Polygenic scores (PGS) are becoming an increasingly popular approach to predict complex disease risk, although they also hold the potential to develop insight into the molecular profiles of patients with an elevated genetic predisposition to disease.We sought to construct an atlas of associations between 125 different PGS derived using results from genome-wide association studies and 249 circulating metabolites in up to 83,004 participants from the UK Biobank.As an exemplar to demonstrate the value of this atlas, we conducted a hypothesis-free evaluation of all associations with glycoprotein acetyls (GlycA), an inflammatory biomarker. Using bidirectional Mendelian randomization, we find that the associations highlighted likely reflect the effect of risk factors, such as adiposity or liability towards smoking, on systemic inflammation as opposed to the converse direction. Moreover, we repeated all analyses in our atlas within age strata to investigate potential sources of collider bias, such as medication usage. This was exemplified by comparing associations between lipoprotein lipid profiles and the coronary artery disease PGS in the youngest and oldest age strata, which had differing proportions of individuals undergoing statin therapy. Lastly, we generated all PGS–metabolite associations stratified by sex and separately after excluding 13 established lipid-associated loci to further evaluate the robustness of findings.http://mrcieu.mrsoftware.org/metabolites_PRS_atlas.We envisage that the atlas of results constructed in our study will motivate future hypothesis generation and help prioritize and deprioritize circulating metabolic traits for in-depth investigations. All results can be visualized and downloaded at This work is supported by funding from the Wellcome Trust, the British Heart Foundation, and the Medical Research Council Integrative Epidemiology Unit. Complex traits and disease have a polygenic architecture meaning that they are influenced by many genetic variants scattered throughout the human genome . An incrThe human metabolome consists of over 100,000 small molecules and is a rich source of potential risk factors and biomarkers, as well as therapeutic targets , for comIn this study, we sought to construct an atlas of associations between 125 PGS and the 249 circulating metabolic traits in the UKB study . We demor2). These were (1) ‘lenient’ thresholds of p < 0.05 and r2 < 0.1 and (2) a ‘stringent’ threshold of p < 5 × 10−8 and r2 < 0.001. PGS were generated using the software PLINK . All PGS were standardized to have a mean of 0 and standard deviation of 1 and similarly all metabolites were subject to inverse rank normalization transformations prior to analysis allowing cross-PGS/metabolite comparisons to be made. Analyses were conducted using linear regression adjusting for age, sex, and the top 10 principal components (PCs).We investigated the association between each PGS in turn with 249 circulating metabolites measured using targeted high-throughput NMR metabolomics from Nightingale Health Ltd (biomarker quantification version 2020) . Our finhttp://mrcieu.mrsoftware.org/metabolites_PRS_atlas/) to query and visualize metabolic signatures for a given PGS. In this paper, we have discussed findings using PGS that were derived using the more lenient criteria (i.e. p < 0.05 and r2 < 0.1), although all findings based on both thresholds can be found in the web atlas. PC analysis suggested that the first 19 PCs captured 95% of the variance in the NMR metabolites data (whereas the first ten PCs captured 90% and the first 41 PCs captured 99% of the variance)   . We thern = 217) . Our atln = 217) .In this paper, we provide several examples of how results from this atlas can be used to generate hypotheses and pave the way for in-depth and careful evaluations of associations between PGS and circulating traits. Specifically, we believe our findings can facilitate a ‘reverse gear Mendelian randomization’ approach to disentangle whether associations likely reflect metabolic traits acting as a cause or consequence of disease risk  as illus−35 and Beta = 0.012, 95% CI = 0.006 to 0.019, p = 2.7 × 10–4, respectively) and also coronary heart disease (CHD) liability . Conducting bidirectional MR suggested that the associations with average diameter of VLDL particles are likely attributed to a consequence of BMI and CHD liability as opposed to the size of VLDL particles having a causal influence on these outcomes (–7) which may explain associations between the CHD PGS and this metabolic trait within our atlas. Similar MR analyses to investigate findings from our atlas can be conducted using the full GWAS summary statistics for all 249 circulating metabolic traits available via the GWAS catalog (https://www.ebi.ac.uk/gwas/) under accession IDs GCST90092803 to GCST90093051  and genetic liability to cigarettes smoked per day . Estimates based on the IVW method were typically supported by the weighted median approach, although only cigarettes smoked per day were supported by both the weighed median  and MR-Egger  methods   as instrumental variables for these proteins (–3), although this was not replicated when using estimates of genetic associations with GGT levels from a larger GWAS conducted in the UKB data . For details of pleiotropy robust analysis and replication results see Next, we investigated the converse direction of effect using MR to assess whether genetically predicted GlycA may influence any of the 44 complex traits or disease endpoints highlighted by our atlas of results. Undertaking a GWAS of GlycA in the UKB identified 59 independent genetic variants which were harnessed as instrumental variables (mean = 100.1) . In cont= 100.1) . We alsonsferrin . Using cproteins  did not proteins . The onlA critical challenge when analysing the NMR metabolites data in UKB concerns the most appropriate manner to account for participants taking medications which may undermine inference . For exar2 < 0.1) under a p value <0.05/19 for multiple testing correction (–15), which acts as an index of the number of circulating atherogenic lipoprotein particles and has been postulated previously to be the predominating lipoprotein lipid trait indexing CHD risk . In stark contrast, there was weak evidence of an inverse association between apo B and the CHD PGS in the oldest tertile  , which is likely attributed to the higher proportion of participants undergoing statin therapy in this sample. Similarly, concentrations of VLDL, LDL, and IDL provided evidence of a positive association with the CHD PGS in the youngest tertile  cholesterol, which apo B particles carry. In the youngest tertile , the association between the CHD PGS with apo B was markedly stronger than in the total sample  cohort . For detN = 209), waist circumference (N = 206), and BMI (N = 205) still provided strong evidence of association with the majority of metabolic measurements on the NMR panel based on multiple testing corrections. Elsewhere however, the Alzheimer’s disease PGS, which was associated with 60 metabolic traits robust to p < 0.05/19 in the initial analysis including these lipid loci (APOE locus (near one of the major lipid regulators APOC1) which are recognized to exert their influence on phenotypic traits via horizontally pleiotropic pathways . Previous studies in the field have demonstrated the strong influence that adiposity has on circulating traits from across the metabolome (–9). In our atlas, evidence of these associations strongly attenuated in the youngest age tertile, where the influence of such factors in the UKB population may be weakest . In addition to the striking difference highlighted in our study between the CHD PGS and apolipoprotein B across age tertiles, findings such as this further emphasize the importance of evaluating results from the full sample analysis together with those derived in age-stratified subsamples. Moreover, we suggest that users interested in the sex-stratified estimates within our atlas should interpret them in conjunction with estimates derived across age tertiles as in this example, given that the proportions of males and females in UKB taking certain medications may differ (e.g. statins).Amongst the thousands of PGS associations identified in this study with a p value <0.05/19, we observed an enrichment of scores derived using GWAS of anthropometric traits. This was exemplified by the waist circumference PGS which yielded the largest number of associations in our atlas  to formally assess putative causal relationships which may underlie findings in our atlas however, as well as to help orient their directionality. This is particularly important given that PGS may be more prone to recapitulating sources of bias commonly encountered in observational studies in comparison to formal MR analyses  in large numbers with GWAS genotyping will aid in this effort . FurtherWe envisage that findings from our atlas will motivate future study hypotheses and help prioritize (and deprioritize) circulating metabolic traits for further in-depth research. Although we highlight several key findings in this manuscript, all our findings can be queried using our web application which provides a platform to inform researchers in the field planning similar analyses. Similar evaluations to those conducted in this manuscript should help develop a deeper understanding into how circulating metabolic traits contribute towards complex trait variation and assess their putative mediatory roles along the causal pathways between modifiable lifestyle risk factors and disease endpoints.n = 409,703). In total, 107,162 pairs of related individuals were removed based on estimated kingship coefficients derived using the KING toolset. An in-house algorithm was then applied to preferentially remove individuals related to the greatest number of other individuals until no related pairs were left . A further 2 individuals were removed as they were related to over 200 other individuals. There were n = 814 individuals with sex-mismatch (derived by comparing genetic sex and reported sex) or individuals with sex chromosome aneuploidy excluded from analyses.Metabolic profiling was undertaken on a random subset of individuals from the UKB study  range b. In brieIn total, 249 metabolic biomarkers were generated using non-fasting plasma samples  taken from UKB participants at initial or subsequent clinical visits. Targeted high-throughput NMR metabolomics from Nightingale Health Ltd (biomarker quantification version 2020) were used to generate data on each of the 249 measures. These included biomarkers on lipoprotein lipid traits, their concentrations and subclasses, fatty acids, ketone bodies, glycolysis metabolites, and amino acids. Further details are described elsewhere . For QC,Ethical approval for this study was obtained from the Research Ethics Committee  and informed consent was collected from all participants enrolled in UKB. Data were accessed under UKB application #15825 and #81499.https://gwas.mrcieu.ac.uk/) and publicly available repositories (Publicly available GWAS summary statistics were extracted from the OpenGWAS platform (sitories . We idensitories . All detr2 < 0.001) which were robustly associated with their traits or disease based on conventional genome-wide corrections (i.e. p < 5 × 10–8). The second versions of scores were derived using more lenient thresholds which were r2 < 0.1 and p < 0.05. LD to estimate correlation between variants was based on a previously constructed reference panel of 10,000 randomly selected unrelated UKB individuals of European descent (K-means clustering (K = 4). These scores were built by summing trait/disease risk increasing alleles which participants harboured weighted by their effect size reported by GWAS using genotype data from hard call dosages files (plink binaries bed/bim/fam) and the software PLINK v2.0 . Details of the 13 genes are presented in The majority of PGS were constructed in all eligible participants, with the exception of those based on GWAS in sex-stratified populations. These were breast cancer (including ER+ and ER− PGS), endometrial cancer, ovarian cancer, endometrioid ovarian cancer, age at menarche, age at menopause, and bulimia nervosa, which were derived in females only, as well as the prostate cancer PGS derived in males only. Additionally, we also built two versions of PGS for all complex traits excluding variants at 13 lipid-associated gene loci, which were princomp function from the ‘stats’ R package.PC analysis was performed on the post-QC metabolite data to identify the number of independent traits among the 249 highly correlated metabolites. The analysis was conducted using the –8 necessary for the more stringent criteria. To account for multiple testing in this study, we applied a p value threshold of 0.05/19  to highlight findings worthwhile evaluating in further detail. However, all results from our analyses are available in the web application should users decide to apply a more stringent (or lenient) heuristic to prioritize findings for in-depth analyses.To allow us to draw comparisons between PGS–metabolite associations, we standardized all PGS to have a mean of 0 and standard deviation of 1 and additionally applied inverse rank normalization transformations to all metabolic traits prior to analysis. Associations between PGS and normalized metabolites were determined by linear regression with adjustment for age, sex (where appropriate), genotyping chip, the top 10 PCs, and fasting time. Each analysis was conducted initially in the full sample, followed by analyses after stratification into age tertiles to investigate the influence of medication use on findings. Sex-stratified association analyses in the full sample were also conducted whereby metabolic traits were transformed separately among males and females before applying linear regression. All analyses were undertaken using both versions of each PGS as long as their corresponding GWAS had at least one variant with p < 5 × 10clump_data function from this package to identify them. Instruments for GlycA and particle size of very low-density lipoprotein (VLDL) were identified by conducting a GWAS of this trait in the UKB study using the BOLT-LMM (linear mixed model) software to control for population structure (K-mean clustering of K = 4) and standard exclusions, including withdrawn consent, mismatch between genetic and reported sex, and putative sex chromosome aneuploidy. Analyses were adjusted for age, sex, fasting status, and a binary variable denoting the genotyping chip used in individuals (the UKBB Axiom array or the UK BiLEVE array). Genetic instruments were defined as variants with p < 5 × 10–8 after removing those in LD using the clump_data function as above. The full GWAS summary statistics for GlycA as well as the other 248 circulating metabolic traits are available via the GWAS catalog (https://www.ebi.ac.uk/gwas/) under accession IDs GCST90092803 to GCST90093051  for ORM1 from the eQTLGen Consortium  for these proteins identified from 35,559 Icelanders , HP , SERPINA1 , SERPINA3 , or TF . Independent instruments were identified using the same protocol as above  . Thus, ansortium  due to aelanders . To idenas above .F-Statistics were calculated to assess weak instrument bias  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, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Carlos Isales as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Scott Ritchie (Reviewer #2); Maik Pietzner (Reviewer #3).Thank you for submitting your article "Constructing an atlas of associations between polygenic risk scores from across the human phenome and circulating metabolic biomarkers" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) While this work provides a potentially important resource for the community, the examples provided tend to be illustrative, and do not really do justice to the resource. We strongly encourage the authors to revisit the data analysis for more-compelling examples of the types of impactful work that can be achieved using this resource. How and why should users want to use this Atlas?2) One key question is whether these analyses bring us closer to causality compared to 'classical' observational associations, given LD confounding and strong metabolite variants included in PRS driving the associations. For example, how robust are PRS associations to the exclusion of individual regions? Second, can we determine whether the associations are causally upstream, or downstream of the identified phenotypes? At minimum the authors should demonstrate for examples how these issues can be teased apart .3) The atlas would also be potentially strengthened by including additional results for sex-stratified models. Previous work has shown significant differences in NMR biomarker concentrations between males and females, so it would be valuable to see if these result in any differences in PRS associations which may arise despite PRSs largely being constructed from autosomal GWAS.Furthermore, the reviewers provide numerous additional specific and insightful comments; we strongly encourage the authors to consider these points seriously in their revisions.Reviewer #1 (Recommendations for the authors):Specific comments:Some of the text on figures is very small and it may be worth revisiting figure design.P8: the positive control of creatinine and kidney disease seems a bit too trivial, given that creatinine is a diagnostic criterion. Perhaps there may be a better example?P7 Para1: It would be helpful to the reader to say a bit more about the range of types of traits considered here, as well as typical sample sizes given that the data come from a variety of sources.Consider using PGS instead of PRS as more accurate for quantitative and non-disease traits.Reviewer #2 (Recommendations for the authors):Introduction/discussion: this is not the first study that has demonstrated the utility of PRS associations for prioritising molecular traits for follow-up. We have recently published a paper doing so for cardiometabolic PRS and protein levels  and this has been available as a preprint in various forms since 2019. This should be noted and cited appropriately at the relevant points in the introduction and discussion.The associations in the atlas for the systolic blood pressure (SBP) and diastolic blood pressure (DBP) are invalid. As the authors note in the methods, PRSs for these two traits are constructed from GWAS that include UK Biobank samples. PRSs are invalid when used in samples that contributed to their underlying GWAS  as this causes them to have dramatically inflated associations. Associations for these two PRS should be removed from the atlas and study, or if the authors think these two traits are of critical interest, an alternate source of GWAS that do not include UK Biobank participants should be used to construct these two PRSs.We have found significant sources of technical variation exist in the NMR metabolomics data for UK Biobank , which may confound some of the PRS associations here. Of particular concern in the context of this study are the presence of outlier shipping plates, on which samples have systematically high (or low) concentrations of non-biological origin . This affects up to 5% of samples depending on the biomarker, but will have an outsized effect on associations as they result in samples being spuriously located at the extremities of biomarker distributions. I.e. this may result in associations being weaker or not significant, or less likely, false positives may be introduced if outlier plates happen to correlate with PRS. There are also a small number of biomarkers significantly impacted by other sources of technical variation, e.g. drift over time within spectrometer, or sample degradation due to extended time between sample preparation and sample measurement. We have made available an R package: ukbnmr (https://github.com/sritchie73/ukbnmr), for correcting for this technical variation, and removing samples on these outlier plates. Although this preprint is still under review, we would suggest using this package to remove the described technical variation, or at least checking whether doing so significantly changes associations in the atlas.There are also major biological determinants of NMR biomarker concentrations that have not been accounted for, and thus may confound, PRS to biomarker associations. In addition to age, sex, and 10 genetic principal components that the authors already adjust for, NMR biomarker concentrations are also strongly correlated with body mass index (BMI), fasting time, and lipid lowering medication (statin) usage . The atlas would be greatly improved by either appropriately accounting for these in the basic models, or including additional results using models that do so.Regarding statin usage in particular, we appreciate that the authors have conducted a separate age-stratified analysis to evaluate the impact of medication usage on PRS to biomarker associations. However, the fact remains that statin usage will be confounding the PRS to biomarker associations in the main results . This confounding is typically removed in one of two ways: either (1) fitting associations excluding participants taking statins, or (2) applying biomarker-specific correction factors to concentrations prior to association analysis, such as those estimated by . The latter is probably the better approach as the former is likely to introduce further bias towards young and healthy participants.The atlas would also be potentially strengthened by including additional results for sex-stratified models. We have observed significant differences in NMR biomarker concentrations between males and females, so it would be interesting to see if these result in any differences in PRS associations which may arise despite PRSs largely being constructed from autosomal GWAS.Regarding the bi-directional Mendelian randomization analysis of GlycA, we would suggest using an alternative biomarker as exemplar in the study, as GlycA is heterogeneous and needs to be more carefully instrumented than has been done in the study currently. GlycA is an NMR signal that quantifies the total concentrations of five proteins in circulation : α-1-acid glycoprotein (AGP), α-1-antitrypsin (AAT), α-1-antichymotrypsin (AACT), haptoglobin (HP), and transferrin (TF). These are acute-phase reactants whose concentrations each change in response to acute inflammation or in chronic inflammation . Moreover these changes are differential with respect to each other, and over time . Further, a single GlycA measurement cannot be simply decomposed as two people with the same GlycA concentration can have different concentrations of the underlying proteins . This heterogeneity means that using genome-wide significant QTLs for GlycA is unlikely to be appropriate as these QTLs may include (1) protein-QTLs for one or more of the five proteins GlycA captures, and (2) signals involved in initiation of acute-phase response . To use GlycA as an exposure in Mendelian randomization analysis, the QTLs selected as instruments should be restricted to cis-pQTLs for the five proteins. These signals may need to be treated separately, as in the scenario GlycA is truly causal, it is unlikely that all five proteins are causal, or that they have similar causal effect sizes.Data availability: the PRS should also be made publicly available, e.g. downloadable via the atlas. These should include at minimum: the set of variants in each PRS, variant identifier information (e.g. chromosome and position), genome build, effect allele, and weight (β or log odds from the underlying GWAS).ReferencesBretherick AD, Canela-Xandri O, Joshi PK, Clark DW, Rawlik K, Boutin TS, Zeng Y, Amador C, Navarro P, Rudan I, Wright AF, Campbell H, Vitart V, Hayward C, Wilson JF, Tenesa A, Ponting CP, Baillie JK, Haley C. 2020. Linking protein to phenotype with Mendelian Randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet 16:e1008785.Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O'Connell J, Cortes A, Welsh S, Young A, Effingham M, McVean G, Leslie S, Allen N, Donnelly P, Marchini J. 2018. The UK Biobank resource with deep phenotyping and genomic data. Nature 562:203-209.Connelly MA, Otvos JD, Shalaurova I, Playford MP, Mehta NN. 2017. GlycA, a novel biomarker of systemic inflammation and cardiovascular disease risk. J Transl Med 15:219.Ebersole JL, Cappelli D. 2000. Acute-phase reactants in infections and inflammatory diseases. Periodontol 2000 23:19-49.Gabay C, Kushner I. 1999. Acute-phase proteins and other systemic responses to inflammation. N Engl J Med 340:448-454.Kofink D, Eppinga RN, van Gilst WH, Bakker SJL, Dullaart RPF, van der Harst P, Asselbergs FW. 2017. Statin Effects on Metabolic Profiles: Data From the PREVEND IT . Circ Cardiovasc Genet 10. doi:10.1161/CIRCGENETICS.117.001759Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, Buniello A, McMahon A, Abraham G, Chapman M, Parkinson H, Danesh J, MacArthur JAL, Inouye M. 2021. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat Genet 53:420-425.Otvos JD, Shalaurova I, Wolak-Dinsmore J, Connelly MA, Mackey RH, Stein JH, Tracy RP. 2015. GlycA: A Composite Nuclear Magnetic Resonance Biomarker of Systemic Inflammation. Clin Chem 61:714-723.Ritchie SC, Kettunen J, Brozynska M, Nath AP, Havulinna AS, Männistö S, Perola M, Salomaa V, Ala-Korpela M, Abraham G, Würtz P, Inouye M. 2019. Elevated serum α-1 antitrypsin is a major component of GlycA-associated risk for future morbidity and mortality. PLoS One 14:e0223692.Ritchie SC, Lambert SA, Arnold M, Teo SM, Lim S, Scepanovic P, Marten J, Zahid S, Chaffin M, Liu Y, Abraham G, Ouwehand WH, Roberts DJ, Watkins NA, Drew BG, Calkin AC, Di Angelantonio E, Soranzo N, Burgess S, Chapman M, Kathiresan S, Khera AV, Danesh J, Butterworth AS, Inouye M. 2021a. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat Metab 3:1476-1483.Ritchie SC, Surendran P, Karthikeyan S, Lambert SA, Bolton T, Pennells L, Danesh J, Di Angelantonio E, Butterworth AS, Inouye M. 2021b. Quality control and removal of technical variation of NMR metabolic biomarker data in ∼120,000 UK Biobank participants. medRxiv. doi:10.1101/2021.09.24.21264079Sliz E, Kettunen J, Holmes MV, Williams CO, Boachie C, Wang Q, Männikkö M, Sebert S, Walters R, Lin K, Millwood IY, Clarke R, Li L, Rankin N, Welsh P, Delles C, Jukema JW, Trompet S, Ford I, Perola M, Salomaa V, Järvelin M-R, Chen Z, Lawlor DA, Ala-Korpela M, Danesh J, Davey Smith G, Sattar N, Butterworth A, Würtz P. 2018. Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment. Circulation 138:2499-2512.Wand H, Lambert SA, Tamburro C, Iacocca MA, O'Sullivan JW, Sillari C, Kullo IJ, Rowley R, Dron JS, Brockman D, Venner E, McCarthy MI, Antoniou AC, Easton DF, Hegele RA, Khera AV, Chatterjee N, Kooperberg C, Edwards K, Vlessis K, Kinnear K, Danesh JN, Parkinson H, Ramos EM, Roberts MC, Ormond KE, Khoury MJ, Janssens ACJW, Goddard KAB, Kraft P, MacArthur JAL, Inouye M, Wojcik GL. 2021. Improving reporting standards for polygenic scores in risk prediction studies. Nature 591:211-219.Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM. 2013. Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 14:507-515.Zheng J, Haberland V, Baird D, Walker V, Haycock PC, Hurle MR, Gutteridge A, Erola P, Liu Y, Luo S, Robinson J, Richardson TG, Staley JR, Elsworth B, Burgess S, Sun BB, Danesh J, Runz H, Maranville JC, Martin HM, Yarmolinsky J, Laurin C, Holmes MV, Liu JZ, Estrada K, Santos R, McCarthy L, Waterworth D, Nelson MR, Davey Smith G, Butterworth AS, Hemani G, Scott RA, Gaunt TR. 2020. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat Genet 52:1122-1131.Reviewer #3 (Recommendations for the authors):I have the following more specific comments that could possibly improve the study by Fang et al.:1. The NMR platform used by the authors measures only a small number of small molecules and the vast majority of the derived measures refer to characteristics of lipoprotein particles, which are not 'classical' metabolites. The paper would benefit from a paper distinction between both measures. Therefore, it is questionable how many of the 100,000 metabolites mentioned by the authors are captured by the technology used and further it is even of interest how many approximately independent features are indeed captured by the technology. A principal component analysis or similar dimension reduction techniques would provide an important correction/estimate of/to the metabolic space captured. Given the high correlation among the NMR traits, it would be important to state how many likely independent associations have been found, for instance by clumping highly correlated metabolites in clusters, similar as the authors do for SNPs.2. I find the section about collider bias in the introduction a bit as surprise and it is unclear to me, how this relates to the overall aim of the study. The high amount of self-citation in this section and in general throughout the paper makes me wonder, how much of an issue this really is compared to more substantial questions about the suitability of PRS to identify disease biomarkers or causal metabolites.3. Investigating different types of genetic scores is a clear strength of this work. However, the study currently stops early leaving important questions unanswered. For instance, how many more 'helpful' associations between PRS and NMR measures are really gained by going genome-wide, that is, how many of the added associations are mainly due to unspecific pleiotropy? The authors have outstanding methodological skills in MR and related causal inference techniques, and I find it somewhat wasted here to go simply for more associations instead of digging into the relevant part, which in turn defines the usefulness of the whole atlas and hence this study. It currently reads mostly like a computational exercise.4. In my opinion, the key question is, what do all those associations deliver, do they bring us any closer to causality compared to 'classical' observational associations, given LD confounding and strong metabolite variants included in PRS driving the associations. For example, how robust are PRS associations to the exclusion of single regions, Ritchie et al., 2021 Nat Metab provide a neat framework to address such questions. The APOE example goes in that direction and other locus-specific effects are likely underlying other disease PRS – NMR measure associations.5. One might argue that the CKD example is somewhat self-fulling, given that the selection of cases is mainly done based on serum creatinine (or the eGFR derived from creatinine), but is a good example how strong and specific examples might point to disease biomarkers. Are there more examples for a given NMR trait being strongly and possibly specifically associated with a trait or a cluster of highly related traits? I would also tone the creatinine example done, since it is obvious that this marker is used for clinical decision making.6. I am not quite sure about the bidirectional MR approach. By only testing diseases for which the PRS showed an association with the metabolite of interest, isn't it quite likely that an effect of the metabolite on the disease would be missed, as one would assume that a true causal association between a metabolite and a disease might well be lost in the large set of SNPs associated with the endpoint of interest.7. I disagree with the statement on page 12 that all lipid traits associated with the CHD PRS are causal risk factors, most of them are likely not and the true underlying risk factor or biological mechanism is likely hidden among all those associations. The general inability to distinguish about the causal relevance of all those highly related measures is a massive challenge working with NMR data, in particular given that many are not real measurements but are only derived as proportion from other measures, including apolipoprotein B.8. The authors need to do better in distinguishing between all the different lipid measures that are derived from the NMR platform, this is also seen in the discussion of Apo B. Apo B is the main structural protein for many lipoprotein particles of different densities and not just for atherogenic ones.9. Instead of stratified analysis, why not correcting for statin intake to estimate lipid levels of participants without the use of statins? What about the effect of many other medications widely prescribed, with strong effects on NMR measures as described in van Duijn et al., Nat Med 2020? Essential revisions:1) While this work provides a potentially important resource for the community, the examples provided tend to be illustrative, and do not really do justice to the resource. We strongly encourage the authors to revisit the data analysis for more-compelling examples of the types of impactful work that can be achieved using this resource. How and why should users want to use this Atlas?Many thanks for your comments to help us refine this resource and manuscript. We have now revisited the examples in this manuscript to showcase the value of the PGS atlas with the help of the reviewer comments:1. We have undertaken a new application of bi-directional Mendelian randomization (MR) to demonstrate how readers may use this approach to disentangle whether associations in our atlas likely reflect either causes or consequences of PGS traits/diseases. This example is described on page 9 and discussed in the discussion on page 21:‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’2. Based on reviewer #2’s comments regarding our previous example using results for GlycA, we have performed further sensitivity analyses to support the conclusions from this example.3. Based on reviewer #3’s comments we have included further explanation regarding collider bias as well as a Figure (page 14) to emphasize the importance of age-stratified analyses in our atlas for users to evaluate the robustness of findings.4. Finally, we have also recalculated all PGS-metabolite associations in the atlas after removing established lipid loci across the genome based on the publication by Gallois et al., (2019 Nature Communications https://www.nature.com/articles/s41467-019-12703-7). As an example of why this sensitivity analysis may be helpful to the user, we compared metabolomic profiles for the anthropometric and the Alzheimer’s disease PGS before and after removing these loci (page 19):APOE locus which are recognised to exert their influence on phenotypic traits via horizontally pleiotropic pathways .’‘For PGS with these lipid loci excluded, anthropometric traits such as waist-to-hip ratio (N=209), waist circumference (N=206) and body mass index (N=205) still provided strong evidence of association with the majority of metabolic measurements on the NMR panel based on multiple testing corrections. Elsewhere however, the Alzheimer’s disease PGS, which was associated with 60 metabolic traits robust to P<0.05/19 in the initial analysis including these lipid loci (Supplementary Table 17), provided no convincing evidence of association with the 249 circulating metabolites after excluding the lipid loci based on the same multiple testing threshold (Supplementary Table 18). Further inspection suggested that the likely explanation for this were variants located at the 2) One key question is whether these analyses bring us closer to causality compared to 'classical' observational associations, given LD confounding and strong metabolite variants included in PRS driving the associations. For example, how robust are PRS associations to the exclusion of individual regions?Thank you for the suggestion. As recommended by reviewer #2 we have looked into the analysis conducted in the Ritchie et al., 2021 Nat Metab paper. The authors examined the association between 5 different genome-wide polygenic scores (PGSs) and systematically removed 1,703 approximately independent LD blocks with replacement in their association analyses.APOA5, CETP, PCSK9, LDLR, GCKR, APOC1, LPL, GALNT2, CELSR2, TRIB1, DOCK7, FADS2 and APOE) in the PGS-metabolites associations as reported on page 19:We agree that it is very important to consider the contribution of individual gene regions when examining genetic associations. Whilst this was feasible for 5 different PGS in the study by Ritchie et al., this becomes a lot more computational intensive for our study whereby we construct 125 different PGS using 2 different selection criteria. Therefore, we have added a new analysis to evaluate the contribution of 14 previously discovered lipid-associated genes  cohort . For details of these genes (see Supplementary Table 5).’Followed by the example described above to help demonstrate to users to importance of these results for further evaluate the robustness of findings.Second, can we determine whether the associations are causally upstream, or downstream of the identified phenotypes? At minimum the authors should demonstrate for examples how these issues can be teased apart .As described above we have now added an example evaluating associations with measures of triglyceride-rich VLDL captured by the NMR panel by applying bi-directional MR to disentangle whether they may reflect causes or consequences of traits/disease.The main reason for not conducting these types of evaluations atlas-wide is because we present this resource as one to generate hypotheses which users can then conduct in-depth evaluations of in a timely and careful manner. This was highlighted by reviewer #2 who notes that challenges of appropriately instrumenting the metabolic trait GlycA which we have also address below in response to their comment. This overall point has been discussed on page 8 of the manuscript:‘In this paper, we provide several examples of how results from this atlas can be used to generate hypotheses and pave the way for in-depth and careful evaluations of associations between PGS and circulating traits. Specifically, we believe our findings can facilitate a ‘reverse gear Mendelian randomization’ approach to disentangle whether associations likely reflect metabolic traits acting as a cause or consequence of disease risk  as illustrated using triglyceride-rich very low density lipoprotein (VLDL) particles in the next section. Furthermore, in-depth evaluations allow careful consideration of appropriate instrumental variables for circulating metabolites which can be a challenging task as highlighted in our exemplar analysis of glycoprotein acetyls. Finally, we provide examples of how the plethora of sensitivity analyses within our atlas can help users further investigate the robustness of findings.’3) The atlas would also be potentially strengthened by including additional results for sex-stratified models. Previous work has shown significant differences in NMR biomarker concentrations between males and females, so it would be valuable to see if these result in any differences in PRS associations which may arise despite PRSs largely being constructed from autosomal GWAS.Many thanks for this suggestion. We have undertaken extensive analyses to generate all PGS-metabolite association stratified by sex and added these to the web platform. This has been discussed on page 8:‘Our atlas also includes sex-stratified estimates for PGS weighted by GWAS undertaken in female only (such as breast cancer and age at menarche) and male only  populations, as well as sex-stratified estimates in both females and males separately for all other PGS-metabolite associations. We encourage users interested in these sex-stratified estimates to interpret them with caution however, given the widespread sex-differential participation bias in UKB ).’And page 20:‘Moreover, we suggest that users interested in the sex-stratified estimates within our atlas should interpret them in conjunction with estimates derived across age tertiles as in this example, given that different proportions of males and females in UKB may be taking certain medications (e.g. statins).’Furthermore, the reviewers provide numerous additional specific and insightful comments; we strongly encourage the authors to consider these points seriously in their revisions.Reviewer #1 (Recommendations for the authors):Specific comments:Some of the text on figures is very small and it may be worth revisiting figure design.eLife which is an online only journal, therefore meaning that readers will have the added benefit of always being able to zoom in of the figures generated in our manuscript.Many thanks for this suggestion. We have now regenerated our large Circos plot figure where text was particularly small (now as a forest plot) and moved it to the supplementary Figure. Furthermore, we note that this work has been submitted to P8: the positive control of creatinine and kidney disease seems a bit too trivial, given that creatinine is a diagnostic criterion. Perhaps there may be a better example?The creatinine-CKD example was originally included to demonstrate the predictive ability of this PGS; however, this has now been removed as per your suggestion. Instead, we have further highlighted associations between lipoprotein lipid metabolic traits and the coronary heart disease (CHD) PGS, which we feel is powerful positive control given the overwhelming evidence supporting a causal relationship between lipids and CHD risk.P7 Para1: It would be helpful to the reader to say a bit more about the range of types of traits considered here, as well as typical sample sizes given that the data come from a variety of sources.We have now added a pie chart to illustrate the range of PGS traits/disease in our atlas into the Figure 1. A similar figure reflecting associations across NMR subcategories has also been generated alongside and added to Figure 1. All sample sizes are reported in Supplementary Table 1.Consider using PGS instead of PRS as more accurate for quantitative and non-disease traits.Thank you – we have now changed ‘PRS’ to ‘PGS’ throughout.Reviewer #2 (Recommendations for the authors):Introduction/discussion: this is not the first study that has demonstrated the utility of PRS associations for prioritising molecular traits for follow-up. We have recently published a paper doing so for cardiometabolic PRS and protein levels  and this has been available as a preprint in various forms since 2019. This should be noted and cited appropriately at the relevant points in the introduction and discussion.We have now referenced your paper on pages 4 and 12 of the manuscript.The associations in the atlas for the systolic blood pressure (SBP) and diastolic blood pressure (DBP) are invalid. As the authors note in the methods, PRSs for these two traits are constructed from GWAS that include UK Biobank samples. PRSs are invalid when used in samples that contributed to their underlying GWAS  as this causes them to have dramatically inflated associations. Associations for these two PRS should be removed from the atlas and study, or if the authors think these two traits are of critical interest, an alternate source of GWAS that do not include UK Biobank participants should be used to construct these two PRSs.Many thanks for your suggestion. We have now removed the SBP and DBP PGS from our atlas due to overlapping samples in UKB.We have found significant sources of technical variation exist in the NMR metabolomics data for UK Biobank , which may confound some of the PRS associations here. Of particular concern in the context of this study are the presence of outlier shipping plates, on which samples have systematically high (or low) concentrations of non-biological origin . This affects up to 5% of samples depending on the biomarker, but will have an outsized effect on associations as they result in samples being spuriously located at the extremities of biomarker distributions. I.e. this may result in associations being weaker or not significant, or less likely, false positives may be introduced if outlier plates happen to correlate with PRS. There are also a small number of biomarkers significantly impacted by other sources of technical variation, e.g. drift over time within spectrometer, or sample degradation due to extended time between sample preparation and sample measurement. We have made available an R package: ukbnmr (https://github.com/sritchie73/ukbnmr), for correcting for this technical variation, and removing samples on these outlier plates. Although this preprint is still under review, we would suggest using this package to remove the described technical variation, or at least checking whether doing so significantly changes associations in the atlas.Thank you for this suggestion. As mentioned in response to the public review, we have now repeated our entire pipeline after applying your R package.There are also major biological determinants of NMR biomarker concentrations that have not been accounted for, and thus may confound, PRS to biomarker associations. In addition to age, sex, and 10 genetic principal components that the authors already adjust for, NMR biomarker concentrations are also strongly correlated with body mass index (BMI), fasting time, and lipid lowering medication (statin) usage . The atlas would be greatly improved by either appropriately accounting for these in the basic models, or including additional results using models that do so.Regarding statin usage in particular, we appreciate that the authors have conducted a separate age-stratified analysis to evaluate the impact of medication usage on PRS to biomarker associations. However, the fact remains that statin usage will be confounding the PRS to biomarker associations in the main results . This confounding is typically removed in one of two ways: either (1) fitting associations excluding participants taking statins, or (2) applying biomarker-specific correction factors to concentrations prior to association analysis, such as those estimated by . The latter is probably the better approach as the former is likely to introduce further bias towards young and healthy participants.We have responded to this suggestion in the public review. However, just to add that the previous studies referenced here are noticeably much smaller in sample size compared to our study (n=864 & n=5359 respectively). Conditioning on colliders has become a lot more problematic with the advent of biobank scale cohort studies such as UKB. As such, previously modest sources of bias induced by conditioning on colliders, which may have not led to spurious findings in smaller cohort studies, are now large enough such that precautions  are necessary to investigate the robustness of findings.The atlas would also be potentially strengthened by including additional results for sex-stratified models. We have observed significant differences in NMR biomarker concentrations between males and females, so it would be interesting to see if these result in any differences in PRS associations which may arise despite PRSs largely being constructed from autosomal GWAS.We have undertaken extensive analyses to additionally evaluate sex-stratified associations (page 8):‘Our atlas also includes sex-stratified estimates for PGS weighted by GWAS undertaken in female only (such as breast cancer and age at menarche) and male only  populations, as well as sex-stratified estimates in both females and males separately for all other PGS-metabolite associations. We encourage users interested in these sex-stratified estimates to interpret them with caution however, given the widespread sex-differential participation bias in UKB ).’And page 20:‘Moreover, we suggest that users interested in the sex-stratified estimates within our atlas should interpret them in conjunction with estimates derived across age tertiles as in this example, given that different proportions of males and females in UKB may be taking certain medications (e.g. statins).’Regarding the bi-directional Mendelian randomization analysis of GlycA, we would suggest using an alternative biomarker as exemplar in the study, as GlycA is heterogeneous and needs to be more carefully instrumented than has been done in the study currently. GlycA is an NMR signal that quantifies the total concentrations of five proteins in circulation : α-1-acid glycoprotein (AGP), α-1-antitrypsin (AAT), α-1-antichymotrypsin (AACT), haptoglobin (HP), and transferrin (TF). These are acute-phase reactants whose concentrations each change in response to acute inflammation or in chronic inflammation . Moreover these changes are differential with respect to each other, and over time . Further, a single GlycA measurement cannot be simply decomposed as two people with the same GlycA concentration can have different concentrations of the underlying proteins . This heterogeneity means that using genome-wide significant QTLs for GlycA is unlikely to be appropriate as these QTLs may include (1) protein-QTLs for one or more of the five proteins GlycA captures, and (2) signals involved in initiation of acute-phase response . To use GlycA as an exposure in Mendelian randomization analysis, the QTLs selected as instruments should be restricted to cis-pQTLs for the five proteins. These signals may need to be treated separately, as in the scenario GlycA is truly causal, it is unlikely that all five proteins are causal, or that they have similar causal effect sizes.Thank you for the suggestion. We have repeated the GlycA Mendelian randomization analysis using the pQTLs  and eQTL (for AGP) of these proteins. Detailed methods are presented on page 26 of the manuscript:et al., 2015). Thus, another set of genetic instruments for GlycA were selected among SNPs strongly associated with these five proteins for further evaluation of the genetically predicted effect of GlycA. Instrumental variables for alpha1-acid glycoprotein are identified from the gene expression quantitative trait loci (eQTL)  of ORM1 from the eQTLGen Consortium . Genetic instruments for haptoglobin, alpha1-antitrypsin, alpha1-antichymotrypsin, and transferrin are their protein quantitative trait loci (pQTL)  identified from 35,559 Icelanders . To identify cis-acting genetic variants, genetic variants of the five proteins are limited to those located within 1Mb around their encoding genes: ORM1 , HP , SERPINA1 , SERPINA3  or TF . GWAS summary statistics of the protein data were performed using an in-house reference panel consisting of 10,000 randomly selected European participants of the UK Biobank  in PLINK v1.90. The eQTL data from the eQTLGene Consortium was extracted from the OpenGWAS portal.’‘The NMR signal of serum GlycA is contributed by five acute-phase proteins  . Using cis-acting plasma protein (where possible) and expression quantitative trait loci (pQTLs and eQTLs) as instrumental variables for these proteins (Supplementary Table 12) did not provide convincing evidence that they play a role in disease risk for associations between PGS and GlycA (Supplementary Table 13). The only effect estimate robust to multiple testing was found for higher genetically predicted alpha1-antitrypsin levels on γ glutamyl transferase (GGT) levels , although this was not replicated when using estimates of genetic associations with GGT levels from a larger GWAS conducted in the UK Biobank data . For details of pleiotropy robust analysis and replication results see Supplementary Table 14.’‘We also conducted further sensitivity analyses given that the NMR signal of GlycA is a composite signal contributed by the glycan Data availability: the PRS should also be made publicly available, e.g. downloadable via the atlas. These should include at minimum: the set of variants in each PRS, variant identifier information (e.g. chromosome and position), genome build, effect allele, and weight (β or log odds from the underlying GWAS).All weights used to generate the PGS in our atlas are now available at https://tinyurl.com/PGSweights as mentioned on page 7:‘The specific weights for clumped variants used in all PGS can be found at https://tinyurl.com/PGSweights.’Reviewer #3 (Recommendations for the authors):Apart from the general comments made above, I have the following more specific comments that could possibly improve the study by Fang et al.:1. The NMR platform used by the authors measures only a small number of small molecules and the vast majority of the derived measures refer to characteristics of lipoprotein particles, which are not 'classical' metabolites. The paper would benefit from a paper distinction between both measures. Therefore, it is questionable how many of the 100,000 metabolites mentioned by the authors are captured by the technology used and further it is even of interest how many approximately independent features are indeed captured by the technology. A principal component analysis or similar dimension reduction techniques would provide an important correction/estimate of/to the metabolic space captured. Given the high correlation among the NMR traits, it would be important to state how many likely independent associations have been found, for instance by clumping highly correlated metabolites in clusters, similar as the authors do for SNPs.As suggested we have conducted a PCA analysis to investigate the amount of metabolic space captured. Findings are reported on page 8:‘Principal component analysis suggested that the first 19 principal components (PCs) captured 95% of the variance in the NMR metabolites data (whereas the first ten PCs captured 90% and the first 41 PCs captured 99% of the variance) (Supplementary Table 3).’The full results from PCA analysis are presented in Supplementary Table 3.2. I find the section about collider bias in the introduction a bit as surprise and it is unclear to me, how this relates to the overall aim of the study. The high amount of self-citation in this section and in general throughout the paper makes me wonder, how much of an issue this really is compared to more substantial questions about the suitability of PRS to identify disease biomarkers or causal metabolites.We have now added a more detailed section (including a new figure in the main document) discussing the challenges of addressing potential collider in a biobank-scale dataset which is discussed on page 14:‘Directed acyclic graphs illustrating the potential collider bias involved in the causal relationship between the coronary artery disease polygenic score and circulating metabolites. (A) The likelihood of participants in UK Biobank taking medication such as statins is influenced by having a higher genetic predisposition to coronary artery disease but may also be influenced by certain metabolic traits measured on the NMR panel (e.g. having elevated low-density lipoprotein cholesterol levels). Either stratifying or adjusting for statin use in regression models may therefore induce collider bias into the association between disease liability and metabolic traits. (B) Age is commonly adjusted for in association analyses due to its role as a confounder and cannot be a collider (i.e. exposures and outcomes cannot influence the age of participants). Stratifying samples by age therefore enables the analysis of exposure-outcome associations in a group of participants with relatively consistent confounding effect from age, leading to more robust association estimates in the lower age tertile where the percentage of participants who are regularly taking medication is low. Furthermore, comparisons with participants in the highest age tertile can help highlight associations between polygenic scores and metabolic traits most likely distorted by potential colliders such as statins in the full sample.’3. Investigating different types of genetic scores is a clear strength of this work. However, the study currently stops early leaving important questions unanswered. For instance, how many more 'helpful' associations between PRS and NMR measures are really gained by going genome-wide, that is, how many of the added associations are mainly due to unspecific pleiotropy? The authors have outstanding methodological skills in MR and related causal inference techniques, and I find it somewhat wasted here to go simply for more associations instead of digging into the relevant part, which in turn defines the usefulness of the whole atlas and hence this study. It currently reads mostly like a computational exercise.Thank you for your complement re. methodological skills. We have emphasized in our introduction (page 8) that the main purpose of this resource is to generate hypotheses and therefore facilitate in-depth, carefully designed follow-up analyses (such as those applying the principles of MR). We advocate in this section that causal inference is challenging to robustly undertake which is why we suggest readers conduct bespoke analyses for this task motivated by findings in this atlas of results:‘In this paper, we provide several examples of how results from this atlas can be used to generate hypotheses and pave the way for in-depth and careful evaluations of associations between PGS and circulating traits. Specifically, we believe our findings can facilitate a ‘reverse gear Mendelian randomization’ approach to disentangle whether associations likely reflect metabolic traits acting as a cause or consequence of disease risk  as illustrated using triglyceride-rich very low density lipoprotein (VLDL) particles in the next section. Furthermore, in-depth evaluations allow careful consideration of appropriate instrumental variables for circulating metabolites which can be a challenging task as highlighted in our exemplar analysis of glycoprotein acetyls. Finally, we provide examples of how the plethora of sensitivity analyses within our atlas can help users further investigate the robustness of findings.’4. In my opinion, the key question is, what do all those associations deliver, do they bring us any closer to causality compared to 'classical' observational associations, given LD confounding and strong metabolite variants included in PRS driving the associations. For example, how robust are PRS associations to the exclusion of single regions, Ritchie et al., 2021 Nat Metab provide a neat framework to address such questions. The APOE example goes in that direction and other locus-specific effects are likely underlying other disease PRS – NMR measure associations.We have undertaken extensive sensitivity analyses to compute all PGS association in this atlas after removing established lipid gene loci to evaluate the sensitivity of results driven by variants at these regions of the genome. This has been added to page 19 of the manuscript:et al., 2019). For details of these genes (see Supplementary Table 5).‘The polygenic nature of complex traits means that the inclusion of highly weighted pleiotropic genetic variants in PGS may introduce bias into genetic associations within our atlas. To provide insight into this issue, we constructed PGS excluding variants within the regions of the genome which encode the genes for 14 major regulators of NMR lipoprotein lipids signals which captured 75% of the gene-metabolite associations in the Finnish Metabolic Syndrome In Men (METSIM) cohort .’For PGS with these lipid loci excluded, anthropometric traits such as waist-to-hip ratio (N=209), waist circumference (N=206) and body mass index (N=205) still provided strong evidence of association with the majority of metabolic measurements on the NMR panel based on multiple testing corrections. Elsewhere however, the Alzheimer’s disease PGS, which was associated with 60 metabolic traits robust to P<0.05/19 in the initial analysis including these lipid loci (Supplementary Table 17), provided no convincing evidence of association with the 249 circulating metabolites after excluding the lipid loci based on the same multiple testing threshold (Supplementary Table 18). Further inspection suggested that the likely explanation for this attenuation of evidence were due to variants located within the 5. One might argue that the CKD example is somewhat self-fulling, given that the selection of cases is mainly done based on serum creatinine (or the eGFR derived from creatinine), but is a good example how strong and specific examples might point to disease biomarkers. Are there more examples for a given NMR trait being strongly and possibly specifically associated with a trait or a cluster of highly related traits? I would also tone the creatinine example done, since it is obvious that this marker is used for clinical decision making.We have now removed the creatinine example and instead included a bi-directional MR analysis of VLDL metabolic traits (page 9).-35 & Β=0.012, 95% CI=0.006 to 0.019, P=2.7x10-4 respectively) and coronary heart disease (CHD) . Conducting bi-directional MR suggested that the associations with average diameter of VLDL particles are likely attributed to a consequence of BMI and CHD liability as opposed to the size of VLDL particles having a causal influence on these outcomes (Supplementary Table 6). In contrast, MR analyses suggested that the concentration of VLDL particles increases risk of CHD  which may explain associations between the CHD PGS and this metabolic trait within our atlas. Similar MR analyses to investigate findings from our atlas can be conducted using the full GWAS summary statistics for all 249 circulating metabolic traits available via the GWAS catalog (https://www.ebi.ac.uk/gwas/) under accession IDs GCST90092803 to GCST90093051.’‘For example, we applied Mendelian randomization (MR) to further evaluate associations highlighted in our atlas with triglyceride-rich very-low-density lipoprotein (VLDL) particles. For instance, both VLDL particle average diameter size and concentration were associated with the PGS for body mass index (BMI) . This was one of the primary motivations for choosing the VLDL example mentioned above as well as providing insight into lipoprotein lipid biology (page 21):‘We likewise conducted bi-directional MR to demonstrate that associations between the CHD PGS and VLDL particle size likely reflect an effect of CHD liability on this metabolic trait. In contrast, the association between the CHD PGS and VLDL concentrations are likely attributed to the causal influence of this metabolic trait on CHD risk, suggesting that it is the concentration of these triglyceride-rich particles that are important in terms of the aetiology of CHD risk as opposed to their actual size. We envisage that findings from our atlas, as well as other ongoing efforts which leverage the large-scale NMR data within UKB, should facilitate further granular insight into lipoprotein lipid biology.’7. I disagree with the statement on page 12 that all lipid traits associated with the CHD PRS are causal risk factors, most of them are likely not and the true underlying risk factor or biological mechanism is likely hidden among all those associations. The general inability to distinguish about the causal relevance of all those highly related measures is a massive challenge working with NMR data, in particular given that many are not real measurements but are only derived as proportion from other measures, including apolipoprotein B.We have now rephrased this sentence (now on page 15) to address this comment:‘The vast majority of these were lipoprotein lipid traits, which are likely capturing causal risk factors for CHD.’8. The authors need to do better in distinguishing between all the different lipid measures that are derived from the NMR platform, this is also seen in the discussion of Apo B. Apo B is the main structural protein for many lipoprotein particles of different densities and not just for atherogenic ones.We agree that it is important to distinguish between the various metabolic traits measured by the NMR platform. Given that this work is a resource paper, we have pointed readers to a review of this platform to help them navigate findings within our atlas (page 4):et al., 2022).’‘The 249 metabolite measurements include the particle concentration, size, and composition of 14 lipoprotein subclasses, as well as the levels of phospholipids, fatty acids, amino acids, ketone bodies and others biomarkers as discussed in a recent review  has become a lot more problematic with the advent of biobank scale cohort studies such as UKB. As such, previously modest sources of bias induced by conditioning on colliders, which may have not led to spurious findings in smaller cohort studies such as the one referenced here by the reviewer, are now large enough such that precautions (e.g. those taken in our study by stratifying on age) are necessary to investigate the robustness of findings.


In order to address possible explanations for these contradictions we examined trait–trait variations of 11 genomic traits from ~18,000 sequenced genomes. The studied trait–trait variations suggested: (i) the predominance of two resistance and resilience-related orthogonal axes and (ii) at least in free living species with large effective population sizes whose evolution is little affected by genetic drift an overlap between a resilience axis and an oligotrophic-copiotrophic axis. These findings imply that resistance associated traits of prokaryotes are globally decoupled from resilience related traits and in the case of free-living communities also from traits associated with resource availability. However, further inspection of pairwise scatterplots showed that resistance and resilience traits tended to be positively related for genomes up to roughly five million base pairs and negatively for larger genomes. Genome size distributions differ across habitats and our findings therefore point to habitat dependent tradeoffs between resistance and resilience. This in turn may preclude a globally consistent assignment of prokaryote genomic traits to the competitor - stress-tolerator - ruderal (CSR) schema that sorts species depending on their location along disturbance and productivity gradients into three ecological strategies and may serve as an explanation for conflicting findings from earlier studies. All reviewed genomic traits featured significant phylogenetic signals and we propose that our trait table can be applied to extrapolate genomic traits from taxonomic marker genes. This will enable to empirically evaluate the assembly of these genomic traits in prokaryotic communities from different habitats and under different productivity and disturbance scenarios as predicted  Ruderals have an advantage in habitats with high disturbance levels, stress tolerators dominate habitats with high stress levels such as low nutrient conditions or extreme temperatures, while competitors profit from their competitive advantage over other organisms in resource-rich habitats  that determine their response to disturbances . High leResource availability can shape the distribution of resistance or resilience related life histories due to the above mentioned potential costs that are linked to the response to disturbance. Traditionally, in microbial ecology, large emphasis has been put in sorting species according to the nutrient levels required for their growth, in either oligotrophic or copiotrophic species . It has The more complex CSR theoretical schema, that goes beyond a binary classification of life-history traits, sorts species into three classes of ecological strategies  depending on their location along two major environmental gradients: disturbance and productivity . This frvia machine learning approaches that the presence of functional genes within genomes was tightly linked to the ecological niche occupied by the corresponding prokaryotes, explaining ~50% of their niche variability in a high-dimensional niche space  would increase the versatility of prokaryotes and allow them to grow in a larger number of environments  in prokaryote genomes could not be associated significantly to the classification as either generalist or specialist . InsteadA significant correlation between growth rate and competitive ability  or carboIt had been hypothesized that the evolution towards GC depleted genomes is an adaption to nutrient poor conditions because GC pairs contain one more nitrogen atom compared to AT pairs . HellwegA number of earlier studies explored pairwise correlations among the above-mentioned genomic traits or with the organism’s physiologically measured maximal growth rate. Most of these studies found significant positive correlations, e.g., between genome size and %TF, RRN and growth rates . As a covia the JGI/IMG platform . These genomes cover a broad phylogeny  and include genomes from yet uncultured candidate phyla . Below we will highlight some of the 35 pairwise correlations whose strength or shape displayed via local fitting can be interpreted well in the light of possible physiological constraints.Covariation patterns among the above introduced genomic traits in the multivariate trait space were illustrated sis PCA, . The firsis PCA, . A randosis PCA, . An inspsis PCA,  revealedGenome size, gene duplication level, gene richness and the %TF aligned along the first principal component (PC1) while covarying positively with each other ; Table 1Earlier studies suggested moreover that the % HGT, the CUB and the RRN were either linked to generalist-specialist classifications or correlated positively with genome size , 2, whicvia ATP should lead to enhanced growth rates  versus and traits that covaried positively with CUB  displayed similar profiles .via prophages is one of several mechanisms leading to HGT. It is therefore remarkable that we found a negative correlation between the number of prophages with the %HGT  represent resistant related traits ; Table 1We did not assign %GC to resistance or resilience related traits as it was positioned in-between PC1 and PC2 and did not align closely to any of the other traits. Although a high GC content can protect cells from heat or desiccation stress resistance, it can furthermore not be considered as a general resistance trait enabling for instance tolerance against changes in the resource type supply. A significant alignment with both, PC1 and PC2  underlinThe not fully congruent overlap between the genomic traits that we assigned to either resistance or resilient related traits suggests that these traits cover different aspects of resistance and resilience, such as increased growth rates versus shorter lag phases in the case of resilience related traits.The roughly orthogonal position of several resistance and resilience traits in prokaryotes across whole range genome sizes reflects the results of some previous studies, which suggested that genome size and growth rate are not related . HoweverNoticeably, not all partial pairwise correlations were strong or resulted in a differential correlation profile for larger and smaller genomes . Still, n ≤ 565 species per habitat, Habitat specific PCA patterns with genomes originating from soils, aquatic habitats or from the digestive tract furthermore supported our findings: depending on whether mean genome size of genomes from the habitats was >5 or <4 mbp resilience trait rotated either counterclockwise or clockwise relative to the global PCA . As a coLiterature suggests consistently that increasing resource availability leads to the selection of fast growing opportunists  with decreased resource usage efficiency . ProkaryImportantly, the proposed non-monotonous relationship of resistance versus resilience and resource usage efficiency dimensions entails that a consistent assignment of traits to the CSR or YAS schemes with validity for all prokaryotes may not be possible: in environments inhabited by prokaryotes with small genomes, such as aquatic habitats a nutrient rich and frequently disturbed habitat should select species with increased CUB and comparably large genomes, while the same scenario in soil environments should instead rather select species with increased CUB, but comparably small genomes. Genome size dependent trait–trait covariation patters might furthermore be the reason for conflicting assignments of genome size either with the C  or R catvia their genomic traits into life history categories suffers from inaccuracies. This is on the one hand due to the fact that different evolutionary mechanisms and selective forces in combination lead to the selection of genomic trait values, which causes noisy correlations between trait values and the functional characteristics of a species. On the other hand, the assignment of species for instance along the specialist-generalist gradient is highly context-dependent and a resource specialist might be at the same time a temperature generalist , but also the community weighted variance, skewness or kurtosis belong to the key drivers of community functioning and assembly . While tAnother option to assess the distribution of genomic traits is to infer genomic traits from taxonomic marker genes of species in a community based on sequenced reference genomes of close relatives . This prThe PICRUSt2 software  that hadBox 3All above evaluated genomic traits featured overall significant phylogenetic signals . In agreDue to possible biases of RRN values given in the JGI database , S5 we eOne may wonder why genomic traits, such as prophages or %HGT at all exhibit phylogenetic signals, although individual events contributing to these traits are not inherited vertically. However, while the presence of a specific prophage or HGT gene in a genome should indeed not have a phylogenetic signal, it has been argued as outlined above that the characteristic of a genome to host multiple prophages or HGT derived genes is linked to the life-history of the corresponding organism. In agreement with our observation , life hiRecent publications claimed that trait dimensions that are apparent among heterotroph prokaryotes are due to different physiological constraints and tradeoffs not directly comparable to those of autotroph plants . Based oEven though specific CSR or YAS trait attributions may not generally be applicable for all prokaryotes, they should be applicable with adapted trait associations for communities harboring species within certain ranges of genome sizes, such as aquatic or soil communities. We argue that, beside potential differences between heterotrophs and autotrophs outlined elsewhere , disturbhttps://img.jgi.doe.gov/ and https://rrndb.umms.med.umich.edu/.Publicly available datasets were analyzed in this study. This data can be found at: The theoretical frame work for this study was developed by SB in collaboration with CV, NM, and TB. All data analyzes were performed by SB with support from JW. All authors contributed to the article and approved the submitted version.The study was supported by a grants from the German Science Foundation (DFG) awarded to SB (BE 5937/2–1 and BE 5937/2–3). Bioinformatic analyzes were supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) .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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Within-family predictive ability was moderately high for heading date and winter survivorship . A multi-trait model did not improve predictive ability for either trait. Progeny predictive ability was 0.71 for winter survivorship and 0.53 for heading date. These results suggest that lowland ecotype populations can obtain sufficient survival rates in the northern United States with two or three cycles of effective selection. Despite accurate genomic prediction, naturally occurring winter mortality successfully isolated winter tolerant genotypes and appears to be an efficient method to develop high-yielding, cold-tolerant switchgrass cultivars.In the North-Central United States, lowland ecotype switchgrass can increase yield by up to 50% compared with locally adapted but early flowering cultivars. However, lowland ecotypes are not winter tolerant. The mechanism for winter damage is unknown but previously has been associated with late flowering time. This study investigated heading date (measured for two years) and winter survivorship (measured for three years) in a multi-generation population generated from two winter-hardy lowland individuals and diverse southern lowland populations. Sequencing data  from 1,306 individuals were used to evaluate genome-wide trait prediction through cross-validation and progeny prediction (n = 52). Genetic variance for heading date and winter survivorship was additive with high narrow-sense heritability  and reliability . The initial negative correlation between winter survivorship and heading date degraded across generations (F Panicum virgatum L.) is undergoing breeding for improved agronomic performance as a biomass crop  from lowland populations and two lowland genotypes that showed strong winter survivorship as multiple clonal replicates over 6 winters near Arlington, WI. There were also a limited number of crosses between southern genotypes which had not been evaluated for winter survivorship. The winter tolerant genotypes are referred to as Tolerant 1 and Tolerant 2, and they originated from an unknown population originally collected in North Carolina, South Carolina or northern Florida . Collectively, the individuals used for initial crosses will be referred to as Founders. Crosses resulted in 2,058 individuals unevenly distributed across 29 unique F1 sibling matings, used to generate pseudo-F2 families, the success rate was 20%, but with a mean of 74 seedlings generated per successful cross parent.Among controlled greenhouse crosses, the success rate for initial crosses among Founder individuals was 71%, with success defined as resulting in at least one progeny seedling from a parent (mean 36 seedlings per successful cross). Within F1 parents of pseudo-F2 families were maintained in a greenhouse and divided into vegetative replicates by dividing crowns. In July 2018, a completely randomized spaced plant nursery was planted with 195 genotypes. The spaced plants were genotypes maintained in 12-plant rows with 0.7 m between and 0.7 m within rows. Weeds were controlled between individuals genotype crowns using roto-tilling and occasional hand weeding. The nursery contained a minimum of two vegetative replicates per individual. In 2019, vegetative replicates reserved from Founder individuals and F1 parents of pseudo-F2 families were used to replace individuals that were lost to winterkill in the spaced plant nursery during the first winter.All Founder individuals and F1 families and pseudo-F2 families in the spring of 2018. Each row contained 10 unique genotypes. In the summer of 2018, heavy rain and standing water in sections of the nursery and resulted in uneven and poor plant vigor. To account for establishment damage that was unrelated to winter survival, fall vigor ratings were made on a scale of 0 to 5 during September in 2018 and 2019. Fall vigor was then used as a covariate for the subsequent spring vigor scores. A fall vigor score of 5 indicated a healthy switchgrass plant and zero indicated a deceased plant.In addition, an unreplicated, stratified by genotype spaced row nursery (unique genotypes planted with 0.7 m between rows and 0.3 m within rows) was established of the FWinter survivorship scores and heading date was measured for each individual in both nurseries during 2019, 2020, and 2021 (spring vigor only). Spring vigor was recorded using a scale from 0–20, with 20 indicating no visible damage and 0 indicating mortality. Heading date was recorded as the date in which panicles were observed on at least 50% of an individual's tillers.2 individuals which survived three winters from the primary experiment (described above) were planted as a progeny population in a row-plot trial constructed from greenhouse grown seedlings. The progeny seed was planted alongside half-sib families selected from the Liberty cultivar (9 families), a lowland population (1 family), and upland families (8 families). Each comparison family was the result of multiple selection cycles for late flowering or strong winter survival.A small population of progeny derived from the primary experiment were evaluated as part of a trial to measure yield performance in row plots conditions. Individuals within the rows were used to estimate genomic prediction accuracy. Open-pollinated seed from eight pseudo-FWithin each family, seedlings were randomly assigned to family rows  and rows were assigned using an incomplete block design. Uneven germination and seed quantities resulted in an unbalanced design among the genotypes. Due to strong germination, the bulked progeny seedlings were used as a check family and was assigned to each incomplete block. Therefore, the progeny population was replicated 26 times, while the other families were replicated a mean of 5.6 times. The validation nursery was planted at Arlington, WI in May 2020. All plants and plots were allowed to grow during the establishment year and biomass was removed after killing frost. No fertilizer was applied. Plots were harvested with a flail chopper and plot weights determined by a load cell. Biomass harvest occurred during November 2021 and dry matter adjustment was based on three dry matter samples collected on the same date (∼500 g fresh weight each).Heading date and winter survival was also measured on individual plants during the spring and summer of 2021. Winter survival scores were collected on 366 progeny individuals. Heading date was collected on 175 progeny individuals. Row-plot biomass yield best linear unbiased estimates (BLUEs) were calculated in a mixed-model with incomplete blocks as a random effect and genotypes as a fixed effect. Post hoc means comparison was carried out using a Dunnett's multiple comparison test which compared all families to the progeny population.2 families , 17 F1 families (n = 618), 18 Founder individuals, and 23 F1 parents. The Founder individuals were deeply sequenced (targeting 40 reads per site), while the F1 and pseudo-F2 individuals were shallow sequenced (∼1–5 reads per site).Leaf samples were collected from all individuals after establishment in 2018. In 2019, a subset of the nursery was extracted for sequencing based on observed segregation for winter survival during spring 2019 and sufficient sample sizes within families. This resulted in genetic data from 18 pseudo-FSequencing data were generated at the DOE Joint Genome Institute using an Illumina NovaSeq S4 platform. Briefly, plate-based DNA library preparation for Illumina sequencing was performed on the PerkinElmer Sciclone NGS robotic liquid handling system using Kapa Biosystems library preparation kit (Roche). Next, 200 ng of sample DNA was sheared to 500 bp using a Covaris LE220 focused ultrasonicator. The sheared DNA fragments were size selected by double-SPRI and then the selected fragments were end-repaired, A-tailed, and ligated with Illumina compatible sequencing adaptors from IDT containing a unique molecular index barcode for each sample library.The prepared libraries were then quantified using KAPA Illumina library quantification kit (Roche) and run on a LightCycler 480 real-time PCR instrument (Roche). The quantified libraries were then multiplexed and the pool of libraries was prepared for sequencing on the Illumina NovaSeq 6,000 sequencing platform using NovaSeq XP v1 reagent kits (Illumina), S4 flow cell, following a 2 × 150 indexed run recipe.The program BBDuk (version 38.87) was used to remove contaminants, remove adapter sequences and trim reads where quality drops below 6  counting reads from either grandparent was used to assign ancestral probability within each pseudo-F2. Specifically, for each position parental calls were made with probability <0.1 or >0.9 assigned as homozygote of the given Founder ancestor and probabilities >0.2 and <0.75 assigned as heterozygotes. Run-length equivalents of parentage calls were calculated. Then assigned calls were decoded into haplotype breakpoints and short runs of heterozygosity between two homozygous regions were dropped. Specifically, short runs of <100 sites were dropped with recalculation of run-length equivalents to construct a final haplotype map. Individuals were removed from the haplotype map if they contained greater than 85% heterozygosity (n = 8) or only contained haplotypes from only one parent (n = 21). These individuals are most likely the result of pollen contamination.To compensate for shallow sequencing within pseudo-FP. virgatum v5.1), assigning each tag to a position with the best unique alignment. The occupancies of tags for each sample were observed from barcode data. Resulting files were used to call single-nucleotide polymorphism markers (SNPs) at the tag locations on the genome, resulting 1,072,642 SNPs.Leaf samples were collected for sequencing from 52 individuals within the progeny population. Since the identification of outliers is the most critical goal of genomic selection, a sampling method was used increase the incorporation of outlier individuals in the validation set. Specifically, individuals were sampled using weights from an inverted density distribution of the population's mean Z-scores. The mean Z-scores were calculated from each individual's heading date and winter survivorship scores. This resulted in a subset of the population with trait values slightly oversampled from the tails of the gaussian distribution. Genotyping by sequencing occurred on an Illumina sequencer (NovaSeq 6000) through the University of Wisconsin Biotechnology Center using PstI-MspI restriction enzyme digestion before ligating fragments to barcoded adaptors prior to polymerase chain reaction amplification. Data analysis of sequencer output used TASSEL . Next, imputation of the remaining sites was carried out using the expectation-maximization algorithm , minor allele frequency (<0.05), and linkage disequilibrium (<0.90 with a marker within the nearest 15 variant sites) within the Founder individuals. The analysis with the progeny used a less stringent minor allele frequency (<0.025) to maximize the number of overlapping sites between the two sequencing runs. These initial filters resulted in 365,996 markers within the primary marker array and 204,682 with the array including progeny. To supplement shallower sequencing within the FVariance estimation was carried out for heading date and winter survival scores using two single-trait models and a multi-trait model. The following model was used:Z represent the incidence matrix for individual genotypes, a, ud, ue,u and Ru vectors represent the additive, dominance, epistatic and residual genetic effects respectively. The e represents a vector of the residuals. Variance structures are au ∼N, du ∼N, Eu ∼N, Ru ∼N and e ∼N, where σa2, σd2, σE2, and σR2 are the additive, dominance, epistatic, and residual genotypic variance. The matrices G, D, and E are the realized additive relationship matrix, realized dominance relationship matrix, and realized epistatic relationship matrix. The matrix I is an identity matrix and used for isolating residual genotypic effects. Relationship matrices were derived from marker data using the R package sommer , e sommer . The matReliability was estimated for each trait and model. Reliability was calculated as:i , and G matrix diagonal  included. Genomic prediction was evaluated by cross-validation among families and by prediction of a generation of progeny individuals. Cross-validation used a two-stage process, where BLUEs were generated from a fixed effect model and BLUEs were then used in a model which accounted for relatedness between genotypes to predict breeding values for heading date and winter survivorship. The second model included weights based on the inverse of the square root of their standard error from the BLUE model .Three cross-validation methods were used to assess model performance. This included masking half of a single family, masking and entire family, and masking the entire pseudo-F2 individuals to estimate the erosion of predictive ability when training data is not updated.Progeny predictive ability was evaluated using individuals from the progeny performance experiment (n = 52) for both winter survivorship scores and heading date. For progeny prediction, an additional set of predictions was carried out integrating epistatic variance and dominance variance for winter survivorship score prediction and heading date prediction, respectively. In addition, predictive ability of the progeny set was evaluated by masking all pseudo-FOverall, winter survivorship in Arlington, WI, was variable between and within families. Within the Founder individuals, mean winter survivorship ranged from 0 to 20 . The Tol1 families sustained 48% mortality and the pseudo-F2 families sustained 66% mortality. During the second winter (2018–2019), mortality among the remaining individuals dropped to 9% for F1 families and 16% for the pseudo-F2 families. During the final year of measurements, mortality rate was 10% for F1 families and 12% for pseudo-F2 families.Winter survivorship scores were collected for 3,158 individuals, 1,306 of which were re-sequenced. Since heading date measurements required at least 1 year of survival, only 1,458 individuals were measured for heading date, 609 of which were re-sequenced. During the initial winter (2018–2019), the F1 families and 2.9 for pseudo-F2 families. In the spring of 2020, mean survivorship scores were 3.7 for F1 families and 1.3 for pseudo-F2 families. In the spring of 2021, mean survivorship scores were 7.1 for F1 families and 7.2 for pseudo-F2 families. Overall, a strong parent–progeny relationship was observed, with a winter survivorship mid-parent regression narrow-sense heritability of 0.71 . The mean heading date was 210 DOY (range = 197 to 222 DOY). The phenotypic correlation between heading date and winter survivorship scores was pulation . The cor1 and pseudo-F2 populations, respectively. In the single-trait model, genetic variance for heading date was primarily additive, with moderate residual genetic variance, no epistatic variance, and a small amount of dominance variance. Mean reliability was 0.68, with means of 0.50, 0.70, and 0.62 within the Founder, F1 and pseudo-F2 populations, respectively. In the multi-trait model, variances were similar to those from the single-trait models . When whole families were removed from the dataset, mean predictive ability was 0.52 and mean predictive ability was greater for pseudo-F2 relative to F1 families . When observations of the entire pseudo-F2 generation were removed from the training data and predicted, the mean predictive ability was 0.79. However, high predictive ability in this iteration was largely due to the accuracy associated with predicting the mean performance of families. The mean predictive ability within families when the entire pseudo-F2 generation was masked was 0.40.Broadly, genomic predictive ability of winter survivorship was high . Mean pr2 relative to F1 families . When whole families were removed from the dataset, mean predictive ability was 0.53. Mean predictive ability was greater for pseudo-F2 relative to F1 families . Predictions of the entire pseudo-F2 generation resulted in a mean predictive ability of 0.74. Similar to observations for winter survivorship, the mean predictive ability within families was substantially lower when the entire pseudo-F2 generation was predicted (0.26).Due to the smaller training dataset, the predictive ability of heading date was generally lower than winter survivorship . Mean pr2 families removed from the training data, the predictive ability unexpectedly increased to 0.73. A model including epistatic genetic variance resulted in a predictive ability of 0.72. Predictive ability for heading date within the progeny population was 0.53. A model including dominance genetic variance resulted in a predictive ability of 0.56. If the pseudo-F2 families were removed from the training data, the predictive ability increased slightly to 0.55. A post hoc analysis found that the inverted density sampling regime used for progeny population selection could inflate predictive ability by approximately 8% .Strong predictive ability could be an artifact of pedigree constructed in this study. Specifically, with only two major genetic donors of winter survivorship in the study (Tolerant1 and Tolerant2), winter survivorship could be proportional to the percentage of ancestry from a tolerant founder. A post hoc examination found significant correlations between winter survivorship scores of an individual and the genetic distance of that individual to the tolerant founders. The mean genetic distance between the progeny population (n = 52) and two tolerant founders was negatively correlated with progeny winter survival scores (−0.64). However, the relationship between genetic distance from tolerant founders and winter survivorship BLUPs was comparable witho the pseudo-FSimply as a survey of biological adaptability, this study highlights how polyploid grasses can rapidly adapt to new environments through strong within-family segregation , many fuG=σair), one can calculate the expected gain from selection using the narrow-sense heritability for winter survivorship  that unseen damage or nutrient loss is occurring due to excessively late flowering, and that late flowering genotypes will have poor vigor in commercial sward conditions. In the progeny row-plot evaluation, two families derived from the Liberty cultivar produced comparable biomass yield to the lowlands despite earlier flowering time . Future Predictive ability of winter survivorship using genomic selection was relatively strong and this level of predictive ability would result in reliable genetic progress with field evaluations occurring only as needed to recalibrate after one or two generations of selection  and 2. W2 family generation was omitted, which was a surprising result. Usually, genomic prediction performance is superior then the population being predicted is closely related to the training data. This results in a penalty in predictive ability when multiple cycles of selection and recombination are carried out without updating training data , and only a minor decrease was observed between F1 and pseudo-F2 families for heading date reliability .Progeny predictive ability was not reduced when the entire pseudo-FAlternatively, this strong and persistent predictive ability could be an artifact of the population structure generated in this study. Specifically, winter survivorship was proportional to the overall percentage of ancestry from a tolerant founder. This correlation was strong within progeny validation, but not as strong as the predictive ability obtained through genomic prediction . This result is not surprising since this experiment utilized the GBLUP model, which relies heavily on genetic relationships for prediction . OverallThis study described the winter survivorship and survival of multiple lowland switchgrass families across three years in the North-Central United States and found that genetic variance for winter survivorship is largely additive, has high narrow-sense heritability (0.71) and reliability (0.76). Heading date, a potential covariate for winter survivorship, had similarly high reliability (0.68), but a multi-trait model including heading date did not improve winter survival predictive ability. Further, the genetic correlation between heading date and winter survivorship appeared to erode across multiple recombination events. In a single-trait model, genomic predictive ability was generally high, even with large portions of the dataset omitted. Despite these promising results for genomic prediction, phenotypic selection successfully isolated winter tolerant genotypes from multiple crosses of different backgrounds and may continue to be the most efficient selection strategy to develop high-biomass and sustainable switchgrass cultivars. Instead, genomic prediction of winter survival will be applicable in populations already being sequenced for more complex traits, such as biomass yield.jkad014_Supplementary_DataClick here for additional data file.