility effects in this model of acute stress exposure.The kappa-opioid receptor (KOR) / dynorphin system is implicated with behavioral and neurobiological effects of stress exposure (including heavy exposure to drugs of abuse) in translational animal models. Thus some KOR-antagonists can decrease the aversive, depressant-like and anxiety-like effects caused by stress exposure. The first generation of selective KOR-antagonists have slow onsets (hours) and extremely long durations of action (days-weeks),  This mae assay) .11C-LY2795050 has been used as a KOR positron emission tomography radiotracer in humans (i) of LY2795050 for KOR, MOR and DOR is 0.72, 25.8 and 153 nM respectively (in vitro (Kb) at KOR, MOR and DOR is 0.63, 6.8 and 83.3 nM respectively . Adult , gonadally intact C57BL/6 J male and female mice were purchased from the Jackson Laboratory. The facility was approved by the American Association for Accreditation of Laboratory Animal Care (AAALAC). Mice were housed in one room at least 7 days prior to experiments. Mice were housed in same-sex groups (two to four per cage), in transparent plastic shoebox cages . A n = 8 was the standard group size; this could vary slightly due to the availability of mice per cage. The cages had corncob bedding ; shredded paper was added for nesting. The room was maintained at controlled temperature (≈20–22°C) and humidity, and had a 12:12 light:dark cycle (lights on at 0700), with overhead lighting. Experimental procedures occurred in the same room as housing. Procedures took place between 09:00 and 13:00 h.Locomotor activity: Locomotor activity was examined in mice placed individually in rectangular transparent plastic cages (19.7 cm width × 41.3 cm length × 20.3 cm height). Each bin had bedding identical to home cages and was placed in a photocell frame, which had an array of beams in perpendicular directions . Beam breaks caused by the mouse were cumulated for analysis, on a computer interface.2. For comparison, the standard FST test (not used here) provides an approximately 6-fold smaller swim surface area  (Open Space Swim (OSS): This assay has been previously described  and was 133 cm2) .Sessions (15-min long) were recorded with digital videocameras, and analyzed by a trained observer, for “blind” scoring of duration of immobility. The camera was pointed horizontally, at the surface level of the water. This allowed visualization of movements above and below the surface.Locomotor activity: One to three days prior to experiments, mice were habituated individually to locomotor cages, for a 60-min session. Consecutive experimental sessions in the same mice (90 min in duration) were separated by at least 72 h. Beam breaks were quantified as a measure of locomotor activity, over 15-min bins in the session.r = 0.97; p < 0.0001), as was inter-rater correlation .Open Space Swim (OSS): A 1-day OSS procedure was carried out . Each moLocomotor activity: Locomotor experiments were performed to define basic KOR-antagonist effects of LY2795050, to be further examined in the OSS. Experiments were carried out with a within-subjects design, balanced for order. We determined the time course of the antagonist effects of LY2795050  in preventing locomotor deficits caused by U50,488 (10 mg/kg), in males only. These dosing conditions were based on prior dose-effect studies, including determinations in both males and females . LY27950Open space swim (OSS): OSS studies were performed with a between-groups design; each mouse was used for a single session. In the first experiment, LY2795050  was administered as a 1-min pretreatment to OSS in males and females. The second study examined the effects of the same dose of LY2795050 , but with a longer pretreatment time (15 min). Lastly, the effects of a 10-fold smaller dose of LY2795050  was studied, with a 15-min pretreatment before OSS.p ≤ 0.05 level used as the criterion for significance). Mixed effects ANOVAs were used if there were missing data. Data for each 15-min OSS session were also analyzed as 1-min bins, in order to examine in greater detail the emergence of immobility over time. This was done with a non-linear regression with an exponential plateau growth curve (Graphpad Prism), with the following parameters: Y=Ymax -(Ymax-Ymin) * exp(-k*x), where Ymin and Ymax are the minimum and maximum value of the curve, respectively, “k” is the rate constant, and “x” is the time (1-min bins). Since these were 1-min bins measuring immobility, Ymin was constrained at 0 s, and the largest possible Ymax value was 60 s. A lack of overlap in the 95%CI for the maximum plateau value (Ymax) between LY2795050 and vehicle treatment was used as an indicator of a significant difference.Graphpad Prism (version 9) software was used for statistical analyses. Data for locomotor activity and immobility were analyzed with repeated measures ANOVAs and Sidak’s or Tukey’s post-hoc tests (including planned comparisons), or with t-tests  and pretreatment time  but no interaction . A Sidak’s multiple comparisons test determined that LY2795050 pretreatment significantly reduced U50,488-induced locomotor deficits compared to vehicle pretreatment, only at the shortest PT time (15 min).LY2795050, at a dose that is devoid of direct locomotor effects (0.32 mg/kg) , preventp = 0.0178] and of LY2795050 dose . There was no pretreatment X dose interaction . Planned comparisons with Sidak’s tests revealed significant differences between the larger dose of LY2795050 (0.32 mg/kg) pretreatment and its vehicle condition, but not for the smaller dose (0.032 mg/kg) , given as a 15 min pretreatment to U50,488, did not prevent U50,488-induced locomotor deficits . A 2-way2 mg/kg) .X sex X time bin, with repeated measures on the last variable)  = 21.00; p < 0.0001] and of time bin . There was also a significant interaction between sex and time bin , but no other interaction was significant (not shown). Tukey’s post-hoc tests were used to examine sex differences. In the first two time bins , there were no differences between males and females, either for the vehicle or LY2795050 conditions. However, in the third time bin (11–15 min), males had significantly greater immobility scores under the vehicle condition, compared to females . There were no sex differences in the LY2795050 condition.1-min pretreatment with LY2795050 (0.32 mg/kg): A 1-min pretreatment with LY2795050 (0.32 mg/kg) decreased immobility in the OSS session (15-min in duration) compared to vehicle, in males but not in females . In ordeariable) . In thisp = 0.0003] but not of treatment  or their interaction  (max) for immobility in the LY2795050 versus the vehicle group, as denoted by a lack of overlap in 95%CI. Thus, the best fit value for maximum immobility after LY2795050  was lower than after vehicle .In order to further explore the effects of LY2795050, we then analyzed the data separately for males and females. In males, a mixed-effects 2-way ANOVA of three consecutive 5-min time bins (treatment X time bin) detected a main effect of time bin  . A plann.50; ns] . This reIn females, no effect of LY2795050 was detected, in a similar 2-way ANOVA for 5-min bins, neither for treatment , nor time bin , or their interaction  . A non-lp < 0.0001] and of time bin , but no main effect of sex. There no significant interactions between these three variables (not shown). Likewise, there were no significant differences across sex, either in vehicle or LY2795050 conditions (Tukey’s tests).15-min pretreatment with LY2795050 (0.32 mg/kg): When administered with a slightly longer (15 min) pretreatment, LY2795050 decreased immobility in both males and females . As abovp < 0.0001] and time bin , but no interaction  (max) for immobility in the LY2795050 versus the vehicle group. Thus, the best fit value for maximum immobility after LY2795050  was lower than after vehicle .We then further examined effects of LY2795050 in males and females separately, as above. In males, a 2-way mixed-effects ANOVA in three consecutive 5-min time bins (treatment X time bin) detected significant main effect of treatment  . A plannp = 0.0085] and time bin , but no interaction   showed a main effect of treatment  . Planned095; ns] . Thus, t15-min pretreatment with a 10-fold smaller dose of LY2795050 (0.032 mg/kg): At a 10-fold smaller dose (0.032 mg/kg) and with a 15-min pretreatment time, LY2795050 did not decrease immobility in the OSS, in either males or females. For example, in the last 5-min time bin of the session , LY2795050- and vehicle-treated groups were not different, either in males or females .This study shows that the short-acting KOR-antagonist LY2795050 can produce very rapid-onset anti-immobility effects in a model of acute stress exposure. The study also shows that apparent sex differences in the effects of KOR-antagonists may be due to the specific stress paradigm used .Initial examinations in the locomotor activity assay demonstrated that LY2795050 (0.32 mg/kg) prevented U50,488-induced locomotor deficits when administered as a 15-min pretreatment, but was ineffective at longer pretreatments . This is consistent with the effectiveness of LY2795050 (0.32 mg/kg), previously determined in locomotor activity studies in both male and female mice . The effWe found for the first time that a very short (1 min) pretreatment with LY2795050 (0.32 mg/kg) was able to decrease immobility in the OSS assay (15-min duration), but only in male, but not female mice. Growth curve analysis over 1-min bins revealed that LY2795050 was able to decrease maximal immobility, which was observed typically in the last 5 min of the swim exposure.Since KOR-antagonist effects of LY2795050 (0.32 mg/kg) were detected here in the locomotor assay with a 15-min pretreatment in males  , this prThe effects of LY2795050 in the OSS were dose-dependent, since a 10-fold smaller dose (0.032 mg/kg) did not affect immobility. This is consistent previous findings that this smaller dose of LY2795050 does not exhibit KOR-antagonist effects in other assays mice .Limitations and methodological considerations: The largest dose of LY2795050 alone (0.32 mg/kg) does not have locomotor effects in either male or female C57BL/6 J mice . TherefoThe time-dependent increase of immobility within a session of swim stress has been examined in the standard FST, previously . In the In this study, male versus female differences in the effects of LY2795050 were examined in gonadally intact adult mice. Larger doses of LY2795050  were not studied, because they also exhibit MOR-antagonist effects . Future KOR and endogenous dynorphin function can be regulated by stress exposure in several brain areas and pathways . Future Conclusion: This is the first study to examine the effects of a KOR-antagonist in the open space swim stress (OSS) procedure. Very rapid onset anti-stress effects of LY2795050 were detected, more robustly in males than in females. This profile may be of potential interest in the modulation of the behavioral and neurobiological consequences of acute stress exposure, in translational models, and possibly for medication development.


Neonates with genetic disorders or congenital anomalies (CA) contribute considerably to morbidity and mortality in neonatal intensive care units (NICUs). The objective of this study is to study the prevalence of genetic disorders in an academic level IV NICU. We retrospective collected and analyzed both clinical and genetic data of all 1444 infants admitted to the NICU of the Radboudumc (October 2013 to October 2015). Data were collected until infants reached at least 2 years of age. A total of 13% (194/1444) of the patients were genetically tested, and 32% (461/1444) had a CA. A total of 37% (72/194) had a laboratory-confirmed genetic diagnosis. In 53%, the diagnosis was made post-neonatally (median age = 209 days) using assays including exome sequencing. Exactly 63% (291/461) of the patients with CA, however, never received genetic testing, despite being clinically similar those who did.Conclusions: Genetic disorders were suspected in 13% of the cohort, but only confirmed in 5%. Most received their genetic diagnosis in the post-neonatal period. Extrapolation of the diagnostic yield suggests that up to 6% of our cohort may have remained genetically undiagnosed. Our data show the need to improve genetic care in the NICU for more inclusive, earlier, and faster genetic diagnosis to enable tailored management.The online version contains supplementary material available at 10.1007/s00431-021-04213-w. A significant portion of neonates admitted to neonatal intensive care units are diagnosed with a genetic disorder . Congeni%, which can mainly be attributed to cohort selection and the heterogeneity of diagnostic tools used [Previous studies have shown that genetic disorders are a frequent cause of CA, especially MCA , 2. Howeols used . In neonOver the last decade, novel technologies, such as whole exome sequencing (WES), have entered the genetic diagnostic arena. Its use in clinical settings, such as neonatal intensive care, have however been limited, as turnaround times were perceived too long  to impact acute or short-term clinical decision making, and too costly compared to other genetic diagnostic testing options –10. Yet,To facilitate these efforts, insight into current practices, both at the level of clinical presentation as well as the uptake of (the type of) genetic testing, is essential. For this purpose, a retrospective observational study was performed in a cohort of neonates admitted to the NICU of the Radboud university medical center during a 2-year period up to a postnatal age of 2 years.We collected data of all patients born between 1 October 2013 and 1 October 2015 and admitted to the level IV NICU of the Radboud university medical center. Exclusion criteria were genetic testing in the context of a known mutation within the family and/or the identification of disorders through the national neonatal blood spot screening program . For theData was extracted from the electronic medical record (EMR) for each subject until the postnatal age of 2 years. A combination of automatic and manual data extraction was performed. Information regarding demographic data, diagnoses, and clinical geneticist consultations were manually extracted from the EMR of all patients. Genetic diagnosis was defined as a molecular, cytogenetic, or metabolic abnormality explained by a genetic disorder and related to the patient’s presenting phenotype. “No genetic diagnosis” was classified as patients with (non-specific) symptoms, such as feeding difficulties or respiratory distress and physical abnormalities without confirmation of an underlying genetic disorder.For each patient, we retrieved information on whether or not a clinical geneticist was consulted. If so, information on the date, location (inpatient /outpatient), and indication for consultation was obtained. We reviewed all genetic tests and recorded the type and result of the test, the date the specimen was received by the lab, and the date of the final report. We also included relevant tests performed prior to transfer to our institution using the information available in our EMR. Gene tests that were ordered as a panel (more than one gene), but for which results for each gene were provided separately, were entered as individual gene tests as the turnaround time may vary per gene. A conclusive diagnosis was defined as a laboratory-confirmed genetic diagnosis based on the identification of a (likely) pathogenic (classes 4 and 5) variant in concordance with the patient’s phenotype . Of notePrimary end points were (I) confirmed genetic disorders, (II) incidence of genetic testing, (III) diagnostic yield of genetic testing, and (IV) time to diagnosis (TTD). Suspicion of an underlying genetic disorder was based on the presence of one or more CA or other guiding clinical symptoms. The incidence of genetic testing was defined as the percentage of patients that received any molecular, cytogenetic, or metabolic diagnostic testing. The diagnostic yield was defined as the percentage of cases for whom a conclusive molecular or cytogenetic diagnosis was identified, e.g., the identification of a class 3, 4, or 5 variant, that is compatible with the identified phenotype. The TTD was measured from the moment the first test was indicated until the return of the final conclusive genetic diagnostic report.To identify the presence of any CA, we analyzed all EMR and scored the reported anomalies. Only anomalies that were identified prenatally or during the NICU stay were scored. CA were scored using the human phenotype ontology (HPO) terms and concomitantly grouped in 23 different organ systems . CA wereNormal distributed data were expressed in mean and standard deviation. Median and interquartile ranges were used in data with a skewed distribution. Statistical analysis was performed using descriptive and chi-square analyses and a two-sided Fisher’s exact test for continuous variables.n = 22) or they were admitted after the identification of a neonatal bloodspot screening disorder (n = 4) , 410 genetic tests were performed Fig. . Of thesIn a total of 72 patients, a genetic diagnosis could be established Table . Most geThe median time to diagnosis for patients with a confirmed genetic diagnosis in the post-neonatal period was 112 days IQR 234 days). Patients in the post-neonatal period received more genetic tests than patients in the neonatal period (Table 4 days. Pn = 312) presented with an isolated CA and 32% with MCA  of patients, of whom 68%  of infants with an isolated CA were genetically tested, leading to a diagnosis in 25% (20/80) of these patients. Similarly, for patients with MCA, 60% (90/149) of patients received genetic testing, with a diagnostic yield of 52% (47/90). Comparison of the different clinical presentations to determine whether we could identify any clinical indications why some patients with CA were tested, and others were not, did not reveal any specific observations (data not shown). This was not dependent on which organ system was affected. Potential reasons for the reduced uptake of genetic testing in patients with isolated CA or MCA could be unawareness of physicians to order genetic testing, or perceptions of “too long turn-around times to impact clinical decision making” and/or parents rejecting genetic evaluation.Following the above rationale, one may wonder how many patients with CA would have benefited from early genetic testing, thereby reducing their diagnostic odyssey and allowing enhanced patient-tailored medicine. Extrapolation of the data from our cohort and based on the assumption that the genetic diagnostic yield achieved is representative for the remainder of the cohort, an extra 58 (diagnostic yield of 25% in 232 not-tested patients) patients could potentially be diagnosed in the group with an isolated CA, and another 25 (diagnostic yield of 43% in 59 not-tested patients) patients in the sub-cohort with MCA. The diagnostic yield for the group with MCA is corrected for patients with aneuploidies who rarely will not be tested and diagnosed because of the obvious clinical features.Of note, also in the group of patients without CA, genetic diagnoses were made; retrospective analysis of these patients showed that there were specific clinical indications (mostly later in life) for genetic testing, such as neurodevelopmental delay. As we have limited the follow-up period of our cohort to 2 years of age, it is currently not possible to extrapolate the potential for additional diagnosis in the group of patients without CA. Overall, it is speculated that at least 83 (58 plus 25 = 6%) patients in our total cohort of 1444, and 29% (83/291) of not-tested patients with a CA could have likely remained undiagnosed due to a lack in genetic testing.The limited uptake of genetic testing in daily clinical practice on the NICU patients offers opportunities for improvement, for instance, by offering genetic testing to all patients with one or multiple CA. Traditionally, genetic testing has been too time-consuming or perceived to have limited impact on management of the critically ill neonate. Technological advances in recent years have led to the ability to sequence and interpret the entire genome of a neonate in only 1 or 2 days , 23. WheIdeally, one would analyze these aspects in a prospective parallel study that would offer great insight into the opportunities and potential pitfall of a so-called “WES or WGS-first strategy.” Outcome measures should not only focus on quantification of the diagnostic yields via rapid WES, but also on relevant clinical management changes, which are anticipated to range from the initiation of specific patient-tailored supportive management, the transition to palliative care for confirmed lethal conditions to simply refraining from further invasive diagnostic procedures as a consequence of having a final molecular diagnosis . The parWhereas our study has limitations because of its retrospective nature relying only on information available in the patients’ EMR with only a few years of clinical follow-up, its power is reflected by the systematically assessment of all patients admitted to a level IV NICU for their clinical presentation, genetic testing, and genetic diagnosis obtained. We have motivated the speculation that currently admitted neonates to the NICU are underdiagnosed for disorders of genetic origin. Our results contribute to gaining insight in patient populations that would benefit from WES- or WGS-based genetic testing, that not only allows for impact on clinical decision making in the acute setting but would also limit the diagnostic odyssey of these patients. However, further research is needed to determine the best strategy on whom to offer advanced genetic testing, maximizing the potential of WES or WGS in the NICU setting improving the care provided to infants and their families.Supplementary file1 (XLSX 92 KB)Supplementary file2 (XLSX 27 KB)Supplementary file3 (XLSX 28 KB)Below is the link to the electronic supplementary material.


The ancient Hungarians, “Madzsars”, established their control of the Carpathian Basin in the late ninth century and founded the Hungarian Kingdom around 1000AD. The origin of the Magyars as a tribal federation has been much debated in the past. From the time of the conquest to the early fourteenth century they were ruled by descendants of the Arpad family. In order to learn more about the genetic origin of this family, we here analyzed the genome of Bela III one of the most prominent members of the early Hungarian dynasty that ruled the Hungarian Kingdom from 1172 to 1196. The Y-Chromosome of Bela III belongs to haplogroup R1a-Z2123 that is today found in highest frequency in Central Asia, supporting a Central Asian origin for the ruling lineage of the Hungarian kingdom. The autosomal DNA profile of Bela III, however, falls within the genetic variation of present-day east European populations. This is further supported through his mtDNA genome that belongs to haplogroup H, the most common European maternal lineage, but also found in Central Asia. However, we didn’t find an exact haplotype match for Bela III. The typical autosomal and maternal Central Eastern European ancestry among Bela III autosomes might be best explained by consecutive intermarriage with local European ruling families. An alternative hypothesis proposes that Proto-Indo-European speakers were nomadic pastoralists of the Pontic–Caspian steppe and their languages spread into Europe after the invention of wheeled vehicles4. Genetic data from ancient human remains across Neolithic and Bronze Age Europe revealed a massive population turnover mediated by the expansion of pastoralists from the eastern European steppe starting around 3000 years before the common era (BCE)6. The described population movement might have been responsible for replacing the original languages and contributing the “steppe” genetic component that, together with “Hunter-Gatherer” and “Farming” ancestries, describe the genetic makeup of most Europeans today. From this steppe-Indo-European correlation, there are known exceptions like the Basques who despite carrying steppe ancestry speak a non-Indo-European language. Genetic studies have revealed that Basques are the closest modern-day population to Neolithic and Iron Age individuals from Iberia8 suggesting a possible survival of the local language from the Neolithic onwards. The second most spoken language family in Europe is the so-called Finno-Ugric (according to the theory of the language family-tree) that today is distributed across Finland, Estonia, western Russia and Hungary. This is divided into Finnic and Hungarian , both part of the larger Uralic language branch, which is widespread as far as northeastern Eurasia9.The vast majority of Europeans today speak Indo-European languages, but their origins and dispersals are highly debated topics. Bayesian methods applied to date the root of Indo-European languages provide an age estimate of ~ 6,000 years before Common Era (BCE) and suggest Anatolia as the homeland of Proto-Indo-European10. This, however, is not the case for present-day Finnish individuals that are displaced from the European genetic cluster towards East Eurasian populations. A recent ancient DNA study of Fenno-Scandian human remains identified an additional genetic contribution ultimately related to a Northeast Asian population (Nganasan) reaching this region by at least 3,500 BCE11. This genetic component is present in lower proportions in present-day Saami individuals from northern Finland and to an even lesser extent in Finns, who are largely admixed with central European ancestry. Notably, the distribution of the putative Siberian-related ancestry is present in most Uralic speaking populations but absent in modern-day Hungarians12. Contrary to Finns, Hungarians fall almost entirely within the genetic diversity of modern-day Europeans harboring only a minor East Asian component13. Genomic analyses of ancient human remains from Hungary have revealed a process of large genetic replacement during the Early Neolithic with the arrival of “Farming” ancestry followed by the resurgence of local “Hunter-Gatherer” ancestry in the Middle Neolithic, as observed in other European regions15. Furthermore, during the Late Neolithic-Early Bronze Age transition the “steppe” ancestry spread throughout Hungary providing the third genetic component present in most Europeans today16.Genetic studies on modern-day populations have shown that in Europe genetic distance among populations has a high correlation to their geographical distance24, which has not permitted a detailed genome-wide characterization of populations in Hungary through time. After the Iron Age from 35 BC to the early ninth century AD, Hungary was part of the Roman Empire and later underwent several “barbaric migrations” such as from the Huns between the 4th and the sixth century AD and from the Longobards in the sixth century AD, followed by the Avars between the late sixth century and the early ninth century AD25. Genomic data is so far available only from a Longobard-associated cemetery in Pannonia that revealed genetically heterogeneous individuals, thus suggesting that this group was a congregation of people with different origins not resembling previous or present-day Hungarian populations26.The following period of the genetic history in Hungary is less well characterized. In fact, almost all ancient DNA studies on post-Iron Age individuals have been relying on the genetic typing or sequencing of polymorphisms within the Y-chromosome (Ychr), on partial or complete mitochondrial DNA (mtDNA) sequences and on phenotypic SNPs27. Centuries later Álmos the first, Great Prince of the Hungarians, organised the monarchic state in the same region ca. 850 AD, although the links with the previous populations are not fully deciphered. A few decades after the collapse of the Avar Khaganate (c. 822 AD), Álmos and his son Árpád conquered the Carpathian Basin (c. 862–895 AD)28. During the conquest, it is suggested that Hungarian conquerers, together with Turkic-speaking Kabars, assimilated Avars and Slavonic groups31.According to available written records ca. 530 AD, King “Muageris”–which has been associated with the name “Magyar”–was the ruler of the Kutrigur Huns north of the Black Sea32 shows that a paternal line of inheritance has always been followed from Great Prince Álmos—who lead the initial conquest after/ca. 862 AD—to King Andrew III of Hungary, who died in 1301 AD, signing the dynasty’s end. Along this succession line, one of the most prominent kings was Bela III (1172–1196), the first king who adopted the "double cross" as the symbol of the Kingdom of Hungary  Figure 32 shows y Figure . He was 35 from those specimens and converted into single and double-stranded libraries   Table . The com40. Sex determination was performed inspecting the coverage of the captured SNPs overlapping sex chromosomes compared to the autosomes41. Bela III was assigned to be male 45: 263G, 315 + C, 750G, 1438G, 3010A, 3106A, 4769G, 8860G, 15326G, 16183C, 16189C, 16356C, 16519C. Our mtDNA result is consistent with the published mtDNA control region data as reported in Olasz et al.33. We used EMPOP (https://empop.online/) to search if this mitogenome haplotype has been reported before and where. There are 211 samples of H1b haplogroup mainly reported in Europe but also found in Central Asia in EMPOP, but we didn’t find an exact matched haplotype with that of Bela III.We performed mtDNA capture on Bela’s libraries and estimated mtDNA contamination level at 1% and determined his mtDNA hg Table 43. Bela 46, which is consistent with the result reported in previous studies34.The Y chromosomal mutations were inspected at all ~ 40,000 positions present in our captured SNP panel. We could identify 30 derived mutations stemming from the hg R node and leading to hg R1a1a1b2a-Z95 plus an additional mutation on a downstream SNP, which defines Y chromosomal hg R1a1a1b2a2a-Z2123 Table . This is47 built with modern-day West-Eurasian genetic variation ion Fig.  39. The s Figure .Figure 1f3-outgroup statistics49, which measure shared genetic affinity compared to a common outgroup, here the African Mbuti population. Consistent with the PCA result, the genome of Bela III clusters within the diversity of present-day Europeans and indistinguishable from most other European populations , where X is a list of current worldwide populations  people.A few decades after the collapse of the Avar Khaganate (ca. 822 AD), Álmos and his son Árpád conquered the Carpathian Basin (ca. 862–895 AD)22. However, multiple early Medieval human migrations took place after the collapse of the Roman Empire in Hungary. Therefore, the reconstruction of the population dynamics that took place in the region until the establishment of the Árpád dynasty would require archaeogenetic data from this intermediate period. Indeed, mtDNA and Y chromosomal haplogroups of Hun- and Avar-related individuals from Hungary suggest an even stronger Eastern Eurasian genetic influence than in groups from the Hungarian conquest-period24.Genetic studies of mtDNA, Y chromosomal and autosomal markers from those conquering Hungarians have revealed an admixed ancestry characterized by both western and eastern Eurasian genetic componentsOur study presents the first genome-wide analysis of a historical Hungarian individual older than the sixth century AD, by directly investigating the genomic profile of one of the most prominent kings of the Árpád dynasty. Historical evidence reveals that from the initial conquest of the Carpathian Basin by the Hungarian tribal federation in 895 to King Béla III (1172–1196) a paternal line of inheritance was always followed Figure . In 117033. Based on the established genealogy  and were put in sterile Falcon tubes and were immediately frozen in dry ice. Bones in one of the tubes were divided into two approximately equal pieces. They were transferred in May of 2014 to the laboratories of Suzanne Hummel and Johannes Krause. P.I. Nagy was invited to the project in October 2014 by BM. He received DNA isolated from all bones in Hummel’s laboratory in 2015 via BM, he reported his first sequence reports in August 2015. We showed the sample details in Table S9. We presented the preliminary results including the full and complete Y chromosome haplotype at the consortium meeting at the National Institute of Oncology in Budapest on September 1, 2015 (BM presentation).We collected four bone fragments from the tomb associated with King Bela III. His remains were buried in the church (Székesfehérvár) and in 1848 they were transported to Budapest and nowadays the sarcophagus is in the Mathias Church35. Sequencing libraries were initially built with a double-stranded protocol without DNA damage correction36 and then indexed with double barcodes52. After amplification, libraries were diluted equimolarly and shotgun sequenced on Illumina platforms  in paired-end mode. Two out of four libraries form Bela III with highest percentage of aDNA (MA32 and MA33) were enriched for the complete mitochondrial genome using DNA capture53 and then paired-end sequenced on Illumina NextSeq500 machine. Furthermore, from two DNA extracts of Bela III single-stranded libraries were generated (MA172 and MA173) following a protocol that retains C to T damage-caused by substitutions in the last two terminal positions from both ends54. Subsequently an in-solution nuclear DNA capture was used targeting either ~ 390,000 or ~ 1,240,000 SNPs scattered across the human genome38 for the two Bela III double stranded libraries (MA32 and MA33). The enriched libraries were paired-end sequenced on Illumina NextSeq500 machine. DNA sequences from shotgun sequencing screening, nuclear and mtDNA capture were processed through EAGER55. Adapters were trimmed, forward and reverse reads were merged and fragments below 30 bp long were removed55. The resulting sequences were aligned against the mtDNA and nuclear DNA reference genomes  followed by mapping quality filtering (q30) and duplicate removal.The ancient DNA workflow for this study was carried out at the Laboratory of Archaeo- and Paleogenetics at the University of Tübingen. Four skeletal elements associated to King Bela III  were collected from the royal graves discovered in 1848 in the Royal Basilica of Székesfehérvár in Budapest (Hungary). All four specimens were sampled using a dentist drill (Nakanishi (Emax Evolution)) and the resulting bone powder was used to extract DNA following an established protocol38. Reads of high base and mapping quality (SAMtools depth -q30 -Q37) were extracted using SAMtools v1.3.156. Ratios of the numbers of reads mapped on X chromosome or Y chromosome were calculated and compared with that mapped on autosomes (X-rate = xCov/autCov and Y-rate = yCov/autCov).The sex of the newly reported samples in this study was determined by counting the number of reads overlapping with the targets of 1240 k capture reagent as discussed in Fu et al.43. Haplogroup assignment was performed with the online tool Haplofind57.MtDNA contamination level and reconstructed mtDNA consensus sequence of both individuals were estimated using schmutzi58. The Method of Moments (MOM) and Maximum Likelihood (ML) estimate from “Method 1” and “Method 2” likelihood computation were applied using this software package. A consistent estimate of low contamination rate (about 1.2%-2.7%) in different libraries using different methods was determined.An X-chromosomal contamination test was performed for the five libraries of Bela III following an approach introduced by Rasmussen et al. and implemented in the ANGSD software suite53, which included mapping with BWA (v0.6.1)59 against human genome reference GRCh37/hg19, and removing duplicate reads with the same orientation and start and end positions. To avoid an excess of remaining C-to-T and G-to-A transitions at the ends of the reads, three bases of the ends of each read were clipped for each sample. “Pseudo-haploid” calls were generated by selecting a single read randomly at each of the targeted 1240 k SNP positions.The sequence data was demultiplexed, adaptor clipped with leehom and then further processed using EAGERhttps://reich.hms.harvard.edu/allen-ancient-dna-resource-aadr-downloadable-genotypes-present-day-and-ancient-dna-data.The genomic data of populations used for comparisons are from the Human Origin Dataset: 47, using default parameters and the lsqproject: YES and numoutlieriter: 0 options. We project our ancient Bela III sample onto the variation of present-day West Eurasians from published Human Origin Dataset over 591,642 SNPs. We have not used the option autoshrink: YES or shrinkmode: YES.Principal component analysis was carried out in the smartpca program of EIGENSOFT48 analysis after pruning for linkage disequilibrium in PLINK60 with parameters –indep-pairwise 200 25 0.4 which retained 252,493 SNPs. We ran ADMIXTURE with default fivefold cross-validation (–cv = 5), varying the number of ancestral populations between K = 2 and K = 12 in 100 bootstraps with different random seeds. The samples used in this analysis are present-day West Eurasians, and individuals from worldwide representative populations Mbuti, Yoruba, Han, Papuan, Karitiana, Eskimo, Uzbek, Ami, Selkup, and Kalash. We observed the lowest CV errors for K = 7.We carried out ADMIXTUREf3-statistics of the form f3  were used in order to test which West Eurasian populations share the most genetic drift with Bela III. Analysis of f3-statistics was carried out using ADMIXTOOLS49 with standard errors computed with a block jackknife.Outgroup f4-statistics of the form  were used to test if Africans, Europeans, South Asians, East Asians, Native Americans and Oceanians share extra affinity with Bela III than with present-day Hungarian and Croatian. Analysis of f4-statistics was carried out using ADMIXTOOLS with standard errors computed with a block jackknife. The statistics are consistent with 0, implying there was no extra ancestry from outside Europe in Bela III.Outgroup http://isogg.org/) and also Y-full (https://www.yfull.com/tree/) database. To perform this analysis, we restricted our analysis to only include reads with a mapping quality higher than 30. Afterwards, we determined the haplogroups by identifying the most derived Y chromosomal SNP in our individual. The sample has a derived allele at R1a1a1b2a2a-Z2123: C → T with coverage of 3x. Upstream mutations assigning our individual to R1a1a1b2a-Z95 and R1a1a1b2-M746 could be identified.The Y chromosomal haplogroup was determined by examining a set of diagnostic positions on chromosome Y using the ISOGG  bases are counted. rs4988235 is responsible for lactase persistence in Europe. The SNPs at Supplementary Information.Supplementary Information.