Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) trees from four open-pollinated progeny trials were genotyped with 25,099 SNPs, and phenotyped for 15 growth, wood quality, pest resistance, drought tolerance, and defense chemical (monoterpenes) traits. The main objectives of this study were to: (1) identify genetic markers associated with these traits and determine their genetic architecture, and to compare the marker detected by single- (ST) and multiple-trait (MT) GWA models; (2) evaluate and compare the accuracy and control of bias of the genomic predictions for these traits underlying different ST and MT parametric and non-parametric GP methods. GWA, ST and MT analyses were compared using a linear transformation of genomic breeding values from the respective genomic best linear unbiased prediction (GBLUP) model. GP, ST and MT parametric and non-parametric  models were compared in terms of prediction accuracy (PA) and control of bias.Genomic prediction (GP) and genome-wide association (GWA) analyses are currently being employed to accelerate breeding cycles and to identify alleles or genomic regions of complex traits in forest trees species. Here, 1490 interior lodgepole pine  than the ST models, being generally the PA (bias) of the RKHS approach significantly higher (lower) than the GBLUP.The power of GWA and the accuracy of GP were improved when MT models were used in this lodgepole pine population. Given the number of GP and GWA models fitted and the traits assessed across four progeny trials, this work has produced the most comprehensive empirical genomic study across any lodgepole pine population to date.The online version contains supplementary material available at 10.1186/s12864-022-08747-7. Pinus contorta Dougl. ex. Loud. var. latifolia Engelm) is one of the most widely distributed and commercially important conifer species in Northwestern North America  and [2].Univariate [1] and multivariate [2] models were fitted with the R-package  revealed that differences attributable to provenance origins were negligible. For instance, the first principal component (PC) accounted for < 0.7% of the total variation. In addition, design random effects  were also not fitted in the GWA analyses, given that adjusted phenotypes were used. The two traits that showed inconsistent and imprecise genotype by environment interactions (G × E) were MFA and DECL and were therefore removed from further multiple-trait GWA and GP analyses see . Thus, Gp-value for each k SNP from each individual trait using the ST and MT models was computed with the formula [Ф(.) is the cumulative density function of the normal distribution . Followof model , a custoFive statistical methods were assessed for their ability to predict the genomic breeding values using single-trait (ST) models for all traits studied. These methods included BayesC , BayesiaG-matrix (see above) is equivalent to the mixed linear model of GBLUP [K) is defined as: i and i´. The rate of decay imposed by the bandwidth parameter h was estimated using the ST model for each trait, where 11 out of the studied 15 traits showed the highest predictive ability to the h value equal to 0.5 , for thIn order to evaluate and compare the accuracy and bias of the genomic predictions of the studied traits, a 10-fold cross-validation analysis was conducted across all the ST and MT parametric and non-parametric GP models, where one subsample was used as the validation set, and the remaining nine samples as the training set. A total of 5 replicates were conducted at each fold. In the MT cross-validation analysis, when a trait is predicted for a tree in the validation population, the phenotypic measurements for the other traits are available for the tree in the validation set .Predictive ability was estimated by evaluating the Pearson correlation coefficient between the predicted breeding values of the validation trees and the adjusted phenotypes. Then, the prediction accuracy was calculated for each ST and MT model and trait as the predictive ability divided by the square root of the narrow-sense heritably of each trait, computed using the MT-GBLUP model . The preAn analysis of variance (ANOVA) using a linear model with fixed effects of method and replication, and Tukey’s multiple comparison tests were performed at a significance level α = 0.05, to test the significance of the difference in prediction accuracy and prediction bias between the different ST and MT models performed for each trait.Additional file 1.


Recent research has shown that cultural, linguistic, and sociodemographic peculiarities influence the measurement of trait emotional intelligence (trait EI). Assessing trait EI in different populations fosters cross-cultural research and expands the construct’s nomological network. In mental health, the trait EI of clinical populations has been scarcely researched. Accordingly, the present study examined the relationship between trait EI and key sociodemographic variables on Trait Emotional Intelligence Questionnaire (TEIQue-SF) datasets with mental healthcare patients from three different Spanish-speaking countries. Collectively, these datasets comprised 528 participants, 23% from Chile (120), 28% from Peru (150), and 49% from Spain (258). The sociodemographic variables we used for trait EI comparisons were gender, age, educational level, civil status, and occupational status. Analyses involved Multigroup Exploratory Structural Equation Modelling (to test measurement invariance) and analysis of covariance (ANCOVA). Our results revealed significant between-country differences in trait EI across the studied sociodemographic variables and interactions between these variables. Measurement invariance across the datasets was attained up to the scalar level regarding gender and education , although analyses on age, civil status, and occupation displayed non-invariance. The resultant psychometric evidence supports the suitability of the TEIQue-SF for the accurate cross-cultural assessment of trait EI in mental health settings. It also highlights the importance of incorporating trait EI into extant psychotherapeutic frameworks to enhance non-pharmacological treatment efficacy. Trait EI is a personality-based conceptualisation of EI that is consistent with established models of Differential Psychology and has shown exceptionally strong evidence of construct validity . Trait EThe four-factor basis of the TEIQue comprises Wellbeing, Self-control, Emotionality, and Sociability . MoreoveIn contrast to most other EI measures, the suite of Trait Emotional Intelligence Questionnaire (TEIQue) assessments has fully developed theoretical foundations and nomological networks, spanned associations with important health outcomes . This trend was fully supported when including the factor-level as predictors , and partially replicated—with the exceptions of three facets— when testing the same effect after including the fifteen facets that the TEIQue allows.Several other syndromes and disorders have been related to trait EI. For instance, p < 0.01, with R2 of 0.45, 0.16, and 0.18, respectively).Furthermore, In addition, personality disorders , emotionDifferent populations, cultures, and other sociodemographic and economic peculiarities may affect the interpretation and cross-cultural validity of trait EI in clinical settings. Therefore, the present study had two main aims. First, to provide evidence of measurement invariance concerning the preceding sociodemographic variables with clinical populations. Second, to test for trait EI differences across influential sociodemographic variables  in clinical cross-cultural populations. The study compared datasets from clinical populations in three countries with distinct characteristics , albeit sharing the same language (Spanish). To our knowledge, it is the first attempt to profile clinical populations on trait EI from a cross-cultural perspective and to provide cross-country evidence of measurement equivalence. Trait EI theory is especially appropriate for these aims, as its taxonomy and measurement instruments have strong conceptual and explanatory power to predict attitudes, behaviours, and performance .Data were obtained in Chile, Peru, and Spain from clinical populations. Collectively, the three datasets comprised 528 participants, 23% from Chile (120), 28% from Peru (150), and 49% from Spain (258). Only the Chilean dataset has been employed in previous research i.e., . Particiminimum = 17, maximum = 74). Children and adolescent TEIQues are currently available, although they have not yet been validated in Peru and Chile. Regarding main occupations, most participants worked in the private sector (31%), followed by students (30%) and those in the public sector (19%). The unemployed accounted for 12% of the pooled sample. Participants who declared a non-listed occupation were reached 5% of the pooled sample. Those working in the education sector either as teachers or as lecturers accounted for 3% of participants. This categorisation followed https://data.mendeley.com/datasets/f23zhjcwcv/2.In the pooled dataset, 280 participants were women (53%) and 248 were men (47%). Mean age was 34.38 years, standard deviation [SD] = 11.12, We used the Chilean-Spanish-TEIQue-SF in Chile and Peru  and the Chilean and Spanish participants completed  the paper-and-pencil version of the TEIQue-SF, whilst Peruvian participants responded online due to Coronavirus Disease (COVID) restrictions. Pertinent local ethics boards approved all phases of the data collection. In Chile, the Ethics Committee of the University College London (UCL) granted ethical approval with project ID: 12971/00, as the data were originally collected as part of doctoral research . In Spai2) as a measure of effect size. Regarding post hoc analyses, we conducted multiple comparisons through the Bonferroni correction to control the Type I error. We tested for three kinds of measurement invariances: configural, metric, and scalar  and reported Eta Squared  estimations following via Chained Equations (MICE)  . Most ofh, was 68%. In addition, trait EI factors mainly showed adequate-to-high reliability ; although the values were considerably lower than that of the global trait EI score, which the TEIQue-SF was specifically designed to measure. Moreover, lower than desired  reliability scores at the factor level  had been previously reported in clinical TEIQue-SF samples . The proportion of scale variance due to the general factor  only, as estimated by ω samples .The measurement invariance analyses revealed that the trait EI factor structure, as measured by the TEIQue-SF and modelled bifactorially, was invariant up to the scalar (latent means) level for gender and education. The comparative fir index (CFI) reached the 0.9 cutoff threshold, with CFI changes across nested models falling below the 0.01 cutoff criterion recommended by Moreover, the root mean square error of approximations (RMSEAs) and standardised root mean residuals (SRMRs) in all modelled measurement invariance analyses were below their respective recommended thresholds . Regarding occupation, CFI was below the threshold of 0.9 for configural invariance, which prevented progressing to the metric and scalar steps of the process  were within the expected boundaries, CFI changes were not, suggesting that the instrument may be non-invariant in clinical populations of different ages, at least in the countries examined in the present study. Concerning civil status, the analyses revealed non-invariance at the scalar step  showed that global trait EI was normally distributed in all three datasets. Moreover, global trait EI variances across the three-country datasets were homogenous according to the Levene’s test .Descriptive statistics for the trait EI variables in each country are depicted in D(120) = 0.12, p < 0.001; D(120) = 0.11, p = 0.002], whereas Self-control and Sociability were both normally distributed (p = 0.200). In Peru, Self-control and Sociability were normally distributed , whereas Wellbeing and Emotionality were deviated from normality . In Spain, Emotionality was normally distributed (p = 0.200), whereas Wellbeing, Self-control, and Sociability were deviated from the expected distribution . After visual inspection of quantile-quantile (q-q) plots from the pooled dataset, histograms, and the KSL statistic, we concluded that the deviation from normality at the factor level was not substantial. Moreover, the variance was homogenous for three of the four trait EI factors in the pooled dataset: {Wellbeing , Self-control , Emotionality , with Sociability displaying heterogeneity of variances }.At the factor level of trait EI, in Chile, Wellbeing and Emotionality were deviated from normality ; Wellbeing ; Self-control ; Emotionality ; and Sociability }. These analyses revealed that country explained significant trait EI variance over and above the other IVs in the main effects model . In addition, in these analyses, age had a significant main effect on Self-control , occupation had a significant main effect on Emotionality , and education had a significant main effect on Sociability . Last, gender had a significant main effect on Self-control  and on Emotionality . All these main effects were small in size .We conducted ANCOVAs with the sociodemographic variables as independent variables, age as covariate and trait EI  as the dependent variables, the homogeneity of variance assumption was met in all cases {global trait EI , and between gender and occupation  on global trait EI. Gender and country had a significant interaction Self-control , whilst country and occupation had significant interactions on Self-control  and on Emotionality . Finally, gender and occupation had a significant interaction on Sociability . With respect to three-way interactions, the assumption of homogeneity of variances was violated for all trait EI-dependent variables except Emotionality. In addition, there was a high number of parameters in these models, which reduces statistical power. For these reasons, we did not proceed further with these analyses.There were significant two-way interactions between country and occupation on global trait EI [p < 0.001, η2 = 0.09), Wellbeing , Self-control , Emotionality , and Sociability . Chilean participants also scored higher than Peruvians on Self-control . Regarding education, participants with graduate education (Master’s degree or PhD) scored significantly higher than those with secondary education  on Sociability. Regarding occupation, students scored significantly higher than participants working in the private sector . Men scored marginally higher than women  on Self-control, whereas women scored higher on Emotionality . There were no differences in civil status. Effects sizes were generally medium-to-small.Chilean participants scored significantly higher than Spaniards on global trait EI  cross-cultural invariance of trait EI (as measured by the TEIQue-SF) concerning gender and education. The main advantages of the chosen approach in comparison to extant related research e.g.,  are threOur findings were consistent with previous trait EI examinations in clinical transdiagnostic participants  and provide a basis for understanding trait EI differences across the levels of the abovementioned sociodemographic variables. Concerning occupation, although there is a dearth of evidence on the role of this variable to trait EI with clinical populations, the literature with general populations highlights that some professions, especially those from the public sector  or exerting leadership and mentorship roles  usually have higher levels of trait EI . The resThe ANCOVA analyses revealed higher homogeneity between the Chilean and Peruvian datasets, which is expectable as bordering countries tend to display similar means on personality measures , such asThe present study supported mean equivalence  of trait EI across clinical populations from different countries and the studied sociodemographic variables of gender and education, being this the first attempt in the literature, a task of major methodological interest . As a liOverall, the findings highlight the cross-cultural stability and validity of the trait EI construct across clinical populations, as measured by the TEIQue-SF, irrespective of cultural, socioeconomic, and linguistic correlates. This gives the TEIQue-SF potential value as a complementary clinical diagnostic tool of common application in Latin American countries, filling a gap in the screening of emotional vulnerability in transdiagnostic disorders, where the underlying basis is often related to emotional difficulties or emotional dysregulation e.g., . Howeverhttps://data.mendeley.com/datasets/f23zhjcwcv/2.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: In Chile, the University College London (UCL) - Ethics Committee granted ethical approval with project ID: 12971/00, as the data were originally collected in Chile as a part of doctoral research . In SpaiPP-D and KP contributed to conception and design of the study. PP-D, DM-M, and MG-G organised the database. PP-D performed the statistical analysis and wrote the first draft of the manuscript. PP-D, KP, and J-CP-G wrote sections of the manuscript. PP-D, MV-S, RM-R, NF-R, DM-M, and MG-G contributed with data collection. All authors contributed to manuscript revision, read, and approved the submission.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.


Gene–environment correlations affect associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here we showed in up to 43,516 British siblings that educational attainment polygenic scores capture gene–environment correlations, and that migration extends these gene–environment correlations beyond the family to broader geographic regions. We then ran GWASs on 56 complex traits in up to 254,387 British individuals. Controlling for geographic regions significantly decreased the heritability for socioeconomic status (SES)-related traits, most strongly for educational attainment and income. For most traits, controlling for regions significantly reduced genetic correlations with educational attainment and income, most significantly for body mass index/body fat, sedentary behavior and substance use, consistent with gene–environment correlations related to regional socio-economic differences. The effects of controlling for birthplace and current address suggest both passive and active sources of gene–environment correlations. Our results show that the geographic clustering of DNA and SES introduces gene–environment correlations that affect GWAS results. Analyses of gene–environment correlations across geographic regions for 56 complex traits in UK Biobank suggest that both passive and active sources of gene–environment correlation affect genetic association signals. The aim of a GWAS is to estimate associations between genetic variants and complex traits2. The nature of the associations is generally difficult to determine, especially for highly polygenic traits3. Genetic effects are expressed through many cascades of biological processes that influence and react to environmental exposures, which can give rise to gene–environment correlations. Three potential sources of gene–environment correlations are as follows: (1) population stratification, (2) family-level gene–environment correlations and (3) gene–environment correlations across geographic regions.Genome-wide association studies (GWASs) are an important tool for the investigation of the epidemiology and biology of mental and physical health outcomes. GWASs are viewed as essential herein, because for most outcomes in life, individual differences are a consequence of a multitude of genetic and environmental influences5. Genome-wide allele frequency differences between populations due to genetic drift and/or natural selection are detectable even within reasonably homogenous populations8. These older ancestry differences generally show strong correlations with geography9 and could therefore align with regional differences in environmental influences. Population stratification could then lead to biases in GWASs that are not only due to systematic genetic differences but also due to correlated environmental differences between (sub)populations. Bias due to population stratification in GWASs is commonly controlled for with principal components (PCs) that reflect the strongest axes of  genetic variation10.Population stratification affects GWASs when trait differences co-vary with systematic allele frequency differences between ancestries11. These indirect genetic effects have been detected for genes associated with educational attainment on a variety of outcomes. Because parental and offspring genotypes correlate, polygenic effects on education could be mixed with signals of GWASs on physical and mental health outcomes that are influenced by these environmental factors. Some refer to these indirect genetic effects as ‘nature of nurture’ (ref. 11), implying indirect effects that arise by ways of parenting or nurturing a child. Others call it ‘dynastic effects’ (ref. 12), implying that indirect effects can arise from the succession of (economic) (dis)advantage accumulated across generations, improving children’s (socio-economic) positions at birth. As families are nested in neighborhoods, regions and other social structures, accounting for family can partly account for effects at other levels as well.Differences between families in (socio-economic) environmental factors can also induce gene–environment correlations. Outcomes can, for example, be influenced by the parental genotype through parental (rearing) behavior and inherited socio-economic environments13. This process can increase the geographic clustering of genes associated with educational attainment and decrease the geographic clustering of PCs that capture older ancestry differences13. Migration can improve rearing environments for offspring as well, making active gene–environment correlations in one generation induce family-level gene–environment correlations in the next. Passive sources of regional gene–environment correlation could arise, for example, due to government policies that affect certain socioeconomic strata more than others. For instance, a policy change that makes insulin more expensive will introduce a correlation between alleles related to socioeconomic status (SES)  and those related to consequences of untreated diabetes.A third source of gene–environment correlations can be found across geographic regions, which could result from both active and passive processes. Active processes could occur when individuals with favorable genetic predispositions are more likely to improve their environmental circumstances through migration14. In these GWASs, we reduced the part of the signal that arises due to gene–environment correlations by introducing fixed effects for neighborhoods that are economically more homogenous than the country as a whole, effectively performing within-region GWASs for a wide range of complex traits. We investigated the impact of gene–environment correlations on the heritability of complex traits and on their genetic relationships with socio-economic outcomes (education and income).In this study, we investigated the effects of gene–environment correlations across geographic regions on polygenic signals for a wide range of traits. We first examined passive and active gene–environment correlations at the family and regional level. To this end, we used educational attainment polygenic scores and phenotypic measures of 56 complex traits in 22,657 adult sibling pairs from UK Biobank. We then conducted GWASs on 56 complex traits in a dataset of up to 254,557 adult individuals of European descent from Great Britain (UK Biobank)We first show gene–environment correlations across geographic regions using polygenic scores in siblings. We then reduce their impact on GWAS signals by controlling for geographic regions. For both parts, we analyzed 1,246,531 common SNPs and 56 complex traits related to physical and mental health, body composition, and emotional, cognitive, behavioral and socioeconomic outcomes  environment in which they were born and raised, that is, passive gene–environment correlations. We conducted similar analyses in this study in 22,675 adult sibling pairs aged 40–70 years. We extended our analyses to investigate whether gene–environment correlations across geographic regions result in an increase in predictive power and to test whether this was influenced by active gene–environment correlations. Active gene–environment correlations could occur, for example, when adults with a higher educational attainment polygenic score are more likely to move to a more favorable region than their siblings with a lower educational attainment polygenic score , between-family (within-region) and between-region level as predictors; in model 5, we decomposed the within-family polygenic score into within- and between-region effects to test for active gene–environment correlations. Regions are based on Middle Layer Super Output Area (MSOA) regions of the current address 15, individual-level effects became generally weaker after adding between-family effects , waist circumference (P = 1 × 10−4), household income (P = 1 × 10−4), time spent watching television (P = 3 × 10−4) and whole-body fat mass (P = 6 × 10−4), possibly because, of all traits considered, these are most likely to change once siblings migrate away from the parental residence (see Table P = 9 × 10−4). Six traits showed an increase in R2 > 2% after adding the between-region effect to the between-family effect  to local authority areas or MSOA regions . Both local authority regions and MSOA regions explained significant amounts of variation for all 56 complex traits, most for household income We only included regions with ≥ 100 UK Biobank participants and selected only participants with all geographic information available. For local authority areas, this resulted in 282 regions for birthplace and 147 regions for current address. For MSOA regions, this resulted in 816 regions for birthplace and 1,747 regions for current address. A total of 254,557 participants with geographic regions were available for both birthplace and current address , with physical traits showing higher heritability estimates than behavioral traits  score regressionn = 6): household income (from 7% to 4%), educational attainment (from 14% to 9%), age at first birth (from 14% to 9%), time spent watching television (from 12% to 9%), overall health (from 8% to 7%) and fluid intelligence (from 22% to 19%). These traits have a strong relationship with SES, especially income and educational attainment, which showed the strongest reduction and also the strongest geographic clustering , body fat percentage , BMI , alcohol frequency  and time spent watching television . When controlling for MSOA regions based on birthplace and current address, 35 traits showed a significant reduction of the genetic correlation with household income. The five most significant decreases were observed for body fat percentage , BMI , time spent watching television , whole-body fat mass  and waist circumference , which is the same top five as for the polygenic score analyses in siblings summarized in Table Controlling for local authority or MSOA region significantly reduced the genetic correlations between most of the 56 complex traits and education and income  and genetic correlations with income became significantly stronger for six traits . This could mean that regional differences in SES masked genetic correlations between these traits and educational attainment, but could potentially also result from collider bias  decreased significantly for most traits, more so when controlling for current address than for birthplace, in line with increased geographic clustering of complex trait variation (and decreased geographic clustering of ancestry-related variation)13. While the relationship between BMI and SES is positive in poorer countries, where food insecurity relates to food shortages, it is reversed in more developed countries, where food insecurity relates to a lack of access to healthy foods29. The number of fast-food restaurants and diabetes rates in British neighborhoods are significantly correlated with regional differences in (polygenic scores for) educational attainment13. Behavioral traits related to body weight, namely time spent watching television and frequency of alcohol intake, are also among the top five traits showing these gene–environment correlations in their polygenic signals. This suggests that, besides dietary options, behaviors that correlate with regional socio-economic factors may also affect regional differences in body weight and their associated polygenic signals.The most significant reductions in genetic correlations with education and income were observed for traits related to BMI and body fat, suggesting correlations between SES-related genes and obesogenic environments. This may help (partly) explain why the polygenic score for BMI shows the strongest geographic clustering in Great Britain, after educational attainment and cognition31. Geographic clustering of SES is probably stronger in the general population than in UK Biobank13, so effects observed in the current study may be underestimated. Furthermore, the geographic coverage is wider for birthplace than for current address, as participants were sampled to live within a radius of 35 km of one of the 22 UK Biobank assessment centers. Controlling for assessment center, however, had minimal impact on the GWAS signal compared to the other regional measures  and the outcome of interest  also affects current address, controlling for current address in the GWAS on substance use may induce collider bias, biasing estimates of SNP associations, heritability and genetic correlations . A total of 273,402 females and 229,134 males  aged between 37 and 73 years were recruited between 2006 and 2010 across 22 assessment centers throughout Great Britain. The sampling strategy was aimed to cover a variety of different settings providing socio-economic and ethnic heterogeneity and an urban–rural mix. The participants underwent cognitive, health and lifestyle assessments, provided blood, urine and saliva samples, and have their health followed longitudinally.The participants of this study come from UK Biobankn = 49,950) or UK Biobank Axiom Array . Genotypes were imputed using the Haplotype Reference Consortium (HRC) panel as a reference set (pre-imputation QC and imputation are described in more detail in ref. 14). We extracted SNPs from HapMap3  from the imputed dataset. In the pre-principal-component analysis (PCA) QC on unrelated individuals, we filtered out SNPs with a minor allele frequency (MAF) < 0.01 and missingness > 0.05, leaving 1,252,123 SNPs. After filtering out individuals with non-European ancestry (see 'Ancestry and PCA'), we repeated the SNP QC on unrelated Europeans , filtering out SNPs with MAF < 0.01, missingness >0.05 and Hardy–Weinberg equilibrium P < 10 × −10, leaving 1,246,531 SNPs. We then created a dataset of 1,246,531 QC-ed SNPs for 456,064 UK Biobank participants of European ancestry. These QC steps were performed using PLINK 2.0.A total of 488,377 participants had their genome-wide SNPs genotyped on UK BiLEVE array  UK Biobank dataset without long-range LD regions consisted of 131,426 genotyped SNPs. The PCA to construct British ancestry-informative PCs was conducted on this SNP set for unrelated individuals using flashPCA v2 (ref. 33). PC SNP loadings were used to project the complete set of European individuals onto the PCs. PCs that reflect ancestry differences are expected to cluster geographically, which we investigated by computing their Moran’s I: out of the top 100 PCs, 78 PCs showed significant geographic clustering after Bonferroni correction and 94 PCs showed a P-value of < 0.05 .Ancestry was estimated using PCA. We first determined which participants had non-European ancestry by projecting UK Biobank participants onto the first two PCs from the 2,504 participants of the 1000 Genomes Project, using HapMap3 SNPs with MAF > 0.01 in both datasets. Participants from UK Biobank were assigned to one of five super-populations from the 1000 Genomes project: European, African, East Asian, South Asian, or admixed. Assignments for European, African, East Asian and South Asian ancestries were based on > 0.9 posterior probability of belonging to the 1000 Genomes reference cluster, with the remaining participants classified as admixed. Posterior probabilities were calculated under a bivariate Gaussian distribution, where this approach generalizes the r2 cutoff = 0.9 and MAF > 0.01, resulting in 575,293 SNPs). The GRMs were computed using GCTA34 on individuals of European descent. We created a sparse GRM, containing only the relationships of related individuals  to control for the presence of closely related individuals .We created genetic relatedness matrices (GRMs) to include in our LMM GWASs to control for cryptic relatedness. The GRMs contain genetic relationships between all individuals based on a slightly LD-pruned HapMap 3 SNP set 36, but also all British cohorts, as an extra measure against residual population stratification within Great Britain. The polygenic scores were computed with the SBLUP approach28, which maximizes the predictive power by creating scores with best linear unbiased predictor (BLUP) properties and accounts for LD between SNPs. As a reference sample for the LD, we used a random sample of 10,000 unrelated individuals from UK Biobank that were imputed using the HRC reference panel37. To control for population stratification, we regressed out the first 100 PCs from the polygenic scores.We computed polygenic scores, that is, the genome-wide sum of alleles weighted by their estimated effect sizes, for educational attainment. The allelic effect size estimates were obtained from the GWAS from ref. 38, and educational attainment, which was transformed to years of education as defined according to the International Standard Classification of Education (ISCED) coding as analyzed in ref. 16.Selection of phenotypic outcomes was based on the relevance of the metric to broad mental and physical health outcomes, resulting in the selection of 56 complex traits encompassing domains of anthropomorphic traits, cardiovascular outcomes, cognition, SES, depressive symptoms, sedentary behavior, reproductive behavior, risk-taking behavior, physical activity, self-reported overall health, sleep, social connection and substance use  and uk-postcodes (https://www.uk-postcodes.com). Further details of this process are available from relevant websites.” The participants’ coordinates, which were rounded to 1 km, were mapped to the nearest MSOA region using a shapefile obtained from the InFuse website, which is part of UK Data Service Census Support39. The R-packages sp (v1.4-4) and rgdal (v1.5-18) were used to merge the spatial data from the MSOA shapefile42.Birthplace location was based on the coordinates in UK Biobank fields 129 (latitude) and 130 (longitude). UK Biobank verbal interview includes a procedure to ascertain a participant’s birthplace described as follows: “The interviewer is provided with a tree structure that lists place names and counties in England, Wales and Scotland. They were instructed to enter at least the first three letters of the town/village/place that the participant provides. If there are too many matches to the first three letters, no place names appear, and more letters need to be entered. If there is more than one listing of the relevant place name, then they were asked to choose the one with correct district or county. In order to narrow down the search, the interviewer may also need to type in the district or county to find a match. If they cannot find the place name, they were instructed to use the Enter Other facility to enter free text to state the town, county (or district) and country. If the participant does not know, there is the option to enter Unknown. Place of birth in the United Kingdom is then converted into North and East coordinates.” It is not clear whether birthplace refers to the residence at birth or the place of the hospital of birth. The current address location was based on the coordinates in UK Biobank fields 22702 (longitude) and 22703 (latitude). Deriving these coordinates is described by UK Biobank as follows: “Where the full address is present and is verified to be valid, software package DataPlus (provided in the QuickAddress Batch packet) is used to transform the address data into the grid coordinates. Where only the postcode is present, the grid coordinates are generated with the aid of online mapping tools: doogal :We extended the model from ref. Model 1: The model, with the polygenic score on an individual level as fixed effect:15:Model 2: The model with between-family fixed effects added, as used in ref. Model 3: The model with between-region fixed effects added:Yijk denotes the phenotypic outcome, where i =  corresponds to the sibling (1 is the oldest sibling) from family j, who lives in MSOA region k. PRS denotes the polygenic score, α0 denotes the intercept; γj denotes the random intercept with γj ~ N, accounting for between-family variation in the intercept; γk denotes the random intercept with γk ~ N, accounting for between-region variation in the intercept. The residual is represented as εijk with εijk ~ N. This random intercept mixed-effects model can include multiple fixed effects to separate the association between the polygenic score and the phenotype into within-family  and between-family and between-region effects. The random effect term σ2γ, which estimates the difference between the group intercepts γj, γk and the overall intercept α0, accounts for the residual structure in the data corresponding to all unaccounted familial and regional factors  that contribute to phenotypic similarities within families and within regions.Model 4: The model with both between-family and between-region fixed effects added:Next, to test for active gene–environment correlations, we decomposed the within-family effect into within- and between-region effects. This allowed us to investigate whether the between-family model captures all gene–environment correlations in the individual-level polygenic score or whether part of the within-family effect also contains between-region effects. We fitted the following model 5:All models included sex and age as fixed covariates (not displayed in the formulas above).βW) differs between models 2 and 4, we need to account for the fact that we rely on near perfectly overlapping data. We do so by way of a bootstrap procedure. We resampled, with replacement, a number of observations equal to the number of observations in the original dataset from rows of the dataset. In 1,000 bootstrap samples, we re-estimated models 2 and 4, computed the difference in βW between models 2 and 4, and computed the standard deviation of this difference, which served as the bootstrapped standard error. Any correlation between βW2 and βW4 is reproduced faithfully across the bootstrap samples, meaning that the standard error of their differences accounts for this correlation. We then computed a bootstrap P value assuming the distribution of  in Extended Data Fig. P represents the previous generation , BP is birthplace, SNP represents the genes carried by the individual and Y corresponds to the phenotypic outcome. Along similar lines, a GWAS was performed using dummy variables based on current address instead of birthplace and an analysis where dummy variables for both birthplace and current address were included. The plausible causal models in the GWASs that correct for current address are more complicated. We propose three DAGs, which we feel are abstractions of potential causal processes that relate confounding parental influences (P), genotype (SNP), current address (CA) and outcome (Y). We include these DAGs and their descriptions because they are useful to have in mind when evaluating our results. In practice, we expect that a mix of these processes, or more complex processes altogether, underpins the relationship between genotype and outcome.The second equation omits sex and age for brevity and includes dummy variables Y on SNP is no longer confounded by P (similar to the process that underlies controlling for birthplace).The first causal process  of interest. Here, if we control for current address, we test for a more ‘direct’ effect, not mediated by environmental exposures, of genotype (SNP) on outcome (Y). The (probable) presence of mediation would mean that controlling for current address is likely to lead to qualitative changes in the genetic effect estimates to a different extent than when controlling for birthplace. However, even in the absence of mediation, we could expect differences herein, as current address appears to be measured more precisely.The second causal process   also influences current address. In this case, conditioning on current address in a GWAS on, for example, substance use would potentially induce collider bias, which is undesirable. In practice, we do not know which of the causal processes, or which mix of causal processes, underlies the data. The effects of correcting for current address are therefore more complex to interpret.Finally, as depicted in the causal model in Extended Data Fig. 20 and Genomic SEM21 version 0.0.3. The genetic correlations represent the genetic covariation between two traits based on all polygenic effects captured by the included SNPs, and are estimated with the slope from the regression of the product of the two z-scores from the two GWASs on the LD score20. The genome-wide LD information used was based on European populations from the HapMap3 reference panel43. All LD score regression analyses included the ~1.3 million genome-wide HapMap SNPs used in the original LD score regression studies20. The standard error of the difference between the heritability and genetic correlations based on the different specifications could not easily be estimated because the GWASs, for which we wanted to obtain the differences were based on the exact same sample and their standard errors were therefore highly dependent. Thus, we estimated the standard error of the differences in heritability and genetic correlations with Genomic SEM, which allowed us to account for the dependence between the estimates of SNP-based heritability and genetic correlations. Genomic SEM accounts for this dependence by explicitly considering the correlation between estimates of heritability and/or genetic correlations that are modeled jointly (for details see ref. 21). To obtain the P value of the difference, we divided the estimate of the difference by its standard error and performed a Z test. Significance was then determined after FDR correction of the P value. For geography-corrected traits, genetic correlations were computed with either educational attainment or income, and these were also corrected for the same geographic variable.Heritability and genetic correlation were estimated using LD score regressionFurther information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgments, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01158-0.Supplementary InformationSupplementary Tables 1–3.Reporting SummaryPeer Review FileSupplementary DataAll SNP-based heritability estimates and genetic correlations before and after controlling for geography.


Solanum pennellii introgression lines (ILs) is conducted. Five quantitative trait loci (QTL) affecting yield stability are mapped; IL10‐2‐2 is unique as this introgression improved yield stability without affecting mean yield both in the historic data and in four years of field validations. Another dimension of the stability question is which genes when perturbed affect yield stability. For this the authors tested in the field 48 morphological mutants and found one ‘canalization’ mutant  with a consistent effect of reducing the stability of a bouquet of traits including leaf variegation, plant size and yield. canal‐1 mapped to a DNAJ chaperone gene (Solyc01g108200) whose homologues in C. elegans regulate phenotypic canalization. Additional alleles of canal‐1 are generated using CRISPR/CAS9 and the resulting seedlings have uniform variegation suggesting that only specific changes in canal‐1 can lead to unstable variegation and yield instability. The identification of IL10‐2‐2 demonstrates the value of historical phenotypic data for discovering genes for stability. It is also shown that a green‐fruited wild species is a source of QTL to improve tomato yield stability.Breeding plant varieties with adaptation to unstable environments requires some knowledge about the genetic control of yield stability. To further this goal, a meta‐analysis of 12 years of field harvest data of 76  Breeding tomatoes for stable yield. Using 12 years of field data, five quantitative trait loci that affect the variation in fruit yield are found in introgression lines from wild tomato. One QTL stabilizes yield without affecting mean yield. In contrast, a screen for morphological mutants in production tomato lines found one locus that reduces stability in fruit yield, leaf variegation, and plant size. The datasets generated by past variety trials harbor a trove of information for the future identification of quantitative trait loci (QTL) and genes that regulate stability of agricultural performance.To maximize crop productivity, farmers select for cultivation those varieties that produce high yields in diverse agricultural environments.1, 2 The best‐known example of a master regulator is the molecular chaperone HSP90, which assists in the folding of many proteins. Although there is still some ambiguity regarding the role and mode of action of the chaperone HSP90 it is tempting to speculate that among the many “genes of unknown function” there exist additional factors that act on the biological system as a whole. Support for this comes from a mutant screen for master regulators in Saccharomyces cerevisiae that showed more than 300 gene products that when disrupted led to increased morphological variation.From the plant breeding and the biological points of view, considerably more is known about genes that modify the means than about genes that affect its dispersion. The ability of a crop variety to perform in a consistent manner can be attributed to simple genetic factors such as susceptibility to a particular disease that is prevalent in certain sites and not others. In such a case, the yield instability can be simply be overcome by introducing a single resistance gene. Beyond such obvious cases the search is on for the so called ‘master‐regulators’ that buffer traits against genetic and environmental perturbations by modulating genetic networks.3, 4 CANAREP estimated CV for multiple traits generated values that could be statistically compare between genotypes. The advantage of the CANAREP is that the estimation of stability is robust but at a price of a ten‐fold increase of the number of plants in the experiments.To identify genes that may act as master regulators we screened in the field tomato diversity of wild species introgression lines (ILs) in a genetic background of a cultivated inbred variety M82 and also induced mutations in the same inbred processing tomato variety. To phenotype the tomato entries for yield stability we applied a ‘canalization replication’ (CANAREP) design where each such replication consisted of eight plants of identical genotypes (in a row), where yield of each of the plant was used to estimate the mean and the standard deviation that was used to calculate the coefficient of variation (CV = standard deviation/mean) for the canalization replication.7 CANA22.1S. pennellii (LA716) IL population, for which historical data on yield associated traits was available from replicated trials conducted over 12 years (DRYAD). We observed that different ILs that share a common S. pennellii genomic segment often exhibited similar phenotypic effects on yield and its stability  and in the F2 we counted 48 plants with no‐variegation and 19 variegated ones. The preliminary mapping of canal‐1 to chromosome‐1 was achieved using DNA from the above F2 plants using an indel markers kit covering the entire genome. Higher‐resolution mapping was performed on an additional 120 variegated F2 plants, which localized canal‐1 to an interval of 210 000 base pairs that included two dozen genes. Among them was Solyc01g108200, a homologue of At3g19220, that was identified as cyo‐1 and later named Snowy Cotyledon 2 (SCO2).To genetically map the variegation effect of canal‐1 prompted us to search for additional alleles by crossing canal‐1e4058 with different variegated accessions. The mutant ghost‐2  from the Tomato Genetics Resource Center  was found to be allelic to canal‐1e4058, while the mutants e1458, e9515, and a Core Collection variety (CC1300) did not exhibit variegation in their respective F1s with canal‐1e4058 and thus were considered non‐allelic. The F1 hybrids of canal‐1 and gh‐2 were very stable in their drastic variegation which was higher than their homozygous parent. The best candidate for being canal‐1 was Solyc01g108200.2 (Solanaceae Genome Network https://solgenomics.net/) which is 3600 base pairs‐long and composed of 3 exons, which are translated to a protein of 203 amino acids. Both alleles result from mutations in the second exon; canal‐1e4058 was shown to have a T→G (W→G) substitution 923 bp downstream of the transcription start site, while the mutation of gh‐2 was delimited to a C→T (P→L) substitution 1011 bp downstream of the transcription start. A CAPS marker for gh‐2 designed to distinguish between M82 and gh‐2 alleles, co‐segregated perfectly with the phenotype of variegated leaves of 100 plants. Targeted editing of the first exon of Solyc01g108200 using CRISPR/Cas9, led to identification of two sterile mutant plants. The first (named CR9) germinated well but had white cotyledons and was unable to form true leaves  experimental design that enables the measurement of yield associated traits for each member of a group of eight plants at equidistance along the row in the field. Using this approach, we can estimate the diversity of plants that are exposed to a similar field environment in a manner that allows the calculation of the mean, standard deviation and the CV (ratio of standard_deviation/mean) for each replication unit. When multiple such units are used for a number of genotypes the CV for yield can be statistically compared and the most stable and unstable lines can be selected.Geneticists know considerably more about specific genes that affect the mean value of traits than about those that control the degree of dispersion of the means. The latter stability trait is particularly important for agriculture where we aim grow varieties that are relatively stable in their good productivity also in climate fluctuations. A common parameter that is used to estimate yield stability is the coefficient in linear regression that describe the performance of genotypes across different trial environments. The deviation from the estimated regression describes a ‘stability index’ which can be used to compare yield consistency of different genotypes. Regression analysis of the stability index on polymorphic genetic markers has been performed to map candidate genomic regions that are associated with yield stability in barley,12 soyS. pennellii introgression lines. This set of fixed genotypes was phenotyped in the field, with multiple replicates per trial, over a 12 year period. This database allowed us to identify specific QTL that based on the historic data altered CV of yield associated traits. The objective of the 12 years of trials was to identify and validate QTL that affect the means of traits while in this paper, we use the data to analyze the stability of the phenotype over years. To validate the historic data, we established canalization replications trials with 15–20 such CANAREPS for each genotype. The two unstable genomic regions IL1‐2, and the overlapping ILs that share a common genomic region on chromosome 3 (IL3‐3/IL3‐4) were easily validated as the phenotype was altered for a bouquet of correlated traits; additionally, the mean yield of lines homozygous for these segments were dramatically reduced compared to the control M82. The stable pairs IL10‐2‐2 and IL12‐1‐1 both had a similar mean yield as the control M82 but only IL10‐2‐2 validated in our canalization replication set‐up. It is possible that we could not detect a yield stability effect for IL12‐1‐1 due to the experimental set‐up: in the historical trials the entire IL collection was grown in a randomized manner in the experimental plot while the validation was conducted using CANAREPS where the neighbors of each plant were others of the same genotype. To eliminate the possibility that the yield stability phenotype is not due to IL10‐2‐2 we derived every year different stocks from self‐pollination of heterozygotes followed by marker analysis and field planting. Since the tomato variety M82 has been one of the top producers in the pre‐hybrid era of processing tomatoes it initially seemed surprising that a genomic segment of S. pennellii could improve yield stability of a very successful variety. On the other hand, yield in tomatoes correlates to fruit number and so is reproductive success in nature. Thus, it makes sense that some genes that confer selective advantage in natural habitats could also stabilize production in agricultural fields. Finally, this IL analysis is an example for the potential value of sharing the raw phenotypic data with the community. When scientists involved in plant genetics research deposit the raw data in public databases it could be analyzed in the future to probe other biological aspects that were not addressed in the original paper.The first tomato genetics resource we used to map stability QTL was the genotypes8 was  This landmark study identified 300 genes that, when deleted, result in an increase of the morphological variation and were therefore defined as “phenotypic capacitors”. Among these capacitors, there was an overrepresentation of genes that are involved in essential cellular processes, such as maintaining DNA stability, RNA processing and cell cycle. The yeast screen suggested that ≈5% of the genes are involved in buffering the phenotype against environmental and genetic perturbations.The genetic basis of the buffering capacity of the organismal phenotype against environmental and genetic perturbations  was investigated in yeast, in a study which quantitatively phenotyped the budding of 4718 viable single‐gene deletions in haploid strains.6 This Of the 48 mutations tested the most pronounced effect on yield stability was observed for the e4058 allele of SOLYC01G108200.2, a tomato homologue of the Arabidopsis thaliana mutant snowy cotyledons‐2 . The Arabidopsis gene encodes a DNAJ‐like protein, with a zinc‐finger domain that has a chaperone activity and is involved in thylakoid biogenesis.SCO2 is required for normal assembly of the photosynthetic machinery in cotyledons and possibly, for the targeting of the light harvesting chlorophyll‐binding protein LHCB1 to the thylakoid membrane. While the mutant was initially thought to be cotyledon‐specific, it was recently shown that under short‐day conditions, the true leaves could also become variegated. Over four years of field studies, the tomato canal‐1e4058 mutant showed an unstable phenotype, that is, plants of identical genotypes homozygous for the mutation showed large variation in plant size, leaf variegation and yield. Similarly, a C. elegans study showed that seven of eight endoplasmic reticulum associated DNAJ genes are involved in trait canalization. It is important to note that an independent allele at the same locus (ghost‐2), as well as two CRISPR/CAS9‐generated mutations had leaf variegation but did not exhibit the unstable variegation phenotype. ghost‐2 produced uniformly large plants and the CRISPR/CAS9 mutants did not survive beyond the first true leaf stage. This result is consistent with other studies that showed that independent mutations in the same gene may result in different phenotypes. The instability of canal‐1e4058 that is located on chromosome 1 (Genome coordinates: SL2.50ch01:95554613‐95560134) suggests that the site of the mutation in the second exon (T→G (W→G) substitution, situated 923 bp downstream of the transcription start site, has a function associated with the stability of the phenotype. However, we did not explore the molecular function of canal‐1e4058 and this must await additional investigations.These results prompted us to screen for yield stability a subset of 48 single gene mutants derived from a large mutagenesis project of the variety M82.9 Of tcanal‐1e4058 reduced yield stability, another locus on IL10‐2‐2 improved yield stability of a tomato variety without a major effect on the mean yield. Wild species are selected in nature to survive and reproduce in diverse environments, whereas crop plants rely on human agronomic care. Here we show that natural biodiversity can be a source of genes and QTLs to improve yield stability even when benchmarked to an elite processing tomato variety.While 4Introgression Lines: To identify QTL that affect the stability of yield associated traits, the authors used the introgression lines (IL) population that covered the entire genome of Solanum pennellii in 76 overlapping segments in the genetic background of domesticated processing tomato (S. lycopersicum) variety M82. Because the ILs differ from each other by only a single S. pennellii chromosome segment, they are extremely useful for identification of QTLs, since any phenotypic difference between an IL and the recurrent parent M82 can be attributed to the introgressed chromosome segment.S. pennellii ILs were phenotyped on multiple occasions in a homozygous and heterozygous condition in replicated field trials conducted at the Akko experiment station. In each year IL 6–12 individual plants per IL were randomized in the field (1 plant per m2) and were measured for the yield‐associated traits. This enabled for every trait to calculate a single CV estimate per IL, per year. The estimates from 12 years of trialing and the SNP genotypes of the ILs allowed the authors to compare trait stability (CV) among the lines and to map putative wild species genomic regions affecting this dispersion parameter. By normalizing the results of each season relative to the common control M82 we selected based on the historic data five lines with the most stable and unstable yield associated phenotypes for validation.Between the years 1993 and 2004, the  station.8 In eMutants: The second genetic resource that was used in our search for stability genes was an M82 mutant collection generated by chemical and physical means that is called “The Genes that Make Tomatoes” that includes 3500 phenotyped hits. 48 mutants from the collection with effects on a range of traits compared to the isogenic control M82 was selected , the following phenotypes relating to total yield were measured: plant weight (PW kg), total yield (TY kg), harvest index (HI), fruit weight (FW g) and Brix .233 of water per 1000 m2) and in five canalization replications under drought stress (≈40 m3 of water per 1000 m2 applied immediately after transplantation). Each replication was composed of 10 plants of the same genotype, planted at equal spacing (50 cm between plants) where the eight middle plants were measured for morphological and yield‐associated traits and the CV for the entire group was calculated. To eliminate the possibility that other undetected genetic variations contributed to the observed results, F2 seeds resulting from selfing of the respective IL hybrids (ILHs) were planted. In the nursery the F2 seedlings were separated to the three genotypic groups using PCR markers for the targeted wild genome introgression  that affected yield stability were tested in spring season of 2013 in Akko Experiment Station. Each IL was grown in five ‘canalization replications’ under optimal irrigation conditions  was conducted in four different seasons.The 48 mutants were initially screened using three canalization replications grown in optimal irrigation conditions and three grown under drought stress . Evaluation of trait dispersion was based on the mean CV of the six plots that included the same mutant; no difference in the CV was observed between the wet and the dry treatments.7 An icanal‐1, a strategy of finer mapping was followed. Five indel markers were designed for the introgressed region of IL10‐2‐2 by comparing of the DNA sequence of S. pennellii (LA716) and M82. The indels were validated by amplifying the flanking regions using Primer 3 program (http://bioinfo.ut.ee/primer3‐0.4.0/) (Table To further the understanding of the QTL for yield stability (IL10‐2‐2 spans ≈1.7 million base pairs) and the mutant e4058canal‐1, in the genetic background of M82, was fine mapped in an F2 family from the red fruited wild species Solanum pimpinellifolium (LA1589). The F2 population was screened for individuals homozygous for the mutation that were used for mapping of a genomic region that does not have the S. pimpinellifolium polymorphism since all plant that were homozygous for e4058canal‐1 mutant carried only the cultivated tomato alleles in the vicinity of the mutation. Using a set of 48 indel markers, designed by exploiting the natural genomic diversity between the red tomato ancestor S. pimpinellifolium and the cultivated tomato S. lycopersicum, M82. The 48 indel markers were distributed over all tomato chromosomes.The variegated mutant, S. pimpinellifolium, and S. lycopersicum, were compared and aligned. When a polymorphic site was identified, primers were designed for the flanking region. When a single nucleotide polymorphism (SNP) site was identified, a dCAPS designer tool was used to identify restriction enzymes that recognize the polymorphic site. A DNA sequence for the candidate gene Solyc01g108200 was prepared at The Center for Genomic Technologies at the Hebrew University of Jerusalem using the Sanger DNA sequencing method.Additional markers were designed to fine map the mutated gene. Selected genomic regions showing high diversity between the genomes of gner tool24 was with two guided mRNA sequences primers, was used for the CRISPR/Cas9 gene editing of Solyc01g108200 :The protocol of Brooks et al.25 witCR‐CANAL1 F1 tgtggtctcaATTggaagtaatgaagaacttaggttttagagctagaaatagcaagCR‐CANAL1 F2 gtggtctcaATTgaaccaccagcgaaaggacggttttagagctagaaatagcaagM82 was the genetic background that was selected for this gene editing activity. DNA from mutant plants showing the expected phenotype were sequenced using the flanking primers:5’‐ TTGGTCCTATCTCCGTCCAT‐3’5’‐ TACCCTGACACTTCCCCTTG‐3’The authors declare no conflict of interest.D.Z. conceived the research plans; J.F. performed the experiments; D.Z. and J.F. wrote the paper.Data table IL historical data contains phenotypes of yield related traits in the S. pennellii (LA716) IL population, from replicated trials conducted over 12 years 1993–2004.Data table Akko 13 contains detailed phenotyping for five ILs  that affected yield stability tested in spring season of 2013 in Akko Experiment Station.Data table 10‐2‐2 Akko 13–17 raw CANAREPS contains detailed phenotyping for CANAREPS experiments on IL10‐2‐2 shown in Figure Supporting InformationClick here for additional data file.


Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases. A central challenge in the field of human genetics is understanding the mechanism of how genetic variants influence complex traits and diseases. Genome-wide association studies (GWAS) have begun characterizing the genetic architecture of complex traits, but the molecular mechanisms connecting genetic variants to these traits are rarely understood. This is particularly true for understanding pleiotropy, when a variant affects multiple traits . It is pIn this study, we use metabolites as model traits to understand pleiotropic features of genetic architecture. Metabolites are small molecules interconverted by a series of biochemical pathways and are an appealing model system for studying pleiotropy because their pathways are typically well-documented and biologically simpler than those underlying other complex traits . PreviouNumerous GWAS have begun characterizing the genetic architecture of metabolites and found them to be heritable and polygenic . Recent In this article, we demonstrate that investigating the effects of pleiotropic variants on biologically related metabolites allows for a better understanding of why these variants have their observed joint effects. Our results reveal striking heterogeneity in genetic correlation across the genome and provide a biologically intuitive basis for understanding this heterogeneity. Together, this allows us to dissect the molecular basis of metabolic disease GWAS variants and enables us to directly define the mechanism relating an example variant to its associated disease.We chose 16 metabolites from the 249 available through the Nightingale NMR platform in a subset of the UK Biobank (GLS2), which was significant in 9 of the 16 metabolites, and rs1260326 (GCKR), which was significant in 8. Glycine also had the highest Heritability Estimate from Summary Statistics (HESS) total SNP heritability of 0.284  genetic correlation across the 16 metabolites. We found substantial genome-wide sharing for many pairs of metabolites, especially for metabolites within the same biochemical group . We thenAn important step in understanding the pathway-level mechanisms of variants is knowing which gene a variant is affecting and how that gene relates to the biology of the pathway. Different types of genes influence trait biology through distinct mechanisms. Metabolite biology is documented in genetic and biochemical databases based on the extensive history of biochemical research . Thus, wBCAT2 encodes an enzyme responsible for the first step in the breakdown of all three BCAAs and was assigned to the BCAA group. OXCT1 encodes an enzyme responsible for the conversion of acetoacetyl-CoA to the ketone body acetoacetate and was assigned to the Ketone Body group. Similarly, SLC7A9 encodes a protein that transports amino acids and was assigned to the Other Amino Acid group, while TCF7L2 is a TF assigned to the Glycolysis group and involved in blood glucose homeostasis. These results confirm that these variants are affecting known trait-relevant biology and reflecting the local structure of these pathways.Next we sought to understand which genes and subpathways were most relevant to each biochemical group. We assigned each gene to the biochemical group with the most associated metabolites . Similarly, plotting the effects of the 213 GWAS variants on these two traits indicates a strong positive correlation (PDPR) and rs77010315 (SLC36A2), which have strong discordant effects. We were intrigued to understand why these two variants had discordant effects on alanine and isoleucine relative to their overall positive genetic correlation, while the majority of other variants had concordant effects.Given the overlap between the biology of these metabolites and their hits, we next sought to understand the molecular causes of pleiotropy in trait pairs. We found 26 genetically correlated metabolite pairs at a local false sign rate < 0.005. For example, alanine and its strongest genetic correlation partner, isoleucine, share a genetic correlation of relation . NonethePDPR and SLC36A2 are both located between alanine and isoleucine in the biochemical pathway . PDPR activates the enzyme that catalyzes the conversion of pyruvate to acetyl-CoA , and alanine with leucine .As an illustration, we first consider variant rs370014171. This variant was assigned to gene etyl-CoA . A candietyl-CoA  and runnetyl-CoA . This woSLC36A2. SLC36A2 encodes a transporter for small amino acids such as alanine , it was classified as ‘discordant.’ For example, a discordant variant for a metabolite pair with a positive genetic correlation would have a negative association in one of the metabolites and a positive association in the other. If a variant had an effect direction consistent with the overall genetic correlation for its significant metabolite pairs, it was classified as ‘concordant.’ Variants without multiple associations, or where associated traits were not significantly genetically correlated, were classified as ‘neither.’ In total, of the 62 metabolite GWAS hits that had at least one significant metabolite pair, we found 26 total discordant variant–metabolite pairs across 14 variants .We then investigated overall properties of discordant variants relative to concordant ones. We discovered that discordant variants are more likely to affect genes encoding enzymes and transporters than all other genes types, including TFs, general cell function genes, and those of unknown function  . The varxidation .PCCB variant was negatively associated with glycine and HDL-C, and positively associated with isoleucine, leucine, valine, tyrosine, total fatty acids, total triglycerides, and LDL-C .Not marked as outliers for heterozygosity and missing rates (putative_sex_chromosome_aneuploidy column).Do not show putative sex chromosome aneuploidy (excess_relatives column).Have at most 10 putative third-degree relatives . As was done for the White British in the initial UK Biobank study design  plasma samples  from approximately 120,000 UK Biobank participants were measured in molar concentration units. No power calculation was performed, but we anticipated numerous discoveries on the basis of prior GWAS with similar or smaller sample sizes . The meaPCCB variant using the same pipeline as for the 16 metabolites below.Sixteen metabolites were chosen from the available Nightingale metabolites based on their biochemical proximity, relevance to health and disease, and because the genes and enzymes involved in their metabolism are well-characterized. Specifically, we first filtered to the 168 metabolites that were not metabolite ratio measurements (n = 81) because we wanted to focus on absolute metabolites levels. We then filtered out the lipids and lipoprotein measures, including cholesterol and fatty acids, because the complexity of their biochemistry make it difficult to map out the chemical reactions directly interconverting one to another, and because many of these metabolites have already been extensively studied in large GWAS . HoweverFinally we removed remaining derived measures  and those primarily reflecting physiological conditions such as fluid balance  and GlycA (inflammation). One exception to this filtering was the three ketone bodies , which were included due to their proximity and clear direct interconversions connecting them to the metabolic pathways of the remaining amino acid and glycolysis-related metabolites. Metabolites were classified into four biochemical groups based on biochemical similarity. The three branched chain amino acids – valine, leucine, and isoleucine – were classified as “BCAA”; the remaining amino acids in the dataset – glycine, alanine, glutamine, tyrosine, phenylalanine, and histidine – were classified as ‘Other Amino Acid’; the three ketone bodies – 3-hydroxybutyrate, acetone, and acetoacetate – were classified as ‘Ketone Body’; and the four metabolites in or immediately adjacent to glycolysis – glucose, pyruvate, lactate, and citrate – were classified as ‘Glycolysis'.Trait measurements were filtered to only include baseline samples then log-transformed. Outlier removal was performed by dropping any sample that had a metabolite level greater than 20-fold the interquartile range or greater than 10-fold below the median across all samples for that metabolite. Principal component analysis (PCA) was run for the remaining samples and outliers were dropped using aberrant (lambda = 20) on the top two PCs . RemainiWe performed GWAS in BOLT-LMM v2.3.2  adjustinbolt --phenoCol= [Metabolite] \          --covarCol=sex \      --covarCol=Array \      --qCovarCol=age \      --qCovarCol=PC{1:10} \      --lmmForceNonInf \      --numThreads=24 \      --bgenMinMAF=1e-3 \      --bgenMinINFO=0.3The resulting GWAS summary statistics were then filtered to minor allele frequency (MAF) > 0.01 and INFO score >0.7 for further analyses (referred to as the Filtered Metabolite Sumstats). The LDSC munge_sumstats.py script was then used to munge the data (referred to as the Munged Metabolite Sumstats) .To evaluate GWAS hits, we took the Filtered Metabolite Sumstats and ran the following command using plink version 1.9 :plink --bfile  --clump [GWAS input file] --clump-p1 1e-4       --clump-p2 1e-4 --clump-r2 0.01 --clump-kb 1000 --clump-field P_BOLT_LMM --clump-snp-field SNPWe greedily merged GWAS hits across the 16 metabolites located within 0.1 cM of each other and took the SNP with the minimum p-value across all merged lead SNPs. In this way, we avoided potential overlapping variants that were driven by the same, extremely large, gene effects. This resulted in 213 lead GWAS variants, referred to as the metabolite GWAS hits.We defined all genes in any Gene Ontology (GO) , Kyoto EWe classified each gene using GeneCards  into oneGene-type enrichments were calculated with a Poisson rate test. The baseline was the total of the GWAS hits among the 1.95 Gb of the genome within 100 kb of a gene in any pathway, and the test was performed with the number of GWAS hits within 100 kb of each pathway of interebst. There were 2.8 GWAS hits per megabase within 100 kb of a pathway-relevant enzyme versus 0.1 GWAS hits per megabase among all genes . There were 0.58 GWAS hits per megabase within 100 kb of a transporter versus 0.1 GWAS hits per megabase for all genes . We also repeated this analysis using closest genes rather than assigned genes, which allowed us to use a Fisher’s exact test (as each variant has a single closest gene). This resulted in a 27-fold (p<2e-16) enrichment for pathway-relevant enzymes and an 11-fold (p<2e-15) enrichment for transporters, respectively.For TF enrichments, we used TF-Marker  to annotWe next filtered tissue-specific TFs to those acting in liver (28 relevant and 30 putative), kidney (25 relevant and 20 putative), or pancreas (14 relevant and 3 putative). Kidney and pancreas TFs had no more than one GWAS hit each and were excluded for these analyses. For liver TFs, we observed an 18-fold enrichment  within gene bodies and a 7.6-fold enrichment  within 100 kb of genes. Results were similar when removing the cancer and stem cell filter  and dropped slightly when further including putatively relevant TFs (0.71 hits/Mb and 0.48 hits/Mb). Together, this suggests that liver marker TFs are specifically enriched for variants affecting our metabolite levels.For the ancestry-inclusive analysis, we performed the same method as the European-ancestry-only analysis except we omitted the step filtering individuals on the basis of self-identified race/ethnicity and ancestry PC outlier status. For the ancestry-inclusive analysis, we again used the European ancestry LD matrix as European-ancestry individuals were the overwhelming majority in the study. This resulted in a total of 98,189 individuals for the GWAS, which identified 238 lead GWAS variants across the 16 metabolites. This was inspired by recent ‘mega-analysis’ studies . We examcoloc.abf was run using the default arguments  was plotted for all pairs of traits run individually.Full summary statistics for all quality-controlled SNPs within 1 Mb of the target gene were considered at each locus, and these were loaded for all the evaluated traits. Standard deviations of the technical covariate-adjusted trait measurements were used for input standard deviation calculations, and dichotomous traits were set to ‘cc’-type datasets while continuous trait were set to ‘quant.’ rguments . Output We ran HESS  using thhess.py --local-hsqg {\filtsumstats} --chrom {chrom} \         --bfile 1kg_eur_1pct_chr{chrom}         --partition EUR/fourier_ls-chr{chrom}.bed \         --out {Metabolite}_step1 hess.py --prefix {Metabolite}_step1 --out {Metabolite}_step21kg_eur_1pct_chr{chrom} were downloaded from here and EUR/fourier_ls-chr{chrom}.bed were downloaded from here.where We intersected the resulting heritability estimates per LD block with gene lists from each pathway  and MR-Egger with fixed and random effects, and selection between different methods for results to present was based on the goodness-of-fit and heterogeneity parameters for the individual MR regressions as previously described .All pairwise combinations of LDSC genetic correlation (as described above) were performed for the 16 metabolites. Pairs were filtered to those that had a genetic correlation significantly different than 0 using ashR  with a lThe ‘between’ region for a given pair of metabolites was defined as the shortest realistic biochemical path connecting them, and any alternative paths of reasonably similar distance and likelihood. This region can include a path converting one metabolite to the other, as well as other scenarios such as those involving colliders. This is because all biochemical reactions, including one-way reactions, can have bidirectional causal relationships due to Le Châtelier’s principle. In other words, even in a one-way (irreversible) reaction, changes in product levels can induce changes in reactant levels in order to re-establish equilibrium. Genes were defined as acting between a given metabolite pair either if they encoded an enzyme catalyzing a reaction in the ‘between’ region defined above or if they encoded a transporter that primarily transports either of the two metabolites themselves or an intermediate metabolite in the ‘between’ region. Variants were defined as acting between a given metabolite pair if the gene they affect was defined as between. Pathways were defined as between a given metabolite pair if many of the genes defined as between the metabolites were part of the pathway or if many of the genes in the pathway were defined as between. Note that even if a pathway is defined as ‘between,’ not all genes in the pathway will always be between and vice versa; however, this is likely to only make the resulting differences in genetic correlation for ‘between’ vs. not ‘between’ pathways more conservative.We ran HESS  using thhess.py --local-rhog {Met1_sumstats}          {Met2_sumstats} --chrom {chrom} --bfile 1kg_eur_1pct_chr{chrom} \          --partition EUR/fourier_ls-chr{chrom}.bed --out {Met1_Met2}_step1 \ hess.py --prefix {Met1_Met2}_step1_trait1 \         --out {Met1_Met2}_step2_trait1 hess.py --prefix {Met1_Met2}_step1_trait2 \         --out {Met1_Met2}_step2_trait2 hess.py --prefix {Met1_Met2}_step1 \         --local-hsqg-est {Met1_Met2}_step2_trait1 {Met1_Met2}_step2_trait2 \         --num-shared 94464 \         --pheno-cor {gcov_int from LDSC genetic correlation for Met1_Met2} \         --out {Met1_Met2}_step31kg_eur_1pct_chr{chrom} were downloaded here and EUR/fourier_ls-chr{chrom}.bed were downloaded here.where We then used the local rho HESS results and estimated the local genetic covariance and correlation across all LD blocks overlapping pathway regions.OXCT2, HMGCL, HMGCS1, HMGCS2, ACAT1, ACAT2, OXCT1, DLD, AGXT2, ABAT, and AACS and also included ECHDC1. ‘All Regions Outside Pathway Genes’ was defined as all LD blocks not overlapping any of the regions defined above. ‘Metabolite-Associated TF Genes’ and ‘Metabolite-Associated Transporters Genes’ were defined as all LD blocks overlapping any of TFs or transporters, respectively annotating the metabolite GWAS hits.We defined the pathway regions based on gene boundaries of relevant genes in rg and Rather than aggregating variant effects and estimating total genetic covariance and heritability per pathway, which is not robust to outlier effects, we additionally tried a nonparametric approach. Individual Genotyped variants within 100 kb of genes in each pathway were aggregated, and the resulting matrices were tested using the following command in BOLT-LMM:bolt      --remove {non-European ancestry individuals}      --phenoFile={Technical-adjusted metabolites} \      --phenoCol=Ala \      --phenoCol=Gln \      --covarCol=sex --covarCol=Array      --qCovarCol=age     --qCovarCol=PC{1:10} \      --geneticMapFile=genetic_map_hg19_withX.txt.gz ‘# downloaded with bolt‘ \      --numThreads=24      --verboseStats \      --modelSnps {pathway SNPs} \      --reml \ --noMapCheckStandard errors were as reported by BOLT-REML.Genotyped variants were pruned to MAF > 1% and approximate linkage equilibrium among individuals included in the GWAS using,{GCTA} --HEreg-bivar {trait1} {trait2} --thread-num 16 --grm {GRM}--mgrm) to jointly test all pathways were qualitatively similar outside of the genome-wide GRM, which no longer captured the within-pathway component.Results using multiple GRMs  --condition<conditional SNP>--condition-list for conditioning on multiple variants. Associations were visually inspected to detect highly linked variants and conditioning tests were repeated with top associations in any of the key traits until there were no significant variants remaining.For single SNP conditioning tests and PCCB vignette, additional traits were included in the analysis, including fatty acids and lipids in the Nightingale-assayed individuals and clinical biomarkers in the full cohort of European-ancestry UK Biobank participants, where traits were residualized as previously described  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, including Alexander Young as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by George Perry as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Mark I McCarthy (Reviewer #2).Thank you for submitting your article "Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation" 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:The reviewers all agreed that your article presented interesting and potentially important results about how to link GWAS results to biochemical mechanisms. However, the reviewers have raised issues with the presentation that need to be addressed before the manuscript can be accepted. Furthermore, please address reviewer #3's comment about colocalization, and reviewer #1's comment about the ancestry-inclusive GWAS.Reviewer #1 (Recommendations for the authors):I am quite confused as to where the ancestry inclusive analysis was used? It is mentioned in the introduction as identifying additional hits, but are these used anywhere in the main text results? I also found the details on the ancestry inclusive analysis in the methods lacking. Was the same GWAS procedure used as for the European ancestry GWAS? Did the results look reasonable in terms of QQ-plots, etc.?For the model proposed in Figure 5a, this seems quite different from the example given in Figure 4c for the variant affecting PDPR. For the variant affecting PDPR, it appears that this discordant effect is generated due to the variant affecting the conversion of pyruvate into Acetyl-CoA, and the corresponding increase in conversion of alanine into pyruvate, and a decrease in the conversion of BCAAs into Acetyl-CoA. So the key here seems to be the level of something (Acetyl-CoA) that is downstream of both of the pair of metabolites. The model in Figure 5a, in contrast, appears to be implying that discordant effects are generated by variants affecting the pathway leading to the conversion of one metabolite into another. So is the PDPR example different from the model proposed in Figure 5a? It does not appear to be consistent with the definition of 'between' biology as being a short pathway converting one metabolite to another, but perhaps I am misunderstanding.I also found the description of the mechanism in Figure 4d a little confusing. isoleucine is described as 'downstream' of alanine (line 185), but the diagram in Figure 4d does not appear to show that.For the discussion of the putative mechanism linking the PCCB variant with CAD: I think you need to be more careful about whether you are talking about correlation or causation when you talk about a 'pathogenic constellation of metabolite effects'. Many of the correlations between metabolites and CAD risk may not reflect the causal effects of those metabolites on CAD.Figure 4a: what is the regression line? Did it adjust for sampling error for effects on both phenotypes?Figure 4b: the position of the 'star' SLC36A2 I found a little ambiguous.Figure 5 panels c and d: some of the confidence intervals go into negative territory. I don't think it's critical for the paper, but you could try alternative ways of constructing the confidence interval for a binomial proportion, such as a Wilson Score Interval, that should not have this issue.Often point estimates are given along with P-values (e.g. for genetic correlations), but no confidence interval is given. Please also give confidence intervals.Line 382: "The included participants are therefore meant to be representative of the 502,543 participants in the full cohort." How is this known?Line 411: I think alanine is missing from the listeLife. Your revised article has been evaluated by George Perry (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation" for further consideration by Please address the remaining comment from reviewer 1.Reviewer #1 (Recommendations for the authors):With respect to my comment about the definition of 'between' biology, I thank the authors for the inclusion of Figure 5 – Supplement 1, which is helpful in tying this to the example to Figure 4c. However, I'm still not convinced that the example given in Figure 5 – Supplement 1a is consistent with the definition in the main text: 'We consider biology "between" a given pair of metabolites as the shortest realistic biochemical path converting one to the other'. The example in Figure 5 – Supplement 1a appears to show two metabolites both being converted into something else, rather than one being converted into the other. In the language of directed acyclic graphs, Figure 5 Supplement 1a shows a 'collider' and Figure 5 Supplement 1b shows a 'chain', but the main text definition appears to only consider the 'chain' model. Some further clarification on this in the main text is warranted. Essential revisions:The reviewers all agreed that your article presented interesting and potentially important results about how to link GWAS results to biochemical mechanisms. However, the reviewers have raised issues with the presentation that need to be addressed before the manuscript can be accepted. Furthermore, please address reviewer #3's comment about colocalization, and reviewer #1's comment about the ancestry-inclusive GWAS.Thank you for the feedback. We have now made these changes as well as the additional ones below. See responses to individual reviewer comments below and the document with tracked changes for more specifics.Reviewer #1 (Recommendations for the authors):I am quite confused as to where the ancestry inclusive analysis was used? It is mentioned in the introduction as identifying additional hits, but are these used anywhere in the main text results? I also found the details on the ancestry inclusive analysis in the methods lacking. Was the same GWAS procedure used as for the European ancestry GWAS? Did the results look reasonable in terms of QQ-plots, etc.?Thank you for mentioning this. We have now made the following changes to improve the clarity around this analysis and answer your questions:1. We have removed mention of the ancestry-inclusive analysis from the introduction and instead only discussed it during a dedicated paragraph in the main text results (lines 139-144). We made this change to make it clear that the majority of the analysis and results are from the European-ancestry only GWAS as these are less susceptible to confounding by population structure. However, we still ran and included the ancestry-inclusive GWAS to evaluate the utility of GWAS on diverse ancestries in discovering additional biologically important hits. In the main text we directly mention the number of additional hits identified by the ancestry-inclusive analysis and in a supplementary table (Supplementary File 5) we list the SNPs and their respective summary statistics from each of the GWAS for the associations at additional pathway-relevant genes.2. We have now created a dedicated section in the methods called “Ancestry-Inclusive GWAS” to describe the details of the method (lines 544-552) and clarify that the method was identical to that of the European-ancestry only GWAS, other than the omission of a single filtering step .eLife 2019).3. We have now added an additional supplementary figure  with QQ-plots for each of the 16 metabolite ancestry-inclusive GWAS. These results and the overlap in these summary statistics and hits with those from the European-ancestry only GWAS support that the ancestry-inclusive GWAS results are reasonable. Please note that for the ancestry-inclusive GWAS we opted to only focus on variant discovery and use the largest European ancestry for methods examining sub-threshold variants to minimize the risk of population structure confounding in those analyses  that is downstream of both of the pair of metabolites. The model in Figure 5a, in contrast, appears to be implying that discordant effects are generated by variants affecting the pathway leading to the conversion of one metabolite into another. So is the PDPR example different from the model proposed in Figure 5a? It does not appear to be consistent with the definition of 'between' biology as being a short pathway converting one metabolite to another, but perhaps I am misunderstanding.I also found the description of the mechanism in Figure 4d a little confusing. isoleucine is described as 'downstream' of alanine (line 185), but the diagram in Figure 4d does not appear to show that.Thank you for your feedback on this. We agree that the “between” concept for more complicated examples like that in Figure 4c and 4d may be confusing to readers, especially those used to looking at gene regulatory networks where the concept of downstream/upstream means something different than that in pathways of chemical reactions. To address this, we have now added an additional supplementary figure  with two additional example models. The first model is based on the metabolite pathway in Figure 4c and 4d and more explicitly defines the regions of between biology vs outside biology , as well as gives an example of a discordant  and concordant model . The second model is an example of a variant affecting a gene encoding a transporter and more explicitly defines the regions of between biology vs outside biology , as well as gives an example of a discordant  and concordant model  for that situation.For the discussion of the putative mechanism linking the PCCB variant with CAD: I think you need to be more careful about whether you are talking about correlation or causation when you talk about a 'pathogenic constellation of metabolite effects'. Many of the correlations between metabolites and CAD risk may not reflect the causal effects of those metabolites on CAD.This is an important point and definitely something we agree with. We have now modified the language in the “Using metabolites to understand the mechanism of a disease-associated variant” Results section to more clearly describe the analysis as a proof-of-concept analysis in terms of hypothesis generation and a new way of thinking about these kinds of data, as opposed to implying causality between the PCCB variant or relevant metabolites and CAD. We have also softened the language in the section, replacing many “would”s with “could”s. For example, the phrase you mentioned now reads “A potential mechanism that we hypothesized could result in this pathogenic constellation of metabolite effects is that the variant could decrease PCCB activity…”. We also added a sentence explicitly warning against causal conclusions being drawn: “… in vivo functional validation would be needed to draw causal conclusions about the effect of this variant on these metabolites and of these metabolites on CAD…”.Figure 4a: what is the regression line? Did it adjust for sampling error for effects on both phenotypes?Thank you for pointing this out. We have decided to remove the regression line from Figure 4a altogether because it may confuse readers and isn’t explicitly used in our results.Figure 4b: the position of the 'star' SLC36A2 I found a little ambiguous.Thank you for mentioning this, we have now modified the location of the star in Figure 4b to more clearly represent that it is a transporter of alanine.Figure 5 panels c and d: some of the confidence intervals go into negative territory. I don't think it's critical for the paper, but you could try alternative ways of constructing the confidence interval for a binomial proportion, such as a Wilson Score Interval, that should not have this issue.Thank you for the suggestion to use an alternative way of constructing the confidence interval for the data shown in Figure 5c and 5d. We have now updated the figure to show the 95% confidence interval calculated by Wilson Score Interval, which you were correct did not have the issue of going into negative values.Often point estimates are given along with P-values (e.g. for genetic correlations), but no confidence interval is given. Please also give confidence intervals.Thank you, we have now added SE for all the genetic correlation values and GWAS summary statistics, and 95% confidence intervals for the Fisher's exact tests and enrichment analyses mentioned in the text.Line 382: "The included participants are therefore meant to be representative of the 502,543 participants in the full cohort." How is this known?This is a good point that just because the participants were randomly selected to undergo metabolomic analysis, that does not mean the random sample will be representative for all relevant characteristics. We have now removed this sentence from the text. In case you are interested, a table comparing some basic characteristics of the ~120k randomly selected participants vs the full ~500k cohort is available in Nightingale’s recent medRxiv paper describing the measurements (Table 1 in https://www.medrxiv.org/content/10.1101/2022.06.13.22276332v2).Line 411: I think alanine is missing from the listThank you for catching this. We have now added alanine to the list.Please address the remaining comment from reviewer 1.Reviewer #1 (Recommendations for the authors):With respect to my comment about the definition of 'between' biology, I thank the authors for the inclusion of Figure 5 – Supplement 1, which is helpful in tying this to the example to Figure 4c. However, I'm still not convinced that the example given in Figure 5 – Supplement 1a is consistent with the definition in the main text: 'We consider biology "between" a given pair of metabolites as the shortest realistic biochemical path converting one to the other'. The example in Figure 5 – Supplement 1a appears to show two metabolites both being converted into something else, rather than one being converted into the other. In the language of directed acyclic graphs, Figure 5 Supplement 1a shows a 'collider' and Figure 5 Supplement 1b shows a 'chain', but the main text definition appears to only consider the 'chain' model. Some further clarification on this in the main text is warranted.Thank you for this important feedback! We have now updated the definition in the main text (lines 201-205) to read “We consider biology "between" a given pair of metabolites as the shortest biochemical path connecting them, which can include a path converting one metabolite to the other, as well as other scenarios such as those involving colliders. This is because all biochemical reactions, including one-way reactions, can have bi-directional causal relationships due to Le Châtelier's Principle .”We also updated the expanded definition given in the methods section (lines 605-611) to “The ``between'' region for a given pair of metabolites was defined as the shortest realistic biochemical path connecting them, and any alternative paths of reasonably similar distance and likelihood. This region can include a path converting one metabolite to the other, as well as other scenarios such as those involving colliders. This is because all biochemical reactions, including one-way reactions, can have bi-directional causal relationships due to Le Châtelier's Principle. In other words, even in a one-way (irreversible) reaction, changes in product levels can induce changes in reactant levels in order to re-establish equilibrium.”