Genetic predictions of height differ among human populations and these differences have been interpreted as evidence of polygenic adaptation. These differences were first detected using SNPs genome-wide significantly associated with height, and shown to grow stronger when large numbers of sub-significant SNPs were included, leading to excitement about the prospect of analyzing large fractions of the genome to detect polygenic adaptation for multiple traits. Previous studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the analyses in the UK Biobank, a much more homogeneously designed study. We show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population stratification. More generally, our results imply that typical constructions of polygenic scores are sensitive to population stratification and that population-level differences should be interpreted with caution.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (ddressed . Most human complex traits are highly polygenic . For exaHere we focus on a series of recent studies—some involving co-authors of the present manuscript—that have reported evidence of polygenic adaptation at alleles associated with height in Europeans. One set of studies observed that height-increasing alleles are systematically elevated in frequency in northern compared to southern European populations, a result that has subsequently been extended to ancient DNA . AnotherMost of these previous studies have been based on SNP associations and effect sizes (summary statistics) reported by the GIANT Consortium, which most recently combined 79 individual GWAS through meta-analysis, including a total of 253,288 individuals . Here, w−8)  . This re−8)  or in ge−8)  across E−8) , althoug−8)  and that−8) .−65, Next, we assessed if an independent measure, the ‘singleton density score (SDS)', which uses a coalescent approach to infer adaptation within a population, is equally as susceptible to biases in GWAS . SDS canWe propose that the qualitative difference between the polygenic adaptation signals in GIANT and the UK Biobank is due to the cumulative effect of subtle biases in each of the SNPs estimated in GIANT. This bias can arise due to incomplete control of the population structure in GWAS . For exaWe obtained direct confirmation that population structure is more correlated with effect size estimates in GIANT than to those in the UK Biobank. We analyzed previously released family-based effect size estimates based on an approach of We also note that the white British subset of the UKB data is not completely free of population stratification (as shown previously ), althouWe proceeded to investigate the effects of uncontrolled population stratification in GWAS discussed above on a coalescent approach such as tSDS that relies on singleton density . In prin−8). This effect persists when using UK Biobank family-based estimates (UKB sibs WB) for genome-wide significant SNPs  and 2) sub-significantly associated with height  in different GWAS datasets. For each of these, we tested if population differences were significant due to an overall overdispersion (PQx), and if they were significant along a north-south cline (Plat)  and gw-sig  height scores. In comparison, scores that were computed using the UK Biobank (UKB) effect sizes showed substantially attenuated differences using both sub-sig  and gw-sig  SNPs, and a smaller difference between the two scores. This suggests that the attenuation of the signal in GIANT is not only driven by a loss of power when using fewer gw-sig SNPs, but also reflects a decrease in stratification effects. The overdispersion signal disappeared entirely when the UK Biobank family based effect sizes were used  –2. Both e (Plat) –2. Howevere used –2. Moreos absent . The poss absent . TherefoWe have shown, by conducting a detailed analysis of human height, that estimates of population differences in polygenic scores are reduced when using the UK Biobank GWAS data relative to claims of previous studies that used GWAS meta-analyses such as GIANT. We find some evidence for population-level differences in genetic height, but it can only be robustly seen at highly significant SNPs, because any signal at less significant P values is dominated by the effect of residual population stratification. Even genome-wide significant SNPs in these analyses may be subtly affected by population structure, leading to continued overestimation of the effect. Thus, it is difficult to arrive at any quantitative conclusion regarding the proportion of the population differences that are due to statistical biases vs. population stratification of genetic height. Further, estimates of the number of independent genetic loci contributing to complex trait variation are sensitive to and likely confounded by residual population stratification.We conclude that while effect estimates are highly concordant between GIANT and the UK Biobank when measured individually , they arOur results have implications in other areas of human genetics research. For example, there is growing interest in polygenic scores that predict complex phenotypes from the aggregate effects of all allelic variants . The obset al.’s GWAS uses a BOLT-LMM Bayesian mixed model   and a GWed model . Associaed model .We also used previously published family-based effect size estimates   were computed based on the methodology developed in references Al polygenic scores are plotted in centered standardized form (https://github.com/msohail88/polygenic_selection (https://github.com/elifesciences-publications/polygenic_selection) and https://github.com/uqrmaie1/sohail_maier_2019 (https://github.com/elifesciences-publications/sohail_maier_2019).where 2 threshold, 0.1) and within a physical distance of them (1 Mb) into clumps with them. Clumps are preferentially formed around index SNPs with the lowest P value in a greedy manner. The index SNP from each clump is then picked for further polygenic score analyses. The algorithm is also greedy such that each SNP will only appear in one clump if at all. We clumped each GWAS to obtain (2) a set of independent sub-significant SNPs associated with height (p<0.01) similarly to −8). The 1000 genomes phase three dataset was used as the reference panel for computing LD for the clumping procedure.Polygenic scores were computed using independent GWAS SNPs associated with height in three main ways: (1) The genome was divided into ~1700 non-overlapping linkage disequilibrium (LD) blocks , where VA is the additive genetic variance of the ancestral population and F is a square matrix describing the population structure. This is equivalent to the univariate case of the test statistic used in A Y’FY), where Y is a mean centered vector of latitudes for each population (The Qpulation .The Singleton Density Score (SDS) method identifies signatures of recent positive selection based on a maximum likelihood estimate of the log-ratio of the mean tip-branch length of the derived vs. the ancestral allele at a given SNP. The tip-branch lengths are inferred from the average distance of each allele to the nearest singleton SNP across all individuals in a sequencing panel. When the sign of the SDS scores is aligned with the trait-increasing or trait-decreasing allele in the effect estimates of a GWAS, the Spearman correlation between the resulting tSDS scores and the GWAS P values has been proposed as an estimate of recent positive selection on polygenic traits.Here, we applied the tSDS method using pre-computed Singleton Density Scores for 4,451,435 autosomal SNPs obtained from 3195 individuals from the UK10K project  for SNPs2 <0.2 relative to each other, PCA was performed in PLINK on the 187,160 remaining SNPs. In order to get SNP PC loadings for more SNPs than those that were used to compute PC scores, we performed linear regressions of the PC scores on the genotype allele count of each SNP (after controlling for sex) and used the resulting regression coefficients as the SNP PC loading estimates. The 1000 genomes phase one dataset  in the 1000 genomes data , we firs dataset  was used In the interests of transparency, eLife includes the editorial decision letter, peer reviews, and accompanying author responses.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed.][eLife. Your article has been reviewed by three peer reviewers, including Magnus Nordberg as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Mark McCarthy as the Senior Editor. The following individuals involved in review of your submission have also agreed to reveal their identity: Nicholas H Barton (Reviewer #2); Joachim Hermisson (Reviewer #3).Thank you for submitting your article "Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies" for consideration by The Reviewing Editor has summarized the major concerns shared by all reviewers, and we have also included the separate reviews below for your consideration.If you have any questions, please do not hesitate to contact us.Summary:This is one of two papers demonstrating that published signals of selection on human height cannot be replicated in the recently released UK Biobank data, apparently because these signals were caused by confounding population structure that is absent in UK Biobank data.Major concerns:We were struck by how both papers focus on spurious signals of selection rather than the underlying cause, which is that the GWAS effect-size estimates are confounded. The former is a somewhat esoteric question, but the latter may have enormous implications for much of human genetics, and these papers are likely to be heavily cited because of this. However, the papers seem to go out of their way to avoid discussing this topic. Of course we are not the authors, but, for the record, it looks odd.Furthermore, the papers seem to suggest that confounding is not present in the UK Biobank data, but isn't it more likely that the magnitude is simply smaller?Both papers also present evidence that a sib-based study by Robinson et al., 2015, that was meant to eliminate confounding did no such thing. This is disturbing, and while we understand that identifying the reason may be beyond the present papers, the general implications should again probably be discussed.Finally, this paper often seems stream-of-consciousness: it lacks detailed explanations as well as a coherent outline, making it very difficult to follow unless you are a specialist in the field. We urge the authors to explain better for a general audience.Separate reviews (please respond to each point):Reviewer #1:This one of at least two papers appearing simultaneously and reaching exactly the same conclusion. It is well written.The only thing that surprises me about this paper is that it, as well as the other one I have seen, focuses on the relatively obscure issue of whether height has been under selection, tiptoeing around the much bigger issue (the elephant in the room) that the reason the claims for selection do not stand is that the GWAS estimates of effect sizes are biased because of population structure. It is not just the selection signals that do not replicate, but the polygenic scores. I'm not surprised, but, as you know, there are probably at least a hundred papers out there that are based on the infallibility of LD score regression and genomic prediction. I understand the need for caution before attacking this edifice, but I nonetheless think some clarification is unavoidable.Reviewer #2:This paper identifies a discrepancy between signs of selection estimated from UK Biobank data, compared with previous studies, and suggest that those earlier signals were caused by subtle stratification in the data. This is a useful contribution to an important question. I have only minor comments (below), but overall, urge the authors to try to rewrite the text to make it more accessible to those not immersed in the field. I find it hard to make specific suggestions, but it comes across as a list of statistical tests, without enough flow to carry the reader along with the argument. Admittedly, given the quite intricate arguments, this is not easy to do.Minor Comments:Why should one believe that using the first 15 PC corrects for stratification? Even this is somehow traditional in the field, it needs explanation, since the failure of the correction is the key point of the paper.Figure 1B: – The x axis needs to be labelled. More important, Spearman's correlation seems far too small, given that by eye, the points follow the linear regression rather well. This may be related to the large values seen at the right of each figure, fitting a single regression is clearly inappropriate. There needs to be a test which separates these two sets of points in some way: as it stands the significance test is just not appropriate.Figure 3: – b in the figure should be β. Also, there is a paragraph break before "The patterns" which makes it hard to work out what is main text and what is caption.Figure 4: – I do not understand what the "six summary statistics" are here.Discussion section: – The concluding paragraph seems too weak, especially the sentence "In no way..". Surely the point of the paper is to "question the statistical methodology.… in polygenic tyests for adaptation", since that methodology seems to give spurious results? It is also not at all clear how much the stratification implied here influences effect size estimates in GWAS.Paragraph five of subsection “Polygenic scores”: Does the distribution in fact follow a β?Reviewer #3:Both manuscripts by Berg et al. and Sohail et al. present thorough and insightful analyses with highly relevant results for current and future GWAS studies. Even prior to publication, the manuscripts have considerable impact. They will be widely read and cited. I do not think that further analyses are needed, with the potential exception of the third point below. All other points concern the discussion, in particular the guidance for further research that will surely emerge from these studies.How safe are results based on the UK Biobank data?This refers to the weak signals reported (with much caution) in the present studies, but also to potential future results on other traits. You recommend using data "such as UKB" and we will certainly see many more studies based on this resource. I would therefore appreciate a more specific discussion of risks connected to this particular data set.1) Stratification even within the UKB-GB data: It is well known that height and socioeconomic status are correlated in modern societies , and social status correlates with descent. In the UK, both factors are also geographically stratified, with people living in the north of the country having lower socioeconomic status and shorter stature, on average, than those in the south. Furthermore, the percentage of Anglo-Saxon admixture varies across the UK. How could these factors influence results based on UKB data, both here and otherwise?2) Potential influence of GxE interactions: The manuscripts focus (for good reason) on issues connected with stratification. However, if polygenic scores depend on the environment , GxE interactions are an alternative confounding factor. Importantly, use of a homogeneous detection panel (to avoid stratification), such as UKB-GB, could increase these effects. Maybe this should be briefly discussed in the context of the present results and mentioned as a necessary caveat also for future studies that use detection panels from narrow geographic regions.What, exactly, causes the problems with the previous data?3) There seem to be two relevant differences of the GIANT data relative to UKB: 1) UKB is much more homogeneous and 2) GIANT is a meta-study, collecting summary statistics from many sources that are individually corrected for stratification. One would like to know better which factor is decisive. This could be further addressed by combining summaries from sub-samples of the "UKB-all" data in an artificial meta-study.4) The Robinson et al., 2015 GWAS: Sib-based studies are done to avoid / minimize stratification effects and the Robinson 2015 data have been used as a proof of robustness in several previous studies. The fact that you find clear signs of stratification is sobering and one would like to know what has gone wrong. You may not currently have any explanation and this is fair enough. However, the discussion should be clearer and say upfront that results based on these data cannot be trusted until we understand the issues.Minor Comments:a) You use 11 different summary statistics, with partly inconsistent naming strategy. I had to look up names in the methods part a number of times. I think this can be improved. Maybe even use the same names as Berg et al. where the summaries are identical.b) The switch from 1000 genomes to POPRES complicates comparison between figures. If there are advantages of POPRES, why not use it throughout? This holds, in particular, for the test of the latitudinal slope, which would be more convincing with many populations rather than just 4 from the 1000 genomes data.c) Figure 4: "The overdispersion signal disappeared entirely when the UK Biobank family based effect sizes were used": Is this due to the smaller sample size of the sib data or due to residual stratification issues in UKB? This could be tested using a sub-sample from UKB of the same size as the sib data.d) Figure 3 legend: "suggesting that tSDS shift at the gw-significant SNPs is not driven by population stratification": only true for stratification due to this particular axis.Additional data files and statistical comments:All necessary information is provided and the UKB sib data is on Dryad. I think the other newly generated GWAS data should go there, too. Major concerns:We were struck by how both papers focus on spurious signals of selection rather than the underlying cause, which is that the GWAS effect-size estimates are confounded. The former is a somewhat esoteric question, but the latter may have enormous implications for much of human genetics, and these papers are likely to be heavily cited because of this. However, the papers seem to go out of their way to avoid discussing this topic. Of course we are not the authors, but, for the record, it looks odd.We agree that our analysis raises broader issues beyond detection of polygenic adaptation. However, we do not find that our results question the whole GWAS enterprise. Almost all genome-wide significant signals identified by the GIANT consortium replicate in the UK Biobank. Overall, there is a high correlation between the effect size estimates between the two studies. However, certain aspects of current human genetics research outside evolutionary biology are obviously affected. The prime example is the transferability of polygenic risk scores between populations. We added a detailed discussion of this in the revised manuscript.Furthermore, the papers seem to suggest that confounding is not present in the UK Biobank data, but isn't it more likely that the magnitude is simply smaller?The revised version of the manuscript discusses the confounding in the UK biobank data. We clearly demonstrate that uncorrected summary statistics of the UK Biobank GWAS show signals of stratification even if the analysis is restricted to White British individuals. Interestingly, in the UK the north-south genetic cline tracks the height gradient in the opposite direction than in Continental Europe. Obviously, the magnitude of the confounding is much smaller. When principal components are included in the UK Biobank GWAS, we do not find any evidence of residual stratification when testing for a correlation between effect size estimates and twenty 1000 genomes principal components . However, this does not preclude the possibility of residual stratification along axes that are not captured by these principal components.Both papers also present evidence that a sib-based study by Robinson et al., 2015, that was meant to eliminate confounding did no such thing. This is disturbing, and while we understand that identifying the reason may be beyond the present papers, the general implications should again probably be discussed.In the revised manuscript, we clarify that we agree with the conceptual approach of Robinson et al. but that the discrepancy is likely to be due to a technical error in the computations of Robinson et al. We have in fact now confirmed this through correspondence with the authors of Robinson et al., and they are currently preparing a manuscript revisiting these analyses and correcting the technical issues. We emphasize that family-based effect size estimates computed in the UK Biobank following the Robinson et al. methodology behave as expected.Finally, this paper often seems stream-of-consciousness: it lacks detailed explanations as well as a coherent outline, making it very difficult to follow unless you are a specialist in the field. We urge the authors to explain better for a general audience.We edited the text making it more accessible.Separate reviews (please respond to each point):Reviewer #1:This one of at least two papers appearing simultaneously and reaching exactly the same conclusion. It is well written.The only thing that surprises me about this paper is that it, as well as the other one I have seen, focuses on the relatively obscure issue of whether height has been under selection, tiptoeing around the much bigger issue (the elephant in the room) that the reason the claims for selection do not stand is that the GWAS estimates of effect sizes are biased because of population structure. It is not just the selection signals that do not replicate, but the polygenic scores. I'm not surprised, but, as you know, there are probably at least a hundred papers out there that are based on the infallibility of LD score regression and genomic prediction. I understand the need for caution before attacking this edifice, but I nonetheless think some clarification is unavoidable.As noted above, we added a discussion on the potential impact of our findings on the debate about the transferability of polygenic scores between populations. In this work we have focused on the effects of residual population stratification on tests of selection because they appear to be particularly sensitive. While we cannot exclude the possibility that some other methods are also sensitive to residual stratification, neither our analyses nor previous publications provide evidence that this is a widespread problem for other applications of GWAS data, even though our results certainly do highlight the importance of revisiting the importance of population stratification in all analyses of polygenic predictors. As for polygenic scores, it has been demonstrated previously that results can be unreliable when predicting across populations . The fact that polygenic scores from the UK Biobank tend to have a higher out of sample prediction accuracy than polygenic scores from GIANT is just one of many pieces of evidence showing that polygenic scores do not just pick up residual stratification, but rather a signal from the trait of interest.Generally, any method that uses genetic data from multiple populations is prone to be susceptible to bias from residual stratification. However, our SDS results show that even methods which do not use data from distinct populations can be affected by residual stratification. In the case of SDS, the problem is that both the singleton density scores and GIANT height summary statistics are stratified across the European north south cline. We cannot exclude the possibility that similar biases can exist in other methods, but we have not found any other examples of it. LD score regression is unlikely to be affected, since residual  stratification should affect high and low LD score SNPs to a similar extent, and thus not have a large impact on the parameter estimates. To confirm the robustness of LD score regression, we have compared bivariate LD score regression estimates from GIANT to estimates from the UK Biobank. In each case, we used LD hub to obtain genetic correlation estimates between height and 832 other traits. We found very high concordance between the estimates in GIANT and UK Biobank, which further supports that bivariate LD scores regression results are in fact robust to population stratification.While it is outside the scope of this work to provide a complete characterization of the extent to which residual population stratification affects the plethora of methods that make use of GWAS summary data, we hope that it provides a useful case study and stimulates more research in this area, as well as more careful study design in general.Reviewer #2:This paper identifies a discrepancy between signs of selection estimated from UK Biobank data, compared with previous studies, and suggest that those earlier signals were caused by subtle stratification in the data. This is a useful contribution to an important question. I have only minor comments (below), but overall, urge the authors to try to rewrite the text to make it more accessible to those not immersed in the field. I find it hard to make specific suggestions, but it comes across as a list of statistical tests, without enough flow to carry the reader along with the argument. Admittedly, given the quite intricate arguments, this is not easy to do.We have substantially edited the text and hope that it has become more accessible.Minor Comments:Why should one believe that using the first 15 PC corrects for stratification? Even this is somehow traditional in the field, it needs explanation, since the failure of the correction is the key point of the paper.One can certainly envision a scenario where very few samples in the dataset have a substantially different ancestry or there is an axis of very mild stratification affecting many samples. Both of these would not be captured by top principal components. However, investigation of these effects is not a focus of this manuscript, which attracts attention to a problem rather than suggests new analytical standards.Figure 1B: The x axis needs to be labelled. More important, Spearman's correlation seems far too small, given that by eye, the points follow the linear regression rather well. This may be related to the large values seen at the right of each figure, fitting a single regression is clearly inappropriate. There needs to be a test which separates these two sets of points in some way: as it stands the significance test is just not appropriate.We have labeled the x-axis for Figure 1B. We agree with the reviewer and, in our work, present an analysis that studies the tSDS distribution for only genome-wide SNPs . We use the Spearman correlation coefficient to recapitulate the original analysis. The points and the linear slope in the figure are for visualization only. The Spearman correlation coefficient is computed based on raw data and is unrelated to the dots that correspond to binned data.Figure 3: b in the figure should be β. Also, there is a paragraph break before "The patterns" which makes it hard to work out what is main text and what is caption.We have updated the figure and removed the paragraph break.Figure 4: I do not understand what the "six summary statistics" are here.We have corrected the caption to say “four” summary statisticsDiscussion section: The concluding paragraph seems too weak, especially the sentence "In no way..". Surely the point of the paper is to "question the statistical methodology.… in polygenic tyests for adaptation", since that methodology seems to give spurious results? It is also not at all clear how much the stratification implied here influences effect size estimates in GWAS.We have edited this sentence as well as the concluding paragraphs in general.Paragraph five of subsection “Polygenic scores” Does the distribution in fact follow a β?Again, we have re-implemented the existing approach used by previous work . Although not shown in the paper, we have investigated the issue and found that different ways to compute confidence intervals yield very similar results .Reviewer #3:Both manuscripts by Berg et al. and Sohail et al. present thorough and insightful analyses with highly relevant results for current and future GWAS studies. Even prior to publication, the manuscripts have considerable impact. They will be widely read and cited. I do not think that further analyses are needed, with the potential exception of the third point below. All other points concern the discussion, in particular the guidance for further research that will surely emerge from these studies.How safe are results based on the UK Biobank data?This refers to the weak signals reported (with much caution) in the present studies, but also to potential future results on other traits. You recommend using data "such as UKB" and we will certainly see many more studies based on this resource. I would therefore appreciate a more specific discussion of risks connected to this particular data set.1) Stratification even within the UKB-GB data: It is well known that height and socioeconomic status are correlated in modern societies , and social status correlates with descent. In the UK, both factors are also geographically stratified, with people living in the north of the country having lower socioeconomic status and shorter stature, on average, than those in the south. Furthermore, the percentage of Anglo-Saxon admixture varies across the UK. How could these factors influence results based on UKB data, both here and otherwise?This is an important point, which we took into account. We repeat here the same response as above (the Major comments section). The revised version of the manuscript discusses the confounding in the UK biobank data. We clearly demonstrate that uncorrected summary statistics of the UK Biobank GWAS show signals of stratification even if the analysis is restricted to White British individuals. Interestingly, in the UK the north-south genetic cline tracks the height gradient in the opposite direction than in Continental Europe. Obviously, the magnitude of the confounding is much smaller, compared to the confounding that can arise from residual stratification in a trans-European sample. It seems likely that both genetic and environmental stratification are present along similar dimensions in the UK Biobank population, which could explain this finding. Principal components can correct for stratification effects, regardless of whether they are of genetic or environmental origin. We therefore have made no attempts to disentangle the causes of stratification in the UK Biobank, but other researchers have .2) Potential influence of GxE interactions: The manuscripts focus (for good reason) on issues connected with stratification. However, if polygenic scores depend on the environment , GxE interactions are an alternative confounding factor. Importantly, use of a homogeneous detection panel (to avoid stratification), such as UKB-GB, could increase these effects. Maybe this should be briefly discussed in the context of the present results and mentioned as a necessary caveat also for future studies that use detection panels from narrow geographic regions.We thank the reviewer for bringing this up and agree that GxE is another potential confounder affecting transferability of polygenic scores. At the same time, presence of GxE interactions without selection are not expected to generate a non-zero covariance between effect size estimates and allele frequency differences (basis of Qx) or between effect size estimates and allelic ages (basis of tSDS), although such interactions are an important caveat for how adaptation signals should be interpreted. We think that the countergradient due to stabilizing selection in a changing environment (with or without presence of GxE) can indeed lead to a signal of adaptation. Whether this should be considered a false signal or as a true adaptation to the same phenotypic value might be a matter of a terminology debate.We briefly mention the potential effect of GxE on the transferability of polygenic scores in the Discussion. We decided not to include the technical points above in the manuscript because we are trying to make it more accessible.What, exactly, causes the problems with the previous data?3) There seem to be two relevant differences of the GIANT data relative to UKB: 1) UKB is much more homogeneous and 2) GIANT is a meta-study, collecting summary statistics from many sources that are individually corrected for stratification. One would like to know better which factor is decisive. This could be further addressed by combining summaries from sub-samples of the "UKB-all" data in an artificial meta-study.We believe that we have a good understanding of the factors that can lead to residual population stratification in the UK Biobank. First, not including PCs as covariates in the estimation of effect sizes. Second, extending the studied samples from the very homogeneous set of white British individuals to a wider range of samples with more diverse ancestry.Conducting a meta-analysis per se should not be a factor that compromises stratification correction, as long as the principal components were computed on the whole sample. However, as the reviewer has pointed out, correcting for stratification within each cohort individually can lead to ineffective stratification correction, if cohort sizes are too small to allow PCA to capture the underlying population structure.The distributed nature of a meta-analysis, and the difficulty of balancing transparency with data privacy concerns, make it easier for inconsistencies or mistakes to remain undetected and to have an impact on stratification correction. As we do not currently have access to cohort level data in GIANT, we are unable to comment on the causes of residual population stratification in the GIANT meta-analysis.It would be possible to conduct an artificial meta-analysis in the UK Biobank and to induce residual stratification effects by computing PCs in very small cohorts or by omitting some PCs as covariates in some cohort. However, as there are several potential ways for stratification effects to enter into a meta-analysis, we would be unable to make strong conclusions about the origin of the observed differences between GIANT and the UK Biobank.4) The Robinson et al., 2015 GWAS: Sib-based studies are done to avoid / minimize stratification effects and the Robinson 2015 data have been used as a proof of robustness in several previous studies. The fact that you find clear signs of stratification is sobering and one would like to know what has gone wrong. You may not currently have any explanation and this is fair enough. However, the discussion should be clearer and say upfront that results based on these data cannot be trusted until we understand the issues.Again, repeating the above (the Major comments section). In the revised manuscript, we clarify that we agree with the conceptual approach of Robinson et al. but that the discrepancy is likely to be due to a technical error. We have in fact now confirmed this through correspondence with the authors of Robinson et al., and they are currently preparing a manuscript revisiting these analyses and correcting the technical issues. We emphasize that family-based effect size estimates computed in the UK Biobank following the Robinson et al. methodology behave as expected.Minor Comments:a) You use 11 different summary statistics, with partly inconsistent naming strategy. I had to look up names in the methods part a number of times. I think this can be improved. Maybe even use the same names as Berg et al. where the summaries are identical.We have made sure the names of the 11 summary statistics are consistent throughout the paper and figures. The main "UKB" dataset is referred to as "UKB Neale" in the supplemental figures to distinguish it from "UKB Neale new" and from the UKB LMM summary statistics "UKB Loh".b) The switch from 1000 genomes to POPRES complicates comparison between figures. If there are advantages of POPRES, why not use it throughout? This holds, in particular, for the test of the latitudinal slope, which would be more convincing with many populations rather than just 4 from the 1000 genomes data.We use 1000 genomes as it is publically available and several of the previous studies we analyze in this paper use 1000 genomes populations for claims of polygenic adaptation . The switch to POPRES for the test of latitudinal slope and overall overdispersion is to ensure that we do not see a nonsignificant P-value simply due to a lack of power in 1000 genomes. We realize this makes comparing Figures 1 and 4 difficult but we believe our paper is stronger for analyzing both datasets due to their different strengths and because both datasets have been used by previous studies of polygenic adaptation.c) Figure 4: "The overdispersion signal disappeared entirely when the UK Biobank family based effect sizes were used": Is this due to the smaller sample size of the sib data or due to residual stratification issues in UKB? This could be tested using a sub-sample from UKB of the same size as the sib data.Even without running another GWAS on a subset of the UK Biobank, we have some reasons to believe that the lack of overdispersion signal in the family-based estimates reflects a lack of power rather than residual stratification in the UK Biobank: Row 7 in Figure 4—figure supplement 1 shows that in the UK Biobank GWAS of white British samples without PC correction, no latitude signal is detectable, unless only genome-wide significant SNPs are used, which are least affected by stratification effects. We therefore think that the latitude signal at the genome-wide significant SNPs is likely real, rather than driven by stratification in the UK Biobank, as the stratification effects in UKB WB no PCs tend to go in the opposite direction. This leads us to believe that given enough power, a real overdispersion signal should probably be detected. However, we can’t help but remain agnostic as even for genome-wide significant SNPs , polygenic scores for both modern and ancient individuals change when the main UKB summary statistics are used (WB ancestry controlling for 10 PCs) instead of GIANT. This shift, for example, for the hunter-gatherer (HG) polygenic score is troubling as we know that different European populations have variable amounts of ancient HG vs. EF vs. SP ancestry, and could reflect residual stratification in the UKB GWAS not captured by our PCs.d) Figure 3 legend: "suggesting that tSDS shift at the gw-significant SNPs is not driven by population stratification": only true for stratification due to this particular axis.We agree with the reviewer and have changed this sentence to “There is no significant difference in frequency in these two populations, suggesting that tSDS shift at the gw-significant SNPs is not driven by population stratification at least due to this particular axis.”Additional data files and statistical comments:All necessary information is provided and the UKB sib data is on Dryad. I think the other newly generated GWAS data should go there, too.We have placed all newly generated GWAS data on Dryad.


Behavioral and cognitive traits have a genetic component even though contributions from individual genes and genomic loci are in many cases modest. Changes in the environment can alter genotype–phenotype relationships. Space travel, which includes exposure to ionizing radiation, constitutes environmental challenges and is expected to induce not only dramatic behavioral and cognitive changes but also has the potential to induce physical DNA damage. In this study, we utilized a genetically heterogeneous mouse model, dense genotype data, and shifting environmental challenges, including ionizing radiation exposure, to explore and quantify the size and stability of the genetic component of fear learning and memory-related measures. Exposure to ionizing radiation and other external stressors altered the genotype–phenotype correlations, although different behavioral and cognitive measures were affected to different extents. Utilizing an integrative genomic approach, we identified pathways and functional ontology categories associated with these behavioral and cognitive measures. These exposures pose a significant hazard to space flight crews not only during the mission but also at later times after the mission when slow-developing adverse effects could finally become apparent. The hazards associated with the space environment will likely impact many organs, including the brain.A unique feature of the space radiation environment is the presence of galactic cosmic rays (GCRs) and solar particle events (SPEs). GCR involves protons and fully ionized atomic nuclei such as Mus musculus  genotyping, and new analytical approaches enables studying gene–environment interactions and genotype–phenotype relationships involved in radiation effects on the brain. Analyzing CNS radiation effects in genetically heterogeneous mouse populations therefore likely offer a better model for genetically diverse human populations. In this study, we utilized animals from the HS/Npt colony, which was developed in 1991 as a tool to investigate complex genetic traits and captures a significant amount of the genetic diversity that is available in musculus . These m28Si ion irradiation (56Fe ion irradiation (16O ion irradiation (40Ca ion exposure on locomotor baseline activity (prior to the first tone) and responses to tone and shock without affecting fear learning and memory  and the number of mice required for studying genotype–phenotype relationships, we searched for cognitive tests that: (1) allow testing relatively large groups of mice; (2) can be performed by mice of various genetic backgrounds, including HS/Npt mice; (3) are sensitive to detect effects of irradiation on the brain; and (4) are relevant with regard to environmental psychological and physical stressors astronauts experience during space missions. Contextual and cued fear learning and memory fit these criteria. They allow testing of relatively large groups of mice and can be used to distinguish hippocampus-dependent function  from hippocampus-independent, amygdala-dependent functions  , 2010. Fadiation , 2015b aadiation  on hippoadiation , 16O ionadiation  on hippod memory . In thisThere are three possibilities arising from cognitive tests following ionizing radiation. Cognitive measurements could be strongly altered resulting in significant group mean differences. Second, cognitive measurements could be affected in more subtle ways mitigated by internal mechanisms. Third, ionizing radiation could have minimal effects on cognition. In the case of compensatory mechanisms, genetic factors are a distinct possibility. Genome-wide association studies have detected numerous genetic variants associated with complex behavioral and cognitive traits. For the most part, the QTL identified so far explain only a small proportion of phenotypic variability . DissectGiven these observations, our strategy for analyzing the HS/Npt-derived genetic, behavioral and cognitive data was twofold. First, we employed a distance/similarity based multi-locus technique that offers high flexibility in the number of loci included in the analysis ; this is56Fe (600 MeV/n), or 0.4°Gy 28Si (240 MeV/n), or 3°Gy of 137Cs gamma-rays at the NASA Space Research Laboratory (NSRL) building. While the shipping and irradiation conditions were the same between the cohorts, urine was collected from the second cohort 24 h after sham-irradiation or irradiation. Urine collection involved firmly holding the mouse over parafilm. In some cases, it was necessary to gently massage the mouse’s abdomen. Restraint time was generally, but not always, less than a minute. Urine was not collected from the first cohort. The week following irradiation, the mice were shipped to Colorado State University. Three months later, the mice were weighed and tested for contextual (hippocampus-dependent) and cued  fear conditioning. Potentially due to the additional stressor of the urine collection in the second cohort, there was a significant cohort effect and the mean and variance of a majority of phenotypes differed in the two cohorts of animals. Therefore, we analyzed the two cohorts separately and subsequently compared and contrasted the resulting findings.HS/Npt is a reverse engineered outbred mouse population derived from the A/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, CBA/J, DBA/2J, and LP/J strains as progenitors. The breeding colony consist of 48 families that are maintained by a circular breeding scheme that maximizes genetic heterogeneity. Three HS/Npt breeder mice of each family at generation 71, one male and two females, were provided by Dr. Robert Hitzemann, OHSU, and shipped to Colorado State University for breeding the experimental mice of the current study. Because of the limited number of breeders, matings were set up and litter generation was performed as follows. A first mating was set up which generated about 600 pups (cohort 1). When these animals reached 8–12 weeks of age they were shipped to BNL, Upton, NY, United States for irradiation. A second mating was then set up which generated about 1,200 pups (cohort 2) that were also shipped to BNL and similarly irradiated. Consequently, all of the mice in cohort 1 were from first litters, and those from cohort 2 were from second or third litters or first surviving litters from dam that had not previously had a surviving litter. Irradiation was either sham-irradiation or 0.4°Gy The mice were tested for fear conditioning using Med Associates NIR Video and automated analysis  utilizing Med Associates Video Freeze automated scoring system. Pavlovian fear conditioning is a versatile and well-understood method of assessing associative learning and memory. In this task, mice learn to associate a conditioned stimulus  with an unconditioned stimulus . CS–US pairings are preceded by a short habituation period, from which a baseline measure of locomotor activity is measured. On day 1, training, the mice were placed inside a white LED lit (100 lux) fear conditioning chamber (Context A). Context A consists of a metal grid floor with gray and white walls. There was a 90-s baseline followed by five CS–US pairings. During acquisition, the 30-s tone (CS)  co-terminated with 2-s foot shocks (0.7 mA) (US). The inter-tone interval (ITI) was 90 s. Motion during shock (arbitrary units from proprietary index) was measured to explore potential treatment-induced differences in response to the aversive stimulus. Percent time freezing during each subsequent ITI and tone presentation was measured to assess acquisition of the fear response. On day 2, mice were exposed first to Context A for 300 s and to a new environment, Context B, 4 h later. Context B consists of a smooth white plastic floor, with a “tented” black plastic ceiling and scented with a 10% isopropanol solution. There was a 90-s baseline and a 180-s tone. Freezing was defined as the absence of motion with the exception of respiration. The freezing response is a widely used indicator of a conditioned fear response , 2010. MDNA was prepared from tail snips (∼2 mm) collected at weaning. Each mouse was genotyped for 77.8 K SNP markers by GeneSeek . We identified ∼40 K markers with sufficient genetic diversity . The mapping resolution was in the 200 kb to 2 Mb range for this HS mapping population.P variables to M factors collected on N individuals, where P >> N. The most important feature of this procedure is the capacity to evaluate individual markers as well as groups of markers. In the present study, this feature was utilized to select groups of markers that collectively display the strongest association with the phenotype. MDMR analysis involves computing the distance between all pairs of individuals with respect to P variables of interest and constructing an N × N matrix whose elements reflect these distances. Permutation tests are used to test hypotheses and derive p-values that consider whether or not the M genetic factors (loci) can explain variation in the observed distances between and among the N individuals as reflected in the matrix of phenotype differences.For the QTL detection, we used a modified version of multivariate distance matrix regression (MDMR), which relates Figure 1.An essential step in the MDMR procedure involves selecting appropriate distance measures for each data modality – SNPs for the genotype and composite measures for the behavioral and cognitive data. For both the genotype data and phenotype data, we utilized the Manhattan distance, assigning equal weight to each genotype and phenotype. The essential steps of this approach are outlined in Figure 3A).For the purpose of finding optimal groups of genetic markers, we further combined the MDMR procedure with a pattern detection algorithm known as forward selection. Starting with the most significant genetic marker, we included additional markers to the analysis, selecting them in decreasing order of significance as quantified by the Mantel correlation with the phenotype. For each additional marker, the genotype distance matrix was recomputed and correlated once more with the phenotype distance matrix. The typical outcome of this procedure consists of initial increases in the cumulative/group correlation, followed by a peak reaching a plateau, after which including additional markers starts to decrease the correlation , as well as the associated Statistical analyses were performed in the R programming and analysis framework .r > 0.8 – see Supplementary Table 1). Most of the strong correlations were detected among related phenotypes. For example, correlations between the several “context ”-related phenotypes are in the range of 0.5–0.9. Similarly, high correlations were found when “cued” (cued fear memory) and “train” (learning day) phenotypes were compared within the same category. In contrast, we only detected small to moderate correlations (<0.6) among phenotypes that capture distinct behavioral and cognitive measures . In most cases, we detected very low correlations, as in between “shock” (learning) and “context” phenotypes. These observations justified the grouping of related phenotypes into six main categories: context_pctfrze , context_avgmot , cued_pctfrze (percent freezing during the cued fear memory test), train_pctfrze (percent freezing during acquisition/learning of fear memory on the training day), shock_avgmot (average motion during the shocks), and train_avgmo (average motion during the training/learning session). To perform subsequent analyses and in particular multi-locus QTL mapping, related individual behavioral and cognitive measures were combined in a vector and, for each vector, we constructed pairwise distance matrices between all individuals.An initial examination of the behavioral data identified numerous phenotypes that displayed high correlations ]. This illustrates the sensitivity of the behavioral and cognitive measures assessed to alterations in the environment. Based on this result, subsequent analyses were performed independently for the two cohorts although we compared and contrasted quantitatively and qualitatively the findings from the two cohorts.Due to the large number of animals utilized in this study , the shipment to BNL and radiation and sham-irradiation there, and return shipment from BNL to CSU, behavioral and cognitive testing was performed in two cohorts, of ∼600 and ∼1,200 animals, respectively. For the second, but not first, cohort, there was urine collection 24 h after radiation or sham-irradiation. Due to the fact that the urine collection procedure and associated handling has the potential to serve as an additional environmental challenge and alter subsequent behavioral performance, we tested for the presence of differences in behavioral and cognitive measures between the two cohorts. For nearly all behavioral and cognitive measures we found significant differences in mean and variance [see p-values. Second, we utilized the forward selection machine learning technique and we combined the most significant markers for each phenotype and derive cumulative Mantel statistics. Third, we utilized randomization procedures to evaluate the robustness of this approach.Behavioral and cognitive measures generally fall into the category of complex traits, which implies that they do not follow simple Mendelian models of inheritance and their genetic control is dispersed throughout the genome . Our anap-value falls under 0.01. An example of the performance of this procedure is outlined in Figure 3A for the average movement during the contextual fear memory test (context_avgmo) phenotype. In this case, the performance (magnitude of the Mantel correlation) reached a plateau after 500 markers . We next repeated this procedure on randomized data (order of the samples in the phenotype datasets was scrambled). Examples of the resulting curves are illustrated in Figure 3A (thin gray lines). This procedure illustrates that peak performance in the original data (not randomized) was unlikely to be achieved by chance alone. We further quantified these results by constructing Z-statistics from the randomized and un-randomized data ; this revealed that as the number of markers grows beyond 2–300, Z-scores increase >3 (p < 0.0014) and eventually achieve levels >5 (p < 00001), is highly unlikely due to chance. Qualitatively similar results were obtained for all phenotypes (data not shown). We conclude that our procedure robustly detects groups of genetic markers that achieve a high correlation with the behavioral phenotypes. Importantly, both overall performance (correlation) and robustness are achieved when including larger number of genetic markers. Uncertainty remains about the influence of single markers, even when examining the marker achieving the greatest genome wide significance; this manifests in the data by sharp jumps in the significance and Z-score curves .As outlined above, our procedure starts from the most significant marker and expands the group of markers until either a peak is achieved or the individual marker significance p < 0.001 based on 1,000 permutation tests) . The average motion during the contextual fear memory test (context_avgmo) phenotype had the highest correlation with the genotype in both cohorts, while the percent freezing during the contextual fear memory test (context_pctfrze) had the lowest correlation ; the rankings of “genetic correlation” for the six composite phenotypes were identical in the 600 and 1,200 cohorts, as can be seen in Figure 3C. However, there was a significant shift downward in the magnitude of correlations between the 600 and 1,200 groups, as indicated by the departure from the diagonal of the linear relationships in Figure 3C. This set of results illustrates that: (1) all behavioral and cognitive measures had a significant genetic component, albeit dispersed among numerous genomic locations; (2) changes in environmental conditions between the two cohorts resulted in differences in genetic components of behavioral and cognitive measures, as quantified by the Mantel correlation; and (3) the ranking of the strength of the genetic control over behavioral and cognitive performance was preserved across the two cohorts.We detected sets of markers that cumulatively raised to as high as Mantel correlation of 0.35 (train_avgmo) measure being the most affected . We conclude that a change in the environmental conditions, which in our case was a recent intrusive urine collection, changed the identity of the genomic locations showing an association with certain behavioral and cognitive measures.Given the fact that the different testing environment in the two cohorts was associated with differences in the strength of genetic control over behavioral and cognitive performance, we inquired whether the identity of the significant genomic locations was altered as well. We utilized the Fisher exact test to quantify the level of overlap between sets of significant markers independently detected in each cohort. We found that for most behavioral and cognitive measures, the genomic locations that showed association with behavioral and cognitive performance shifted, with the average motion during training . Lack of overlap was quantified by the magnitude of the Fisher test odds ratio, with values below one being no different from chance. For nearly all behavioral and cognitive measures, the identity of the significant markers changed dramatically to the point that there was no overlap beyond what can be achieved by chance. It is important to note that the one phenotype that showed concordance between sham-irradiated and irradiated animals, average motion during training (train_avgmo) was the same in the two cohorts. We conclude that radiation exposure has the potential to alter phenotype–genotype correlations.Given the fact that shifts in environmental setting (urine testing) induced changes in phenotype–genotype correlations, we inquired whether radiation exposure has similar effects. This hypothesis was evaluated independently in the two cohorts. We separated the two main cohorts between the sham versus irradiated animals . We then performed genetic scans independently in the irradiated and sham-irradiated animals. Comparing the identity of the sets of genetic markers detected revealed large shifts in their composition . For example, the average motion during training (train_avgmo) phenotype following sham-irradiation showed enrichment in “response to pheromone” and “response to chemical stimulus” GO functional categories. For the percent freezing during the contextual fear memory test (context_pctfrze) with Gamma radiation, we detected “regulation of proteolysis” and “negative regulation of endopeptidase activity.”We next inquired whether biological pathways or other functional groups are enriched in genes within the significant genomic locations. For each behavioral phenotype, we intersected the significant markers detected in each cohort. Next, we collected genes within 1056Fe irradiation and train_avgmo under 28Si irradiation (Supplementary Table 2). These two phenotypes also displayed the strongest genetic basis in both batches . Importantly, 56Fe and 28Si are two radiation types uniquely encountered in space flight. Given these findings, an in-depth examination of the specific GO and KEGG pathways uncovered by the genetic analysis is warranted.The phenotype-irradiation combinations with the most abundant annotations were context_pctfrze under 28Si, we have the PPAR_SIGNALING_PATHWAY (p < 2.2 × 10-9). This pathway is associated with regulation of peroxisome proliferation and adipocyte differentiation. However, emerging evidence suggests that within the adult mouse brain PPAR is highly expressed primarily not only in the hypothalamus, but also in the neocortex, the olfactory bulb, the organ of the vasculosum of the lamina terminalis (VOLT), and the subfornical organ . There is extensive literature on the effect of ionizing radiation on fatty acid metabolism (p < 1.6 × 10-6), there is evidence that retinoids reduce cellular apoptosis following irradiation . The mitochondrial activity of ATPASE has been shown to be affected by radiation in female mice (56Fe irradiation (Supplementary Table 2).Among the annotation categories associated with context_pctfrze under ale mice . Additioale mice ; we alsoOur main finding is that phenotype/genotype correlations are plastic and can shift depending on the external environment, and in particular following ionizing radiation. Recent studies have revealed that a significant proportion of QTLs reported in the literature fail to replicate in subsequent studies . Given tThis observation has important implications regarding potential effects of space radiation on behavioral and cognitive performance. Our results suggest that complex behaviors and their genetic basis have to be studied under realistic conditions, which in our case means irradiated animals. Behavioral QTLs from non-irradiated animals will have limited predictive power once the animals are subjected to irradiation.Figure 3D). In contrast, ionizing radiation does not directly alter behavioral and cognitive performance but has profound effects on the genotype/phenotype correlations . It is striking that in the larger cohort of 1,200 mice in which there was urine collection 24 h following radiation exposure, the mean percent freezing during training, a measure of learning, as well as the variability in this measure in individual mice was much higher than that in smaller cohort of 600 mice in which there was no urine collection . Striking cohort effects were also seen in Figure 4. There was more overlap between the genetic effect on activity during the contextual fear memory test. This was detected the day following training and between irradiated and sham-irradiated mice in the cohort of 1,200 mice. As noted above, in the 1,200 mice cohort there was urine collection 24 h following radiation exposure. In contrast, in the cohort of 600 mice cohort there was no urine collection. In other words, there appear long-term effects of urine collection that diminishes the ability to detect genetic effects of irradiation on behavioral performance in a hippocampus-dependent fear memory test. No such difference was seen for hippocampus-independent cued fear memory. These data indicate that the hippocampus might be particularly sensitive to long-term effects of environmental stressors that might affect radiation effects. However, the cohort effects were also seen when activity during fear conditioning training was compared. The odds ratio for activity during the training session was much higher in the cohort of 1,200 than in the cohort of 600 mice. These data suggest that the urine collection causes a dramatic increase in overlap between the genetic effects on activity during the session in which the mice learned this task. As astronauts during space missions encounter environmental challenges in addition to radiation exposures, increased efforts are warranted to determine how environmental challenges other than radiation might modulate long-term effects of space radiation on the brain.We find that alterations in environmental conditions and environmental challenges other than irradiation also have the potential to alter behavioral and cognitive performance and their genetic basis, an observation consistent with previous findings . StressfNumerous studies have encountered and commented on the varying levels of reproducibility of genetic association studies . BenefitWithin behavioral genetics there is wide recognition that Mendelian mutations leading to large shifts in behavioral and cognitive performance are extremely rare . Given twhen considered as a group, their collective significance will clearly point to the implication of the relevant pathway.Our annotation approach closely follows  and is b56Fe irradiation and train_avgmo under 28Si irradiation. There are several conclusions that we draw from this analysis. First, at the level of annotations and pathways, there is no overlap in the categories detected for different radiation types and behavioral and cognitive phenotypes. This finding suggests that distinct radiation types lead to the “engagement” of different biological mechanisms, pathways, and ontologies. This observation is concordant with previous experimental work showing that irradiation parameters induce different types of DNA damage (We examined closely some of the pathways associated with the two behavioral phenotypes with the strongest genetic component, context_pctfrze under A damage , and alsAt the conceptual level, our findings suggest the following model of interaction between irradiation, genetics, and behavioral and cognitive patterns. Ionizing radiation engenders effects on behavioral and cognitive performance, and internal compensatory mechanisms mitigate these effects. However, genetics plays a part as to which compensatory mechanisms are engaged in each individual animal. Individual genetic variability interacts with variation in the irradiation type, resulting in heterogeneity in the identity of individual genetic loci and of the functional pathways affected.The experiments were approved by the IACUC committees from BNL and CSU.OI, JB, MW, and JR designed the experiments and wrote the manuscript. SB, RO, MD, BS, ME, TM, CF, and EE acquired and analyzed the data and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Aegilops tauschii and was absent in all elite lines used in study. In silico analysis of the SNP showed hits with a candidate gene coding for isoflavone reductase IRL-like protein in Ae. tauschii. Rare haplotypes were also identified on chromosomes 1A, 6A and 2B effective against abiotic/biotic stresses. Results demonstrate positive contributions of exotic germplasm to PBLs derived from crosses of exotics with CIMMYT’s best elite lines. This is a major impact-oriented pre-breeding effort at CIMMYT, resulting in large-scale development of PBLs for deployment in breeding programs addressing food security under climate change scenarios.The value of exotic wheat genetic resources for accelerating grain yield gains is largely unproven and unrealized. We used next-generation sequencing, together with multi-environment phenotyping, to study the contribution of exotic genomes to 984 three-way-cross-derived (exotic/elite1//elite2) pre-breeding lines (PBLs). Genomic characterization of these lines with haplotype map-based and SNP marker approaches revealed exotic specific imprints of 16.1 to 25.1%, which compares to theoretical expectation of 25%. A rare and favorable haplotype (GT) with 0.4% frequency in gene bank identified on chromosome 6D minimized grain yield (GY) loss under heat stress without GY penalty under irrigated conditions. More specifically, the ‘T’ allele of the haplotype GT originated in  In the pre-Green Revolution period, breeding efforts relied on selection from locally adapted landraces or traditional cultivars, but a paradigm shift occurred with the extensive use of semi-dwarf wheat cultivars beginning during the Green Revolution. Further, the development and use of synthetic wheats in breeding brought diverse alleles from the tertiary gene pool into varietal pipelines3. These and other advances in breeding enabled annual genetic gains for GY between 0.6 and 1.0 percent for varieties released between 1994 and 20104. However, rates of gain must increase to meet the food and nutrition demand of our growing global population under the likely, unfavorable climate change scenarios.The twentieth century witnessed the impact of traditional breeding methods in enhancing genetic gains for wheat grain yield (GY)8. Nevertheless, breeders are generally reluctant to use exotic genetic resources because of the long-term commitment required to identify useful, novel diversity and introgress it into well-adapted elite cultivars while reducing the effects of undesired, linked genes. A commonly reported, intriguing analogy, is that the “bad” parent of good × bad crosses often contributes one or more QTL with favorable effects on the trait of interest9. Approximately 800,000 wheat accessions, including landraces and synthetics, exist in germplasm banks globally as reservoirs of useful alleles for breeding, but only a very small proportion of these have been used in varietal improvement programs10. Low-cost high-density genomics and growing bioinformatics capacity are increasing the feasibility of identifying and using the “diamonds in the rough” within these genetic resources.The impact of landraces, wild relatives and synthetic wheats on genetic gains for GY, by providing resistance against biotic stresses, and to a much lesser extent against abiotic stresses, is well recognized12. CIMMYT’s Seeds of Discovery13 is an innovative project initiated to use next generation genome sequencing technology, envirotyping and analytical methods to characterize and enhance the use of germplasm bank accessions16. This is the third major germplasm infusion effort at CIMMYT that has resulted in strategic development of large-scale pre-breeding germplasm  into elite germplasm, plays a key role in broadening the genetic base of breeding germplasm pools. Pre-breeding programs that create freely available, pre-competitive, bridging or intermediate germplasm by introgressing desirable genes from exotic into elite lines empower breeding programs to use germplasm bank diversity cost effectively. Realizing this, a few wheat pre-breeding projects have been launchedhttp://www.diversityarrays.com/sequence-maps), resulted in 361, 115 and 367 haplotype blocks (HBs) in pre-breeding lines (PBLs), elite and exotic parents, respectively, based on average linkage disequilibrium (LD) distance of 5 cM. Supplementary Table 1). Haplotype block-by-block comparison by chromosome revealed that 58 (16%) of the 361 HBs identified in PBLs originated from or were specific to their exotic parents. Of the 58 exotic-specific HBs in PBLs, 11 (19%) were positively associated with agronomic traits and disease resistance [Fig. ]. Elite-specific HBs were not estimated in PBLs due to the small number of elite parents used.Haplotype block analysis for the complete set of 984 PBLs, performed by localizing the genome-wide SNPs to a high-density consensus map  population. Supplementary Fig. In another approach to investigate the exotic parent contribution to PBLs, SNP allele frequencies were evaluated for 278 PBLs derived from 10 crosses, each with progeny number ≥16, involving 9 different exotic and 7 elite parents. Of the homozygous PBL alleles that could be traced to either exotic or elite parents, an average of 23.4% were inherited from their exotic parents and 65.9% from one of their elite parents. Exotic introgression patterns varied among chromosomes (Supplementary Table The genome wide association (GWA) analysis identified HBs significantly associated with grain yield Table  and 4 anPuccinia striiformis f. sp. tritici) resistance in both years of evaluation [Fig. Blumeria graminis f. sp. tritici – Bgt), two genomic regions on 5B and 6B had significant effects , was associated with yellow rust  domains known to interact with pathogen effectors to induce defense responses. Of the GY-associated HBs; HB10.5 spanned ~13.8 Mbp containing 61 genes, HB16.10 spanned ~28.5 Mbp containing 138 genes and HB18.1 spanned ~2.3 Mbp containing 48 genes. Using KnetminerAegilops tauschii via synthetic pedigree. Further, this SNP (belonging to clone ID 1067078) showed similarity with a candidate gene Traes_6DS_84A4D85F.1 through BLAST analysis. This gene is homologous to a rice gene LOC_Os06g27770.1 coding for isoflavone reductase. Phylogenetic analysis revealed a high level of similarity of Traes_6DS_84A4D85F.1 with the gene F775_22033 in Ae. tauschii, also coding for isoflavone reductase IRL-like protein  has been shown to be involved in homoeostasis of reactive oxygen species18. In wheat, detailed physiological dissection of this gene is underway to identify the underlying mechanism conferring heat tolerance.Figure Supplementary Table PBLs with resistance to yellow rust and powdery mildew are shown in Fig. −1 more Zn than the check in years 1 and 2, respectively  Zn concentration than the best check line, Reedling#1 in the two years’ evaluation. One PBL //KACHU/3/BAJ #1) had 18 and 17 µg gely Fig. . The Pea20. Wheat has been exposed to intense artificial (through breeding) and natural selection21 since its domestication, resulting in large HBs as observed for the elite germplasm evaluated herein. These HBs may inadvertently fix unfavorable alleles linked with selected genes; for example, as demonstrated by Voss-Fels et al.22 for root traits negatively affected by linkage drag with the selected Vrn gene for heading date. Thus, HBs prevalent in elite germplasm may need to be broken to introduce and capture valuable diversity within them that may otherwise remain undiscovered and unused. The pre-breeding strategy reported here successfully disrupted many large HBs present in the elite lines  and 16.1% Fig. , respectThe identification of rare haplotypes in HBs on chromosomes 6A and 6D associated with GY across environments, and on chromosome 2B for yellow rust resistance demonstrated the value of crosses with exotic germplasm Figs . The GY Ae. tauschii via synthetic wheat (Ae. tauschii × Triticum durum) parents. More specifically, the ‘T’ allele of haplotype GT was absent in all elite parents studied here, whereas it was present in the exotic (synthetic) parents and their derived PBLs. Following this discovery, we screened 62,000 previously sequenced CIMMYT germplasm bank accessions for the presence of this favorable haplotype and found it in only 262 (0.42%) accessions. The majority of germplasm bank accessions with the ‘T’ allele were synthetic-derived lines such as Sokoll (released in 1997). All of the PBLs with the ‘T’ allele was susceptible to stem rust (Puccinia graminis f. sp. tritici), which may be coincidental or could suggest that selection for stem rust resistance led to negative selection of this haplotype. This would be similar to the inadvertent selection for the Lr67 susceptible allele, which was associated with intense selection for RhtD1b semi-dwarf gene in CIMMYT’s elite germplasm24.It is noteworthy that the rare haplotype (GT) of HB18.1, which had a significant positive effect on biomass and grain yield of PBLs under drought and heat stresses, was inherited from To follow-up on the candidate gene within HB18.1 (that affected grain yield and biomass under heat and drought stress) Fig. , we have25, were identified within the HB5.23 intervals. Most of these genes were located in a small cluster, similar to those observed in Arabidopsis26 and rice27, with 5 located within a 115 Kbp region and 3 within a sub-cluster of 22 Kbp. Nine genes viz. Yr5, Yr7, Yr27, Yr31, YrV23, YrSp, YrQz, YrTp1, and YrCN1928 showing resistance to multiple yellow rust strains have previously been mapped on chromosome 2B. Most of these known yellow rust genes, except Yr5, are not effective under Punjab conditions, where the association reported herein was detected. Molecular marker analysis confirmed that HB5.23 is not linked with Yr5 (data not shown), and warrants an in-depth analysis to determine the novelty of the identified gene.The rare and favorable haplotype of HB5.23 for yellow rust resistance had a frequency of 19% in 62,000 germplasm bank accessions investigated. Ten NBS-LRR genes, which are well-known disease resistance proteins in plants), for GY advantage under heat and drought  or allele(s) for disease resistance  to generate PBLs, in a way so that each PBL possessed approximately 25% of the exotic and 75% of the elite genomes at an early stage. Therefore, exotic alleles were incorporated into elite backgrounds even before their trait values were identified. This strategy enabled investigation of greater number of genetic variants at a time and also allowed recombination between exotic and elite genomes to be exploited for genetic improvement of elites. Further, bulk selection in subsequent segregating generations (TC1F2 to TC1F5) helped in capturing maximum useful diversity. In addition, HB analysis suggests that this strategy retained rare and useful allelic variation. The simultaneous evaluation of the derived germplasm at multiple locations ensured minimum loss of useful diversity, identify useful, and novel diversity into well-adapted regional elite cultivars. The agronomic competitiveness of many PBLs with elite check lines further indicates that this approach addressed the bottlenecks of undesirable drag, possibly by breakage of haplotype blocks and thereby selection of useful alleles or interaction of exotic with elite alleles.Conventional way to utilize germplasm bank accessions is to identify useful trait donors and then use them in pre-breeding. This approach is successful to improve specific trait and are being used in most breeding programs. However, in this investigation, exotics alleles were first brought into elite backgrounds and then useful alleles were selected. We pursued a three-way cross strategy  to bring in the untapped diversity of wheat exotics in gene bank into PBLs. CIMMYT provided first research breakthrough by providing semi-dwarf materials to the global research community. Followed by this, second generation germplasms were provided by CIMMYT i.e. synthetics. This is the third major impact-oriented germplasm infusion effort that has resulted in strategic development of large-scale pre-breeding germplasm Fig.  followedTwenty-five elite CIMMYT wheat lines were used for developing pre-breeding germplasm. These 25 elites were either released varieties (Supplementary Table T. turgidum subsp. durum) or emmer wheat (T. dicoccum) with diverse Ae. tauschii accessions), 784 landraces and 34 other materials, were used to make three-way crosses (exotic/elite1//elite2), resulting in 1,200 TC1F1 (top cross) populations. Only 244 of these TC1F2 populations were advanced, based on their agronomic performance. The diversity analysis of the exotics involved in generating these 244 populations revealed them genetically diverse , identified using the focused identification of germplasm strategy approach29; 33 heat-adapted materials from the Australian germplasm bank in Horsham, Victoria and termed ‘Australia hot’15; and 15 Iranian landraces, 13 Mexican landraces and 8 inbred lines from CIMMYT’s germplasm bank. The selected exotic accessions were grown along with staggered planting of the 25 elite lines to make F1 crosses that were subsequently crossed with a second elite line to form three-way cross populations.Around 1,711 exotic accessions from CIMMYT’s germplasm bank, including 893 synthetic wheats , 86 synthetics and 4 other germplasm bank lines. The 165 exotics represented different gene pools and from parts of the world. The exotics and PBLs have been enlisted in Supplementary Table The 244 three-way cross populations were advanced by selected bulk method up to TC1F1 plants were advanced to the TC1F2 generation and approximately 2000 seeds for each TC1F2 were grown in 50 m plots at CIMMYT’s experimental station at Ciudad Obregon, Mexico under drought- and heat-stress environments. Approximately 488,000 TC1F3 plants were visually selected for good performance, and spikes from them were selected and bulked for each cross. The TC1F3 bulks were grown at El Batan and Toluca, Mexico, for bulk advancement following mild selection under natural infection of yellow rust disease (with susceptible checks ‘Avocet’ and ‘Morocco’). The TC1F4 bulk populations were grown at the Ciudad Obregon station under managed heat- and drought-stress for a second round of selection, resulting in 8157 TC1F4:5 selections that were grown in 1-m2 plots for evaluations of plant-type (at El Batan) and resistance to yellow rust (at Toluca). 984 TC1F5:6 plants were selected and subsequently advanced as “pre-breeding lines” (PBLs). The 984 PBLs originated from 183 top-crosses that used 165 exotic accessions. The general pre-breeding strategy is outlined in Fig. The 244 TC1F5 plant following a modified CTAB (cetyltrimethylammonium bromide) method30. DNA samples were quantified with a Nano-Drop 8000 spectrophotometer V 2.1.0. Genotypic characterization used DArTseq™ technology (http://www.diversityarrays.com/dart-application-dartseq) at the Genetic Analysis Service for Agriculture (SAGA) service unit at CIMMYT headquarters . The methodology described by Vikram et al.16 was followed to generate a total of 58,378 high quality SNP markers. The main parameters to select markers were call rate (the proportion of samples with genotypic score and not recorded as missing data) and average reproducibility . 12,071 SNP markers belonging to 10,111 sequence tags were identified based on these criteria, out of which 7,180 were used for the final analysis (Supplementary Table (http://www.diversityarrays.com/sequence-maps).Genomic DNA was extracted from leaf samples harvested from each TChttp://www.R-project.org)31 using a script based on the algorithm from Gabriel et al.32. Briefly, 95% confidence bounds on D prime were generated and each comparison was called “strong LD”, “inconclusive” or “strong recombination”. A block was created if 95% of informative (i.e. non-inconclusive) comparisons were “strong LD”. This method defined pairs to be in strong LD if the one-sided upper 95% confidence bound on D’ was >0.98 and the lower bound above 0.7. The Hardy Weinberg p-value cut off was set to 0.001, and minimum marker allele frequency was set to 0.05. Individuals with more than 75% of missing data were excluded from haplotype construction. When multiple SNPs had the same genetic position, only the first marker was used in haplotype construction. The haplotypes were displayed as blocks of marker numbers and alleles. These were named with the prefix ‘HB’ for haplotype block, followed by a number for the chromosome , followed by a dot and incrementing numbers  of the haplotype blocks along the chromosome. For example, HB1.1 and HB2.2 designate the first and second haplotypes on chromosomes 1A and 1B, respectively.Haplotypes were generated in R  utilizing PCA as fixed and kinship matrix as random effect was used. The Bayesian Information Criterion (BIC) was used to select the appropriate number of principal components for each trait35.Genome-wide association (GWA) analysis was conducted for two population panels: (1) the panel of 984 PBLs and (2) a subset of 134 PBLs selected from the initial set of 984 PBLs based on agronomic performance at multiple locations. The covariance matrix was derived by PCA using the PRCOMP function from the STATS package in Rp-value within the bottom 0.1 percentile of the distribution. This approach avoids risk of type II error and has been used in recent studies for wheat36. A threshold p-value of 0.001 and 0.0001 corresponded to the bottom 0.1 percentile of the distribution for GY and yield components and for disease resistance, respectively. Hence, a marker was declared significant if it showed (a) p-value above the threshold and (b) deviation of its p-value from the normal distribution curve in the quantile-quantile (QQ) plot.Significant marker-trait associations (MTAs) were declared using a threshold 2 in 2016 and 2017, respectively. Whereas irrigated and drought experiments were sown in the second fortnight of November, the heat stress trials were sown in early March to expose them to 35–40 °C temperatures during anthesis. Agronomic management was the same for all trials, except for irrigation. Approximately 600 mm of water was provided in the irrigated and the heat experiments, while the drought experiment received ~200 mm of total soil moisture during the crop season. The statistical analyses to estimate means and variances were performed using R software.Phenotypic evaluation of the 984 PBLs for GY under heat, drought and irrigated treatments was conducted at CIMMYT’s experimental station near Ciudad Obregon , Mexico. 984 PBLs were evaluated for GY under heat stress in 2016 and 2017, as well as for GY under drought- and well-irrigated conditions in 2016. Further, a sub-set of 200 best-performing PBLs was selected from the 2016 trial for evaluation in 2017. The experimental designs were alpha-lattices with two or three replications, and plot size of 2.0 and 4.8 m−1) was estimated using a “bench-top,” non-destructive, energy-dispersive X-ray fluorescence spectrometry (EDXRF) instrument  according to the method described by Paltridge et al.37.Zinc concentration was measured for grain of the 984 PBLs grown in a well-irrigated experiment with 2 replications at Ciudad Obregon in 2016. The 50 PBLs with highest grain Zn concentration (in 2016) were evaluated again in 2017 in an experiment with two replications. Grain Zn concentration (µg gBlumeria graminis f. sp. tritici – Bgt) resistance of the 134 PBL sub-set was evaluated at Malan research station in Himachal Pradesh, India , which is a natural hotspot for the disease. Experiments used randomized complete block designs (RCBD) with two replications of 1.0 m2 plots in which row-to-row spacing was 20 cm. Sowing was in the first fortnight of November (2016 and 2017) and standard agronomic practices (as explained in the above section) were followed. The susceptible varieties Lehmi and HPW 155 were sown between every tenth test genotype and on the outer boundaries of the plots for use as susceptible checks and to multiply and spread inoculum. The experiments were dust inoculated with a locally available isolate of Bgt. The inoculum was multiplied on seedlings of HPW155, Lehmi, and Agra Local grown in 4-inch pots. Data were recorded for overall disease reaction on a 0–9 scale as described by Saari and Prescott38.Powdery Mildew (Puccinia striiformis f. sp. tritici) virulent pathotypes 46S119 and 78S84, at IARI-New Delhi , and PAU, Ludhiana , India. Experiments were performed at both locations during two consecutive years, using RCBDs with two replications of 0.5 m2 plots with 20 cm inter-row spacing. Avocet was used as a susceptible check grown all-around the experimental blocks. Methodology explained by Hao et al.39. was used to inoculate spreader/border rows. Disease symptoms were scored when the susceptible check showed 100% yellow rust infection. The percent of infection was estimated according to the modified Cobb’s scale40.The 134 PBLs were evaluated against yellow rust  in GWA using multi-location data, were subjected to bioinformatics analyses. Sixty base pair sequences of each clone ID associated with each SNP marker were anchored to the Refseq. 1 physical map of wheat using BLAST. Markers were anchored based on the top hit (taking into account both query length and percentage match). All genes were extracted between the outermost markers associated with each haplotype +/− 500 Kbp. The size of each interval and number of genes within these intervals are enlisted in Supplementary Table 41.The world map presented in Supplementary Fig. Harnessing genetic potential of wheat germplasm banks through impact-oriented-prebreeding for future food and nutritional securityDataset 2Dataset 3Dataset 6Dataset 9Dataset 10Dataset 11